Kenneth Kai-chung Yung’s Chinese Émigré Intellectuals and Their Quest for Liberal Values in the Cold War presents the philosophical and political development of Chinese intellectuals who fled the mainland after the Communist takeover in 1949. Focusing on Yin Haiguang 殷海光, Zhang Junmai 張君勱, and Xu Fuguan 徐復觀, the author provides a comparative account and comprehensive overview of the many facets of intellectual discourse among Chinese post-war philosophers and public intellectuals.Yung’s book is structured into five chapters. While the (...) first two chapters introduce the reader to the general trends and historical circumstances of the post-war intellectual sphere, the ensuing chapters... (shrink)

This essay reconstructs and explores the fundamental premises of the arguments in section two of Sex Robots: Their Social Impact and the Future of Human Relations. This section compasses essays from scholars both East and West. Mark J. Cherry argues, for example, that while a Traditional Christian could easily appreciate the sinfulness of sex with a robot, such a conclusion will make little sense from a purely secular perspective. Ellen Zhang explores such issues from within the richness of the (...) Daoist tradition, while Lawrence Yung and Hanhui Xu draw on the intellectual and moral riches of Confucianism. Here, my goal is critically to examine their assumptions and arguments, so as better to comprehend their conclusions regarding sex robots. As readers will discover, one general conclusion is clear, however, despite what many may argue, there is little, if any, moral consensus on such matters. (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 Bayesian vector autoregressive model introduces the statistical properties of variables as the prior distribution of the parameters into the traditional vector autoregressive model, which can overcome the problem of too little freedom. The BVAR model established in this paper can overcome the problem of short time series data by using prior statistical information. In theory, it should have a good effect in China’s regional economic forecasting. Most regional forecasting model literature lacks out-of-sample forecasting error evaluation research in the real (...) sense, but our early forecasts of major economic indicators provide an excellent opportunity for this paper to evaluate the actual forecast errors of the BVAR model in detail. The analysis in this paper shows that the prediction error of the BVAR model is very small and the prediction ability is very satisfactory. At the same time, this article also analyzes and points out the direction of efforts to further improve the prediction accuracy of the BVAR model. (shrink)

This study examines the influence of firm governance structures (board size, independence, CEO duality, director share ownership, and board meeting frequency) in relation to carbon emission disclosures by high-polluting Chinses firms. In addition, the study further examined the moderating role of earnings management on this relationship. In line with stakeholder and agency theories, our study identified that the large and independent boards exercise and demonstrate a higher degree of carbon emission disclosures. However, CEO duality and director share ownership are associated (...) with lower carbon emission disclosures. In addition, the study determined that higher earnings management results in a reduced level of carbon emission disclosures. Lastly, a firm earnings management strategy moderates the relationship between a firm governance structure and its carbon emission disclosures. The findings from the study are consistent with multiple econometric models and variables. The findings from the study contribute to the literature in the areas of firm corporate governance and carbon emission disclosures by documenting the moderating role of earnings management, which is not evident in previous studies; provide an enhanced perspective on the implications for firms, regulators, policymakers, and stakeholders who have an interest in reducing carbon emissions and advancing climate change mitigation goals in line with UN's Sustainable Development Goal (SDG) 7: climate action, and zero emissions goal by 2050. (shrink)

We investigated the pairing of a financial news article prediction system, AZFinText, with sentiment analysis techniques. From our comparisons we found that news articles of a subjective nature were easier to predict in both price direction (59.0% vs 50.4% without sentiment) and through a simple trading engine (3.30% return vs 2.41% without sentiment). Looking into sentiment further, we found that news articles of a negative sentiment were easiest to predict in both price direction (50.9% vs 50.4% without sentiment) and our (...) simple trading engine (3.04% return vs 2.41% without sentiment). Investigating the negative sentiment further, we found that AZFinText was best able to predict price decreases in articles of a positive sentiment (53.5%) and price increases in articles of a negative or neutral sentiment (52.4% and 49.5% respectively). (shrink)

Extreme events, which are usually characterized by generalized extreme value models, can exhibit long-term memory, whose impact needs to be quantified. It was known that extreme recurrence intervals can better characterize the significant influence of long-term memory than using the GEV model. Our statistical analyses based on time series datasets following the Lévy stable distribution confirm that the stretched exponential distribution can describe a wide spectrum of memory behavior transition from exponentially distributed intervals to power-law distributed ones, extending the previous (...) evaluation of the stretched exponential function using Gaussian/exponential distributed random data. Further deviation and discussion of a historical paradox are also provided, based on the theoretical analysis of the Bayesian law and the stretched exponential distribution. (shrink)

