The solution of this problem is extremely important to the process of the development of logic. Its solution is of great theoretical significance in upholding the methodology of dialectical materialism; criticizing the sophistry of metaphysics, skepticism, and relativism; developing formal logic and symbolic logic; forming the scientific system of dialectical logic in the strict sense; and conducting research to strengthen all the concrete sciences. It also has enormous significance for the criticism of "leftist" and right-wing opportunism and revisionism, and especially (...) for criticizing the counterrevolutionary revisionist line of the "gang of four.". (shrink)

Simpson’s paradox (SP) is a statistical phenomenon where the association between two variables reverses, disappears, or emerges, after conditioning on a third variable. It has been proposed (by, e.g., Judea Pearl) that SP should be analyzed using the framework of graphical causal models (i.e., causal DAGs) in which SP is diagnosed as a symptom of confounding bias. This paper contends that this confounding-based analysis cannot fully capture SP: there are cases of SP that cannot be explained away in terms of (...) confounding. Previous works have argued that some cases of SP do not require causal analysis at all. Despite being a logically valid counterexample, we argue that this type of cases poses only a limited challenge to Pearl’s analysis of SP. In our view, a more powerful challenge to Pearl comes from cases of SP that do require causal analysis but can arise without confounding. We demonstrate with examples that accidental associations due to genetic drift, the use of inappropriate aggregate variables as causes, and interactions between units (i.e., inter-unit causation) can all give rise to SP of this type. The discussion is also extended to the amalgamation paradox (of which SP is a special form) which can occur due to the use of non-collapsible association measures, in the absence of confounding. (shrink)

In this paper, we formulate and investigate a two-microorganism and single nutrient chemostat model with Monod-Haldane response function and random perturbation. First, for the corresponding deterministic system, we introduce the conditions of the stability of the equilibrium points. Then, using Lyapunov function and Itô’s formula, we investigate the existence and uniqueness of the global positive solution of the stochastic chemostat model. Furthermore, we explore and obtain the criterions of the extinction and the permanence for the stochastic model. Finally, numerical simulations (...) are carried out to illustrate our main results. (shrink)

Taking the semiotic landscaping of the Occupy Movement in Hong Kong in 2014 as an example, the article explores the semiotic relation between the materialization of signs and the constitution of a collectivity. Juxtaposing the concepts of ‘collective assemblage of enunciation’, ‘landscaping’ and ‘autoglottic space’, the article argues that collective enunciations are not autonomous from human agency. Instead, collective enunciations can be created by deliberate de-subjectification and de-personification in the materialization of signs.

Donating money to a charity based on consumer purchase is referred to as cause-related marketing . In this research, we profile consumer psychographics for skepticism toward advertising in a CRM context. To be specific, this study investigates whether and how psychological antecedents and gender differences influence consumer skepticism toward advertising. An empirical study was conducted with 291 participants. Structural equation modeling was employed for hypothesis testing. The results suggest that a utilitarian orientation and an individualistic mindset are positively related to (...) skepticism toward advertising, while a hedonic orientation and a collectivistic mindset are negatively related to skepticism toward advertising. Gender differences are also found in the aforementioned relationships. The segmentational approach of gender and psychographics can assist marketers to explain consumer attitudes toward CRM and then to communicate with those CRM advocates better. (shrink)

From the end of 2018 in China, the Big-data Driven Price Discrimination (BDPD) of online consumption raised public debate on social media. To study the consumers’ attitude about the BDPD, this study constructed a semantic recognition frame to deconstruct the Affection-Behavior-Cognition (ABC) consumer attitude theory using machine learning models inclusive of the Labeled Latent Dirichlet Allocation (LDA), Long Short-Term Memory (LSTM), and Snow Natural Language Processing (NLP), based on social media comments text dataset. Similar to the questionnaires published results, this (...) article verified that 61% of consumers expressed negative sentiment toward BDPD in general. Differently, on a finer scale, this study further measured the negative sentiments that differ significantly among different topics. The measurement results show that the topics “Regular Customers Priced High” (69%) and “Usage Intention” (67%) occupy the top two places of negative sentiment among consumers, and the topic “Precision Marketing” (42%) is at the bottom. Moreover, semantic recognition results that 49% of consumers’ comments involve multiple topics, indicating that consumers have a pretty clear cognition of the complex status of the BDPD. Importantly, this study found some topics that had not been focused on in previous studies, such as more than 8% of consumers calling for government and legal departments to regulate BDPD behavior, which indicates that quite enough consumers are losing confidence in the self-discipline of the platform enterprises. Another interesting result is that consumers who pursue solutions to the BDPD belong to two mutually exclusive groups: government protection and self-protection. The significance of this study is that it reminds the e-commerce platforms to pay attention to the potential harm for consumers’ psychology while bringing additional profits through the BDPD. Otherwise, the negative consumer attitudes may cause damage to brand image, business reputation, and the sustainable development of the platforms themselves. It also provides the government supervision departments an advanced analysis method reference for more effective administration to protect social fairness. (shrink)

