The pathogen-prevalence hypothesis postulates that collectivism would be strengthened in the long term in tandem with recurrent attacks of infectious diseases. However, it is unclear whether a one-time pathogen epidemic would elevate collectivism. The outbreak of COVID-19 and the widespread prevalence of online social networks have provided researchers an opportunity to explore this issue. This study sampled and analyzed the posts of 126,165 active users on Weibo, a leading Chinese online social network. It used independent-sample t-tests to examine whether COVID-19 (...) had an impact on Chinese collectivistic value-related behaviors by comparing the usage frequency of personal pronouns, group-related words, and relationship-related words before and after the outbreak. Overall, most collectivist words exhibited a significant upward trend after the outbreak. In turn, this tendency pointed to a rising sense of collectivism. Hence, this study confirmed the pathogen-prevalence hypothesis in real settings, finding that an outbreak of an infectious disease such as COVID-19 could exert an impact on collectivism and may deliver a theoretical basis for psychological protection against the threat of COVID-19. However, further evaluation is required to ascertain whether this trend is universal or culture-specific. (shrink)

An esssential work of this enigmatic sage, draws from the ancient traditions of Buddhism, Taosim, and Advaita Vedanta.

In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy is used (...) to gradually reduce population to improve the search capabilities and balance the exploitation and exploration capabilities. The less and more greedy mutation strategy is used to enhance the exploitation capability and the exploration capability. Finally, a parameter optimization method based on EBLSHADE is proposed to optimize parameters of PV models. The different PV models are selected to prove the effectiveness of the proposed method. Comparison results demonstrate that the EBLSHADE is an effective and efficient method and the parameter optimization method is beneficial to design, control, and optimize the PV systems. (shrink)

This study investigates whether and how the intensity of social distancing from the Coronavirus disease 2019 (COVID-19) pandemic influences the corporate social responsibility (CSR) disclosure index. An empirical examination is carried out based on data from the Shanghai Stock Exchange from 2020 to 2022. CSR disclosure index is measured by the percentage of CSR-related press releases from the total press releases published on a certain day. The intensity of the COVID-19 pandemic is measured by the daily confirmed cases among the (...) population in China. This research uses the two-stage least squares regression model to alleviate the endogeneity issues. Findings reveal a reverse correlation between the intensity of the COVID-19 pandemic and the CSR disclosure. The results are consistent and robust to endogeneity tests and sensitivity analyses. Findings support the stakeholder–agency theory and indicate that Chinese managers tend to satisfy the shareholders' interests rather than those of other broad stakeholders. Moreover, Chinese managers tend to choose short-term survival rather than long-term development in times of social distancing. (shrink)

In this paper, we improve a result of Seetapun and prove that above any nonzero, incomplete recursively enumerable degree a, there is a high2 r.e. degree c>ac>a witnessing that a is locally noncappable . Theorem 1.1 provides a scheme of obtaining high2 nonboundings , as all known high2 nonboundings, such as high2 degrees bounding no minimal pairs, high2 plus-cuppings, etc.

An esssential work of this enigmatic sage, draws from the ancient traditions of Buddhism, Taosim, and Advaita Vedanta.

Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an (...) eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks. (shrink)

Monoidal t-norm based logic $\mathbf {MTL}$ is the weakest t-norm based residuated fuzzy logic, which is a $[0,1]$ -valued propositional logical system having a t-norm and its residuum as truth function for conjunction and implication. Monadic fuzzy predicate logic $\mathbf {mMTL\forall }$ that consists of the formulas with unary predicates and just one object variable, is the monadic fragment of fuzzy predicate logic $\mathbf {MTL\forall }$, which is indeed the predicate version of monoidal t-norm based logic $\mathbf {MTL}$. The main (...) aim of this paper is to give an algebraic proof of the completeness theorem for monadic fuzzy predicate logic $\mathbf {mMTL\forall }$ and some of its axiomatic extensions. Firstly, we survey the axiomatic system of monadic algebras for t-norm based residuated fuzzy logic and amend some of them, thus showing that the relationships for these monadic algebras completely inherit those for corresponding algebras. Subsequently, using the equivalence between monadic fuzzy predicate logic $\mathbf {mMTL\forall }$ and S5-like fuzzy modal logic $\mathbf {S5(MTL)}$, we prove that the variety of monadic MTL-algebras is actually the equivalent algebraic semantics of the logic $\mathbf {mMTL\forall }$, giving an algebraic proof of the completeness theorem for this logic via functional monadic MTL-algebras. Finally, we further obtain the completeness theorem of some axiomatic extensions for the logic $\mathbf {mMTL\forall }$, and thus give a major application, namely, proving the strong completeness theorem for monadic fuzzy predicate logic based on involutive monoidal t-norm logic $\mathbf {mIMTL\forall }$ via functional representation of finitely subdirectly irreducible monadic IMTL-algebras. (shrink)

