Public companies in the US stock market must annually report their activities and financial performances to the SEC by filing the so-called 10-K form. Recent studies have demonstrated that changes in the textual content of the corporate annual filing can convey strong signals of companies’ future returns. In this study, we combine natural language processing techniques and network science to introduce a novel 10-K-based network, named Lazy Network, that leverages year-on-year changes in companies’ 10-Ks detected using a neural network embedding (...) model. The Lazy Network aims to capture textual changes derived from financial or economic changes on the equity market. Leveraging the Lazy Network, we present a novel investment strategy that attempts to select the least disrupted and stable companies by capturing the peripheries of the Lazy Network. We show that this strategy earns statistically significant risk-adjusted excess returns. Specifically, the proposed portfolios yield up to 95 basis points in monthly five-factor alphas, outperforming similar strategies in the literature. (shrink)

Prioritization of activities is a multicriteria problem that includes both quantitative and qualitative factors. Moreover, due to the nature of organizations and activities, the impact of activities on each other is fuzzy. Prioritizing activities in real and fuzzy situations will help an organization’s decision makers to make the right decisions. In this paper, a new fuzzy hybrid methodology is proposed to describe and prioritize the activities of the organization in fuzzy conditions. First, the activities of the organization are described in (...) a new fuzzy format based on activity model of the C4ISR framework. Furthermore, fuzzy DEMATEL is used to calculate the interdependencies between activities, and then fuzzy ANP is used to calculate the weight of each activity. Finally, by combining the results of the two methods, the priority of activities was calculated. Additionally, an empirical study is presented to illustrate the application of the proposed method. (shrink)

A topological index can be focused on uprising of a chemical structure into a real number. The degree-based topological indices have an active place among all topological indices. These topological descriptors intentionally associate certain physicochemical assets of the corresponding chemical compounds. Graph theory plays a very useful role in such type of research directions. The hex-derived networks have vast applications in computer science, physical sciences, and medical science, and these networks are constructed by hexagonal mesh networks. In this paper, we (...) determined the exact values of vertex-edge-degree-based topological descriptors for hex-derived networks HDN 1 p and HDN 2 p, which are generated by the hexagonal network of dimension p. (shrink)

Rockburst phenomenon is the primary cause of many fatalities and accidents during deep underground projects constructions. As a result, its prediction at the early design stages plays a significant role in improving safety. The article describes a newly developed model to predict rockburst intensity grade using Adaptive Boosting classifier. A database including 165 rockburst case histories was collected from across the world to achieve a comprehensive representation, in which four key influencing factors such as maximum tangential stress of the excavation (...) boundary, uniaxial compressive strength of rock, tensile rock strength, and elastic energy index were selected as the input variables, and the rockburst intensity grade was selected as the output. The output of the AdaBoost model is evaluated using statistical parameters including accuracy and Cohen's kappa index. The applications for the aforementioned approach for predicting the rockburst intensity grade are compared and discussed. Finally, two real-world applications are used to verify the proposed AdaBoost model. It is found that the prediction results are consistent with the actual conditions of the subsequent construction. (shrink)

Fibre suspension has garnered considerable attention in turbulent flows that are used in many industries. Solid particles, such as dust particles, notably affect the turbulent flow field in a rotational frame. In assessing their impacts, the dusty turbulent flow for fibre suspensions needs to be studied in a frame of rotation that can be substantially applied in many industries. This study, therefore, aims to build a theoretical model for the energy motion of dusty turbulent flow of fibre suspensions in a (...) rotational frame. The turbulence momentum equation was considered to formulate the model in presence of dusty fluid rotating flow of fibre suspensions. The newly derived equation was derived in second-order correlation tensors Fi,j, Wi,j, Gi,j, Si,j, Xi,j, Yi,j, Qi,j, and Ri,j at any two points in the flow domain, where the tensors were expressed as space, time, and distance functions. The developed model is a considerable improvement because it takes into account all of the potential influential parameters that could affect the motion of turbulent energy, such as dust particles, suspending particles, and rotating frame. However, the impact of these parameters on turbulence energy motion must be evaluated in order to assess the performance of turbulence systems utilized in a variety of industries, such as paper manufacturing. The present theoretical development will contribute to open up experimental and numerical research opportunities for the advancement of the industry, science, and technology. (shrink)

Liquidity risk arises from the inability to unwind or hedge trading positions at the prevailing market prices. The risk of liquidity is a wide and complex topic as it depends on several factors and causes. While much has been written on the subject, there exists no clear-cut mathematical description of the phenomena and typical market risk modeling methods fail to identify the effect of illiquidity risk. In this paper, we do not propose a definitive one either, but we attempt to (...) derive novel mathematical algorithms for the dynamic modeling of trading volumes during the closeout period from the perspective of multiple-asset portfolio, as well as for financial entities with different subsidiary firms and multiple agents. The robust modeling techniques are based on the application of initial-value-problem differential equations technique for portfolio selection and risk management purposes. This paper provides some crucial parameters for the assessment of the trading volumes of multiple-asset portfolio during the closeout period, where the mathematical proofs for each theorem and corollary are provided. Based on the new developed econophysics theory, this paper presents for the first time a closed-form solution for key parameters for the estimation of trading volumes and liquidity risk, such as the unwinding constant, half-life, and mean lifetime and discusses how these novel parameters can be estimated and incorporated into the proposed techniques. The developed modeling algorithms are appealing in terms of theory and are promising for practical econophysics applications, particularly in developing dynamic and robust portfolio management algorithms in light of the 2007–2009 global financial crunch. In addition, they can be applied to artificial intelligence and machine learning for the policymaking process, reinforcement machine learning techniques for the Internet of Things data analytics, expert systems in finance, FinTech, and within big data ecosystems. (shrink)

In the past two decades, especially after the financial crisis of 2007–09, the literature for examining the availability of integration between the stock exchanges in developed and developing markets has grown. The importance of this topic stems from the significant implications of the linkage between exchange markets on various decisions taken by interested parties, such as policymakers and investors, in the decisions for portfolio diversification. This study examines the relationship between a developing stock exchange index, Amman Stock Exchange Index, and (...) the number of international indices, including S&P 500, NASDAQ, Nikkei, DAX, CAC, and HSI for 2008-2019. To validate the availability of the linkage between the indices, the author includes various tests of a correlation coefficient, stepwise regression analysis, and artificial neural network. Despite the results indicating that the ANN is more efficient than linear regression in investigating the availability of the relationship between ASEI and international indices, stepwise regression and neural network support this relationship. Furthermore, ANN results revealed that the S&P 500 index and year have the most substantial relationship with ASEI. Our research is theoretically and practically important; policymakers and investors can benefit from our findings. Future studies may explore the effect of different international stock market indices on ASEI or other developing markets. Further studies can use macroeconomic factors to build prediction models for stock market indices. (shrink)

In this paper, we deal with an alternative analytical analysis of fractional-order partial differential equations with proportional delay, achieved by applying Yang decomposition method, where the fractional derivative is taken in Caputo sense. The suggested series results are discovered to quickly converge to an exact solution. The computation of three test problems of fractional-order with proportional delay partial differential equations was presented to confirm the validity and efficiency of suggested method. The system appears to be a very dependable, effective, and (...) powerful method for solving a variety of physical problems that arise in engineering and science. (shrink)

With the increasing volume of data transmission through insecure communication channels, big data security has become one of the important concerns in the cybersecurity domain. To address these concerns and keep data safe, a robust privacy-preserving cryptosystem is necessary. Such a solution relies on chaos encryption algorithms over standard cryptographic methods that possess multistage encryption levels, including high speed, high security, low compute overheads, and procedural power, among other characteristics. In this work, a secure image encryption scheme is proposed using (...) linear feedback shift register and chaos-based quantum chaotic map. The focus of the scheme is mainly dependent on the secret keys from the input of the algorithm. The threat landscape, the statistical test analysis, along critical comparisons with other schemes indicate that the presented algorithm is significantly secure and is resistant to a wide range of different attacks such as differential and statistical attacks. The proposed method has sufficiently higher sensitivity and security when compared to existing encryption algorithms. Several security parameters validated the security of proposed work such as correlation coefficient analyses among the neighboring pixels, entropy, the number of pixels change rate, unified average change intensity, mean square error, brute force, key sensitivity, and peak signal to noise ratio analyses. The randomness of the ciphers produced by the proposed technique is also passed through NIST-800-22. The results of NIST indicate that the ciphers are highly random and do not produce any type of periodicity or pattern. (shrink)

This article constitutes the new fixed point results of dynamic process D through FIC-integral contractions of the Ciric kind and investigates the said contraction to iterate a fixed point of set-valued mappings in the module of metric space. To do so, we use the dynamic process instead of the conventional Picard sequence. The main results are examined by tangible nontrivial examples which display the motivation for such investigation. The work is completed by giving an application to Liouville‐Caputo fractional differential equations.

The proposed work describes an approach for the segmentation of abnormal lung CT scans of COVID-19. Lung diseases are the leading killer in both men and women. The pulmonary experts normally make attempts, such as early detection of patients by tomography tests before lung specialists treat patients who are tortured by lung disease. Moreover, lung specialists do their best to detect the presence of lung conditions. X rays or CT scan checks are performed for tomography tests. The finest approach for (...) medical diagnosis and a wide range of uses is computed tomography. This kind of imaging offers elaborate cross-sectional pictures of skinny slices of the organic structure. However, the preprocessing and denoising methods of Lung CT scans may mask some important image features. To address this challenge, we propose a novel framework involving an optimization technique algorithm to solve a multilevel thresholding problem based on information theory to segment abnormal lung CT scans. The proposed framework will evaluate a sample of CT scan images taken from a well-known benchmark database. The evaluation results will assess subjectively and objectively to demonstrate the effectiveness of the proposed framework. (shrink)

In this contribution, we use the connection between stable polynomials and orthogonal polynomials on the real line to construct sequences of Hurwitz polynomials that are robustly stable in terms of several uncertain parameters. These sequences are constructed by using properties of orthogonal polynomials, such as the well-known three-term recurrence relation, as well as by considering linear combinations of two orthogonal polynomials with consecutive degree. Some examples are presented.

Owing to the adverse impact of the COVID-19 pandemic on world economies, it is expected that information flows between commodities and uncertainties have been transformed. Accordingly, the resulting twisted risk among commodities and related uncertainties is presumed to rise during stressed market conditions. Therefore, investors feel pressured to find safe haven investments during the pandemic. For this reason, we model a mixture of asymmetric and non-linear bi-directional causality between global commodities and uncertainties at different frequencies through the information flow theory. (...) Consequently, we utilise the complete ensemble empirical mode decomposition with adaptive noise and the Rényi effective transfer entropy techniques to establish the dynamic flow of information. The intrinsic mode functions from the CEEMDAN are carefully extracted into multi-frequencies through cluster analysis to reconstruct the series into high, medium, and low frequencies in addition to the residue. We utilise daily data from December 31st, 2019, to March 31st, 2021, to provide insights into the COVID-19 pandemic. The correlation coefficients and variances demonstrate that the high frequency which measures the short-term dynamics is the dominant frequency, suggesting short-lived market fluctuations relative to real economic growth for institutional investors. Moreover, outcomes from the multi-frequency entropy indicate a negative bi-directional causality of information flow between global commodities and uncertainties, especially in the long term. Generally, the findings present pertinent inferences for portfolio diversification, policy decisions, and risk management schemes for global commodities and markets volatilities. We, therefore, advocate that market volatilities act as effective hedges for global commodities, and they clearly act as balancing assets rather than substitutes in the long-term dynamics of the COVID-19 pandemic. Investors who delayed in investing within financial markets of commodities and market volatilities are likely to minimise their portfolio risks. (shrink)

Emotion-based sentimental analysis has recently received a lot of interest, with an emphasis on automated identification of user behavior, such as emotional expressions, based on online social media texts. However, the majority of the prior attempts are based on traditional procedures that are insufficient to provide promising outcomes. In this study, we categorize emotional sentiments by recognizing them in the text. For that purpose, we present a deep learning model, bidirectional long-term short-term memory, for emotion recognition that takes into account (...) five main emotions. We use our experimental assessments on the emotion dataset to accomplish the emotion categorization job. The datasets were evaluated and the findings revealed that, when compared to state-of-the-art methodologies, the proposed model can successfully categorize user emotions into several classifications. Finally, we assess the efficacy of our strategy using statistical analysis. This research’s findings help firms to apply best practices in the selection, management, and optimization of policies, services, and product information. (shrink)

A quadratic utility function is introduced in this paper to study the dynamic characteristics of Cournot duopoly game. Based on the bounded rationality mechanism, a discrete dynamical map that describes the game’s dynamic is obtained. The map possesses only one equilibrium point which is Nash point. The stability conditions for this point are analyzed. These conditions show that the point becomes unstable due to two bifurcation types that are flip and Neimark–Sacker. The synchronization property for that map is studied. Through (...) local and global analysis, some dynamics of attracting sets are investigated. This analysis gives some insights on the basis of those sets and the shape of the critical curves. It also shows some lobes found in those attracting sets which are constructed due to the origin focal point. (shrink)

This study aims to examine the main determinants of efficiency of both conventional and Islamic Saudi banks and then choose the best fit model among machine learning prediction models, Chi-squared automatic interaction detector, linear regression, and neural network ). The data were collected from the annual financial reports of Saudi banks from 2014 to 2018. The Saudi banking sector consists of 11 banks, 4 of which are Islamic. In this study, the major financial ratios are subgrouped into the profitability ratios, (...) managerial practices, asset and loans, capital adequacy ratios, and liquidity. First, regression analysis is implemented with efficiency ratio as a dependent variable and the proxies of banks’ profitability, liquidity, asset quality, management ratios, and capital adequacy ratios as independent variables. Next, the feature selection is applied for different prediction models. Subsequently, 4 prediction models were developed to choose the best fit. The performance metrics have also been evaluated. Regression results exhibit that the efficiency of both conventional and Islamic banks is highly affected by profitability, liquidity, and managerial practices. Finally, we choose the best prediction model with the highest R2 in the training and the testing phases with/out feature selection that is the CHAID model. The best predictors of cost efficiency for Saudi banks are the capital ratios, namely, CAR total and CAR tier 1. Findings are theoretically and practically important to academics, investors, and policymakers. Policymakers can benefit from the novelty of this study in building an early warning system using the CHAID model to predict different financial distress scenarios. (shrink)

In our current era, a new rapidly spreading pandemic disease called coronavirus disease, caused by a virus identified as a novel coronavirus, is becoming a crucial threat for the whole world. Currently, the number of patients infected by the virus is expanding exponentially, but there is no commercially available COVID-19 medication for this pandemic. However, numerous antiviral drugs are utilized for the treatment of the COVID-19 disease. Identification of the appropriate antivirus medicine to treat the infection of COVID-19 is still (...) a complicated and uncertain decision. This study’s key objective is to develop a novel approach called q-rung orthopair probabilistic hesitant fuzzy rough set, which incorporates the q-rung orthopair fuzzy set, probabilistic hesitant fuzzy set, and rough set structures. New q-ROPHFR aggregation operators have been established: the q-ROPHFR Einstein weighted averaging operator and the q-ROPHFR Einstein weighted geometric operator. In this study, we explored some basic features of the developed operators. Afterward, to demonstrate the viability and feasibility of the established decision-making approach in real-world applications, a case study related to selecting drugs for COVID-19 pandemic is addressed. Furthermore, a comprehensive comparison with the q-rung orthopair probabilistic hesitant fuzzy rough TOPSIS technique is also presented to illustrate the benefits of the new framework. The obtained results confirm the reliability and effectiveness of the proposed approach for finding uncertainty in real-world decision-making. (shrink)

This article concerns with the existence and uniqueness theory of solutions for sequential fractional differential system involving Caputo fractional derivatives of order 1

Natural Sciences

Direct download (2 more)  

 

Export citation  

 

Bookmark  

We employ a frequency-dependent asymmetric and causality analysis to investigate the connectedness between gold and cryptocurrencies during the COVID-19 pandemic. Hence, the variational mode decomposition-based quantile regression is utilised. Findings from the study divulge that the variational mode functions at the lower quantiles are mostly significant and negative indicating that gold acts as a safe haven, a diversifier at most market conditions with insignificant coefficients, and a hedge at normal market conditions for most cryptocurrencies at various investment horizons. Particularly, hedging (...) benefits mostly occur in the short- and medium-term for Bitcoin and Ripple, as well as Bitcoin and Dogecoin in the long-term with gold. This implies that there is high persistence in the hedging properties of gold with Bitcoin, followed by Ripple. We notice more significant relationship between gold and some cryptocurrencies in the long-term of the COVID-19 pandemic relative to the medium-term emphasising the delayed responses of prices to information. Investors are recommended to be observant and mindful of investing in these markets due to the different dynamics. (shrink)

The present study addresses the fluid transport behaviour of the flow of gold -copper /biomagnetic blood hybrid nanofluid in an inclined irregular stenosis artery as a consequence of varying viscosity and Lorentz force. The nonlinear flow equations are transformed into dimensionless form by using nonsimilar variables. The finite-difference technique is involved in computing the nonlinear transport dimensionless equations. The significant parameters like angle parameter, the Hartmann number, changing viscosity, constant heat source, the Reynolds number, and nanoparticle volume fraction on the (...) flow field are exhibited through figures. Present results disclose that the Lorentz force strongly lessens the hybrid nanofluid velocity. Elevating the Grashof number values enhances the rate of blood flow. Growing values of the angle parameter cause to reduce the resistance impedance on the wall. Hybrid nanoparticles have a superior wall shear stress than copper nanoparticles. The heat transfer rate is amplifying at the axial direction with the growing values of nanoparticles concentration. The applied Lorentz force significantly reduces the hybrid and unitary nanofluid flow rate in the axial direction. The hybrid nanoparticles expose a supreme rate of heat transfer than the copper nanoparticles in a blood base fluid. Compared to hybrid and copper nanofluid, the blood base fluid has a lower temperature. (shrink)

Fractional calculus is nowadays an efficient tool in modelling many interesting nonlinear phenomena. This study investigates, in a novel way, the Ulam–Hyers and Ulam–Hyers–Rassias stability of differential equations with general conformable derivative. In our analysis, we employ some version of Banach fixed-point theory. In this way, we generalize several earlier interesting results. Two examples are given at the end to illustrate our results.