The capacitive voltage transformer is a special measuring and protecting device, which is commonly applied in high-voltage power systems. Its measurement accuracy is affected seriously by the stray capacitances of the capacitance voltage divider to ground and other charged parts. In this study, based on the boundary element method, a mathematical model was established firstly to calculate the stray capacitance. Then, the voltage distribution of the CVD was obtained by the CVD’s equivalent circuit model. Next, the effect of stray capacitance (...) on the voltage distribution and the voltage difference ratio of CVD was analysed in detail. We finally designed three types of shield and optimized their structure parameters to reduce VDR. The results indicated that the average deviation rate between calculated and experimental measured voltages is only 0.015%; that is to say, the method has high calculation precision. The stray capacitance of the CVD to ground is far larger than that of the CVD to the high-voltage terminal. It results in the inhomogeneous distribution of voltage and the increase of VDR. For the test CVT, its VDR exceeds the requirement of class 0.2. Among all of the three types of shield, the C type reduced the VDR of the test CVT the most. After optimizing the structure parameters of C-type shield, the VDR is further reduced to 0.08%. It is not only in accord with the requirement of class 0.2 but also has an adequate margin. (shrink)

We propose a fast and reliable corner detector that can detect corners under non-uniform illumination and fuzzy mineshaft images effectively. First, we presented an inner mask that used only four pixels to determine the flat and corner regions of an image, which could eliminate unnecessary computation of flat regions, thus reducing computing cost. Second, we separated the corner regions into background and foreground and computed the separate corner threshold to settle non-uniform illumination. Third, we proposed a fast corner-detection algorithm to (...) compute the nucleus continuous contributive segment based on the corner state. Finally, we proposed two effective methods to remove the false corners. Experimental results showed that our approach has a better detection quality and is less time consuming than three other algorithms on an artificial image, a noisy image, and non-uniform images and could meet the real-time requirement of mineshaft applications. (shrink)

In face recognition systems, highly robust facial feature representation and good classification algorithm performance can affect the effect of face recognition under unrestricted conditions. To explore the anti-interference performance of convolutional neural network reconstructed by deep learning framework in face image feature extraction and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the random gradient descent and (...) triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild face database. Then, the portrait pyramid segmentation and local feature point segmentation are applied to extract the features of face images, and the matching of face feature points is achieved using Euclidean distance and joint Bayesian method. Finally, Matlab software is used to simulate the algorithm proposed in this paper and compare it with other algorithms. The results show that the proposed algorithm has the best face recognition effect when the learning rate is 0.0004, the attenuation coefficient is 0.0001, the training method is SGD, and dropout is 0.1, and the algorithm proposed in this paper has the largest mean average precision compared to other CNN algorithms. The correct rate of face feature matching of the algorithm proposed in this paper is 84.72%, which is higher than LetNet-5, VGG-16, and VGG-19 algorithms, the correct rates of which are 6.94%, 2.5%, and 1.11%, respectively, but lower than GoogleNet, AlexNet, and ResNet algorithms. At the same time, the algorithm proposed in this paper has a faster matching time and a higher correct matching rate than the joint Bayesian method, indicating that the deep reconstruction network algorithm proposed in this paper can be used in face image recognition, FE, and matching, and it has strong anti-interference. (shrink)

As one of the key technologies for accelerating the construction of the ubiquitous Internet of Things, demand response not only guides users to participate in power market operations but also increases the randomness of grid operations and the difficulty of load forecasting. In order to solve the problem of rough feature engineering processing and low prediction accuracy, a short-term load forecasting model of LSTM neural network considering demand response is proposed. First of all, in view of the strong randomness and (...) complexity of input features, the weighted method is used to process multiple input features to strengthen the contribution of effective features and tap the potential value of features. Secondly, an improved genetic algorithm is used to obtain the best LSTM parameters; finally, the special gate structure of the LSTM model is used to selectively control the influence of input variables on the model parameters and perform load forecasting. The experimental results show that the research has high prediction accuracy and application value and provides a new way for the development of power load forecasting. (shrink)

In a complex network, each edge has different functions on controllability of the whole network. A network may be out of control due to failure or attack of some specific edges. Bridges are a kind of key edges whose removal will disconnect a network and increase connected components. Here, we investigate the effects of removing bridges on controllability of network. Various strategies, including random deletion of edges, deletion based on betweenness centrality, and deletion based on degree of source or target (...) nodes, are used to compare with the effect of removing bridges. It is found that the removing bridges strategy is more efficient on reducing controllability than the other strategies of removing edges for ER networks and scale-free networks. In addition, we also found the controllability robustness under edge attack is related to the average degree of complex networks. Therefore, we propose two optimization strategies based on bridges to improve the controllability robustness of complex networks against attacks. The effectiveness of the proposed strategies is demonstrated by simulation results of some model networks. These results are helpful for people to understand and control spreading processes of epidemic across different paths. (shrink)

