Pharmaceutical supply chains are often highly complex with conflicting objectives of social welfare and profit maximization. Furthermore, there are various stakeholders including pharmaceutical manufacturer, distributors, retailers, patients, and the government. In this paper, we consider a two-stage supply chain consisting of one pharmaceutical manufacturer and a pharmacy with online and offline channels. We focus on four price cap models: no price cap regulation, pharmaceutical manufacturer’s price cap regulation, pharmacy price cap regulation, and linkage price cap regulation. We apply game theory, (...) investigate how the price cap regulations affect the firms’ pricing, and evaluate the economic performance and social welfare of the dual-channel pharmaceutical supply chain. Our findings show that first, like the single-channel pharmaceutical supply chain, the profit of the regulated firm always decreases and the profit of the unregulated firm always increases when they are under one-sided price cap regulations. Second, the impacts of the linkage price cap regulation on the supply chain are more complicated depending on the linkage coefficient and market share. Overall, our findings can provide theoretical and practical insights to help the government devise price cap regulations for complex modern pharmaceutical supply chains. (shrink)

ObjectiveTo investigate the attitudes of Chinese parents regarding the storage of dried blood spots collected for newborn screening (NBS) and their use in research.MethodsWe conducted a hospital-based survey of parents and examined parental attitudes regarding (a) allowing NBS sample storage, (b) permitting use of children’s NBS samples for research with parental permission, and (c) permitting use of children’s NBS samples for research without parental permission.ResultsThe response rate was 52 percent. Of parents surveyed, 68 percent would permit their infant’s NBS sample (...) to be stored for at least some length of time. If permission is obtained, 69 percent of parents “strongly agreed” or “agreed” to permit use of the NBS sample for research. If permission is not obtained, only 14 percent of parents “strongly agreed” or “agreed.” There was no significant association between permitting use of NBS samples for research and parental gender, education, household income, number of children, or site of residence.ConclusionsThis is the first survey of Chinese parents regarding the use of NBS samples for different types of research, with results indicating that most parents would permit their infant’s sample to be stored and would support the use of NBS dried blood spots for research purposes. (shrink)

Major construction projects have a great influence on the national economy and society, wherein cooperative relationship between construction suppliers plays an increasingly significant role in the overall supply chain system. However, the relationships between suppliers are noncontractual, multistage, dynamic, and complicated. To gain a deeper insight into the suppliers’ cooperative relationships, an evolutionary game model is developed to explore the cooperation tendency of multisuppliers. A replicator dynamic system is further formulated to investigate the evolutionary stable strategies of multisuppliers. Then, fourteen (...) “when-then” type scenarios are concluded and classified into six different evolutionary tracks. Meanwhile, the critical influencing factors are identified. The results show that the suppliers’ production capacity, owner-supplier contract, and the owner’s incentive mechanism influence the cooperation tendency of suppliers directly. The managerial implications contribute to insightful references for a more stable cooperative relationship between the owner and suppliers. (shrink)

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose “The lockdown: One month in Wuhan” YouTube documentary, and applied emotion analysis as well as a (...) machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust, joy, and anticipation are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing. (shrink)

A fuzzy predictive fault-tolerant control scheme is proposed for a wide class of discrete-time nonlinear systems with uncertainties, interval time-varying delays, and partial actuator failures as well as unknown disturbances, in which the main opinions focus on the relevant theory of FPFTC based on Takagi-Sugeno fuzzy model description of these systems. The T-S fuzzy model represents the discrete-time nonlinear system in the form of the discrete uncertain time-varying delay state space, which is firstly constructed by a set of local linear (...) models and the nonlinear membership functions. The novel improved state space model can be further obtained by extending the output tracking error to the constructed model. Then the fuzzy predictive fault-tolerant control law based on this extended model is designed, which can increase more control degrees of freedom. Utilizing Lyapunov-Krasovskill theory, less conservative delay-range-dependent stable conditions in terms of linear matrix inequality constraints are given to ensure the asymptotically robust stability of closed-loop system. Meanwhile, the optimized cost function and H-infinity performance index are introduced to the stable conditions to guarantee the robust performance and antidisturbance capability. The simulation results on the temperature control of a strong nonlinear continuous stirred tank reactor show that the proposed control scheme is feasible and effective. (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)