The race among global firms to launch its respective products and services into the market sooner than the competitors puts pressure to equip its employees with job-related skills at the pace of business. Today’s global and dynamic business requires employees to develop highly complex cognitive skills such as decision-making, problem-solving, troubleshooting to perform their jobs proficiently. Traditional training models used by some organizations lead to a very slow speed at which employees gain an acceptable level of proficiency in the targeted (...) job skills. Also, these models have long and regimented instructional development cycle. Thus, traditional models are inherently counter-productive to the business and do not enable employees and organizations for today’s business needs. Therefore, business organizations need to explore new training models and strategies that could reduce the time an employee takes to reach target proficiency in complex skills without compromising the effectiveness or outcome. A comprehensive review of the literature shows a very limited amount of academic or practitioner research on this topic. This doctorate research study aims to find various training strategies that have proven successful in organizations for accelerating proficiency of employees in complex job skills. The researcher collected data primarily through 74 in-depth interviews with 86 training experts with known work experience of reducing time-to-proficiency in various settings. A total of 105 project cases have been collected across 42 industries to date. A grounded theory approach with constant comparison method is used to guide the theoretical saturation, analyze the data and to develop a theoretical model of training strategies. This unique study revealed a range of training strategies from non-training solutions to classroom instructions to workplace training to accelerate proficiency of employees in acquiring complex skills. This paper will specifically report the preliminary findings and conceptual model of major classroom-based instructional strategies only that has been found to hold the potential to shorten time-to-proficiency of employees at the workplace. This paper will also discuss the implications of the findings for the practitioners. Results in this study confirmed that classroom-based instructional strategies need to be designed and delivered the way the work is being performed in reality by the learners. Research findings suggest a pattern of five classroom-based instructional strategies that are more successful in reducing time-to-proficiency - 1) Contextualized learning 2) Performance analysis of star performer 3) Active processing and emotional involvement 4) Access to self-guided learning resources 5) Social interconnectivity and informal learning. (shrink)

With the rapid development of renewable generators such as distributed photovoltaic and profound changes of the power structure, this paper analyzes the frequency characteristics of the power system with high penetration of renewable generations in the process of low-frequency load shedding and discusses the influence of the distributed renewable generator frequency endurance capabilities on the implementation effect of low-frequency load shedding in detail. Finally, the influences of the distributed renewable generator frequency endurance capability and the capacities of the distributed renewable (...) generator on frequency response characteristics of an actual Hunan power grid are simulated. The simulation results show that more distributed photovoltaic capacity without frequency endurance capability leads to deeper frequency drops after the disturbance and requires more basic rounds of load shedding. When the penetration rate of distributed photovoltaic is too high, it may cause load shedding, resulting in power grid load losses and, at the same time, leading to an overshoot phenomenon in the process of frequency recovery. Therefore, the influence of distributed photovoltaic on the control of low-frequency load shedding should be considered when designing low-frequency load shedding schemes for the power grid with high penetration of distributed photovoltaic. (shrink)

Customers are skeptical about shopping online because e-commerce environments are typically considered impersonal. To assure product quality and to enhance customer proclivity in such environments, post-sale services may be considered to alleviate customers’ skepticism. Therefore, this study’s objective is to investigate the role of an online retailer’s post-sale services on customers’ attitudinal and behavioral aspects. Structural equation modeling is applied to data collected through an online survey answered by 409 online customers of jd.com. Research findings show that product return, exchange, (...) and maintenance services are strongly predictive of online customer satisfaction, and satisfaction significantly impacts customer trust. Both customer satisfaction and trust, as indicators of relationship quality, further mediate the links between product return, exchange, and maintenance services and online customer repurchase intention. In addition, differences between male and female customers were found in various aspects of online retailers’ product return, exchange, and maintenance services. This is the first empirical study that not only examines the influence of all three dimensions of online retailers’ post-sale services on customers’ online shopping perceptions and decisions, but also considers differences between male and female customers. Finally, this research provides theoretical and managerial implications based on conceptual and empirical evidence. (shrink)

本书对自组织理论进行综合性研究,并探讨了关于自然界自组织演化的自然图景和自然观,关于自然科学自组织演化的科学图景和科学观。.

This article puts forward a novel class of Bézier smooth semi-supervised support vector machines(BS4VMs) for classification. As is well known, semi-supervised support vector machine is introduced for dealing with quantities of unlabeled data in the real world. Labeled data is utilized to train the algorithm and then adapting it to classify the unlabeled data. However, the objective semi-supervised function is not differentiable globally. It is required to endure heavy burden in solving two quadratic programming problems with inversion matrix operation. To (...) solve this problem, the interpolation Bézier functions for replacing the non-smooth term have been investigated. Firstly, the development of the novel BS4VMs classifier is introduced. Then, the non-linear model and convergence analysis are presented. To show how the BS4VMs can be practically implemented, experiments on synthetic and real data sets are offered. The theoretical analysis and experiments comparisons clearly illustrate the BS4VMs are superior to the proposed methods in both cross-validation classification accuracy and calculating time. (shrink)

Developing World Bioethics, Volume 22, Issue 3, Page 170-178, September 2022.

This study employs an experiment investigating cognitive mapping of fifth-grade children living in a remote village environment, wherein characteristics of the landscape included paths, landmarks, nodes, edges, and districts. Two aspects of analysis were salient in this study. First, important landscape characteristics and their frequency of appearance in the cognitive maps were tabulated and illustrated as a layout map. Second, inaccurate cognitive maps were structurally analyzed to account for any incompleteness, distortions, and augmentation of actual environments found in some map (...) samples. Focus on gender differences in children’s environmental cognition in terms of symbolic representation skills utilized in cognitive mapping is of special interest in this case study. Results confirm Piaget’s theory that older children, aged ten or more, begin to use projective and Euclidean concepts. Furthermore, boys used a greater variety of symbols to represent a particular landscape characteristic, a cultural temple, than did the girls. Finally, we hypothesize that the ‘hunter–gatherer’ social divisions of labor between men and women in the village’s early historical social structure are consequentially related to gender discrepancies in cognitive mapping symbolic representation skills, in non-English-speaking children. (shrink)