We revisit the flight-to-quality and flight-from-quality occurrences vis-à-vis the stock-bond nexus across differing investment time scales in the COVID-19 era, using a novel technique hinged on a denoised frequency-domain transfer entropy. Our findings divulge that flights, both FTQ and FFQ, could be attained during stress periods. Generally, in the intermediate term of the COVID-19 pandemic, both Islamic and conventional bonds could act as safe havens, diversifiers, and hedges for international equities, and the same could be observed for international equities. We (...) reiterate empirically that flights may improve the financial system’s stability and robustness by allowing diversity to be effective when it is most required. The findings have financial and portfolio implications for investors considering how to deploy their investments in the COVID-19 era. Our findings may impact policymakers’ responses to changes in various asset classes, allowing them to better monitor financial markets and adjust macroeconomic policies. (shrink)

The car-sharing system is a popular rental model for cars in shared use. It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road and fewer (...) in the parking stations. To achieve that, various machine learning models, namely vector autoregression, support vector regression, eXtreme gradient boosting, k-nearest neighbors, and deep learning models specifically long short-time memory, gated recurrent unit, convolutional neural network, CNN-LSTM, and multilayer perceptron, were performed on different kinds of features. These features include the past usage levels, Chongqing’s environmental conditions, and temporal information. After comparing the obtained results using different metrics, we found that CNN-LSTM outperformed other methods to predict the future car usage. Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a time. (shrink)

There is a growing concern about the effects that the relationship between the activity of society in the physical world and in the digital world could have. In this study, we address this question in a context of social crisis. Our quantitative and qualitative analysis of the data associated with the critical process suggests a deep and nontrivial relationship between both worlds. Perhaps the most important result refers to the leading role of language, its meaning, and symbolism in the development (...) of social transformation processes linked to the complexity of the social system and its adaptive nature. (shrink)

Sharpe’s ratio is the most widely used index for establishing an order of priority for the portfolios to which the investor has access, and the purpose of this investigation is to verify that Sharpe’s ratio allows decisions to be made in investment portfolios considering different financial market conditions. The research is carried out by autoregressive model of the financial series of returns using Sharpe’s ratio for evaluations looking over the priority of financial assets which the investor can access while observing (...) the effects that can cause autocorrelated series in evaluation measures for financial assets. The results presented in this study confirm the hypothesis proposed in which Sharpe’s ratio allows decisions to be made in the selection of investment portfolios under normal conditions thanks to the definition of a robustness function, whose empirical estimation shows an average 73% explanation of the variance in the degradation of the Spearman coefficient for each of the performance measures; however, given the presence of autocorrelation in the financial series of returns, this similarity is broken. (shrink)

This study investigates the sampled-data admissibility problem for a singular system. The objective of this paper is to design a sampled-data controller to ensure the admissibility of a singular system and to construct an appropriate Lyapunov–Krasovskii functional to get less conservative results for the sampled-data singular system. To accomplish these objectives, the system is converted into a time-delay system by the input delay approach firstly, and both lower and upper bounds of the delay are considered. Secondly, by introducing a suitable (...) LKF, the admissibility criteria are obtained. Then, when estimating the derivative of the LKF, a relaxation variable is introduced by the method of reciprocally convex inequality, and it is proved that the conservatism is reduced. Finally, two numerical examples are given to prove that the designed sampled-data controller can ensure the states of the systems under the influence of external interference. (shrink)

Digital currency is considered a form of currency which is used in the digital world such as digital forms or electronic devices. Several terms are synonyms for digital currency like digital money, electronic money, and cyber cash. Accurate prediction of the digital currency is an urgent necessity due to its impacts on the economic community. The electronic economy is very dangerous and must be approached with great caution, so as to avoid or minimize the risks that occur in such cases. (...) Deep neural network algorithm was improved to predict the Bitcoin price and then achieve the main goal of reducing financial risks to proceed with electronic business, and good estimation was achieved by using informative data such as transactions and currency return. The proposed method extracted features of related Bitcoin and used the informative ones. Transaction plan considered building nodes in terms of network. Development of deep learning algorithms opens the horizons for the development of electronic businesses that use digital currency. The proposed method achieved worthy results in terms of accuracy and correct prediction and offers the prospect of other research in this area. (shrink)

Photovoltaic power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder and Bayesian regularized NARX network for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance is applied to classify the dataset into multiple clusters, (...) including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R2 for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC-MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods. (shrink)

Electric-mechanical equipment manufacturing industries focus on the implementation of intelligent manufacturing systems in order to enhance customer services for highly customized machines with high-profit margins such as electric power transformers. Intelligent manufacturing consists in using supply chain data that are integrated for smart decision making during the production life cycle. This research, in cooperation with a large electric power transformer manufacturer, provides an overview of critical intelligent manufacturing technologies. An ontology schema forms the terminology relationships needed to build two intelligent (...) supply chain management modules for the IM system demonstration. The two core modules proposed in this research are the intelligent supplier selection and component ordering module and the product quality prediction module. The intelligent supplier selection and component ordering module dispatches orders that match the best options of suppliers based on combined analytic hierarchy process analysis and multiobjective integer optimization. In the case study, the intelligent supplier selection and component ordering module demonstrates several acceptable Pareto solutions based on strict constraints, which is a very challenging task for decision makers without assistance. The second module is the product quality prediction module which uses multivariate regression and ARIMA to predict the quality of the finished products. Results show that the R square values are very close to 1. The module shortens the time for the company to accurately judge whether the two semifinished iron cores for the product meet the quality requirements. The component supplier selection module and the finished product quality prediction module developed in this research can be extended to other IM systems for general high-end equipment manufacturers using mass customization. (shrink)

This paper proposes the CorpRank algorithm to extract social hierarchies from electronic communication data. The algorithm computes a ranking score for each user as a weighted combination of the number of emails, the number of responses, average response time, clique scores, and several degree and centrality measures. The algorithm uses principal component analysis to calculate the weights of the features. This score ranks users according to their importance, and its output is used to reconstruct an organization chart. We illustrate the (...) algorithm over real-world data using the Enron corporation’s e-mail archive. Compared to the actual corporate work chart, compensation lists, judicial proceedings, and analyzing the major players involved, the results show promise. (shrink)

The ageing population has become one of the major issues, with manifold consequences upon the economic welfare and elderly living standards satisfaction. This paper grasps an in-depth assessment framework of the ageing phenomenon in connection with the labor market, with significant implications upon economic welfare, across the European Union. We configure our research on four distinctive groups of the EU–27 countries based on the Active Ageing Index mapping, during 1995–2018, by acknowledging the different intensities of ageing implications on economic well-being (...) from one group of countries to another. The methodological endeavor is based on Structural Equation Modelling. Empirical results highlight that the ageing dimensions and labor market productivity notably shape the socioeconomic development of EU countries, visibly distinguished across the four panels. The economic development induced remarkable positive spillover effects on the welfare of older people, under the influence of the ageing credentials and dynamic shaping factors. Our research advances the literature underpinnings on this multifaceted topic by investigation made on specific groups of the EU countries and distinctive strategies proposed for each group of countries, as effective results for improving the well-being of older people. Constant policy rethinking and adequate strategies should be a top priority for each specific group of EU countries, to further sustain the ageing phenomenon, with positive implications mostly on elderly welfare. (shrink)

In the realistic world, various individuals have distinct personalities, preferences, and attitudes toward new information and behavior acceptance, called population heterogeneity. It is seldom taken into account and theoretically analyzed in information propagation on a weighted network. Therefore, we divide individuals into fashionable and conservative individuals according to their passion degree and willingness for novel behaviors acceptance. Then, we build two behavior adoption threshold models corresponding to fashionable and conservative individuals on the weighted network to explore the effect of population (...) heterogeneity on information propagation. Next, a partition theory based on edge weight and population heterogeneity is proposed to qualitatively analyze the information propagation mechanism. The theoretical analyses and simulation results show that fashionable individuals promote information propagation and behavior adoption. More importantly, the crossover phenomena of phase transition appear. When the fraction of fashionable individuals is relatively large, the increasing pattern of the final adoption size shows a second-order continuous phase transition. In comparison, the increasing pattern alters to first-order discontinuous phase transition with the decrease of the fraction of fashionable individuals. Moreover, reducing weight distribution heterogeneity promotes information propagation and slightly accelerates the change of the phase transition pattern from the first-order discontinuous to the second-order continuous. Besides, increasing the degree distribution heterogeneity accelerates the change of the phase transition pattern. Finally, our theoretical analyses coincide well with the simulation results. (shrink)

Statistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary, while in reality, the competition strength may vary among products. In this paper, we model the strength of the product competition by employing a statistical network model, with an emphasis on how product attributes affect which products are considered together and which products are ultimately purchased by (...) customers. We first demonstrate how customers’ considerations and choices can be aggregated as weighted networks. Then, we propose a weighted network modeling approach by extending the valued exponential random graph model to investigate the effects of product features and network structures on product competition relations. The approach that consists of model construction, interpretation, and validation is presented in a step-by-step procedure. Our findings suggest that the weighted network model outperforms commonly used binary network baselines in predicting product competition as well as market share. Also, traditionally when using binary network models to study product competitions and depending on the cutoff values chosen to binarize a network, the resulting estimated customer preferences can be inconsistent. Such inconsistency in interpreting customer preferences is a downside of binary network models but can be well addressed by the proposed weighted network model. Lastly, this paper is the first attempt to study customers’ purchase preferences and car competition together using weighted directed networks. (shrink)

As the per capita income level increases, both environmental quality and income inequality will change significantly, which arouses people’s attention on the relationship between income inequality and environmental quality. Based on mathematical derivations, we first prove that when the relationship between per capita income and environmental pollution is nonlinear, and environmental pollution is not only related to per capita income, but also, among other potential determinants, to income inequality. Then, we use the two-way fixed estimator to estimate the impact of (...) income inequality on environmental quality by decomposing the Stochastic Impacts by Regression on Population, Affluence, and Technology model. The impact of income inequality on environmental pollution is significantly positive, that is, as the income gap widens, industrial pollutant emissions, carbon dioxide emissions, and PM2.5 in the air will increase; there is an “Inverted U-shaped” impact relationship between income inequality and environmental quality, that is, environmental pollution increases first and then decreases with the increase of per capita GDP; per capita income is the intermediate variable between income inequality and environmental quality. The relationship between income inequality and environmental quality should be fully considered when formulating relevant policies. The government should adopt differentiated environmental policies targeted at low-and-high income groups to achieve a win-win situation of economic growth and environmental protection. (shrink)

The spread of the COVID-19 pandemic has severely impacted all aspects of social and economic life, including the evolution of stock markets. Thus, we advance a methodological framework suitable for assessing 2020 year-long shifts in markets’ statistical complexity, and we apply such framework to ten major international developed or emerging stock markets. Our research reveals that this crisis had considerably altered markets’ evolutionary patterns. The network description of markets’ multivocal transmission of complex responses changed in 2020, European and Asian markets (...) playing a pivotal role. Nevertheless, an important regional and time heterogeneity emerges. In addition, we find that the total number of worldwide confirmed COVID-19 cases plays a leading role in the changes in markets’ complexity. (shrink)

This paper proposes the models of allocating defense and recovery resources for spatially embedded networks, respectively, both of which consider the length of links as the allocation cost. In the defense model, the amount of defense resources required for each zone depends on the total length of the links they contain. It is found that dispersed allocation performs better and that parameters that allow for a uniform distribution of link lengths produce better results. In the recovery model, a shortest link (...) hierarchical recovery strategy is proposed and proved to be more effective. In this strategy, the number of repaired links plays a decisive role in the recovery results, while the total length of the links does not seem to matter when the number is constant. In addition, a number of different parameters are adopted to validate the qualitative conclusions. These models might yield insights into studying and protecting spatial infrastructure systems. (shrink)

Considering the reward and punishment mechanism of the management committee and the complexity of innovation path selection of high-tech and general enterprises, this paper constructs an evolutionary game model of independent innovation incentive mechanism in the National Independent Innovation Demonstration Zone of China. Meanwhile, the equilibrium points of the strategy selection are solved for the three. In addition, this paper adopts numerical simulation to analyze the influence of each decision variable on different players’ strategic selections. The results show that the (...) initial willingness of the management committee, high-tech and general enterprises has different influences on each other, and these factors such as independent innovation cost, technology spillover coefficient, and patent royalty significantly affect the strategic selection of enterprises; the reward and punishment mechanism of the management committee can enhance the innovation willingness of high-tech and general enterprises, in which these punitive measures can promote further the independent innovation of the two; the greater the innovation subsidy provided by the management committee to high-tech enterprises, the heavier the punishment for general enterprises, and the better the effect of independent innovation incentives. The results can provide theoretical guidance and practical reference for the management committee to formulate the independent innovation incentive policies in the National Independent Innovation Demonstration Zone. (shrink)

The evaluation index system of ecological security of marine ranching is based on the assumption that there is independence among evaluation indexes in the existing studies, which ignores the complex interactive paths of marine ranching as an artificial ecosystem. In this study, the MRES evaluation network model that includes interdependent relationships is established based on the Driver-Pressure-State-Impact-Response model and the analytic network process method. Then, the probabilistic linguistic term sets and analytic network process methods are used to calculate the weights (...) of the evaluation indexes of MRES. The overall evaluation value and the contribution rate of clusters are consequently defined and analyzed to reflect the performance of MRES. Finally, a case study is carried out for evaluating the MRES of marine ranches in Rongcheng by means of the proposed method. The conclusions are summarized as follows: The weights of clusters are ranked as Responses > Impact > Driver > State > Pressure, and “scientific management of fishery resources” is the most important index; the MRES performance of marine ranches in the city of Rongcheng is at the medium security grade on the whole, and all 11 samples are driven by the response. (shrink)

Due to the advent of deglobalization and regional integration, this article aims to adopt LASSO-based network connectedness to estimate the multiscale tail risk spillover effects of global stock markets. The results show that tail risk varies across frequencies and shocks. In static analysis, the risk is centered mostly on the developed European and North American markets at a low frequency, and regionalization is imposed on the moderate frequency. Moreover, emerging markets could be sources of risk spillover, especially at the highest (...) frequency where there is no absolute risk center. In dynamic analysis, we use rolling window estimation and find that different frequencies identify distinct episodes of shocks, which provides us with the reason for the diverse risk centers at different time scales in static analysis. Our findings provide heterogeneous financial practitioners, regulators, and investors with diverse characteristics of stock markets under multiple time horizons and help them operate their own trading strategies. (shrink)

Sports officials, players, and fans are concerned about overseas player rankings for the IPL auction. These rankings are becoming progressively essential to investors when premium leagues are commercialized. The decision-makers of the Indian Premier League choose cricketers based on their own experience in sports and based on performance statistics on several criteria. This paper presents a scientific way to rank the players. Our research examines and contrasts different multicriteria decision-making algorithms for ranking foreign players under various criteria to assess their (...) performance and efficiency. The paper uses three MCDM algorithms, TOPSIS, TODIM, and NR-TOPSIS, for foreign players ranking in the Indian Premier League. Our analysis is limited to the batsmen and bowlers only. We perform the analysis using Python language, a popular high-level programming language. Finally, we perform a sensitivity analysis to determine the stability of each method when the weights of the criterion or the value of a parameter was changed. Our analysis exhibits the superiority of TODIM over TOPSIS and NR-TOPSIS. (shrink)

When the first r lower extreme order statistics of a sample of large size n, 1 < r < s < n, are observed, asymptotic predictive intervals of the future extreme order statistic with a rank s are constructed. The only assumption that we adopt is that the first failure time is attracted to the Weibull distribution. In addition, we suggest an efficient point estimator of its shape parameter and then a confidence interval is constructed for it. Moreover, new interesting (...) asymptotic properties of the distributions that belong to the minimum domain of attraction of the Weibull distribution are revealed. Furthermore, extensive simulation studies are conducted to demonstrate the efficiency of the proposed methods. Two real datasets are analyzed to illustrate and corroborate the obtained results. The main results concern large samples. (shrink)

A brain tumor is an abnormal mass or growth of a cell that leads to certain death, and this is still a challenging task in clinical practice. Early and correct diagnosis of this type of cancer is very important for the treatment process. For this reason, this study aimed to develop computer-aided systems for the diagnosis of brain tumors. In this research, we proposed three different end-to-end deep learning approaches for analyzing effects of local and deep features for brain MRI (...) images anomaly detection. The first proposed system is Directional Bit-Planes Deep Autoencoder which extracts and learns local and direction features. The DBP-DAE by decomposition of a local binary pattern into eight bit-planes extracts are directional and inherent local-structure features from the input image and learns robust feature for classification purposes. The second one is a Dilated Separable Residual Convolutional Network which extracts high and low-level features. The main advantage of this approach is that it is robust and shows stable results regardless to size of image database and to solve overfitting problems. To explore the effects of mixture of local and deep extracted feature on accuracy of classification of brain anomaly, a multibranch convolutional neural network approach is proposed. This approach is designed according to combination of DBP-DAE and DSRCN in an end-to-end manner. Extensive experiments conducted based on brain tumor in MRI image public access databases and achieves significant results compared to state-of-the-art algorithms. In addition, we discussed the effectiveness and applicability of CNNs with a variety of different features and architectures for brain abnormalities such as Alzheimer’s. (shrink)

Water quality control helps in the estimation of water bodies and detects the span of pollutants and their effect on the neighboring environment. This is why the water quality of the northern part of Lake Manzala has been studied here from January to March, 2016. This study aims to model and create a program for linear and nonlinear regression of the water elements in Lake Manzala to assess and predict the water quality. Water samples have been extracted from various depths, (...) and physio-chemical properties and heavy metal concentrations have been evaluated. This study has proposed a new algorithm for predicting water quality called “Algorithm of Estimation Regression Model”. On the contrary, in renewable energy applications, statistical modeling and forecasting the solar radiation remains a significant issue with detect to reinforce power management. A new proposed method for forecasting the average Monthly Global Solar Energy in Queensland, Australia, is called, Converting Data Set into Markov Model. It was used to obtain Markov transition probability matrices for three and six states of the solar energy. The proposed forecasting method yielded accurate results with minimal error. (shrink)

In recent years, the world knew many challenges concerning the propagation of infectious diseases such as avian influenza, Ebola, SARS-CoV-2, etc. These epidemics caused a change in the healthy balance of humanity. Also, the epidemics disrupt the economies and social activities of countries around the world. Mathematical modeling is a vital means to represent and control the propagation of infectious diseases. In this paper, we consider a stochastic epidemic model with a Markov process and delay, which generalizes many models existing (...) in the literature. In addition, we show the stochastic threshold for the extinction of the disease. Furthermore, numerical examples are discussed to confirm the theoretical result. (shrink)

The aim of this article is to present a chaotic fruit fly algorithm as an optimization approach for solving engineering design problems. In CFFA, the fruit fly algorithm, which is recognized for its durability and efficiency in addressing optimization problems, was paired with the chaotic local search method, which allows for local exploitation. CFFA will be set up to work in two phases: in the first, FFA will be used to discover an approximate solution, and in the second, chaotic local (...) search will be used to locate the optimal solution. As a result, CFFA can address difficulties associated with the basic FFA such as falling into local optima, an imbalance between exploitation and exploration, and a lack of optimum solution acquisition. The chaotic logistic map is employed in the CLS because it has been demonstrated to be effective in improving the quality of solutions and giving the best performance by many studies. The proposed algorithm is tested by the set of CEC’2005 special sessions on real parameter optimization and many EDPs from the most recent test suite CEC’2020. The results have demonstrated the superiority of the proposed approach to finding the global optimal solution. Finally, CFFA′s results were compared to those of earlier research, and statistical analysis using Friedman and Wilcoxon's tests revealed its superiority and capacity to tackle this type of problem. (shrink)

The proposed work describes an approach for segmentation of activated sludge flocs from the microscopic images for monitoring wastewater treatment. The morphological features of flocs and filaments are related to the state of an activated sludge wastewater treatment plant and must be monitored for proper functioning. Hence, image processing and analysis could be a time-saving monitoring tool. To address this challenge, we propose a novel framework involving a multiphase edge detection algorithm based on information theory. The proposed framework is evaluated (...) and scrutinized critically considering the artifacts found in the photographs tested. To evaluate the segmentations, gold approximation of estimated truth images is created. In addition, the performance was subjectively evaluated for its potential for segmenting activated sludge images. Experimental results show that the proposed framework exhibits the good results and demonstrates its effectiveness. (shrink)

There is an increasing interest in expanding the one-parameter Lindley distribution to two-parameter, three-parameter, and five-parameter. The univariate one-parameter Lindley distribution is still one of the most applicable distributions in data analysis especially in lifetime data. Modeling dependent random quantities required bivariate parametric probability distributions. This study presents a new bivariate three-parameter probability distribution called bivariate modified Lindley distribution. The one-parameter modified Lindley distribution is used as a base line to construct the new model. Its statistical properties including cumulative function, (...) density function, marginals, moments, conditional distributions, and copula are discussed. Simulation is constructed to declare theoretical properties, to show the flexibility of the new model and to investigate the goodness of fit. Two sets of real data, financial data and UEFA Champion’s League data, are used to show the applicability of the proposed model for different types of data. (shrink)

The application of recycled aggregate as a sustainable material in construction projects is considered a promising approach to decrease the carbon footprint of concrete structures. Prediction of compressive strength of environmentally friendly concrete containing recycled aggregate is important for understanding sustainable structures’ concrete behaviour. In this research, the capability of the deep learning neural network approach is examined on the simulation of CS of EF concrete. The developed approach is compared to the well-known artificial intelligence approaches named multivariate adaptive regression (...) spline, extreme learning machines, and random forests. The dataset was divided into three scenarios 70%-30%, 80%-20%, and 90%-10% for training/testing to explore the impact of data division percentage on the capacity of the developed AI model. Extreme gradient boosting was integrated with the developed AI models to select the influencing variables on the CS prediction. Several statistical measures and graphical methods were generated to evaluate the efficiency of the presented models. In this regard, the results confirmed that the DLNN model attained the highest value of prediction performance with minimal root mean squared error. The study revealed that the highest prediction performance could be attained by increasing the number of variables in the prediction problem and using 90%-10% data division. The results demonstrated the robustness of the DLNN model over the other AI models in handling the complex behaviour of concrete. Due to the high accuracy of the DLNN model, the developed method can be used as a practical approach for future use of CS prediction of EF concrete. (shrink)