Electroencephalogram has been widely utilized in emotion recognition. Psychologists have found that emotions can be divided into conscious emotion and unconscious emotion. In this article, we explore to classify subliminal emotions with EEG signals elicited by subliminal face stimulation, that is to select appropriate features to classify subliminal emotions. First, multi-scale sample entropy, wavelet packet energy, and wavelet packet entropy of EEG signals are extracted. Then, these features are fed into the decision tree and improved random forest, respectively. The classification (...) accuracy with Ei and WpEn is higher than MSpEn, which shows that Ei and WpEn can be used as effective features to classify subliminal emotions. We compared the classification results of different features combined with the decision tree algorithm and the improved random forest algorithm. The experimental results indicate that the improved random forest algorithm attains the best classification accuracy for subliminal emotions. Finally, subliminal emotions and physiological proof of subliminal affective priming effect are discussed. (shrink)

Nowadays, the development of human society and daily life are inseparable from the power supply. Therefore, people also put forward higher requirements for the reliability of distribution network, but power companies can only passively deal with distribution network failures, which is a bottleneck for the improvement of distribution network reliability. The Internet of Things is the best solution for online equipment status monitoring and basic data sharing for large, widely distributed, relatively fixed, and large numbers of equipment. The construction of (...) the IoT for power distribution equipment faces many important problems, including the selection of networking, equipment selection, and interaction standards. When researching the implementation plan, research on the distribution of IoT market was carried out. Based on the grid, the idea of optimizing the investment selection plan of the power distribution using IoT was discussed, and a result verification model was established. After the completion of the theoretical part, a case study of medium-voltage distribution grid equipment management and medium-voltage distribution network equipment management based on the grid was carried out by taking a real enterprise application situation as an example. Realizing fault diagnosis of distribution network will not only provide decision support for operation and maintenance of distribution network for power companies, but also have great economic and social benefits. Aiming at the shortcomings of single data mining method in distribution network fault diagnosis, hybrid data mining method is proposed. First, rough set theory is used to reduce the original fault data and form a simplified rule set. Because of the non-linearity of distribution network fault and the strong learning ability, adaptability, and robustness of Bayesian network, Bayesian network can be used to classify distribution network faults. Therefore, a simplified fault diagnosis system is established in this paper, and its correctness is confirmed. Then, the learning and training are carried out by using Bayesian network to call the simplest rule set, which has the characteristics of short learning and training time and high diagnostic accuracy. (shrink)

Although some cognitive studies provided reasons that children with low socioeconomic status (SES) showed poor mathematical achievements, there was no explicit evidence to directly explain the root of lagged performance in children with low SES. Therefore, the present study explored the differences in neural correlates in the process of symbolic magnitude comparison between children with different SES by the event-related potentials (ERP). A total of 16 second graders from low SES families and 16 from middle/high SES families participated in this (...) study. According to the results of anterior N1 (early attention) and P2 (extraction of numerical meaning) over frontal region, the differences among children with different SES were manifested as differences in general neural activities in terms of attention and top-down cognitive control. In the late stage of cognitive processing, there was no significant difference in the average amplitude of the LPC between the children with different SES, indicating that low SES did not influence the information encoding and memory updating of numerical representation responsible by the parietal lobe. The educational implications of this study were mentioned in the discussion. (shrink)

The current study mainly explored the influence of fluid intelligence and emotional intelligence on affective decision-making from a developmental perspective, specifically, during the transition from childhood into early adolescence. Meanwhile, their age-related differences in affective decision-making were explored. A total of 198 participants aged 8–12 completed the Iowa Gambling Task, the Cattell’s Culture Fair Intelligence Test and the Trait Emotional Intelligence Questionnaire-Child Form. Based on the net scores of IGT, the development of affective decision-making ability did not increase monotonically with (...) age, and there was a developmental trend of an impaired IGT performance in early adolescence, especially in the early learning phase of the IGT. More importantly, IQ and EI played different roles for children and early adolescents: IQ and EI jointly predicted the IGT performance for 8–10 years old children, whereas only EI contributed to the IGT performance of 11–12 years old early adolescents. The present study extends the evidence how cognitive processing and emotional processing interact in affective decision-making from the developmental perspective. Furthermore, it provides insights of future research and intervention with early adolescents’ poor affective decision-making. (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)