There is a common pattern for researchers to study one particular new religion, write a monograph or article on that specific group, and then begin the cycle all over again with a different group. This approach causes one to remember such groups as relatively stable organizations, fixed in memory at a specific stage of development, rather than as dynamic, evolving groups. In the present article, we will examine new data on contemporary Pagans that takes a quasi-longitudinal approach to survey data. (...) Though our focus will be limited, the result will nevertheless be a partial statistical picture of Paganism as a changing, evolving movement, rather than a static statistical snapshot. (shrink)

Inhibition of return refers to the phenomenon that a person is slower to respond to targets at a previously cued location. The present study aimed to explore whether target-reward association is subject to IOR, using event-related potentials to explore the underlying neural mechanism. Each participant performed a localization task and a color discrimination task in an exogenous cueing paradigm, with the targets presented in colors previously associated with high- or low-reward probability. The results of both tasks revealed that the N1, (...) Nd, and P3 components exhibited differential amplitudes between cued and uncued trials under low reward, with the N1 and Nd amplitudes being enhanced for uncued trials compared to cued trials, and the P3 amplitude being enhanced for cued trials vs. uncued trials. Under high reward, however, no difference was found between the amplitudes on cued and uncued trials for any of the components. These findings demonstrate that targets that were previously associated with high reward can be resistant to IOR and the current results enrich the evidence for interactions between reward-association and attentional orientation in the cueing paradigm. (shrink)

Deep learning models are powerful tools for representing the complex learning processes and decision-making strategies used by humans. Such neural network models make fewer assumptions about the underlying mechanisms thus providing experimental flexibility in terms of applicability. However, this comes at the cost of involving a larger number of parameters requiring significantly more data for effective learning. This presents practical challenges given that most cognitive experiments involve relatively small numbers of subjects. Laboratory collaborations are a natural way to increase overall (...) dataset size. However, data sharing barriers between laboratories as necessitated by data protection regulations encourage the search for alternative methods to enable collaborative data science. Distributed learning, especially federated learning, which supports the preservation of data privacy, is a promising method for addressing this issue. To verify the reliability and feasibility of applying FL to train neural networks models used in the characterization of decision making, we conducted experiments on a real-world, many-labs data pool including experiment data-sets from ten independent studies. The performance of single models trained on single laboratory data-sets was poor. This unsurprising finding supports the need for laboratory collaboration to train more reliable models. To that end we evaluated four collaborative approaches. The first approach represents conventional centralized learning and is the optimal approach but requires complete sharing of data which we wish to avoid. The results however establish a benchmark for the other three approaches, federated learning, incremental learning, and cyclic incremental learning. We evaluate these approaches in terms of prediction accuracy and capacity to characterize human decision-making strategies. The FL-based model achieves performance most comparable to that of the CL-based model. This indicates that FL has value in scaling data science methods to data collected in computational modeling contexts when data sharing is not convenient, practical or permissible. (shrink)

The increase in the size of universities has greatly increased the number of teachers, students, and courses and has also increased the difficulty of scheduling courses. This study used coevolution to improve the genetic algorithm and applied it to solve the course scheduling problem in universities. Finally, simulation experiments were conducted on the traditional and improved genetic algorithms in MATLAB software. The results showed that the improved genetic algorithm converged faster and produced better solutions than the traditional genetic algorithm under (...) the same crossover and mutation probability. As the mutation probability in the algorithm increased, the fitness values of both genetic algorithms gradually decreased, and the computation time increased. With the increase in crossover probability in the algorithm, the fitness value of the two genetic algorithms increased first and then decreased, and the computational time decreased first and then increased. (shrink)

The conditional independence relations present in a data set usually admit multiple causal explanations — typically represented by directed graphs — which are Markov equivalent in that they entail the same conditional independence relations among the observed variables. Markov equivalence between directed acyclic graphs (DAGs) has been characterized in various ways, each of which has been found useful for certain purposes. In particular, Chickering’s transformational characterization is useful in deriving properties shared by Markov equivalent DAGs, and, with certain generalization, is (...) needed to justify a search procedure over Markov equivalence classes, known as the GES algorithm. Markov equivalence between DAGs with latent variables has also been characterized, in the spirit of Verma and Pearl (1990), via maximal ancestral graphs (MAGs). The latter can represent the observable conditional independence relations as well as some causal features of DAG models with latent variables. However, no characterization of Markov equivalent MAGs is yet available that is analogous to the transformational characterization for Markov equivalent DAGs. The main contribution of the current paper is to establish such a characterization for directed MAGs, which we expect will have similar uses as Chickering’s characterization does for DAGs. (shrink)

本书内容包括:虚静无为之术、政治洞察之术、以法治国的艺术、赏罚的艺术、势治的艺术、用人的艺术、政治语言的使用艺术、法家与中国传统政治文化的现代转化。.