River segmentation of remote sensing images is of important research significance and application value for environmental monitoring, disaster warning, and agricultural planning in an area. In this study, we propose a river segmentation model in remote sensing images based on composite attention network to solve the problems of abundant river details in images and the interference of non-river information including bridges, shadows, and roads. To improve the segmentation efficiency, a composite attention mechanism is firstly introduced in the central region of (...) the network to obtain the global feature dependence of river information. Next, in this study, we dynamically combine binary cross-entropy loss that is designed for pixel-wise segmentation and the Dice coefficient loss that measures the similarity of two segmentation objects into a weighted one to optimize the training process of the proposed segmentation network. The experimental results show that compared with other semantic segmentation networks, the evaluation indexes of the proposed method are higher than those of others, and the river segmentation effect of CoANet model is significantly improved. This method can segment rivers in remote sensing images more accurately and coherently, which can meet the needs of subsequent research. (shrink)

Remanufacturing has become an important way to realize sustainable development strategy. For remanufacturing closed-loop supply chain under different circumstances, many factors are considered, such as consumers’ different preferences for the purchase and payment of remanufactured products and the quality of recycled products. In this study, three models are presented for supply chain system, including a manufacturer lead and a retailer recycle. While nongovernment participation and non-supply-chain coordination are considered in model I, model II has government participation but non-supply-chain coordination, and (...) model III has both government participation and supply chain coordination. The equilibrium decisions are derived for each model using the game-theoretic approach. The study found that consumers’ preferences affect the pricing decisions of remanufactured products. When consumers’ willingness to buy and pay for remanufactured products is close to that of new products, the total profit of the supply chain will significantly increase; the quality of recycled products will also influence the pricing decisions of new/remanufactured products, which in turn will affect the market demand for products. Government participation is conducive to encouraging manufacturer, retailer, and consumers to actively participate in the recycling and remanufacturing activities of used products. Supply chain coordination contracts can further enhance the role of the consumer market and promote the implementation of government policies. Under certain conditions, revenue sharing contract coordination can significantly increase the total profit of the supply chain system. The research conclusions have certain reference value for the coordination among the nodes of the remanufacturing supply chain and the formulation of policies with government participation. (shrink)

The structure formed by fossil energy trade among countries can be divided into multiple subcommodity networks. However, the difference of coupling mode and transmission mechanism between layers of the multirelationship network will affect the measurement of node importance. In this paper, a framework of multisource information fusion by considering data uncertainty and the classical network centrality measures is build. Then, the evidential centrality indicator is proposed, by integrating Dempster–Shafer evidence theory and network theory, to empirically identify influential nodes of fossil (...) energy trade along the Belt and Road Initiative. The initial result of the heterogeneity characteristics of the constructed network drives us to explore the core node issue further. The main detected evidential nodes include Russia, Kazakhstan, Czechia, Slovakia, Egypt, Romania, China, Saudi Arabia, and Singapore, which also have higher impact on network efficiency. In addition, cluster analysis discovered that resource endowment is an essential factor influencing country’s position, followed by geographical distance, economic level, and economic growth potential. Therefore, the above aspects should be considered when ensuring national trade security. At last, the rationality and comprehensiveness of EVC are verified by comparing with some benchmark centralities. (shrink)

Machine learning methods have been used in multifactor stock strategy for years. This paper uses three machine learning methods and linear regression method to find the most appropriate approach. First, a framework is established and 10 style factors and 30 industry factors are chosen. Second, four methods are used to forecast portfolio returns and compared by predicting returns, successful rate, and Sharpe ratio. Finally, this paper draws conclusion. The main findings are as follows: the support vector regression has the most (...) stable successful rate for predicting, while ridge regression and linear regression have the most unstable successful rate with more extreme cases; algorithm of support vector regression fitting higher-degree polynomials in Chinese A-share market is optimized, compared with the traditional linear regression both in terms of stock return and retracement control; the results of support vector regression significantly outperforming the CSI 500 index prove further. (shrink)

In terms of image processing, encryption plays the main role in the field of image transmission. Using one algorithm of deep learning, such as neural network backpropagation, increases the performance of encryption by learning the parameters and weights derived from the image itself. The use of more than one layer in the neural network improves the performance of the algorithm. Also, in the process of image encryption, randomness is an important component, especially when used by smart learning methods. Deep neural (...) networks are related to pixels used to manipulate position and value according to the predicted new value given from a variable neural system. It also includes messy encrypted images used via applying randomness and increasing the key space in addition to using the logistic and Henon map for complexity. The main goal of any encryption method is to increase the complexity of the encrypted image to be difficult or impossible to decrypt the image without the proposed key. One of the important measurements for image encryption is the histogram and how it can be uniformed by the proposed method. Variables of randomness are used as features for the deep learning system, with feedback during iteration. An ideal image processing encryption yields high messy images by keeping the quality. Experimental results showed the backpropagation algorithm achieved better results than other algorithms. (shrink)

The study of the chaotic dynamics in fractional-order discrete-time systems has received great attention over the last years. Some efforts have been also devoted to analyze fractional maps with special features. This paper makes a contribution to the topic by introducing a new fractional map that is characterized by both particular dynamic behaviors and specific properties related to the system equilibria. In particular, the conceived one dimensional map is algebraically simpler than all the proposed fractional maps in the literature. Using (...) numerical simulation, we investigate the dynamic and complexity of the fractional map. The results indicate that the new one-dimensional fractional map displays various types of coexisting attractors. The approximate entropy is used to observe the changes in the sequence sequence complexity when the fractional order and system parameter. Finally, the fractional map is applied to the problem of encrypting electrophysiological signals. For the encryption process, random numbers were generated using the values of the fractional map. Some statistical tests are given to show the performance of the encryption. (shrink)

One of the mathematical programming techniques is data envelopment analysis, which is used for evaluating the efficiency of a set of similar decision-making units. Fixed resource allocation and target setting with the help of DEA is a subject that has gained much attention from researchers. A new model was proposed by determining a common set of weights. All DMUs were involved with the aim of achieving higher efficiency in every DMU after the procedure. The minimum resources and targets allocated to (...) each DMU were commensurate to the efficiency of that DMU and the share of DMU in the input resources and the output productions. To examine the proposed method, other methods in the DEA literature were examined as well, and then, the efficiency of the method was demonstrated through a numerical example. (shrink)

The aim of this study is to introduce a novel method to solve a class of two-dimensional fractional optimal control problems. Since there are some difficulties solving these problems using analytical methods, thus finding numerical methods to approximate their solution is a challenging topic. In this study, we use transcendental Bernstein series. In fact, for solving the problem, we generalize the Bernstein polynomials to a larger class of functions which can provide more accurate approximate solutions. The convergence theorem is proved. (...) Some examples are solved to demonstrate the validity and applicability of this technique. Comparing the results with other methods, we can find the efficiency and applicability of the scheme. (shrink)

Unlike previous studies that consider the Chicago Board of Options Exchange implied volatility index, we examine long memory and fractality in the universe of nine CBOE volatility indices. Using daily data from October 5, 2007, to October 5, 2020, covering calm and crisis periods, we find evidence of long memory and fractality in all indices and a change in the degree of volatility persistence, which points to inefficiency. The long memory of the SKEW index is strong before the onset of (...) three crisis periods, but eases afterwards. The findings provide new insights that matter to investment decisions and trading strategies. (shrink)

The volatility of Shanghai crude oil futures prices is researched in this paper. The cusp catastrophe analysis of Shanghai crude oil futures price is based on the perspective of volatility influencing factors. Some important factors are selected based on commodity attributes and financial attributes, including certain China’s macrofactors, such as producer price index and macroeconomic prosperity index. The principal component analysis is used to process the factors. The control variables of the cusp catastrophe model are extracted. The discriminant of the (...) cusp catastrophe model and the principal component analysis are combined to determine the month of mutation. The three-dimensional renderings and plane projections of Shanghai crude oil futures price mutations in 2018 and 2019 are presented. The model is tested by comparing the time points of price sudden change nodes and emergencies. The results show that only nine factors can be used to roughly determine whether the Shanghai crude oil futures price has a sudden change. During the analysis period, the mutation months are May 2018, December 2018, February 2019, June 2019, and September 2019. It can be proved that the established model has certain feasibility and accuracy. (shrink)

This work defines various stakeholder roles to overcome the barriers to implementing Education 4.0, which were recently identified in the domain literature. The stakeholder roles are evaluated against these barriers, and such evaluation is structured as a multicriteria sorting problem. To this end, an integrated entropy-based CRITIC-CODAS-SORT under a Fermatean fuzzy environment addresses epistemic uncertainties inherent in decision-making. The FF CRITIC assigns the priority weights of the barriers, while the FF CODAS-SORT determines the high-priority stakeholder roles. A case of an (...) HEI evaluating 57 possible roles of 5 stakeholders is demonstrated here. Findings suggest the lack of collaboration, apprehensive stakeholders, cybersecurity threats, health issues, and cost as crucial barriers to the HEI. The sorting process yields 13 high-priority roles, encompassing those within the influence of the government, university management, human resource function, and educators. Various managerial insights are offered as inputs to the design of initiatives in EDUC4 implementation. (shrink)

In this work, we introduce families of multimodal maps based on logistic map, i.e., families of m-modal maps are defined on an interval I ⊂ ℝ, which is partitioned into non-uniform subdomains, with m ∈ ℕ. Because the subdomains of the partition are not uniform, each subdomain contains a unimodal map, given by the logistic map, that can have different heights. Therefore, we give the necessary and sufficient conditions for these modal maps present a multimodal family of m-modal maps, i.e., (...) a bifurcation parameter can set a unimodal map, a bimodal map, up to a m -modal map. Some numerical examples are given according to the developed theory. Some numerical examples are given in accordance with the developed theory. (shrink)

A topological index is a numeric quantity related with the chemical composition claiming to correlate the chemical structure with different chemical properties. Topological indices serve to predict physicochemical properties of chemical substance. Among different topological indices, degree-based topological indices would be helpful in investigating the anti-inflammatory activities of certain chemical networks. In the current study, we determine the neighborhood second Zagreb index and the first extended first-order connectivity index for oxide network O X n, silicate network S L n, chain (...) silicate network C S n, and hexagonal network H X n. Also, we determine the neighborhood second Zagreb index and the first extended first-order connectivity index for honeycomb network H C n. (shrink)

e-commerce has promoted the application of the Internet by traditional enterprises. While this phenomenon has deepened industrial embeddedness, it has also profoundly affected the development of traditional manufacturing and retail industries. Based on the embeddedness theory, this paper takes the e-commerce industrial chain in Xiong’an New Area of China as an example, uses location entropy method and local spatial autocorrelation to analyze its spatial characteristics and influencing factors, and also discusses the features of embeddedness from each dimension of industrial chains. (...) The results show the following: ① The supply chain of industry-dependent e-commerce is dominated by production wholesalers, and suppliers with cost advantages and commercial traditions are embedded in local characteristic industries; the e-commerce value chain is dominated by production and processing activities, and the spatial orientation of the business model is affected by regional industrial functions; the e-commerce enterprise chain is dominated by B2B enterprises, the high-level agglomeration of which reflects the absolute scale advantage provided by the e-commercialization of traditional industry. ② The distribution of the e-commerce industrial chains of Xiong’an New Area is characterized by spatial heterogeneity and has obvious spatial agglomeration. The industrial chains are concentrated in Xiong County, followed by Rongcheng County, and Anxin County has the weakest agglomeration. ③ The main factors that cause the uneven distribution of the e-commerce industrial chains in Xiong’an New Area include three aspects: transportation location advantages, traditional industrial foundation, and government policy regulation. Research can provide a scientific reference for accurately identifying the spatial development characteristics of the industrial chains from various perspectives and promoting the sustainable development and e-commercialization of traditional industries. (shrink)

The relationship between urban expansion and population density changes is complex and plays a fundamental role in urban sustainable development research. This relationship has been studied in multiple large cities. However, there is no report of the relationship of the two factors mentioned above in urban agglomeration in a particular region of China. Ten cities located in the central-southern region of Liaoning province are selected as research samples in this study. The spatial growth rate and urban compactness index of the (...) sample cities were calculated using the land use and population data of these cities in three time phases: 1995, 2005, and 2015. Then, the geographical growth features, the population density changes, and the link between these two in the sample cities were investigated at these three periods. Our results revealed that the studied cities of central-southern Liaoning province expanded at the three time phases under the driver of positive population density growth, but the effects of urban population change on urbanization growth modes across time were uneven across different periods. Our study showed a trend that active transition of different urbanization growth modes was associated with decreased population density. (shrink)

Social-bots-mediated information manipulation is influencing the public opinion environment, and their role and behavior patterns in news proliferation are worth exploring. Based on the analysis of bots' posting frequency, influence, and retweeting relationship, we take the diffusion of The New York Times' coverage of Xinjiang issue on the overseas social platform Twitter as an example and employ the two-step flow model. It is found that in the role of second-step diffusion, unlike posting news indiscriminately in first-step diffusion, social bots are (...) more inclined to postcontroversial information in second-step diffusion; in terms of diffusion patterns, although social bots are more engaged in first-step diffusion than in second-step diffusion and can trigger human users to retweet, they are still inferior to humans in terms of influence. (shrink)

Autism spectrum disorder is an inherited long-living and neurological disorder that starts in the early age of childhood with complicated causes. Autism spectrum disorder can lead to mental disorders such as anxiety, miscommunication, and limited repetitive interest. If the autism spectrum disorder is detected in the early childhood, it will be very beneficial for children to enhance their mental health level. In this study, different machine and deep learning algorithms were applied to classify the severity of autism spectrum disorder. Moreover, (...) different optimization techniques were employed to enhance the performance. The deep neural network performed better when compared with other approaches. (shrink)

Positive frequency dependent selection is a natural selection regime where the fitness of a phenotype increases with its frequency in the population. Examples can be typically found in the spread of disease tolerance strategies in a population. A characterizing feature of PFDS is that the focal allele may experience favorable selection only when it becomes more frequent in the population, while being selected against when it is rare. In this paper, by applying a solution concept from evolutionary game theory, we (...) hypothesize that the process of emergence of such alleles triggers selection for silence, where the emergence of alleles that can stay unexpressed for a period of time will be favored by selection forces. We illustrate our hypothesis using a mathematical model of a population of single locus diploid organisms with two alleles where a single mutant of an allele that experiences positive frequency dependent selection appears in a population where the other allele is a dominant. The model results show that the longer the silence interval of the new mutant before its expression, the better its chances of getting fixed in the population. However, this effect will be observable only to a certain limit after which further increase in the interval will not have an effect on the expected fixation rate. Two divergences from the simple scenario are also investigated: the nonsynchronous expression of the focal allele and its emergence in a spatial grid limited by a migration bottleneck. In all cases, it is shown that there is an evolutionary pressure inherent in the dynamics of positive frequency dependent selection on the genes to have a delayed expression. It is hypothesized that the regulation systems of such traits will be under selection for either internal silencing mechanisms or to be triggered by an external environmental condition. (shrink)

The quadruped robot is subject to self and external interference during the running process. In this paper, in order to improve the stability of the quadruped robot, a disturbance suppression strategy based on the kinematic model and the virtual model is proposed. Through the whole-body kinematics modeling of the body, the cause of the disturbance is analyzed. At the same time, two spring-damping virtual elements are introduced on the body. The virtual force generated by the spring-damping model is according to (...) the displacement and speed of the CoM when the robot is moving. By simulating the tort gait of the robot, sorting out the data obtained from the experiment, and drawing a comparison curve with the experiment results of the no spring-damping virtual element under the same conditions, it is proved that the method proposed in this paper has a significant effect on maintaining the dynamic stability of the body, and the actual trajectory is not different from the theoretical trajectory. (shrink)

Presently, the State Grid Corporation of China has accumulated a large amount of maintenance records for power primary equipment. Unfortunately, most of these records are unstructured data which lead to difficultly analyze and utilize them. The emergence of natural language processing technology and deep learning methods provide a solution for unstructured text data. This paper proposes a progressive multitype feature fusion model to recognize Chinese named entity of unstructured maintenance records for power primary equipment. Firstly, the textual characteristics and word (...) separation difficulties of maintenance records are analyzed, then 7 main entity categories of power technical terms from unstructured maintenance records are chosen, and 3452 maintenance records are labeled by these categories, which is so called EPE-MR training dataset. Secondly, the standard test reports, standard maintenance, and fault analysis reports for three types of power primary equipment are employed as corpus to train character embedding in order to obtain certain words representation ability of maintenance records. After that, progressive multilevel radicals feature extraction module is designed to get detailed and fine semantic information in a hierarchical manner. Further, radicals feature representation and character embedding are concatenated and sent to BiLSTM module to extract contextual information in order to improve Chinese entity recognition ability. Moreover, CRF is introduced to handle the dependencies among prediction labels and to output the optimal prediction sequence, which can easily obtain structured data of maintenance records. Finally, comparative experiments on public MSRA dataset, China People’s Daily corpus, and EPE-MR dataset are implemented, respectively, which show the effectiveness of the proposed method. (shrink)

Aerial work platform is a kind of engineering vehicle which is used for hoisting personnel to the appointed place for maintenance or installation. Based on the dynamics model considering the flexible deformation existing in the arm system of folding-boom aerial platform vehicle, this study presents a NN-based backstepping controller used for trajectory tracking control of work platform. The proposed controller can reduce tracking error of work platform and suppress the vibration simultaneously by using the RBF neural network system to compensate (...) model uncertainties and disturbances. Furthermore, we prove that the whole system is stable and convergent by Lyapunov stability theorem. In addition, we give the simulation results which show that the good control performance of the designed controller for trajectory tracking and vibration inhabiting of work platform in the case of model uncertainties. (shrink)

As new energy vehicle is the future of automobile development, it is of great significance to dig deeper into the technical topics and development trends of new energy vehicles for accurately understanding the technical trends of the new energy vehicle industry, grasping development opportunities, and scientifically formulating strategic plans. This paper takes the patent texts in the field of new energy vehicles from 2000 to 2020 in the patent database of CNKI as the data source, identifies 25 technical topics implied (...) in the patent texts by using the LDA topic model, analyzes the evolution trend of the 25 technical topics in terms of importance and popularity, and predicts the popularity and development trend of each technical topic in new energy vehicles from 2021 to 2025 by constructing the ARIMA model. The popularity and development trend of each technology topic of new energy vehicles in China from 2021 to 2025 are predicted by constructing ARIMA model. Drawing on quantitative evidence, the study found that there are top five technical topics in terms of importance in this field, namely, Topic 8, Topic 5, Topic 14, Topic 9, and Topic 15. From 2014 to 2020, the importances of Topic 5, Topic 8, Topic 6, Topic 9, and Topic 15 are gradually rising. In terms of popularity of technical topics, from 2014 to 2020, the first to fifth topics are Topic 20, Topic 8, Topic 3, Topic 15, and Topic 10. Based on the prediction of ARIMA model, it is found that the popularity of these five technical topics is steadily increasing from 2021 to 2025, among which the popularity of Topic 20 will increase from 63.58 to 113.07, the largest increase in popularity among all technical topics. The paper provides implications for countries dedicated to developing the new energy vehicle industry. (shrink)

This paper considers two kinds of stochastic reentrant job shop scheduling problems, i.e., the SRJSSP with the maximum tardiness criterion and the SRJSSP with the makespan criterion. Owing to the NP-complete complexity of the considered RJSSPs, an effective differential evolutionary algorithm combined with two uncertainty handling techniques, namely, DEA_UHT, is proposed to address these problems. Firstly, to reasonably control the computation cost, the optimal computing budget allocation technique is applied for allocating limited computation budgets to assure reliable evaluation and identification (...) for excellent solutions or individuals, and the hypothesis test technique is added to execute a statistical comparison to reduce some unnecessary repeated evaluation. Secondly, a reentrant-largest-order-value rule is designed to convert the DEA’s individual to the SRJSSP’s solution. Thirdly, a conventional active decoding scheme for the job shop scheduling problem is extended to decode the solution for obtaining the criterion value. Fourthly, an Insert-based exploitation strategy and an Interchange-based exploration strategy are devised to enhance DEA’s exploitation ability and exploration ability, respectively. Finally, the test results and comparisons manifest the effectiveness and robustness of the proposed DEA_UHT. (shrink)