Event cameras which transmit per-pixel intensity changes have emerged as a promising candidate in applications such as consumer electronics, industrial automation, and autonomous vehicles, owing to their efficiency and robustness. To maintain these inherent advantages, the trade-off between efficiency and accuracy stands as a priority in event-based algorithms. Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated hardware and (...) datasets constitute an emerging field named event-based data-driven technology. Focusing on data-driven technology in event-based vision, this paper first explicates the operating principle, advantages, and intrinsic nature of event cameras, as well as background knowledge in event-based vision, presenting an overview of this research field. Then, we explain why event-based data-driven technology becomes a research focus, including reasons for the rise of event-based vision and the superiority of data-driven approaches over other event-based algorithms. Current status and future trends of event-based data-driven technology are presented successively in terms of hardware, datasets, and algorithms, providing guidance for future research. Generally, this paper reveals the great prospects of event-based data-driven technology and presents a comprehensive overview of this field, aiming at a more efficient and bio-inspired visual system to extract visual features from the external environment. (shrink)

ABSTRACT This article delves into the advent of Chinese Buddhism in Africa and its entanglement with politics and the contemporary Chinese transnationalism. It explores the previously uncharted territory of the endeavours of Chinese Buddhist organisations and the transnational elements of Chinese religions in Africa. Drawing on ethnographic data from South Africa, Tanzania, Botswana and Malawi, this article examines the mobility of transnational Chinese Buddhism, probes retrospectively into its origins and drives, and investigates its connections with the evolving China–Africa/taiwan–africa engagements. We (...) argue that contemporary Chinese Buddhism in Africa is a product of connections and competitions between states, African politics, and the multi-layered Chinese diaspora. The political, social and cultural influences of transnational Chinese Buddhists in Africa warrant further exploration and analysis. (shrink)

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an (...) automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload. (shrink)

For thousands of human’s history, we have learned how to fake or hide our genuine feelings and emotion to our surrounding folks intentionally or unconsciously. It is an irony that this is what we call emotion intelligence to get more interests, show our politeness, tackle the dilemma, or deal with many other complicated situations. Posed smiles are one of the most common faked expressions in our daily life. Indeed, it is a challenge for computer vision system to recognize genuine from (...) posed smile of an individual, not even for human brains sometimes. Recently, an interesting work by Guo et al.(Guo, et al., 2018) employed computer vision techniques to investigate the potential differences in duration, intensity, speed, symmetry of the lip corners, and certain irregularities between genuine and posed smiles based on the UvA-NEMO Smile Database. The results are quite rewarding since they found that genuine smiles were correlated with higher onset, offset, apex, and total durations, as well as offset displacement, and Irregularity-b, compared with posed smiles. In addition, posed smiles were correlated with higher onset and offset Speeds, Irregularity-a, Symmetry-a, and Symmetry-d.We can’t agree with the saying that only a handful of studies on the dynamic features of facial expressions have been conducted due to the lack of user-friendly analytic tools. For the past decades, hundreds of studies focused on dynamic features of facial expressions (Ko, 2018, Sandbach, et al., 201... (shrink)

Because of the mechanism complexity, coupling, and time-space characteristic of alkali-surfactant-polymer flooding, common methods are very hard to be implemented directly. In this paper, an iterative dynamic programming based on a biorthogonal spatial-temporal Wiener modeling method is developed to solve the enhanced oil recovery for ASP flooding. At first, a comprehensive mechanism model for the enhanced oil recovery of ASP flooding is introduced. Then the biorthogonal spatial-temporal Wiener model is presented to build the relation between inputs and states, in which (...) the Wiener model is expanded on a set of spatial basis functions and temporal basis functions. After inferring the necessary condition of solutions, these basis functions are determined by the snapshot method. Furthermore, a theorem is proved to identify parameters in the Wiener model. Combined with the least square estimation, all unknown parameters are determined. In addition, the ARMA model is applied to build the model between states and outputs, whose parameters are identified by recursive least squares. Thus, the whole modeling process for ASP flooding is finished. At last, the IDP algorithm is applied to solve the enhanced oil recovery problem for ASP flooding based on the identification model to obtain the optimal injection strategy. Simulations on the ASP flooding with four injection wells and nine production wells show the accuracy and effectiveness of the proposed method. (shrink)

As a driver of tourist-oriented citizenship behavior, the effect of social support has not been thoroughly investigated. Grounded in a framework integrating the stimulus-organism-response model and self-determination theory, this study investigates how social support influences TOCB through the sense of self-determination. Structural equation modeling is used to analyze the survey data collected from 377 tourists in China. It is found that social support have a remarkably positive impact on the sense of self-determination which have an intermediary role in the relationship (...) between social support and TOCB. This study provides empirical evidence for the marketing strategies of tourism destinations and enterprises. (shrink)

We derive, in path integral approach, the (anomalous) master Ward identity associated with an infinite set of nonlocal conservation laws in two-dimensional principal chiral models.