本书包括:实践哲学是伽达默尔解释哲学理论运思的主题、伽达默尔从理论解释学到实践解释学的转变逻辑、伽达默尔的“解释学作为实践哲学”等。.

Dynamic capabilities are crucial to the survival and development of enterprises in the BOP market. The research focuses on the double-edged sword impact of relational embeddedness on dynamic capabilities via ambidextrous learning, that is moderate embeddedness facilitates dynamic capabilities while overembeddedness inhibits them. Furthermore, this study investigates whether human capital moderates the relationships between relational embeddedness and ambidextrous learning. Selecting 264 samples for empirical research, firstly, the results show that the relational embeddedness in the BOP cooperation network has an inverted (...) U-shaped influence on ambidextrous learning and dynamic capabilities. Secondly, exploratory learning and exploitative learning play a mediating role in relational embeddedness and dynamic capabilities. Thirdly, prior experience plays a positive moderating role in relational embeddedness and exploitative learning. The conclusions facilitate understanding the antecedents of dynamic capabilities and the black box of “embeddedness paradox,” and provide empirical evidence for adjusting the human capital of enterprises, enhancing the exploratory learning capability and exploitative learning capability, and coping with the overembeddedness effects. (shrink)

Owing to the COVID-19 pandemic, working from home promotes the importance of indoor environment qualities. With the settings and functions of home offices, an experiment was carried out to determine the interaction effects between indoor plants and traffic noise levels on the performance and environmental evaluations of English reading comprehension tasks and the performance of short-term breaks. A sample of 22 Chinese university students took part in the experiment. Two visual conditions and five TNLs were included. Participants’ accuracy rates, eye (...) movements, mental workload, and feelings about the environment were collected. The mental fatigue recovery, visual fatigue recovery, anxiety recovery, and unfriendly recovery were measured for the analysis of a 5-min short-term break. The results demonstrate plants have significant effects on ERCTs and short-term breaks, especially at 45 and 50 dBA TNL; the effects of TNLs on ERCTs’ eye movements and work environment satisfaction differ by the presence of plants, e.g., the average pupil diameter, lighting and layout satisfaction; The effects of indoor plants on ERCT differ by the range of TNLs. In conclusion, indoor plants are beneficial to home workers engaged in ERCT when TNL does not exceed 50 dBA. The current data highlight the importance of audio-visual interaction in home offices and provide insights into the interaction mechanism between indoor plants and traffic noise. (shrink)

This study aims to explore the linking mechanisms underlying the relationship between leader proactivity and follower proactivity. Drawing on social learning theory, the present research investigates the effects of leader proactivity on follower proactivity by developing a chain mediation model. An analysis of three-wave lagged data on 575 employees of six firms in China shows that leader proactivity is positively related to follower proactivity and that employees’ role breadth self-efficacy and felt responsibility for constructive change mediate this relationship. The analytical (...) results also show that leader proactivity facilitates follower value congruence, which in turn enhances followers’ RBSE and FRCC and ultimately promotes followers’ proactivity. The results extend the current proactivity literature and fill the research gap by investigating the relationship between leader proactivity and follower proactivity. The current study also contributes to the literature by identifying the mediating mechanism of the “can do” and “reason to” mechanisms that link leader proactivity to follower proactivity. (shrink)

To survive in a competitive environment, small and medium enterprises have had to adapt to the digital environment in order to adjust to customer needs globally, particularly in the post-COVID-19 world. The advantages of cloud computing provide opportunities for SMEs with a restricted budget and limited resources. To understand how SMEs adopt cloud computing in a complex digital environment, this study examines how antecedents combine with each other to explain the high adoption of cloud computing. From the perspectives of holism (...) and set theory, we draw on complexity and configuration theories, present a conceptual model including seven antecedents based on the technology-organization-environment framework, and conduct an asymmetric fuzzy-set qualitative comparative analysis. Through an empirical study with 123 Chinese companies, we identify nine combinations of determinant antecedents that lead to the high adoption of cloud computing. The results show that none of the factors are indispensable to explain a high adoption on their own; instead, they are insufficient but necessary parts of the causal combinations that explain a high adoption. This study contributes to the literature on cloud computing adoption by extending current knowledge on how antecedents combine to increase the adoption and identify specific patterns of SMEs for whom these factors are essential and greatly influence their adoption. (shrink)