In this research work, we have explored the physical and topological properties of the crystal structure of metal-insulator transition superlattice. In recent times, two-dimensional substantial have enamored comprehensive considerations owing to their novel ophthalmic and mechanical properties for anticipated employment. Recently, some researchers put their interest in modifying this material into useful forms in human life. Also, Metal-Insulator Transition Superlattice is useful in form of a thin film to utilize as two-dimensional transition metal dichalcogenides. Moreover, we have defined the computed-based (...) bond properties such as the degree constructed topological indices and their heat of formation for single crystal and monolayered structure of Ge-Sb-Te. Also, this structure is one of the most interesting composites in modern resources of science. (shrink)

With the expansion of the digital business line, the network flow behind the digital power grid is also exploding. To prevent network congestion, this article proposes a novel network flow forecasting model, which is composed of variational mode decomposition, GRU-xgboost block, and a forecasting adjustment block, to grasp the changing patterns and trends of network flow in advance, and to formulate reasonable and effective flow management strategies and meet the requirements of users for network service quality. The network flow series (...) in power grid enterprise always contain complex patterns and outliers, and VMD is applied to adaptively process complex net flow time series into several subseries with simpler patterns. A GRU-xgboost block is designed to reconstruct the features of historical series. Then, xgboost model is applied to generate predictions for all decomposed subsignals. For the final predictions, we design a forecasting adjustment block to further remove the influence of random noise. Finally, the empirical results show the superior performance of the proposed model on network flow forecasting task. (shrink)

Using devices such as unified power quality conditioners in distribution networks seems essential for higher electricity quality. Moreover, distribution network reconfiguration is a suitable model for improving network characteristics, including loss reduction and voltage increase for distribution networks, and is widely used in this era. Here, the study discusses the rechanging of distribution networks for UPQC via proposing an appropriate model for it. In addition to the optimum structure of distribution networks, the most appropriate branch where UPQC must be located (...) and the most appropriate reactive power size with which series and shunt filters must be injected into the grid are determined. The simulations have been applied on two 69- and 84-standard-bus networks. The results of the simulations indicate much power loss reduction and much voltage increase in the presence of UPQC compensators. (shrink)

A chemical compound in the form of graph terminology is known as a chemical graph. Molecules are usually represented as vertices, while their bonding or interaction is shown by edges in a molecular graph. In this paper, we computed various connectivity indices based on degrees of vertices of a chemical graph of indium phosphide. Afterward, we found the physical measures like entropy and heat of formation of InP. Then, we fitted curves between different indices and the thermodynamical properties, namely, heat (...) of formation and entropy. Curve fitting was done in MATLAB through different methods based on linearity and nonlinearity. Furthermore, we depicted our results numerically and graphically. These numerical systems may give an approach to concentrate on the thermodynamical properties of the compound design of InP at an exceptional level that will help understand the connection between framework measurement and these actions. (shrink)

This paper presents decomposition of the fourth-order Euler-type linear time-varying system as a commutative pair of two second-order Euler-type systems. All necessary and sufficient conditions for the decomposition are deployed to investigate the commutativity, sensitivity, and the effect of disturbance on the fourth-order LTVS. Some systems are commutative, and some are not commutative, while some are commutative under certain conditions. Based on this fact, the commutativity of fourth-order Euler-type LTVS is investigated by introducing the commutative requirements, theories, and conditions. The (...) fourth-order Euler-type LTVSs are investigated into commutative pairs of twice Euler-type second-order linear time-varying systems. The decomposition theories and conditions are derived, proved, and solved to simplify the use of commutativity for practical and industrial uses. Some fourth-order systems are sensitive toward change in initial conditions or parameters while others are not, and the effect due to disturbance also varies within systems. Furthermore, the stability and robustness of systems have so many issues. But we consider fourth-order Euler-type LTVS to observe, investigate, and tackle these issues. Lastly, the realization of fourth-order LTVS from cascaded two second-order systems can be laboratory experimented which is an open problem for future engineers to investigate. However, the theoretical results show a good agreement with the simulation results is considered in this work. Perhaps it might have unlimited physical applications in science and engineering as well as theoretical contribution. But beyond any reasonable doubt, the novelty is guaranteed because this study is the first of its kind that introduces the decomposition of the fourth-order Euler-type linear time-varying system as a commutative pair of two second-order Euler-type systems. Illustrative examples are presented to support the results. (shrink)

Unmanned aerial vehicles, particularly quadcopters, have several medical, agriculture, surveillance, and security applications. However, the use of this innovative technology for civilian applications is still very limited in low-income countries due to the high cost, whereas low-cost controllers available in the market are often tuned using the hit and trial approach and are limited for specific applications. This paper addresses this issue and presents a novel proof of concept low-cost quadcopter UAV design approach using a systematic Model-Based Design method for (...) mathematical modeling, simulation, real-time testing, and prototyping. The quadcopter dynamic model is developed, and controllers are designed using Proportional Integral, and Derivative, Pole Placement, and Linear Quadratic Regulator control strategies. The stability of the controllers is also checked using Lyapunov stability analysis. For verification and validation of the design, Software-in-the-Loop, Processor-in-the-Loop, Hardware-in-the-loop testing, and Rapid Control Prototyping have been performed. The V&V methods of the MBD approach showed practically valid results with a stable flight of the quadcopter prototype. The proposed low-cost POC quadcopter design approach can be easily modified to have enhanced features, and quadcopters with different design parameters can be assembled using this approach for a diverse range of applications. (shrink)

In this paper, a three-dimensional chaotic system with a line equilibrium is studied, in which a single nonbifurcation parameter is used to control the amplitude and frequency. A variety of chaotic signals can be modified using the amplitude-frequency control switch. The realization of circuit simulation based on multisim further verifies the theoretical analysis. Finally, the method for encrypting color images is tested, and the process performance is valued. It shows that the novel chaotic oscillation has a promising application prospect in (...) image encryption. (shrink)

In the present paper, we introduce new models of pendulum motions for two cases: the first model consists of a pendulum with mass M moving at the end of a string with a suspended point moving on an ellipse and the second one consists of a pendulum with mass M moving at the end of a spring with a suspended point on an ellipse. In both models, we use the Lagrangian functions for deriving the equations of motions. The derived equations (...) are reduced to a quasilinear system of the second order. We use a new mathematical technique named a large parameter method for solving both models’ systems. The analytical solutions are obtained in terms of the generalized coordinates. We use the numerical techniques represented by the fourth-order Runge–Kutta method to solve the autonomous system for both cases. The stabilities of the obtained solutions are studied using the phase diagram procedure. The obtained numerical solutions and analytical ones are compared to examine the accuracy of the mathematical and numerical techniques. The large parameter technique gives us the advantage to obtain the solutions at infinity in opposite with the famous Poincare’s method which was used by many outstanding scientists in the last two centuries. (shrink)

Cancer chemotherapy has been the most common cancer treatment. However, it has side effects that kill both tumor cells and immune cells, which can ravage the patient’s immune system. Chemotherapy should be administered depending on the patient’s immunity as well as the level of cancer cells. Thus, we need to design an efficient treatment protocol. In this work, we study a feedback control problem of tumor-immune system to design an optimal chemotherapy strategy. For this, we first propose a mathematical model (...) of tumor-immune interactions and conduct stability analysis of two equilibria. Next, the feedback control is found by solving the Hamilton–Jacobi–Bellman equation. Here, we use an upwind finite-difference method for a numerical approximate solution of the HJB equation. Numerical simulations show that the feedback control can help determine the treatment protocol of chemotherapy for tumor and immune cells depending on the side effects. (shrink)

The grey system model with time power, which is often called the GM, appeals considerable interest of research due to its effectiveness in time series forecasting. Aimed to improve further the GM model, this paper introduces a new whitening equation with variable coefficient into the original whitening equation which extends applicable scope; as a result, an improved grey model with time power, namely, OGM, is proposed. Firstly, the time response function of the novel model and the restored values of original (...) series are deduced through grey modelling techniques. Secondly, the variable coefficient in the whitening equation and the time power are determined by particle swarm optimization algorithm. Two empirical examples are then used to verify the validity of the novel model. Finally, the novel model is applied to predict the oil consumption of China from 2004 to 2018. Results show the novel model outperforms other commonly-used competitive models, which can well serve a benchmark model for scholars and decision-makers. (shrink)

Compared to a charging scheduling and management problem characterized by predetermined trip assignment, this study takes bus and driver scheduling into account, and mealtime windows must be guaranteed as one of the major labor regulations. A discretized mixed-integer linear programming model is developed based on a single electric bus route. We aim to obtain fast and high-quality global solutions for this problem, and the model can be easily executed by bus operators by directly invoking an available optimization solver such as (...) IBM ILOG CPLEX. We test our model on a real round-trip bus route. Numerical experiments show that CPLEX takes approximately 6 sec to obtain an optimal solution. The model can not only reasonably arrange daily trips for each electric bus and driver but also effectively determine the optimal charging schedule and management for an electric bus line. Besides, we analyze the sensitivity of the key parameters in the model. With the increase in the drivers’ maximum workload, the drivers’ average idle time decreases by approximately 11.25%. The objective value decreases by approximately 38.71% and 40.04% with increases in the battery capacity and fleet size, respectively, and the objective value increases by approximately 30.06% with the decrease in the initial battery driving range. In addition, we compare the effectiveness of our time discretization modeling method in solving the same case study to that from other similar studies, and the validity of our method can be verified by the calculation time. We also compare the computational efficiency of CPLEX in solving the same case study problem with and without implementing valid inequalities, and the computational efficiency of the valid inequality method is greatly improved. Finally, through the testing of a multiline network, the potential application of the model to a large-scale traffic network is verified. (shrink)

Tuberculosis is caused by bacillus Mycobacterium tuberculosis. In this study, a mathematical model of tuberculosis is analyzed. The numerical behaviour of the considered model is analyzed including basic reproduction number and stability. We applied three numerical techniques to this model, i.e., nonstandard finite difference scheme, Runge–Kutta method of order 4, and forward Euler scheme. NSFD scheme preserves all the essential properties of the model. Acquired results corroborate that NSFD scheme converges for each step size. While the other two schemes failed (...) to preserve some properties of the model such as positivity and convergence. A graphical comparison presented in this study confirms the numerical stability of the NSFD technique shown here is maintained over a large area. (shrink)

Being building block of data sciences, link prediction plays a vital role in revealing the hidden mechanisms that lead the networking dynamics. Since many techniques depending in vertex similarity and edge features were put forward to rule out many well-known link prediction challenges, many problems are still there just because of unique formulation characteristics of sparse networks. In this study, we applied some graph transformations and several inequalities to determine the greatest value of first and second Zagreb invariant, S K (...) and S K 1 invariants, for acyclic connected structures of given order, diameter, and pendant vertices. Also, we determined the corresponding extremal acyclic connected structures for these topological indices and provide an ordering giving a sequence of acyclic connected structures having these indices from greatest in decreasing order. (shrink)

In this paper, we propose a new and simple method for image encryption. It uses an external secret key of 128 bits long and an internal secret key. The novelties of the proposed encryption process are the methods used to extract an internal key to apply the zigzag process, affine transformation, and substitution-diffusion process. Initially, an original gray-scale image is converted into binary images. An internal secret key is extracted from binary images. The two keys are combined to compute the (...) substitution-diffusion keys. The zigzag process is firstly applied on each binary image. Using an external key, every zigzag binary image is reflected or rotated and a new gray-scale image is reconstructed. The new image is divided into many nonoverlapping subblocks, and each subblock uses its own key to take out a substitution-diffusion process. We tested our algorithms on many biomedical and nonmedical images. It is seen from evaluation metrics that the proposed image encryption scheme provides good statistical and diffusion properties and can resist many kinds of attacks. It is an efficient and secure scheme for real-time encryption and transmission of biomedical images in telemedicine. (shrink)

Background. Imposter syndrome, associated with self-doubt and fear despite clear accomplishments and competencies, is frequently detected in medical students and has a negative impact on their well-being. This study aimed to predict the students’ IS using the machine learning ensemble approach. Methods. This study was a cross-sectional design among medical students in Bangladesh. Data were collected from February to July 2020 through snowball sampling technique across medical colleges in Bangladesh. In this study, we employed three different machine learning techniques such (...) as neural network, random forest, and ensemble learning to compare the accuracy of prediction of the IS. Results. In total, 500 students completed the questionnaire. We used the YIS scale to determine the presence of IS among medical students. The ensemble model has the highest accuracy of this predictive model, with 96.4%, while the individual accuracy of random forest and neural network is 93.5% and 96.3%, respectively. We used different performance matrices to compare the results of the models. Finally, we compared feature importance scores between neural network and random forest model. The top feature of the neural network model is Y7, and the top feature of the random forest model is Y2, which is second among the top features of the neural network model. Conclusions. Imposter syndrome is an emerging mental illness in Bangladesh and requires the immediate attention of researchers. For instance, in order to reduce the impact of IS, identifying key factors responsible for IS is an important step. Machine learning methods can be employed to identify the potential sources responsible for IS. Similarly, determining how each factor contributes to the IS condition among medical students could be a potential future direction. (shrink)

Caricatures can help to understand the perception of a face. The prominent facial feature of a subject can be exaggerated, so the subject can be easily identified by humans. Recently, significant progress has been made to face detection and recognition from images. However, the matching of caricature with photographs is a difficult task. This is due to exaggerated features, representation of modalities, and different styles adopted by artists. This study proposed a cross-domain qualitative feature-based approach to match caricature with a (...) mugshot. The proposed approach uses Haar-like features for the detection of the face and other facial attributes. A point distribution measure is used to locate the exaggerated features. Furthermore, the ratio between different facial features was computed using different vertical and horizontal distances. These ratios were used to calculate the difference vector which is used as input to different machine and deep learning models. In order to attain better performance, stratified k-fold cross-validation with hyperparameter tuning is used. Convolution neural network-based implementation outperformed the machine learning-based models. (shrink)

The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, violent and nonviolent, are (...) created by merging multiple smaller classes of crimes. Exploratory data analysis is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well. (shrink)

In this study, complex dynamics of Briggs–Rauscher reaction system is investigated analytically and numerically. First, the Briggs–Rauscher reaction system is reduced into a new nonlinear parametric oscillator. The Melnikov method is used to derive the condition of the appearance of horseshoe chaos in the cases ω = Ω and ω ≠ Ω. The performed numerical simulations confirm the obtained analytical predictions. Second, the prediction of coexisting attractors is investigated by solving numerically the new nonlinear parametric ordinary differential equation via the (...) fourth-order Runge–Kutta algorithm. As results, it is found that the new nonlinear chemical system displays various coexisting behaviors of symmetric and asymmetric attractors. In addition, the system presents a rich variety of bifurcations phenomena such as symmetry breaking, symmetry restoring, period doubling, reverse period doubling, period-m bubbles, reverse period-m bubbles, intermittency, and antimonotonicity. On the contrary, emerging chaotic band attractors and period-1, period-3, period-9, and period-m bubbles routes to chaos occur in this system. (shrink)

When used in conjunction with the current floorplan and the optimization technique in circuit design engineering, this research allows for the evaluation of design parameters that can be used to reduce congestion during integrated circuit fabrication. Testing the multiple alternative consequences of IC design will be extremely beneficial in this situation, as will be demonstrated further below. If the importance of placement and routing congestion concerns is underappreciated, the IC implementation may experience significant nonlinear problems throughout the process as a (...) result of the underappreciation of placement and routing congestion concerns. The use of standard optimization techniques in integrated circuit design is not the most effective strategy when it comes to precisely estimating nonlinear aspects in the design of integrated circuits. To this end, advanced tools such as Xilinx VIVADO and the ICC2 have been developed, in addition to the ICC1 and VIRTUOSO, to explore for computations and recover the actual parameters that are required to design optimal placement and routing for well-organized and ordered physical design. Furthermore, this work employs the perimeter degree technique to measure routing congestion in both horizontal and vertical directions for a silicon chip region and then applies the technique to lower the density of superfluous routing. Recently, a metaheuristic approach to computation has increased in favor, particularly in the last two decades. It is a classic graph theory problem, and it is also a common topic in the field of optimization. However, it does not provide correct information about where and how nodes should be put, despite its popularity. Consequently, in conjunction with the optimized floorplan data, the optimized model created by the Improved Harmonic Search Optimization algorithm undergoes testing and investigation in order to estimate the amount of congestion that occurs during the routing process in VLSI circuit design and to minimize the amount of congestion that occurs. (shrink)

In practical application studies of glider formations, ocean currents are a major influencing factor in their path planning. The purpose of this paper is to solve the path planning problem of glider formations in time-varying ocean currents and establish gliders, glider formation, and ocean current models based on existing data. The Doc-CNN architecture is tailored to conform to the operation and environment characteristics of gliders in practical application. After experiments, the algorithm of the improved architecture can be used in the (...) path planning task of glider formation. The algorithm architecture is compared and tested on two datasets, grid maps and ocean maps. Doc-CNN has an advantage over architecture without being tailored to glider characteristics and makes full use of known global information and local information collected by gliders themselves. The results show that the path planning problem of glider formation in ocean currents can be solved by using Doc-CNN. (shrink)

The purpose of this paper is to investigate the impact of crude oil market structure on stock market volatility in Asian countries in the period 2008–2017. We integrate network analysis with the SGMM estimation technique to achieve the research objective. Network analysis was conducted with 43 Asian countries, while analysis of the impact of crude oil market structure on stock markets was performed with a sample of 19 countries. The results show that the stock market has a positive growth in (...) countries with stronger export capacity while it is negatively affected in larger importing countries. In addition, the research results show that the stock market’s growth is greater in countries with a central position in the crude oil market. The study results will be useful for countries in reducing the undesirable impact of crude oil market on the stock market. (shrink)

From the perspective of the system of systems development, system-level functional testing is required for designing subsystems. This study utilizes modeling and simulation techniques to analyze the operational behaviors of the subsystems and confirm data communication between them. The targeted system in the study is a naval combat system, which is a typical type of defense cyber-physical system. Three types of models were designed for the simulation testing of the NCS: a combat-management model for simulating the overall computational activities, physical (...) models to confirm the intrasubsystem behaviors, and data integration models to test the intersubsystem communications. These models are realized with the Model-View-ViewModel design pattern, which strongly facilitates graphical user interfaces being decoupled from model logic and data. We consider underwater combat scenarios as an application. Six significant physical subsystems within the NCS are simulated and tested: a ship-steering system, an inertial navigation system, a global navigation satellite system, a periscope, sonar systems, and a plotting board. We expect that the proposed work will play a principal role when analyzing the behaviors and communications of defense CPSs and providing an environment for functional testing as a digital twin. (shrink)

The COVID-19 pandemic heavily influenced human life by constricting human social activity. Following the spread of the pandemic, humans did not have a choice but to change their lifestyles. There has been much change in the field of education, which has led to schools hosting online classes as an alternative to face-to-face classes. However, the concentration level is lowered in the online learning class, and the student’s learning rate decreases. We devise a framework for recognizing and estimating students’ concentration levels (...) to help lecturers. Previous studies have a limitation in that they classified attention levels using only discrete states. Due to the partial information from discrete states, the concentration levels could not be recognized well. This research aims to estimate more subtle levels as specified states by using a minimum amount of body movement data. The deep neural network is used to continuously recognize the human concentration model, and the concentration levels can be predicted and estimated by the Kalman filter. Using our framework, we successfully extracted the concentration levels, which can aid lecturers and can be expanded to other areas. To implement the framework, we recruited participants to take online classes. Data were collected and preprocessed using pose points, and an accuracy of 90.62 % was calculated by predicting the concentration level using the framework. Furthermore, the concentration level was approximated based on the Kalman filter. We found that webcams can be used to quantitatively measure student concentration when conducting online classes. Our framework is a great help for instructors to measure concentration levels, which can increase the learning efficiency. As a future work of this study, if emotion data and skin thermal data are comprehensively considered, a student’s concentration level can be measured more precisely. (shrink)

Speech recognition technology has played an indispensable role in realizing human-computer intelligent interaction. However, most of the current Chinese speech recognition systems are provided online or offline models with low accuracy and poor performance. To improve the performance of offline Chinese speech recognition, we propose a hybrid acoustic model of deep convolutional neural network, long short-term memory, and deep neural network. This model utilizes DCNN to reduce frequency variation and adds a batch normalization layer after its convolutional layer to ensure (...) the stability of data distribution, and then use LSTM to effectively solve the gradient vanishing problem. Finally, the fully connected structure of DNN is utilized to efficiently map the input features into a separable space, which is helpful for data classification. Therefore, leveraging the strengths of DCNN, LSTM, and DNN by combining them into a unified architecture can effectively improve speech recognition performance. Our model was tested on the open Chinese speech database THCHS-30 released by the Center for Speech and Language Technology of Tsinghua University, and it was concluded that the DLD model with 3 layers of LSTM and 3 layers of DNN had the best performance, reaching 13.49% of words error rate. (shrink)