Considering the highly complex structure of quantum chaos and the nonstationary characteristics of speech signals, this paper proposes a quantum chaotic encryption and quantum particle swarm extraction method based on an underdetermined model. The proposed method first uses quantum chaos to encrypt the speech signal and then uses the local mean decomposition method to construct a virtual receiving array and convert the underdetermined model to a positive definite model. Finally, the signal is extracted using the Levi flight strategy based on (...) kurtosis and the quantum particle swarm optimization optimized by the greedy algorithm. The bit error rate and similarity coefficient of the voice signal are extracted by testing the source voice signal SA1, SA2, and SI943 under different SNR, and the similarity coefficient, uncertainty, and disorder of the observed signal and the source voice signal SA1, SA2, and SI943 verify the effectiveness of the proposed speech signal extraction method and the security of quantum chaos used in speech signal encryption. (shrink)

Entrepreneurship may be taught, and entrepreneurship education is flourishing at colleges and universities. However, previous documents show that entrepreneurship education is inconsistent with the research conclusions of entrepreneurial intention, which is a lack of discussion on the mediating effect of government subsidies from external resources. Based on the cognitive behavior theory, a mediating effect of entrepreneurship education on entrepreneurial strategy and entrepreneurial intention is built. By collecting the data of 334 questionnaires of college students in Pearl River Delta in China, (...) a structural equation is used for empirical analysis. The result indicates that entrepreneurship education does not have a significant influence on entrepreneurial intention; exploration innovation and exploitation innovation have a positive influence on entrepreneurial intention, and exploration innovation and exploitation innovation have a mediating effect on entrepreneurship education and entrepreneurial intention; government subsidies have a positive regulating effect on exploration innovation, exploitation innovation, and entrepreneurial intention. In this article, the application of the cognitive behavior theory in the field of entrepreneurship research is expanded to provide the theoretical basis for building the entrepreneurship education ecosystem, which is conductive to innovation and entrepreneurship to promote regional economic development. (shrink)

Business gang refers to the enterprise cluster formed by geographical relationship, which has always been the focus of research on entrepreneurship and regional economic development. The research of new institutional economics shows that culture, as an informal system, will change the social psychology, thinking mode and behavior of economic individuals, and provide a good environment for the growth of start-ups, thus affecting economic activities and economic development. Taking the five modern business gangs in China as the research subject, this paper (...) uses the comparative method to analyze the regional cultural differences of the five modern business gangs, as well as the differences of the entrepreneurs’ psychological characteristics and startup behaviors. Through the analysis of the economic data of the provinces where the modern business gangs are located, this paper summarizes the differences of economic development in different regions. It is concluded that regional culture has a significant impact on the development of modern business gangs and regional economy. It is necessary to give full play to the advantages of regional culture and promote the high-quality development of modern business gangs and regional economy. (shrink)

Neurons encode and transmit signals through chemical synaptic or electrical synaptic connections in the actual nervous system. Exploring the biophysical properties of coupling channels is of great significance for further understanding the rhythm transitions of neural network electrical activity patterns and preventing neurological diseases. From the perspective of biophysics, the activation of magnetic field coupling is the result of the continuous release and propagation of intracellular and extracellular ions, which is very similar to the activation of chemical synaptic coupling through (...) the continuous release of neurotransmitters. In this article, an induction coil is used to connect two HR neurons to stimulate the effect of magnetic field coupling. It is inevitable that time delays can affect the coupling process in the transmission of information, and it should be considered in the coupled model. Firstly, the firing characteristics and bifurcation modes of two coupled HR neurons are studied by using one parameter and two parameters bifurcation. With the increase of propagation delay and coupling gain, the chaotic state of neurons disappears and the high-period window decreases due to the influence of energy transfer between neurons. Then, the synchronization patterns of two HR neurons with different stimulation are analyzed by error diagrams and time series diagrams. It is confirmed that the synchronous pattern has certain regularity and is related not only to the neurons with large stimulation current but also to the time delay and coupling gain. The research conclusions of this article will provide the corresponding theoretical basis for medical experiments. (shrink)