The losses in the radial distribution system are inevitable which needs to be minimized for the proper transmission of power to the end customers. This problem can be solved by the allocation of capacitor banks at proper locations with appropriate sizing. These allocations need an efficient approach for the performance enhancement of RDS. In this paper, several metaheuristic techniques such as particle swarm optimization, Harmony search, Bat, Cuckoo, and Grey-wolf algorithms are employed to find the size of capacitor banks. Loss (...) sensitivity analysis is considered for the indication of candidate buses where a capacitor has to be installed to reduce the total system losses and ultimately increase efficiency. Cost-effectiveness, power loss minimization, and voltage enhancement can be determined and compared for these 5 techniques and are implemented on the IEEE-34 bus system to illustrate the efficacy of each of them. The results show the advantages and drawbacks of the techniques separately. The simulations are carried out in MATLAB. (shrink)

This work presents a distributed generation system that combines system of a wind turbine and photovoltaic using a unified power quality conditioner. Along with providing active power to the utility grid, Wind-PV-UPQC improves PQ indicators, for example, voltage drops/surges, harmonics of grid voltages, and PF. Since Wind-PV-UPQC depends on dual compensation scheme, the parallel converter works as a sinusoidal voltage source, while the series converter works as a sinusoidal current source. In this way, a smooth transition from grid operation to (...) island operation and vice versa can be achieved without load voltage transitions. In addition, in order to overcome the problems through abrupt solar radiation or wind speed variations, a faster power balance is achieved between the wind turbines, the PV array, and the grid, as FFCL pursue the production of the current references of series converter. Consequently, the dynamic reactions of the converter currents and the voltage of dc bus are enhanced. A comprehensive analysis of flow of the AP through the converters is done to ensure a proper understanding of how Wind-PV-UPQC works. Finally, the simulation results are shown to estimate the dynamic and static performance of Wind-PV-UPQC in conjunction with the power distribution system. (shrink)

False information is always produced after the outbreak of major emergencies. Taking this into consideration, this paper discusses the behavior of multiple parties in relation to false information dissemination after major emergencies. First, a game model is constructed, using relevant knowledge of evolutionary game theory, between three parties: regulatory institutions, opinion leaders, and ordinary Internet users. Second, the model equations are solved, and the evolutionary stability strategies of each game party under different circumstances are analyzed. Third, a numerical simulation is (...) applied to the evolutionary trends under different strategy combinations with varying parameters. The results show that the probability of each game party making ideal decisions is positively correlated with the degree of punishment imposed by regulatory institutions on opinion leaders who release false information, the reward provided by regulatory institutions on opinion leaders who release positive information, the degree of participation and satisfaction gained by Internet users in adopting positive information, the richness of authentic content released by opinion leaders, and the psychological identification of Internet users with opinion leaders. Meanwhile, the probability of each game party making ideal decisions is negatively correlated with investigation and evidence collection costs borne by opinion leaders who release positive information, the additional income for opinion leaders who have false information adopted by Internet users, the costs of Internet users’ time and energy when they adopt information released by opinion leaders, and the costs of independently judging the accuracy of information by Internet users. (shrink)

By empirically testing the scale and structure hypotheses of aging’s impacts on the financial market using Tobit, FRM, and Heckman selection models, this paper proves that the number and proportion of elderly family members change the structure of families’ financial assets, though without significant effects on their overall size. Specifically, aging increases the share of cash and deposits in families’ total financial assets and decreases both the quantity and percentage of investment in risky assets. One more family member aged sixty (...) and over, the risky assets decline by 5, 500RMB and its share decreases by 8.8 percent. A Heckman two-step model verifies the robustness of our results. The heterogeneity analysis reveals that aging plays different roles in different types of financial assets. (shrink)

Today, cities account for the majority of global energy consumption and CO2 emissions. The interaction between cities is critical for regional low-carbon development. At this time, China has turned to the full opening of the carbon trading market as an important policy tool for achieving its ambitious emission reduction goals. This paper attempts to explore the spatial relationships among cities from the perspective of carbon trading. A “multiscenarios across different spatial scales” analysis framework applied to the allocation and trading of (...) CO2 emission quotas among cities in China is proposed. Based on this framework, the carbon trading networks of 182 cities in China are specifically simulated under four different scenarios, with a comparative analysis of the networks’ complexity made from different perspectives. The results of the study are as follows: firstly, significant spatial network correlations exist between the cities’ carbon trading under each scenario. Secondly, on a national scale, there are more inner connections in carbon trading networks, with stronger correlations and robustness, while the degree of connection of intercity carbon trading is higher overall on a regional scale. Thirdly, on a national scale, no obvious core nodes exist in the network, while core nodes do exist in the overall network and in each region under the regional scale. Fourthly, cities in the central region have the highest core position overall. Finally, based on the research results, practical guidance in using the framework for achieving carbon reduction, promoting economic growth, and balancing regional development, as well as the important inspiration for policy formulation, is further discussed. (shrink)

The problem of rationalizing denominators for two types of fractions is discussed in the paper. By using the theory and algorithms of Gröbner bases, we first introduce a method to rationalize the denominators of fractions with square root and cube root, and then, for the denominators with higher radical of the general form, the problem of rationalizing denominators is converted into the related problem of finding the minimal polynomials. Some interesting results and an executable algorithm for rationalizing the denominator of (...) these type fractions are presented. Furthermore, an example is also established to illustrate the effectiveness of the algorithm. (shrink)

In this study, for COVID-19, we divide people into four categories: susceptible S t, closely contacted C t, infective I t, and removed R t according to the current epidemic situation and then investigate two models: the SCIR models with immigration and without immigration. For the former, Model 1, we obtain the condition for global stability of its disease-free equilibrium. For the latter, Model 2, we establish the local asymptotic stability of its endemic equilibrium by constructing Lyapunov function. Afterwards, by (...) the bifurcation theory, we qualitatively analyze the properties of its Hopf bifurcations of the latter. Finally, numerical simulations are given to illustrate the obtained results of two models. The results imply the importance of finding closely contacted and overseas imports on epidemic control. It indicates that not only the incubation delay τ is crucial for the containment of the COVID-19 but also the scientific and rigorous containment measures are the key factors of the success of the containment. (shrink)

In this study, under some inequality conditions, necessary and sufficient conditions, using fixed-point theorem in cones, are established for the existence of C 1 -positive solutions for a class of second-order impulsive differential equations. Two examples are given in the last section to illustrate the abstract results.

Existing methods for evaluating manufacturing process chain complexity consider the number of machines, state of machines, number of parts, operation time, and processing sequence of parts. However, such evaluation methods ignore human factors. To consider human factors, human cognitive decision-making process factors are considered in the complexity evaluation of production processes. Accordingly, a new objective evaluation method of the human factor complexity is proposed. In the proposed method, sewing operations are taken as an example, and the human factor complexity is (...) classified into perceived and cognitive complexity. Information entropy is used to measure cognitive complexity according to the type and quantity of sewing workers’ cognitive activities. The results show that various methods have significant differences in the evaluation of the complexity level of the production process chain. Specifically, the calculation results of the proposed evaluation method are much greater than those of other methods. This indicates that human cognitive and perceived complexities account for a large proportion. Therefore, human factor complexity cannot be omitted. (shrink)

China’s E-commerce market is very active. Despite the impact of COVID-19, the market has ushered in major development opportunities. Alongside, the level of intellectual property protection in China is constantly improving. However, there are relatively few studies on intellectual property protection in the field of E-commerce. This study introduces the theory of social cogovernance and explores the construction of China’s E-commerce intellectual property protection system with the participation of collective organizations. Evolutionary game method is applied to model construction. Through numerical (...) simulation, the study analyzes the evolution of E-commerce platform and collective organization strategic choice. The results depict that improving the open space provided by E-commerce platforms for collective organizations and strengthening their public service capabilities can promote the construction of social cogovernance systems. The establishment of reasonable reward and punishment mechanisms can increase cooperation enthusiasm. We also discuss the theoretical and practical implications for governments, E-commerce platforms, and collective organizations, to improve the level of intellectual property protection in E-commerce. This study can provide suggestions for China’s E-commerce intellectual property protection, improve the overall level of intellectual property protection in China, and provide a reference for global E-commerce intellectual property protection. (shrink)

Product configuration is a key enabling technology for mass customization production. Previous studies mainly focus on the solving technologies for obtaining a feasible configuration or optimal configuration but ignore environmental factors like CO2 emissions, especially the environmental regulations from international organizations. In this article, we address product configuration optimization problems considering both carbon cap and carbon tax regulations. A two-stage approach that combines both Particle Swarm Optimization and a greedy algorithm is presented to solve the product configuration problems with carbon (...) emission concerns. A case study of a configurable ranger-drilling machine is illustrated to demonstrate the effectiveness of the presented approach. The impacts of carbon cap and carbon tax regulations on configuration results are compared. Further, the models are extended to analyze the effects of single-sourcing and multisourcing on product configuration. The analysis shows that multisourcing in combination with carbon tax regulation can facilitate a manufacturer to provide more green and low-carbon customized products for customers. Numerical experiments are conducted to validate the effectiveness of the model and the efficiency of the proposed solution method. (shrink)

In this research, the -dimensional variable-coefficient Caudrey–Dodd–Gibbon–Kotera–Sawada model used in soliton hypothesis and implemented by operating the Hirota bilinear scheme is studied. A few modern exact analytical outcomes containing interaction between a lump-two kink soliton, interaction between two-lump, the interaction between two-lump soliton, lump-periodic, and lump-three kink outcomes for the -D VC Caudrey–Dodd–Gibbon–Kotera–Sawada equation by Maple Symbolic packages are obtained. By employing Hirota’s bilinear technique, the extended soliton solutions according to bilinear frame equation are received. For this model, the contemplated (...) model can be got by multi-D binary Bell polynomials. In addition, the analytical analysis of the high-order soliton outcomes to present the discipline of outcomes. The effect of the free parameters on the behavior of acquired figures of a few obtained solutions for the nonlinear rational exact cases was also discussed. The above technique could also be employed to get exact solutions for other nonlinear models in physics, applied mathematics, and engineering. (shrink)

With rapid changes in technology, knowledge innovation has become more and more complex. Complex partners facilitate knowledge creation and innovation in complex problem-solving. Relying on R&D cooperation with forerunners, latecomers can acquire new knowledge factors and new ideas and use them to create new knowledge. This study demonstrated the knowledge innovation mechanism based on linkages between periphery knowledge and core knowledge with knowledge map theory and set methodology. A case of R&D cooperation between latecomers and forerunners was introduced to verify (...) this mechanism. The results show that latecomers could share forerunners’ periphery knowledge in R&D cooperation through formal and informal interaction and acquire the core knowledge factors in periphery knowledge based on the linkages between them. With these core knowledge factors, latecomers integrate them into their internal knowledge system and create new knowledge. By analyzing the linkages between periphery and core knowledge factors, this study creatively demonstrates the knowledge innovation mechanism in knowledge linkage perspective. It deepens the understanding of R&D cooperation between latecomers and forerunners and expands the focus of knowledge innovation theory. This study provides a new insight into latecomers effectively activating internal and external knowledge resources through R&D cooperation. (shrink)

Fourth-order autonomous nonlinear differential equations can exhibit chaotic properties. In this study, we propose a family of fourth-order chaotic systems with infinite equilibrium points whose equilibria form closed curves of different shapes. First, the phase diagrams and Lyapunov exponents of the system family are simulated. The results show that the system family has complex phase diagrams and dynamic behaviors. Simulation analysis of the Poincarè mapping and bifurcation diagrams shows that the system has chaotic characteristics. The circuit simulation model is constructed (...) and simulated in Multisim. The circuit simulation results coincide with the numerical simulation results, which verifies the circuit feasibility of the system. Then, based on Lyapunov stability theory and the adaptive control method, the synchronous control of the system with infinite equilibria is designed. Numerical simulation results verify that the system synchronization with the adaptive control method is well. Finally, the synchronous drive system is used for image encryption, the response system is used for decryption, and color image encryption is realized by combining deoxyribonucleic acid coding and operating rules. Therefore, this study not only enriched the research on infinite equilibria chaotic systems but also further expanded secure communication technology by combining chaotic synchronization control and DNA coding in image encryption. (shrink)

To establish the evaluation index system of urbanization innovation level and ecological efficiency, the entropy method is applied to measure the comprehensive index of urbanization innovation level and ecological efficiency, the VAR model is established, and empirical measurement is used to study the internal relationship and dynamic development between urbanization innovation level and ecological efficiency. The empirical results show the following: The overall development of the innovation level in 30 cities in China is uneven, there is a large gap between (...) the urban innovation level in backwards areas and economically developed areas, and there is a certain coupling effect between the urban innovation level and ecological efficiency. There is a long-term dynamic equilibrium relationship between the urban innovation level and ecological efficiency, which will exert certain pressure on the ecological environment in the process of urbanization. However, the continuous improvement of urbanization will have a positive impact on the improvement of the ecological environment. The improvement of ecological efficiency will also promote the improvement of the urban innovation level. Therefore, this paper puts forwards policy suggestions to promote the harmonious development of urbanization and the ecological environment and provides a reference for realizing the balanced development of urbanization and ecological efficiency in China. (shrink)

The emergence of conflicts between environmental safety incidents and protection rights generates sizeable political costs, which endangers the legitimacy of the government as well as political security and stability. This article further examines the role of political costs in environmental issues. First, political costs in relation to environmental issues are defined. An equilibrium strategic analysis is then presented using an evolutionary game model in which the strategic behavioral choices of government, enterprises, and citizens are investigated by embedding political costs in (...) the environmental governance system. Furthermore, the small-signal model was innovatively applied to simulate and analyze the stability of political costs under different equilibrium strategies in the system. The results show the following. Pubic behavior and government are the dominant factors that impact stability and instability, respectively. Public behavior is the core element that affects political cost consumption. When political costs are extremely depleted, the public will neglect economic interests, turn toward environmental interests, and choose the negative participation strategy, which destabilizes the system. The political cost signal at the optimal equilibrium point not only warns the government not to take the desperate action of concealing information asymmetry but also allows the government to let go of its hands to deal with the environmental issues. Corresponding policy recommendations are proposed. (shrink)

This paper focuses on studying containment control problem with switching communication graphs of continuous-time heterogeneous multiagent systems where the control inputs are constrained in a nonconvex set. A nonlinear projection algorithm is proposed to address the problem. We discuss the stability and containment control of the system with switching topologies and nonconvex control input constraints under three different conditions. It is shown that all agents converge to the convex hull of the given leaders ultimately while staying in the nonconvex set (...) under the premise that at least one directed path from leaders to the agents exists in each bounded time interval. Finally, the validity of the results obtained in this paper is verified by simulation. (shrink)

In literature, the firm’s selection of dual-product strategy under cap-and-trade regulation and the optimal emission reduction decisions are not well studied, especially through an analytical approach. We develop a theoretic model to investigate the firm’s selection on three product strategies in the presence of cap-and-trade policy, including two single product strategies and a dual-product strategy, and identify two types of consumers: consumers with low-carbon preference and regular consumers. Our analysis shows that, in the absence of cap-and-trade policy, it is optimal (...) to adopt dual-product strategy, but in the presence of this policy, it is optimal to adopt dual-product strategy or low-carbon product strategy. Compared with ordinary product strategy, adopting a low-carbon product strategy or dual-product strategy benefits the firm under cap-and-trade regulation. The additional cost of producing low-carbon product has an impact on the firm’s product strategy selection under cap-and-trade regulation. When it falls below a certain threshold, it is optimal to adopt a dual-product strategy; otherwise, it is optimal to adopt a low-carbon product strategy. Our research can provide reference for firm’s decision-making and government’s policy formulation. (shrink)

Financial development is often considered one of the main drivers promoting household consumption. As a form of financial development, whether digital financial inclusion can promote household consumption has been a concern for researchers and policymakers. Considering geographical connectivity characteristics, we examine the effects of digital financial inclusion on household consumption by applying spatial econometric models and using data from 31 provinces in China from 2013 to 2018. The impact of digital financial inclusion is further disaggregated into direct, indirect, and total (...) impacts. The results show that if digital financial inclusion is improved by 1%, household consumption will correspondingly increase by 0.2207%. The spatial spillover effect on neighboring areas is negative: a 1% increase in the level of digital financial inclusion of nearby provinces leads to a 0.1289% decrease in household consumption in the local province. For policymakers, it is necessary to balance the development of digital financial inclusion and view different areas as a whole when making policies to promote consumption. Further analysis based on subsamples finds that the effect of digital financial inclusion on household consumption is more considerable for rural households than for urban households. (shrink)

Predicting stock movements is a valuable research field that can help investors earn more profits. As with time-series data, the stock market is time-dependent and the value of historical information may decrease over time. Accurate prediction can be achieved by mining valuable information with words on social platforms and further integrating it with actual stock market conditions. However, many methods still cannot effectively dig deep into hidden information, integrate text and stock prices, and ignore the temporal dependence. Therefore, to solve (...) the above problems, we propose a transformer-based attention network framework that uses historical text and stock prices to capture the temporal dependence of financial data. Among them, the transformer model and attention mechanism are used for feature extraction of financial data, which has fewer applications in the financial field, and effective analysis of key information to achieve an accurate prediction. A large number of experiments have proved the effectiveness of our proposed method. The actual simulation experiment verifies that our model has practical application value. (shrink)

Dragon boat sport is a traditional activity in China. In recent years, dragon boat sport has become more and more popular around the world. In order to face more challenges, it is urgent for athletes to enhance their own strength. Scientific training methods are particularly important for athletes, and accurate training data are the basis to support scientific training. Traditional mathematical statistic methods neither can sample signals accurately nor can they do real-time analysis and feedback the characteristics to each athlete. (...) In this paper, we use the wearable device with a triaxial accelerometer and heart rate sensor builtin to sample the speed signals and heart rate signals of athletes in various stages of men’s 1000m straight race. Based on the complex network theory, we regard the 23 dragon boat athletes in the dragon boat race as 23 nodes so as to establish a network with 23 nodes and reflect the importance of nodes by measuring the impact of node deletion on the results of the race. The neural network multilayer perceptron model is used for training to obtain the optimal combined value with speed and heart rate for each race stage. The optimal value will be used in the simulated race as the target value to verify if it can help to improve the training efficiency. Experimental results show that the optimal value obtained by this method has a positive effect on the results of the dragon boat race which is beneficial to sports training and tactics planning. (shrink)

Resolving the challenge of flight delays caused by air traffic congestion renders it necessary to explore the mode of congestion propagation. By applying complex network theory, this article establishes a complex network structure where airspace sectors act as nodes, and the edges represent traffic flow relationships between sectors. In addition, a cascading failure model is proposed to analyze the airspace sector network’s invulnerability. The critical threshold and the sector abnormality rate are determined according to the cascading failure measurement indexes. Based (...) on the remaining capacity of sector nodes, two optimization strategies are proposed: adjacent load redistribution strategy and local load redistribution strategy. The simulation results demonstrate that the local load redistribution strategy greatly improves the airspace sector network’s invulnerability to cascading failure. (shrink)

Based on the data of Chinese industrial enterprises from 2004 to 2009, a fixed-effect model is adopted in this paper to analyze the effect and the mechanism of the enterprises using the Internet on tax avoidance. The result shows that using the Internet will produce the peer effect, which enables enterprises to learn tax avoidance strategies on the Internet and makes the degree of tax avoidance between enterprises and other enterprises in the same industry converge. At the same time, using (...) the Internet has a network effect, and the more the enterprises access the Internet, the higher the degree of tax avoidance for enterprises is. In addition, the effect of the Internet on tax avoidance is influenced by the intensity of tax collection and the nature of enterprises. With a further examination, it has been found that enterprises not only acquire the two tax avoidance strategies of underreporting profits and underpaying taxes through the Internet but also master the planning strategy. (shrink)

Let H n be the linear heptagonal networks with 2 n heptagons. We study the structure properties and the eigenvalues of the linear heptagonal networks. According to the Laplacian polynomial of H n, we utilize the method of decompositions. Thus, the Laplacian spectrum of H n is created by eigenvalues of a pair of matrices: L A and L S of order numbers 5 n + 1 and 4 n + 1 n! / r! n − r!, respectively. On the (...) basis of the roots and coefficients of their characteristic polynomials of L A and L S, we get not only the explicit forms of Kirchhoff index but also the corresponding total number of spanning trees of H n. (shrink)

The isolation requirements of the coronavirus epidemic and the intuitive display advantages of live-streaming have led to an increasing number of retailers shifting to social live-streaming platforms and e-commerce live-streaming platforms to promote and sell their products in real time. However, the provision of live-streaming services will also incur high live-streaming effort costs. In this paper, we develop two decision models for retailers to sell goods through a single online shop and both online shop and live-streaming room; we also present (...) the optimal decisions of pricing and live-streaming efforts. Furthermore, we identify the profitability conditions for retailers to determine when to provide live-streaming services. In addition, we examine the impact of the provision of live-streaming services on the optimal price and live-streaming effort. We obtain three findings. First, there is a unique optimal decision on the price and live-streaming effort under certain conditions. Second, when the effect coefficient of the live-streaming room reaches a certain threshold, there are enough customers who enter the live-streaming room to watch and buy and it is profitable for retailers to provide live-streaming service. Finally, the optimal price and live-streaming effort increase with the increase in average return loss, the effect coefficient of live-streaming effort, and the extra return rate and decrease with the increase in the proportion of customers who choose to buy in the online shop and the price discount coefficient in the live-streaming room. (shrink)

A link prediction model for weighted networks based on Dempster–Shafer evidence theory and the influence of common neighbours is proposed in this paper. First, three types of future common neighbours and their topological structures are proposed. Second, the concepts of endpoint weight influence, link weight influence, and high-strength node influence are introduced. Then, the similarity based on the impacts of current common neighbours and FCNs is defined, respectively. Finally, the two similarity indices are fused by the DS evidence theory. This (...) model effectively integrates multisource information and completely exploits the influence of all CCNs and FCNs on similarity. Experiments are performed on 9 real and 40 simulation-weighted datasets, and these findings are compared with several classic algorithms. Results show that the proposed method has higher precision than other methods, which can achieve good performance in link prediction in weighted networks. (shrink)

The clustering of mixed-attribute data is a vital and challenging issue. The density peaks clustering algorithm brings us a simple and efficient solution, but it mainly focuses on numerical attribute data clustering and cannot be adaptive. In this paper, we studied the adaptive improvement method of such an algorithm and proposed an adaptive mixed-attribute data clustering method based on density peaks called AMDPC. In this algorithm, we used the unified distance metric of mixed-attribute data to construct the distance matrix, calculated (...) the local density based on K-nearest neighbors, and proposed the automatic determination method of cluster centers based on three inflection points. Experimental results on real University of California-Irvine datasets showed that the proposed AMDPC algorithm could realize adaptive clustering of mixed-attribute data, can automatically obtain the correct number of clusters, and improved the clustering accuracy of all datasets by more than 22.58%, by 24.25%, by 28.03%, by 22.5%, and by 10.12% for the Heart, Cleveland, Credit, Acute, and Adult datasets compared to that of the traditional K-prototype algorithm, respectively. It also outperformed a modified density peaks clustering algorithm for mixed-attribute data algorithms. (shrink)

In late 2019, the COVID-19 pandemic began to spread over the world, causing millions of deaths. In the first few months of the pandemic, several countries prevented the spread of the pandemic successfully. By contrast, the pandemic in many other countries was not controlled well. For example, India encountered a second serious outbreak of COVID-19 from April 2021 due to the poor resistance measures implemented by the government. To figure out the effective countermeasures to the pandemic, this research proposes a (...) COVID-19 pandemic and its response system, which consists of the infection subsystem, the quarantine subsystem, and the medical subsystem. On this basis, an improved SEIR-SD model is established which is utilized to analyze the response measures to the pandemic quantitatively. This model successfully simulates the actual epidemic scenarios in Wuhan, which verifies its effectiveness. Afterward, the impact of hospital administration rate, quarantine rate, average contact number, and contact infection rate on the cumulative number of infections and deaths are analyzed by simulation. The results show that both the medical and administrative efforts, especially in the early stage of the epidemic, are significant in reducing the number of infections and shortening the epidemic period. In the medical aspect, the more stringent quarantine brings the earlier inflection point of the epidemic; more importantly, improving the treatment rate significantly reduces the scale of the epidemic. In the administrative aspect, enforcing individual protection and strict community closure can effectively cut off the transmission of the virus and curb the spread of the epidemic. Finally, this research proposes several practical suggestions in response to the COVID-19 pandemic. The main contribution of this research is that the effects of different response measures on the number of new infections daily and the cumulative number of deaths of a country or region in the COVID-19 pandemic are estimated quantitatively based on modeling and simulation. (shrink)

A nonlinear coupled wing model subject to unknown external disturbances is proposed in this paper. Since the model is modeled by partial differential equations, the traditional control design scheme based on the ordinary differential equation model is not applicable, and the control law design becomes very complex. In this paper, a new antidisturbance boundary control scheme based on a finite time convergent disturbance observer is proposed. The control laws are designed based on the new disturbance observers to make the external (...) disturbance errors converge to zero in a finite time and ensure the uniformly bounded stability of the controlled system. Finally, the effectiveness of the controllers and the finite-time convergence of disturbance errors are verified by the simulation and comparison. (shrink)

Social security is an important part of the national security system from the perspective of the overall security concept, and social security is an important influencing factor of social security. Using the CFPS2018 data, this study uses the critical weight method to measure residents’ individual security and uses path analysis to empirically study the impact of endowment insurance and medical insurance on residents’ security in urban and rural areas. The results show that the depth of endowment insurance has a significant (...) positive impact on the sense of security of urban residents. The depth of meniscal insurance has no impact on the sense of security of urban residents. The depth of endowment insurance and medical insurance has a significant positive impact on the sense of security of rural residents. The depth of endowment insurance has a significant positive impact on the sense of security of urban residents. The influence of full sense is greater than that in rural areas, and the influence of meniscal insurance coverage depth on residents’ sense of security in rural areas is greater than that in urban areas. Therefore, the impact of social security on residents’ sense of security is heterogeneous between urban and rural areas. We should allocate the limited social security investment to urban and rural areas in a reasonable and balanced way to promote the overall sense of security of social residents. Integrate and improve the social security system and promote regional and group equity. Change the concept of government management, build a communication platform between the government and the people, and improve residents’ subjective sense of security. We should continue to expand the coverage of the system, promote the coverage of basic social security projects, and improve the ability of urban and rural residents to cope with risks. (shrink)

Computational social science, as an emerging interdisciplinary discipline, is a field ushered in by long-term development of traditional social science. It is committed to supplying data thinking, resources, and analytics to study human social behavior and social operation laws to accurately grasp and judge the developing path of the discipline, which is of great significance to promote the innovation and development of social sciences. This study is to conduct a systematic quantitative analysis from a bibliometric perspective, aiming to provide a (...) reference for scholars to explore the paths and changing rules in the field. We use the relevant literature in Web of Science as the dataset. After eliminating journal calls and irrelevant literature, R language and SciMAT tools are used to visualize and analyze the number of articles, keyword clustering, keyword cooccurrence network, and theme evolution, so as to summarize and sort out the paths of computational social science research. The study found that the annual volume of publications has been gradually increasing and will probably remain active in the next few years with high productivity. Subject themes in different periods are diversified, and the evolutionary relationship is found complex as well. Besides, as a cross discipline, scientific knowledge from different fields cross collides and couples with each other in the big data environment, changing the traditional concept of computational social science and forming a new development path. Recently, the emergence of “big data+” has promoted the rise of new subject areas, making the development of new disciplines a reality. (shrink)

The two-dimensional coupled map lattice model has been extensively employed as the basis component for designing various schemes in the cryptography system due to its complicated chaotic dynamic behavior. In this study, we analyze the chaotic characteristics of the 2D CML model, such as the Lyapunov exponent, synchronization stability, bifurcation, and ergodicity. We then show that the chaotic sequences generated by the 2D CML model are random according to the NIST testing. Furthermore, we propose an image encryption scheme based on (...) the 2D CML model and Singular Value Decomposition. In our scheme, the SVD method is used to reduce the image storage, and the Red, Green, and Blue channels of a color image will be encrypted through confusion and diffusion. The simulation results, as well as the results of the comparison with other schemes, demonstrate that our scheme possesses outstanding statistics, excellent encryption performance, and high security. It has great potential for ensuring the security of digital images in real applications. (shrink)

Optimal strategy, one of the main transit assignment models, can better demonstrate the flexibility for passengers using routes in a transit network. According to the basic optimal strategy model, passengers can board trains based on their frequency without any capacity limitation. In the metropolitan cities such as Beijing, Shanghai, and Hong Kong, morning commuters face huge transit problems. Especially for the metro system, there is heavy rush in metro stations. Owing to the limited train capacity, some passengers cannot board the (...) first coming train and need to wait for the next one. To better demonstrate the behavior of passengers pertaining to the limited train capacity, we consider capacity constraints for the basic optimal strategy model to represent the real situation. We have proposed a simulation-based algorithm to solve the model and apply it to the Beijing Subway to demonstrate the feasibility of the model. The application of the proposed approach has been demonstrated using the computational results for transit networks originating from practice. (shrink)

In this paper, we propose the model averaging estimation method for multiplicative error model and construct the corresponding weight choosing criterion based on the Kullback–Leibler divergence with a hyperparameter to avoid the problem of overfitting. The resulting model average estimator is proved to be asymptotically optimal. It is shown that the Kullback–Leibler model averaging estimator asymptotically minimizes the in-sample Kullback–Leibler divergence and improves the forecast accuracy of out-of-sample even under different loss functions. In simulations, we show that the KLMA estimator (...) compares favorably with smooth-AIC estimator, smooth-BIC estimator, and Mallows model averaging estimator, especially when some nonlinear noise is added to the data generation process. The empirical applications in the daily range of S&P500 and price duration of IBM show that the out-of-sample forecasting capacity of the KLMA estimator is better than that of other methods. (shrink)

Today, the risk management of budget challenges throughout the budget process is greater than ever. The process of change has been driven by new information and communication technologies, resulting in e-government. The purpose of this scientific paper is to see whether budgetary challenges have an effect on the performance of e-government in complex financial systems based on factors F1, F2, F3, F4, and F5: lack of information, lack of cooperation, lack of resources and reduction of focus, lack of budget experts (...) and financial stability, and shortcomings and inconsistencies during the budget process. Therefore, this study aims to advance the understanding of how to manage risks from budgetary challenges by focusing on a novel approach to improve e-government performance in complex financial systems. Empirical research was based on three key issues: an approach to e-government, analyzing which variables need more attention to risk, and learning how to meet budgetary challenges to improve performance during governance. For this study, the data were conducted by Kosovo’s public institutions, more specifically at the central level and at the local level. A total of 38 questionnaires were analyzed and divided into three sessions, which were analyzed through three analyses, such as factor analysis, data reliability analysis, and multiple regression analysis, using SPSS version 23.0 for Windows. The research was conducted over the years 2017, 2018, 2019, and 2020, while the analysis involved several processes, where some of the factors were removed in order to make the model acceptable. In this case, 21 variables were tested and divided into 5 factors. The results showed that special attention should be paid to these factors to reduce budgetary challenges and increase the performance of e-government in complex financial systems, such as lack of resources, increasing the focus on risk management even after the transfer of funds from the ministry to the municipality, the selection of programs based on priorities and not on the basis of wishes and policies, having political stability, rule of law, and more control, and having regulations and guidelines from the practices of developed countries as well as taking into account the opinions of budget experts. The implications of this paper have to do with only a considerable number of variables, which were taken in the study as well as only in the municipalities of Kosovo. In this case, for other analyses by other researchers, other variables can be analyzed in other countries by making comparisons. (shrink)

This paper further extends the existing GARCH-MIDAS model to deal with the effect of microstructure noise in mixed frequency data. This paper has two highlights. First, according to the estimation of the long-term volatility components of the GARCH-MIDAS model, rAVGRV is adopted to substitute for the RV estimator. rAVGRV uses the rich data sources in tick-by-tick data and significantly corrects the impact of the microstructure noise on volatility estimation. Second, besides introducing macroeconomic variables, deposits in financial institutions, industrial value-added, and (...) M2), Chinese Economic Policy Uncertainty index and Infectious Disease Equity Market Volatility Tracker are introduced in the long-run volatility component of the GARCH-MIDAS model. As indicated by the results of this paper, the rAVGRV-based GARCH-MIDAS is slightly better than the RV model-based GARCH-MIDAS. In addition to the common macroeconomic variables significantly impacting stock market volatility, CEPU also substantially impacts stock market volatility. Nevertheless, the effect of EMV on the stock market is insignificant. (shrink)

We can observe self-organization properties in various systems. However, modern networked dynamical sociotechnical systems have some features that allow for realizing the benefits of self-organization in a wide range of systems in economic and social areas. The review examines the general principles of self-organized systems, as well as the features of the implementation of self-organization in sociotechnical systems. We also delve into the production systems, in which the technical component is decisive, and social networks, in which the social component dominates; (...) we analyze models used for modeling self-organizing networked dynamical systems. It is shown that discrete models prevail at the micro level. Furthermore, the review deals with the features of using continuous models for modeling at the macro level. (shrink)

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural network, (...) decision tree, random forest, and K-nearest neighbors. The principal component analysis and logistic regression methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F-measure, accuracy, and area under the curve were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC-ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC-ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications. (shrink)

In this study, we implemented a new numerical method known as the Chebyshev Pseudospectral method for solving nonlinear delay differential equations having fractional order. The fractional derivative is defined in Caputo manner. The proposed method is simple, effective, and straightforward as compared to other numerical techniques. To check the validity and accuracy of the proposed method, some illustrative examples are solved by using the present scenario. The obtained results have confirmed the greater accuracy than the modified Laguerre wavelet method, the (...) Chebyshev wavelet method, and the modified wavelet-based algorithm. Moreover, based on the novelty and scientific importance, the present method can be extended to solve other nonlinear fractional-order delay differential equations. (shrink)

Market inefficiency is a latent concept, and it is difficult to be measured by means of a single indicator. In this paper, following both the adaptive market hypothesis and the fractal market hypothesis, we develop a new time-varying measure of stock market inefficiency. The proposed measure, called composite efficiency index, is estimated as the synthesis of the most common efficiency measures such as the returns’ autocorrelation, liquidity, volatility, and a new measure based on the Hurst exponent, called the Hurst efficiency (...) index. To empirically validate the indicator, we compare different European stock markets in terms of efficiency over time. (shrink)

In this note, the problem of tracking random references and rejecting random perturbations in a quadrotor, both generated by an auxiliary system named exosystem, is solved by extending the deterministic tracking problem to the area of stochastic processes. Besides, it is considered that only a part of the state vector of the quadrotor is available through measurements. As a consequence, the state vector of the plant must be estimated in order to close the control loop. On this basis, a controller (...) to track random references and to reject random perturbations is developed by combining a Kalman filter to estimate the references and perturbations of an exosystem and an observer to estimate the states of a quadrotor. Besides, to obtain a more practical controller, the analysis is carried out in discrete time. Numerical simulations are used in a quadrotor to confirm the validity and effectiveness of the proposed control. (shrink)

The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment (...) of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618. (shrink)

Chaos distributes with a covert method to condense the dynamic of complexity and satisfies the security requirements of a cryptographic system. This study gives an ability online/offline ID-based short signature scheme using conformable fractional chaotic maps. Furthermore, we establish its security under IBSS existential unforgeability of identity-based short signature under chosen message attack in the random oracle model. Some of the stimulating preparations of obtainable processes are that they give a multiperiod application of the offline storage, which licenses the agent (...) to recycle the offline pre-registered data in time series, rather than one-period usage in all past IBSS processes. (shrink)

Topic of quantum chaos has begun to draw increasing attention in recent years. So, to ensure the security of digital image, an image encryption algorithm based on combining a hyperchaotic system and quantum 3D logistic map is proposed. This algorithm is applied in four stages. Initially, the key generator builds upon the foundation of mean for any row or column of the edges of the plain image. Its output value is used to yield initial conditions and parameters of the proposed (...) image encryption scheme. Next, it diffuses the plain image by the random sequences generated by 3D hyperchaotic system, and the diffusion process is realized by implementing XOR operation. Then, the diffused image and chaotic sequences are produced by the 3D quantum chaotic logistic map, expressed as a quantum superposition state using density matrix which is a representation of the state of a quantum system, and finally the resulting quantum image is then confused and diffused simultaneously by a unitary matrix generated by logistic chaos using XNOR operation to obtain the final cipher image. Because of the dependence on the plain image, the algorithm can frustrate the chosen-plaintext and known-plaintext attacks. Simulation results and theoretical analysis verify that the presented scheme has high safety performance, a good encryption effect, and a large key space. The method can effectively resist exhaustive, statistical, and differential attacks. Moreover, the encryption time of the proposed method is satisfactory, and the method can be efficiently used in practice for the secure transmission of image information. (shrink)

This work analyzes and characterizes the spread of the COVID-19 disease in Mexico, using complex networks and optimization approaches. Specifically, we present two methodologies based on the principle of the rupture for the GC and Newton's law of motion to quantify the robustness and identify the Mexican municipalities whose population causes a fast spread of the SARS-CoV-2 virus. Specifically, the first methodology is based on several characteristics of the original version of the Vertex Separator Problem, and the second is based (...) on a new mathematical model. By solving VSP, we can find nodes that cause the rupture of the giant component. On the other hand, solving the NLM can find more influential nodes for the entire system’s development. Specifically, we present an analysis using a coupled social network model with information about the main characteristics of the contagion and deaths caused by COVID-19 in Mexico for 19 months. This work aims to show through the approach of complex networks how the spread of the disease behaves, and, thus, researchers from other areas can delve into the characteristics that cause this behavior. (shrink)

This study concentrates on adaptive event-triggered control of complex dynamical networks with unpredictable coupling delays and stochastic deception attacks. The adaptive event-triggered mechanism is used to avoid the wasting of limited bandwidth. The probability of data communicated by the network is established by statistical properties and Bernoulli stochastic variables with an uncertain occurrence probability. Stability analysis based on Lyapunov–Krasovskii functional and the stability of the closed-loop system is guaranteed. Using the LMI technique, we obtain triggered parameters. To demonstrate the feasibility (...) and usefulness of the suggested methodology, two examples are shown. (shrink)

In order to reduce the dimensionality of parameter space and enhance out-of-sample forecasting performance, this research compares regularization techniques with Autometrics in time-series modeling. We mainly focus on comparing weighted lag adaptive LASSO with Autometrics, but as a benchmark, we estimate other popular regularization methods LASSO, AdaLASSO, SCAD, and MCP. For analytical comparison, we implement Monte Carlo simulation and assess the performance of these techniques in terms of out-of-sample Root Mean Square Error, Gauge, and Potency. The comparison is assessed with (...) varying autocorrelation coefficients and sample sizes. The simulation experiment indicates that, compared to Autometrics and other regularization approaches, the WLAdaLASSO outperforms the others in covariate selection and forecasting, especially when there is a greater linear dependency between predictors. In contrast, the computational efficiency of Autometrics decreases with a strong linear dependency between predictors. However, under the large sample and weak linear dependency between predictors, the Autometrics potency ⟶ 1 and gauge ⟶ α. In contrast, LASSO, AdaLASSO, SCAD, and MCP select more covariates and possess higher RMSE than Autometrics and WLAdaLASSO. To compare the considered techniques, we made the Generalized Unidentified Model for covariate selection and out-of-sample forecasting for the trade balance of Pakistan. We train the model on 1985–2015 observations and 2016–2020 observations as test data for the out-of-sample forecast. (shrink)

The Wiener index WI is one of the connectivity parameters used to know the biochemical and physicochemical properties of compounds depending upon their molecular structures. Intuitionistic fuzzy graphs IFG s are a convenient tool to represent the objects and relations between them with two types of information using truth membership degree and falsity membership degree. This research work presents the concept of WI under the structure IFG s, I F trees, and I F cycles. Some bounds on WI are investigated. (...) The relationship between WI and connectivity index CI is also studied. In the end of this study, an application of the WI in transport network flow is proposed. (shrink)

As a common social network, the SNEM plays an important role in emergency management. Magnitude emergencies are characterized by high complexity and uncertainty, and it is impossible to rely on the government for emergency management alone. We should absorb multiple subjects to build the SNEM and carry out extensive emergency mobilization in the whole society. The SNEM can integrate resources, gather consensus, promote participation, and reduce risks. The analysis of the types, generation mechanism, subject behavior, and strategy selection of the (...) SNEM aid in adopting appropriate mobilization strategy based on magnitude emergencies, achieving the adaptation of the SNEM and emergency scenarios. By constructing the evolutionary game model of the SNEM for magnitude emergencies, taking China as an empirical sample, this paper explores the behavior evolution law and stable strategy of the government, social organizations, and the public. The results showed that the symbiotic SNEM with a positive response of social organizations and the public under the path of high-intensity mobilization by the government is the best strategy combination, and it is conducive to maximizing the emergency joint force. (shrink)

Human contact is a key issue in social interactions for autonomous systems since robots are increasingly appearing everywhere, which has led to a higher risk of conflict. Particularly in the real world, collisions between humans and machines may result in catastrophic accidents or damaged goods. In this paper, a novel stop strategy related to autonomous systems is proposed. This control method can eliminate the vibrations produced by a system’s movement by analysing the poles and zeros in the model of autonomous (...) vehicles and goods. Using the pole placement technique, the motion of a system is guaranteed to be more stable, more flexible and smoother. Moreover, several control profiles are employed in the switching mechanism to choose the proper vibration-free effect. The main contributions of this paper are the recommendation of an active stopping planner using different smooth generators from a modelling study, the validation of their physical characteristics and the launching of a switching algorithm based on the socially aware navigation framework of a robot. This theoretical work is based on the virtual environment of MATLAB, and the experiment is implemented in the practical platform of an automated guided vehicle. From these results, it can be seen that the proposed approach is robust, effective and feasible for applications in storehouse management, public transportation or factory manufacturing. (shrink)

The standardized precipitation index is one of the most widely used indices for characterizing and monitoring drought in various regions. SPI's applicability has regional and time-scale constraints when it observes in several homogeneous climatic regions with similar characteristics. It also does not provide sufficient knowledge about precipitation deficits and the spatiotemporal evolution of drought. Therefore, a new method, the regional spatially agglomerative continuous drought probability monitoring system, is proposed to obtain spatiotemporal information and monitor drought characteristics more expeditiously. The proposed (...) framework uses spatially agglomerative precipitation and copulas’ functions to continuously monitor the drought probability in the homogenous region. The RSACDPMS is validated in the region of the Northern area of Pakistan. The outcomes of the current study provide a better quantitative way to obtain appropriate information about precipitation deficits and the spatiotemporal evolution of drought. (shrink)

The classification of collusion behaviors of government-invested project tenderers is one of the important methods to describe the characteristics and laws of collusion behaviors and strengthen the governance of collusion. Firstly, the variables that affect the type of collusion behavior are selected and cluster analysis is carried out on the cases of collusion in government investment project bidding. Then use the social network to mine the types and characteristics of the collusion behavior of the tenderee. Finally, a BP neural network (...) automatic identification model is established to quickly discriminate the types of collusion, which can effectively overcome the subjectivity of traditional methods. As a result, three typical types of collusion of tenderees can be obtained: intervention type, opportunity type, and cooperation type. The study found that the three types of collusion behavior have their own characteristics and laws, and the relationship between the variables that affect the type of collusion behavior is complex and affects each other. (shrink)

We empirically analyze the impact of economic uncertainty due to the COVID-19 pandemic on the trading volume of each sector in the S&P 500 index. Wavelet coherence analysis is carried out using economic policy uncertainty data and the trading volume of each sector in the S&P 500 index from July 2004 to September 2020. Furthermore, we apply multifractal detrended fluctuation analysis to the trading volume series of all sectors. The wavelet coherence analysis shows that the COVID-19 pandemic has substantially influenced (...) trading volume in all sectors. However, the impact of the pandemic is different from that during the global financial crisis in some sectors, such as information technology, consumer discretionary, and communication services. Because of the lockdown taken to suppress COVID-19, increased remote working and remote learning are the main reasons for these results. Additionally, according to the MF-DFA analysis, the trading volume of all the sectors has clear multifractal characteristics, and they are all nonpersistent. Specifically, trading volumes of the real estate and materials sector are highly correlated, whereas the trading volumes of industry and information technology sectors are comparatively less correlated. (shrink)

Conventional methods for fault diagnosis typically require a substantial amount of training data. However, for equipment with high reliability, it is arduous to form a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation. Besides, the generated data have a large number of redundant features which degraded the performance of models. To overcome this, we proposed a feature transfer scenario that transfers knowledge from similar fields to enhance the accuracy of fault diagnosis with small sample. To (...) reduces the redundant information, data were filtered according to manifold consistency. Then, features were extracted based on CNN and feature transfer was conducted. For adequate fitness, the joint adaptation of conditional distribution and marginal distribution was used between the two domains. Minimum structural risk and MMD of adaptation were two indicators weighted for training the model. To test the efficiency of the model, we built an airborne fuel pump testbed, and contributed a new dataset that contained 15 categories of fault data, which serves as the small sample dataset in this research. Then the proposed model was applied in our experimental data. As a result, the fault diagnosis rate increases by 28.6% through our proposed model, which is more precise than other classical methods. The results of feature visualization further demonstrate that the features are more distinguished through the proposed method. All code and data are accessible on my GitHub. (shrink)

For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index. Empirical analysis suggests that the prediction of returns from patterns is more impressive or of (...) less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE, for the period 2010–2020. (shrink)

In recent times, the role of the regional economy changed significantly under certain conditions of globalization and structural adjustment. The process of changing must be crucial to analyse regional economy and develop the planning of regional economy. Developing economies depend often on industries and country policies. Modern studies tend to participate in important factors in this field such as energy intensity, labour skills, local industries, resources, and local expertise. Furthermore, in this study, to start developing the regional economy and make (...) the revolution in this field to connect it with new technology, we train the deep learning algorithm of gathering factors to manage them perfectly and make a good prediction for the future economy. Hybrid sequence to sequence algorithms of deep learning fed with previous information from past years and run the system to compare the predicted result data with current information to evaluate the method to be certified for the coming years. (shrink)

As a NP-hard problem that needs to be solved in real time, the dynamic task allocation problem of unmanned aerial vehicle swarm has gradually become a difficulty and hotspot in the current planning field. Aiming at the problems of poor real-time performance and low quality of the solution in the dynamic task allocation of heterogeneous UAV swarm in uncertain environment, this paper establishes a dynamic task allocation model that can meet the actual needs and uses the binary wolf pack algorithm (...) to solve it, so as to propose a dynamic task allocation method of heterogeneous UAV swarm in uncertain environment. In this method, a dynamic mechanism of attacking while searching and priority attacking of important targets is designed. A dynamic task allocation model of multitarget, multitask, heterogeneous multiaircraft platform and multiconstraint is established based on the target cost-effectiveness ratio and task execution time window. In addition, one-dimensional 0–1 coding method is adopted to encode the task allocation scheme. Furthermore, the wolf pack algorithm is introduced in brief. This paper focuses on the BWPA with the good computational robustness and strong global search ability to solve the dynamic allocation model. According to the simulation results, the designed task allocation method not only has good adaptability to the change of target and UAV number, as well as good stability and scalability, but also can effectively solve the dynamic task allocation problem of heterogeneous UAV swarm in unknown environment. Therefore, the established model and solution method can provide a useful reference for task allocation and other related problems. (shrink)

A landslide inventory is a detailed register of the spatial distribution, geometry, and attributes of landslides and is essential for landslide hazard analysis, risk management, regional planning, and land use management and development, especially in landslide-prone regions. However, the development of a national landslide inventory is time-consuming and costly. Accordingly, most developing countries, including Sri Lanka, have basic landslide databases, which identify the location, date, and time of occurrence on a point map. This study, therefore, aimed to introduce a new (...) method to report landslide information via a mobile application based on a participatory approach with information recorded in a web portal called the participatory landslide inventory. Twenty-one landslide site locations and their attributes were captured on the PLI web portal using the PLI mobile app. The system administrator then demarcated the landslide boundaries and performed geometrical calculations for each landslide to complete the inventory. Finally, the landslide information was published through the PLI web portal. The PLI is an effective and efficient tool for developing a landslide inventory economically. (shrink)

This paper proposes a state-of-the-art methodology for the analysis of stakeholders and their role and performance related to SMEs in the mining industry in the Chilean region of Coquimbo. The relationships between the actors are studied and prioritized according to their support network position. An individual index for each actor based on their influence on solving problems is provided. The social network analysis was used to know the influence of the actors in the sector through the centrality measures. Furthermore, a (...) methodology to measure stakeholders’ influence based on the multicriteria method analytic network process approach is proposed. Both methods are used to identify the main stakeholders, study their relationships, and identify the most influential actors involved in executing strategies to boost the sector performance. The results show that the network remains cohesive thanks to certain actors, while the links between private actors must be strengthened; likewise, some public actors should assume a more proactive role in dealing with the problems of the sector. (shrink)

A personalized recommender system is broadly accepted as a helpful tool to handle the information overload issue while recommending a related piece of information. This work proposes a hybrid personalized recommender system based on affinity propagation, namely, APHPRS. Affinity propagation is a semisupervised machine learning algorithm used to cluster items based on similarities among them. In our approach, we first calculate the cluster quality and density and then combine their outputs to generate a new ranking score among clusters for the (...) personalized recommendation. In the first phase, user preferences are collected and normalized as items rating matrix. This generated matrix is then clustered offline using affinity propagation and kept in a database for future recommendations. In the second phase, online recommendations are generated by applying the offline model. Negative Euclidian similarity and the quality of clusters are used together to select the best clusters for recommendations. The proposed APHPRS system alleviates problems such as sparsity and cold-start problems. The use of affinity propagation and the hybrid recommendation technique used in the proposed approach helps in improving results against sparsity. Experiments reveal that the proposed APHPRS performs better than most of the existing recommender systems. (shrink)

In the traditional case, the uncertainty of the ambient temperature measured by the experiential distributed sensor is considered. In this paper, a model based on the moving least square method in the fusion algorithm is proposed to study the optimal monitoring point of the sensor in the greenhouse and determine the most suitable installation position of the sensor in the greenhouse to improve the control effect of the temperature control device of the system. MATLAB simulation software is used to simulate (...) each working condition of the greenhouse. Temperature data measured at 15 locations in the greenhouse were used to evaluate all possible combinations of monitoring locations and to estimate the optimal location for indoor temperature sensors. Compared with the traditional method, the error is reduced to 0.373, and the data are more accurate. (shrink)

This paper addresses the robust output feedback model predictive control schemes for networked control systems with input quantization. The logarithmic quantizer is considered in this paper, and the sector bound approach is applied, which appropriately treats the quantization error as a sector-bounded uncertainty. The presented method involves an offline designed state observer using linear matrix inequality and online robust output feedback MPC algorithms which optimize one free control move followed by the output feedback using the estimated state. Moreover, due to (...) the uncertainty of estimation error, a technique of refreshing the bound of estimation error which involves the quantization error is provided so as to guarantee the recursive feasibility of the optimization problem. The proposed MPC schemes inherit the characteristics of the synthesis approach of MPC, guaranteeing the recursive feasibility of the optimization problem and the stability of a closed-loop system, and explicitly account for quantization error. Two simulation examples are given to illustrate the effectiveness of the proposed methods. (shrink)

We study the dynamics and stability of the economics of crime and punishment game from an evolutionary perspective. Specifically, we model the interaction between agents and controllers as an asymmetric game exploring the dynamics of the classic static model using a replicator dynamics equation, given exogenous levels of monitoring and criminal sanctions. The dynamics show five possible equilibria, from which three are stable. Our results show that a culture of honest agents is never stable; however when the penalty is high (...) enough, the system will neutrally tend to an equilibrium of honest agents and a monitoring firm. By contrast, when the probability of detecting wrongdoing is small, the system, in some cases, will remain in a transient state, in which it is impossible to predict the proportion of honest agents. (shrink)

In order to build high-quality concrete, it is imperative to know the raw materials in advance. It is possible to accurately predict the quality of concrete and the amount of raw materials used using machine learning-enhanced methods. An automated process based on machine learning strategies is proposed in this paper for predicting the compressive strength of concrete. Fusion-learning-based optimization is used in the proposed approach to generate a strong learner by pooling support vector regression models. The SVR technique proposes an (...) optimization method for finding the kernel radial basis function parameters based on improving the innovative gunner algorithm. As a result of AIG's diverse solutions, local optima are effectively avoided. Therefore, the novelty of our research is that, in solving the uncertainty of predicted outputs based on integrated models, we use fusion-learning-based optimization to improve regression discrimination. We also collected a standard dataset to analyze the proposed algorithm, and subsequently, the dataset was designed from concrete laboratory tests on 244 samples, seven features, and three outputs. Different regression intensities are determined by correlation analysis of responses. Regression fusion is sufficiently accurate to estimate the number of desired outcomes examined based on the appropriate input data sample. The best quality concrete can be achieved with an error rate of less than 5%. (shrink)

During the COVID-19 epidemic, draconian countermeasures forbidding nonessential human activities have been adopted in several countries worldwide, providing an unprecedented setup for testing and quantifying the current impact of humankind on climate and for driving potential sustainability policies in the postpandemic era from a perspective of complex systems. In this study, we consider heterogeneous sources of environmental and human activity observables, considered as components of a complex socioenvironmental system, and apply information theory, network science, and Bayesian inference to analyze their (...) structural relations and nonlinear dynamics between January 2019 and August 2020 in northern Italy, i.e., before, during, and after the national lockdown. The topological structure of a complex system strongly impacts its collective behavior; therefore, mapping this structure is essential to fully understand the functions of the system as a whole and its fragility to unexpected disruptions or shocks. To this aim, we unravel the causal relationships between the 16 environmental conditions and human activity variables, mapping the backbone of the complex interplay between intervening physical observables—such as NO2 emissions, energy consumption, intervening climate variables, and different flavors of human mobility flows—to a causal network model. To identify a tipping point during the period of observation, denoting the presence of a regime shift between distinct network states, we introduce a novel information-theoretic method based on statistical divergence widely used in statistical physics. We find that despite a measurable decrease in NO2 concentration, due to an overall decrease in human activities, locking down a region as a climate change mitigation is an insufficient remedy to reduce emissions. Our results provide a functional characterization of socioenvironmental interdependent systems, and our analytical framework can be used, more generally, to characterize environmental changes and their interdependencies using statistical physics. (shrink)

This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross-validation, and regression ensemble framework. Bagging ), boosting and extreme gradient boosting regression ), and stacking are employed as ensemble models. Different machine learning approaches, including support vector regression, extreme learning machine, and multilayer perceptron neural network, are adopted as reference models. In order to maximize the determination coefficient value and reduce the root mean square error, hyperparameters are set using (...) the grid search method. Using a steel industry dataset, all tests are carried out under identical experimental conditions. In this context, STACK1 and STACK2 models provided better performance than other models. The highest accuracies of R2 of 0.97 and 0.97 are obtained using STACK1 and STACK2, respectively. Moreover, the rank according to performances is STACK1, STACK2, XGBR, GBR, RFR, MLP, ELM, and SVR. As it improves the performance of models and reduces the risk of decision-making, the ensemble method can be used to forecast the demand in a steel industry one month ahead. (shrink)

This paper is reporting on electronic implementation of a three-dimensional autonomous system with infinite equilibrium point belonging to a parabola. Performance analysis of an adaptive synchronization via relay coupling and a hybrid steganography chaos encryption application are provided. Besides striking parabolic equilibrium, the proposed three-dimensional autonomous system also exhibits hidden chaotic oscillations as well as hidden chaotic bursting oscillations. Electronic implementation of the hidden chaotic behaviors is done to confirm their physical existence. A good qualitative agreement is shown between numerical (...) simulations and OrCAD-PSpice results. Moreover, adaptive synchronization via relay coupling of three three-dimensional autonomous systems with a parabolic equilibrium is analysed by using time histories. Numerical results demonstrate that global synchronization is achieved between the three units. Finally, chaotic behavior found is exploited to provide a suitable text encryption scheme by hidden secret message inside an image using steganography and chaos encryption. (shrink)

Dynamical analysis, chaos suppression and electronic implementation of the synchronous reluctance motor without external inputs are investigated in this paper. The different dynamical behaviors found in the SynRM without external inputs are illustrated in the two parameters largest Lyapunov exponent diagrams, one parameter bifurcation diagram, and phase portraits. The three single controllers are designed to suppress the chaotic behaviors found in SynRM without external inputs. The three proposed single controllers are simple and easy to implement. Numerical simulation results show that (...) the three proposed single controllers are effective. Finally, the dynamical behaviors found in the SynRM without external inputs and the physical feasibility of the three proposed single controllers are validated through circuit implementation on OrCAD-PSpice software. (shrink)

This work introduces a three-dimensional, highly nonlinear quadratic oscillator with no linear terms in its equations. Most of the quadratic ordinary differential equations such as Chen, Rossler, and Lorenz have at least one linear term in their equations. Very few quadratic systems have been introduced and all of their terms are nonlinear. Considering this point, a new quadratic system with no linear term is introduced. This oscillator is analyzed by mathematical tools such as bifurcation and Lyapunov exponent diagrams. It is (...) revealed that this system can generate different behaviors such as limit cycle, torus, and chaos for its different parameters’ sets. Besides, the basins of attractions for this system are investigated. As a result, it is revealed that this system’s attractor is self-excited. In addition, the analog circuit of this oscillator is designed and analyzed to assess the feasibility of the system’s chaotic solution. The PSpice simulations confirm the theoretical analysis. The oscillator’s time series complexity is also investigated using sample entropy. It is revealed that this system can generate dynamics with different sample entropies by changing parameters. Finally, impulsive control is applied to the system to represent a possible solution for stabilizing the system. (shrink)

The rapid increase in COVID-19 cases has become the symbol of fear, anxiety, and panic among people around the globe. Mass media has played an active role in community education by addressing the health information of this pandemic. People interact by sharing their ideas and feelings through social media platforms. There is a considerable need to implement different measures and better perceive COVID-19 pertinent facts and information by demystifying public sentiments. In this study, the Quarantine Life dataset of thousand tweets (...) is based on #Quarantine, #Quarantine Days, #Quarantine Life, #My Pandemic Plan, and #Quarantine and Chill from January to September 2020 has been collected from Twitter. The extracted data have been scrubbed through preprocessing techniques. The sentiments and topics extracted from tweets have been analyzed through the TEXT BLOB, VADER, and AFFIN approach. Results show that people were distressed and fearful due to the COVID-19 pandemic. However, most people enjoyed by playing games, watching movies, and reading books during the lockdown period. According to the present meta-analysis, physical activity interventions are beneficial for patients with dementia in terms of cognition. The proposed framework illustrates the insight impact of COVID-19 on human physiological and mainly focuses on the evaluation of sentiment dynamics at the topical level. (shrink)

This study presents a modified simplest equation method to investigate some real and exact solutions of conformable time fractional Benjamin-Bona-Mahony equation and Chan-Hilliard equation. We use traveling wave transformation to obtain the results in the form of series solution. Some calculations are performed through Mathematica software to analyze the accuracy of this approach. Graphical representations are reported for more significant results at different fractional-order which demonstrates that this approach is very simple, adequate, and legitimate.

In this article, complete synchronization and antisynchronization in the identical financial chaotic system are presented. The proposed control strategies depend on first-order sliding mode and adaptive integral sliding mode for complete synchronization and antisynchronization of the identical financial chaotic system. In the primary case, the system parameters should be known, and first-order sliding mode control is utilized for synchronization and antisynchronization while in the second case, the system parameters are considered unknown. An adaptive integral sliding mode control strategy is utilized (...) for synchronization and antisynchronization of the system considering the parameters unknown. The error system is changed into a particular structure containing a nominal part and several unknown terms to utilize the adaptive integral sliding mode control. Then, this error system is stabilized using integral sliding mode control. The stabilizing controller is usually developed based on the nominal part plus the compensator control part. To suppress the high-frequency oscillation phenomenon, smooth continuous compensator control can be used rather than conventional discontinuous control. The compensator controller along with the adapted law is derived such that the time derivative of the Lyapunov function becomes strictly negative. The effectiveness of the proposed method was tested through computer simulations. The proposed control strategies are verified for that Identical 4D hyperchaotic financial system to attain complete synchronization and antisynchronization along with the improved performance. (shrink)

The spread of epidemics, especially COVID-19, is having a significant impact on the world. If an epidemic is not properly controlled at the beginning, it is likely to spread rapidly and widely through the coexistence relationship between natural and social systems. A university community is a special, micro-self-organized social system that is densely populated. However, university authorities in such an environment seem to be less cautious in the defence of an epidemic. Currently, there is almost no quantitative research on epidemic (...) spreading and response strategies in universities. In this paper, a case study of a university community is considered for a simulation of an infection evolving after an epidemic outbreak based on the method of system dynamics of the three stages. The results show the following: By improving the speed of the initial emergency response, the total number of patients can be effectively controlled. A quarantine policy helps to slow down the evolution of infection. The higher the isolation ratio, the higher the cost; therefore, the isolation ratio should be optimized. It is important to make emergency plans for controlling epidemic spreading and carry out emergency drills and assessments regularly. According to the results of this study, we suggest an emergency management framework for public health events in university communities. (shrink)

In computational mathematics, it is a matter of deep concern to recognize which of the given iteration schemes converges quickly with lesser error to the desired solution. Fixed point iterative schemes are constructed to be used for solving equations emerging in many fields of science and engineering. These schemes reformulate a nonlinear equation f s = 0 into a fixed point equation of the form s = g s ; such application determines the solution of the original equation via the (...) support of fixed point iterative method and is subject to existence and uniqueness. In this manuscript, we introduce a new modified family of fixed point iterative schemes for solving nonlinear equations which generalize further recursive methods as particular cases. We also prove the convergence of our suggested schemes. We also consider some of the mathematical models which are categorically nonlinear in essence to authenticate the performance and effectiveness of these schemes which can be seen as an expansion and rationalization of some existing techniques. (shrink)

This paper investigates a distributed energy management strategy for the port power system under false data injection attacks. The attacker can tamper with the interaction information of energy equipment, penetrate the boundary between port information system and port power system, and cause serious operation failure of port energy equipment. Firstly, a hierarchical topology is proposed to allocate the security resources of the port power system. Secondly, by reconstructing the topological structure of the port information system, the robustness of the information (...) system is improved, and the impact of false data injection attacks on the port power system is reduced, which realizes secure distributed energy management of the port power system. Finally, the simulation results show the effectiveness of the proposed strategy, and the defense capability of the port power system is improved. (shrink)

This paper investigates the relationship between technological finance, high-quality economic growth, and financial stability. Based on data of 30 provinces collected between 2004 and 2017, this paper adopts the method of factor analysis to construct comprehensive indexes of technological finance and financial stability before calculating green total factor productivity as the index of high-quality development, using the CRS Multiplicative Model. Then it constructs the spatial SAC model and PVAR model for analyses of the just-mentioned relationship based on the total sample (...) of the nation and regional samples in eastern, middle, and western China, respectively. The results reveal that All samples, whether the total national samples or regional samples of eastern, middle, and western China demonstrate the positive influence of technological finance on high-quality economic development, with an obvious spatial spillover effect. The impact factor is the highest in the eastern region, while the western region holds the lowest factor among the three. Judging by the general national sample, technological finance has an obvious negative shock effect on financial stability within a short period, but the effect gradually dwindles as time goes by. This rule applies to the sample of the eastern region, as its technological finance poses a short-time negative shock effect on financial stability, before gradually diminishing to 0. Neither western nor middle regions have displayed an obvious shock impact on financial stability. (shrink)

This research studies the optimal consumption, investment, and life insurance choices for a wage earner with habit formation, inflation risk, and mortality risk. The wage earner has access to a risk-free asset, an index bond, and a stock in a financial market. The index bond hedges inflation risk, while life insurance hedges mortality risk. The aim of the wage earner is to maximize the expected utility of consumption, bequest, and terminal wealth, where the utility of consumption comes from the part (...) of the consumption that exceeds a minimum consumption requirement given by the habit level. By using the dynamic programming method, we provide and prove a verification theorem and obtain closed-form expressions for the value function, the optimal life insurance premium rate, and the optimal investment and consumption strategies. Numerical results reveal that habit formation and mortality force change play important roles in the financial behaviors of the wage earner. Especially, the impacts of the expected inflation rate and consumption habit on the optimal strategies are mutual restraint; increasing mortality force raises the demand for life insurance; and the effects of other parameters are also affected by consumption habit and mortality force change. (shrink)

In recent years, precise high flow oxygen therapy as a new type of oxygen therapy machine has gradually attracted people’s attention and has been widely used in hospital emergency and clinical treatment of respiratory diseases; especially in recent years, severe coronavirus disease has played an important role in the treatment of patients. This paper presents a new type of precise high flow oxygen therapy machine with electromagnetic pneumatic flow valve as the core control element. A sliding mode control strategy based (...) on the system is proposed to realize the accurate control of oxygen concentration and output flow of oxygen therapy mixture. The physical equipment of the precision high flow system is established, and its working performance is verified through the test platform. The optimization design goal of the precision high flow equipment is achieved. (shrink)

The main objective of Zika transmission studies is to work out the simplest approach to scale back human mortality and morbidity caused by the disease. Therefore, it is essential to spot the relative importance of the various factors contributing to the transmission and prevalence of the disease. Many mathematical models have been formulated incorporating vector-to-human transmission or human-to-human transmission. However, they do not take into consideration the mixture of both sorts of transmission. It raises the question of the impact of (...) both sorts of transmission on the disease dynamics. We develop a mathematical model of Zika with the vertical transmission in the vector population and human-to-human transmission to answer this question. It includes the immature phase of mosquitoes, adult mosquitoes, and human hosts. Results show that neglecting sexual transmission results in an understatement of the proportion of the infected population. Furthermore, it reduces the speed of disease spreading. On the other hand, vertical transmission in mosquitoes has a negligible effect on the dynamics of disease spread. We perform a sensitivity analysis of the reproductive number R 0 to raise to understand the parameters driving the dynamics of the disease. It appears that the most sensitive parameters in decreasing order are as follows: the adult mosquito death rate, the sting rate, the transmission probability of mosquito to human, and, therefore, the transmission probability of human to mosquito. Furthermore, the proportion at the equilibrium of infected humans is extremely sensitive to the transition rate from the immature vector stage to the adult stage, the human-to-human transmission rate, and, therefore, the human recovery rate. These results confirm that control policies targeting the vector population and, therefore, the recovery rate of people are pretty effective solutions. To validate the model and estimate the important parameters of the model and the prediction of the disease, we consider the real cases in Colombia from 2016. In a series of graphic maps, we presented the comparative study to estimate the disease scenarios and to predict the time limit of the epidemic control measure. (shrink)

The past three decades of policy process studies have seen the emergence of a clear intellectual lineage with regard to complexity. Implicitly or explicitly, scholars have employed complexity theory to examine the intricate dynamics of collective action in political contexts. However, the methodological counterparts to complexity theory, such as computational methods, are rarely used and, even if they are, they are often detached from established policy process theory. Building on a critical review of the application of complexity theory to policy (...) process studies, we present and implement a baseline model of policy processes using the logic of coevolving networks. Our model suggests that an actor’s influence depends on their environment and on exogenous events facilitating dialogue and consensus-building. Our results validate previous opinion dynamics models and generate novel patterns. Our discussion provides ground for further research and outlines the path for the field to achieve a computational turn. (shrink)

We present a perspective of the national transplant program based on organizational theory and complexity theory, framing the system’s allocation of donor organs as an interorganizational directed multiplex of agents with diverse belief formation in a cooperative-competitive environment. Simulation and analysis of this macroscale complexity may help explain known behavioural variations across member organizations. However, the transplant community still relies on system-scale simulations since effective macroscale methodologies are not well established. Therefore, we offer this perspective of the national transplant program (...) as a means to stimulate new methods that capture macroscale impacts of policy development for deceased donor organ allocation. (shrink)

In a congested large-scale subway network, the distribution of passenger flow in space-time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path choice with multisource (...) data, in which the impact of the network time-varying state on passenger path choice is fully considered. First, according to the number and characteristics of OD candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real-time probability distribution is formulated with the distribution of walking time, waiting time, and in-vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real-time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time-varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time-varying state, and our model can quantify the time-varying state and its impact on passenger path choice. (shrink)

The impacts of COVID-19 have spread rapidly to global financial markets. In this context, combining the spillover index method introduced by Diebold and Yilmaz and the complex network analysis framework, we examined the volatility connectedness and the topological structure among the top ten cryptocurrencies before and during the COVID-19 crisis. The results revealed that the total volatility connectedness of the cryptocurrency market markedly increased following the outbreak of COVID-19; statically, Bitcoin, Ethereum, Cardano, and Bitcoin Cash were the net transmitters before (...) COVID-19, while Bitcoin, Ethereum, Ripple, Litecoin, Cardano, and Stellar became the major net transmitters in the market after COVID-19. Dynamically, the dynamic performance of different cryptocurrencies during the COVID-19 pandemic was heterogeneous, and the possible driving factors are diverse. Moreover, from network analysis, we further found that the COVID-19 crisis has significantly changed the topological structure of the cryptocurrency market. Our findings may help understand the typical dynamics in the cryptocurrency market and provide significant implications for portfolio managers, investors, and government agencies in times of highly stressful events like the COVID-19 crisis. (shrink)

Over the past century, private tutoring in many countries has increased dramatically. Moreover, the main disadvantage of PT is that has a byproduct and a characteristic on the educational system in developing countries in terms of contributing to conditions such as large class sizes, low public expenditures, and an inadequate number of universities. In Saudi Arabia, the spread of PT at school and university levels has yet to be addressed by researchers. One goal of this examination was to research the (...) spread of PT in relation to mathematics being taught at school and university levels. This investigation used quantitative data of the questionnaire to determine the reasons for the spread. A total of 1000 students responded from University of Ha’il. The edge plans given results that are associated and like the assessment that is performed with the whole data. The current study found that a good family financial situation and lack of parental supervision of the children were significant factors underlying the spread of private tutoring at school and university levels. (shrink)

Bank competition promotes enterprises to obtain credit funds through market mechanisms, and a combination of industry and finance has an important influence on establishing bank-enterprise relationship. Based on data of 2,245 manufacturing enterprises and branches of commercial banks in China from 2007 to 2019, this research establishes a moderated mediation model to verify whether CIF has a moderating effect on bank competition on enterprise innovation. The results are as follows. First, bank competition can both directly and indirectly promote enterprise innovation (...) by alleviating financing constraints. Second, CIF has no moderating effect on the direct effect of bank competition on enterprise innovation but has a negative moderating effect on the first half of mediating effect. (shrink)

The conventional community sports facility allocation methods have high minimum costs, and a new community sports facility allocation performance optimization method is designed based on a linear programming model in order to reduce the performance capital investment. The standardized community sports facility allocation performance objective function is established, and a pairwise model is built to divide the feasible and optimal solutions, and the feasible solutions and their constraints are found out. Establish a community sports facility configuration performance optimization model, delineate (...) the boundaries of the optimal solution by bifurcation, and set the feasible domain of the performance optimization model. The performance optimization algorithm is designed to improve the speed of community sports facility allocation performance optimization. In the experiment, the method is compared with data-driven method, digital management method, and evolutionary game algorithm, which requires less capital investment in spring, autumn, and winter than digital management method and evolutionary game algorithm, and the minimum capital investment in summer is less than the other three methods. From the above experimental data, it is clear that the performance optimization method has the smallest minimum capital investment and achieves the purpose of reducing the input cost. (shrink)

The fire safety of industrial buildings has always been a great concern. An excellent evacuation indicator system can guide the personnel to escape quickly, thus reducing the casualties. In this study, we present a virtual simulation fire scene based on virtual reality to explore the impact of different colors, brightness values, and flashing frequencies on escape time in case of fire emergencies. The presented scene can help shorten the time required for evacuation in an industrial plant by identifying the escape (...) path under the fire and smoke environment and improve the escape efficiency. The results show that the green color is the most suitable color for evacuations; the escape efficiency increases with an increase in brightness, and there is no significant difference between the escape times under high and medium brightness; the evacuation time decreases with an increase in the flicker frequency. Based on these experimental results, the fire evacuation indicating system is designed. A virtual reality verification experiment is used to compare the escape efficiency of the improved design with an original factory’s indicating system. The verification results show that the improved scheme reduces the evacuation time and significantly improves the psychological stress on the evacuees. This work provides a theoretical basis for safe evacuation designs. (shrink)

We investigate whether the determinants of small and medium enterprises’ trade credits taken for purchasing fixed assets suffered substantial changes due to the global financial crisis. The geographical focus of this paper covers 18 former Eastern bloc countries. The data sample comprises opinions of the SMEs top managers relative to the trade credit financing. The two-step Heckman procedure is applied to study complexity of the trade credit determinants. We find that before the GFC the equity concentration and inflation have negatively (...) impacted the trade credit while foreign ownership and company’s longevity have had a positive effect. The GFC has changed this complex relationship. We evidence that, after the GFC, equity concentration and state subsidies have a positive effect. (shrink)

In presence of predator population, the prey population may significantly change their behavior. Fear for predator population enhances the survival probability of prey population, and it can greatly reduce the reproduction of prey population. In this study, we propose a predator-prey fishery model introducing the cost of fear into prey reproduction with Holling type-II functional response and prey-dependent harvesting and investigate the global dynamics of the proposed model. For the system without harvest, it is shown that the level of fear (...) may alter the stability of the positive equilibrium, and an expression of fear critical level is characterized. For the harvest system, the existence of the semitrivial order-1 periodic solution and positive order- q periodic solution is discussed by the construction of a Poincaré map on the phase set, and the threshold conditions are given, which can not only transform state-dependent harvesting into a cycle one but also provide a possibility to determine the harvest frequency. In addition, to ensure a certain robustness of the adopted harvest policy, the threshold condition for the stability of the order- q periodic solution is given. Meanwhile, to achieve a good economic profit, an optimization problem is formulated and the optimum harvest level is obtained. Mathematical findings have been validated in numerical simulation by MATLAB. Different effects of different harvest levels and different fear levels have been demonstrated by depicting figures in numerical simulation using MATLAB. (shrink)

Based on the Lotka–Volterra system, a pest-natural enemy model with nonlinear feedback control as well as nonlinear action threshold is introduced. The model characterizes the implementation of comprehensive prevention and control measures when the pest density reaches the nonlinear action threshold level depending on the pest density and its change rate. The mortality rate of the pest is a saturation function that strictly depends on their density while the release of natural enemies is also a nonlinear pulse term depending on (...) the density of real-time natural enemies. The exact impulsive and phase sets are given. The definition and properties of the Poincaré map corresponding to the pulse points on the phase set are provided. We investigate the existence and stability of boundary and interior order-1 periodic solution. The theoretical analysis developed in the present paper combined with nonlinear controlling measures as well as nonlinear action threshold methods and techniques laid the foundation for the establishment and analysis of other state-dependent feedback control models. (shrink)

With the advancement of scientific collaboration in the 20th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric methods are used to quantify the scientific collaboration among researchers and scientific communities. Among these different bibliometric methods, the co-authorship method is one of the most verifiable methods to quantify or analyze scientific collaboration. In this research, the initial study has been conducted to (...) analyze interdisciplinary research activities in the computer science domain. The ACM has classified the computer science fields. We selected the Journal of Universal Computer Science for experimentation purposes. The J.UCS is the first Journal of Computer Science that addresses a complete ACM topic. Using J.UCS data, the co-authorship network of the researcher up to the 2nd level was developed. Then the co-authorship network was analyzed to find interdisciplinary among scientific communities. Additionally, the results are also visualized to comprehend the interdisciplinary among the ACM categories. A whole working web-based system has been developed, and a forced directed graph technique has been implemented to understand IDR trends in ACM categories. Finally, the IDR values between the categories are computed to quantify the collaboration trends among the ACM categories. It was found that “Artificial Intelligence” and “Information Storage and Retrieval”, “Natural Language Processing and Information Storage and Retrieval”, and “Human-Computer Interface” and “Database Applications” were found the most overlapping areas by acquiring an IDR score of 0.879, 0.711, and 0.663, respectively. (shrink)

Freight transportation plays a critical role in improving company performance in the modern manufacturing industry. To reduce costs, companies must take advantage of the use of large vehicles. It caused fewer deliveries, but inventory costs and degradation quality are high. One of the joint economic lot size problems in supply chain is Integrated Single-Vendor Single-Buyer Inventory Problem. This study developed the I-SVSB-IP model that considers raw materials’ exponential quality degradation and transportation costs. The objective function of this research was to (...) maximize the Joint Total Profit. Three decision variables used were inventory cycle time, raw material ordering frequency, and frequency of delivery of finished products to buyers. This study proposed a sophisticated Chimp Optimization Algorithm procedure to solve the I-SVSB-IP problem. A case study on the food industry in Indonesia was presented to optimize the I-SVSB-IP. The results showed that the ChOA procedure had produced an optimal solution compared to the state-of-the-art algorithm. This study also demonstrated a sensitivity analysis of decision and transportation variables to cost, revenue, and JTP. The results show that increasing transport frequency of ordering raw materials and finished products to buyers enhances the total cost and reduces joint total profit. In addition, increasing the rate of quality degradation of raw materials reduces JTP. (shrink)

Here, a novel conservative chaotic oscillator is presented. Various dynamics of the oscillator are examined. Studying the dynamical properties of the oscillator reveals its unique behaviors. The oscillator is multistable with symmetric dynamics. Equilibrium points of the oscillator are investigated. Bifurcations, Lyapunov exponents, and the Poincare section of the oscillator’s dynamics are analyzed. Also, the oscillator is investigated from the viewpoint of initial conditions. The study results show that the oscillator is conservative and has no dissipation. It also has various (...) dynamics, such as equilibrium point and chaos. The stability analysis of equilibrium points shows there are both stable and unstable fixed points. (shrink)

The discrete-time system has more complex and chaotic dynamical behaviors as compared to the continuous-time system. This paper extends a discrete Leslie–Gower predator-prey system with the Allee effect in the predator’s population, whose dynamics are analyzed and explored. We have determined the equilibrium points and studied their local stability properties. We find that the system undergoes flip bifurcation and Neimark–Sacker bifurcation around the interior equilibrium point by choosing the Allee parameter as a bifurcation parameter. We discuss the stability and direction (...) of both bifurcations with the help of the normal form theory and center manifold theorem. The flip bifurcation and Neimark–Sacker bifurcation are the most common routes to the chaotic orbit in the discrete system. Moreover, we utilize state feedback, pole placement, and hybrid control methods to control the chaos in the system. The work is complete with the numerical simulations to confirm the analytical findings. (shrink)

Spatial predator-prey models have been studied by researchers for many years, because the exact distributions of the population can be well illustrated via pattern formation. In this paper, amplitude equations of a spatial Holling–Tanner predator-prey model are studied via multiple scale analysis. First, by amplitude equations, we obtain the corresponding intervals in which different kinds of patterns will be onset. Additionally, we get the conclusion that pattern transitions of the predator are induced by the increasing rate of conversion into predator (...) biomass. Specifically, pattern transitions of the predator between distinct Turing pattern structures vary in an orderly manner: from spotted patterns to stripe patterns, and finally to black-eye patterns. Moreover, it is discovered that pattern transitions of prey can be induced by cross-diffusion; that is, patterns of prey transmit from spotted patterns to stripe patterns and finally to a mixture of spot and stripe patterns. Meanwhile, it is found that both effects of cross-diffusion and interaction between the prey and predator can lead to the complicated phenomenon of dynamics in the system of biology. (shrink)

Multisource information systems and multigranulation intuitionistic fuzzy rough sets are important extended types of Pawlak’s classical rough set model. Multigranulation intuitionistic fuzzy rough sets have been investigated in depth in recent years. However, few studies have considered this combination of multisource information systems and intuitionistic fuzzy rough sets. In this paper, we give the uncertainty measure for multisource intuitionistic fuzzy information system. Against the background of multisource intuitionistic fuzzy information system, each information source is regarded as a granularity level. Considering (...) the different importance of information sources, we assign different weights to them. Firstly, the paper proposes an optimal source selection method. Secondly, we study the weighted generalized, weighted optimistic, and weighted pessimistic multigranularity intuitionistic fuzzy rough set models and uncertainty measurement methods in the multisource intuitionistic fuzzy information system, and we further study the relationship between the three models and related properties. Finally, an example is given to verify the validity of the models and methods. (shrink)