Zero-shot learning is dedicated to solving the classification problem of unseen categories, while generalized zero-shot learning aims to classify the samples selected from both seen classes and unseen classes, in which “seen” and “unseen” classes indicate whether they can be used in the training process, and if so, they indicate seen classes, and vice versa. Nowadays, with the promotion of deep learning technology, the performance of zero-shot learning has been greatly improved. Generalized zero-shot learning is a challenging topic that has (...) promising prospects in many realistic scenarios. Although the zero-shot learning task has made gratifying progress, there is still a strong deviation between seen classes and unseen classes in the existing methods. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the intrinsic characteristics of visual features which are discriminative enough to be classified by itself. To solve the above problems, we propose a novel model that uses the discriminative information of visual features to optimize the generative module, in which the generative module is a dual generation network framework composed of conditional VAE and improved WGAN. Specifically, the model uses the discrimination information of visual features, according to the relevant semantic embedding, synthesizes the visual features of unseen categories by using the learned generator, and then trains the final softmax classifier by using the generated visual features, thus realizing the recognition of unseen categories. In addition, this paper also analyzes the effect of the additional classifiers with different structures on the transmission of discriminative information. We have conducted a lot of experiments on six commonly used benchmark datasets. The experimental results show that our model outperforms several state-of-the-art methods for both traditional as well as generalized zero-shot learning. (shrink)

This study presents an evolutionary game to model interactions among stakeholders with potential conflicts, including the operational enterprise of incineration plant, the local government, and the residents nearby. System dynamics is used to simulate the change of strategic actions corresponding to the three players, in order to seek for the evolutionary stability strategies. A numerical case is proposed to demonstrate the game theory application, in which the impacts of governmental incentive and punishment on the player’s actions are investigated. The results (...) indicated that administrative penalty is effective not only in motivating the enterprises to upgrade treatment facilities for ensuring environmental quality but also in helping the local government and residents to approach dominant strategies. Policy implications are given based on the results to lay out a foundation for the alleviation of the conflicts. (shrink)

Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. The low-frequency image is (...) processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements. (shrink)

In public health emergencies, people are more willing to save money rather than spending it, which is not conductive to economic development and recovery. Due to the absence of relevant research, the internal logic of this phenomenon is not clear. In the context of the COVID-19 pandemic, this study systematically explored whether and why public health emergencies stimulate consumers' preference for saving. We conducted two online surveys and used methods including stepwise regression analysis and bootstrapping to test the hypotheses. The (...) first survey, with 1,511 participants from China in February 2020, indicates that the severity of emergencies has a significant positive impact on the populations' willingness to save. Risk perception plays a mediating role between the severity of emergencies and consumers' saving willingness. Materialism plays a moderating role between risk perception and an individual's saving willingness, individuals who are more materialistic have a lower saving willingness when they perceive the risks of the pandemic. To verify the duration of the above effects, we conducted a follow-up survey consisted of 466 instances in August 2020. It is noteworthy that the above effects are not significant during the post-pandemic period. Thus, spending behavior in public health emergencies can be motived by reducing risk perception and increasing materialism. These findings can provide a valuable inspiration for public health, crisis management, and economic recovery during public health emergencies. (shrink)

Background The urgency of ensuring adequate moral courage in clinical nursing practice is evident. However, currently, there are few formal intervention plans targeted at enhancing the moral courage of nurses. Aim To develop a training program for improving the moral courage of nurses using the modified Delphi method. Research design A modified Delphi study. Participants and research context From November to December 2022, a literature review and expert group discussion were conducted to develop a preliminary training plan framework. From January (...) to March 2023, a two-round Delphi survey was performed, and a consensus was reached regarding the plan through online questionnaires. Descriptive statistics were used to analyze the data. Ethical considerations This study was approved by the institutional ethics committee (No. 138, 30 August 2021). All participants provided written informed consent. Results Consensus was reached on eight themes and 33 items to strengthen the moral courage training program for nurses. Conclusions Guided by a unified goal of moral education, a multi-level and acceptable intervention plan was designed to enhance the moral courage of nurses. (shrink)

The dynamics behaviors of Kaldor–Kalecki business cycle model with diffusion effect and time delay under the Neumann boundary conditions are investigated. First the conditions of time-independent and time-dependent stability are investigated. Then, we find that the time delay can give rise to the Hopf bifurcation when the time delay passes a critical value. Moreover, the normal form of Hopf bifurcations is obtained by using the center manifold theorem and normal form theory of the partial differential equation, which can determine the (...) bifurcation direction and the stability of the periodic solutions. Finally, numerical results not only validate the obtained theorems, but also show that the diffusion coefficients play a key role in the spatial pattern. With the diffusion coefficients increasing, different patterns appear. (shrink)

Mismatch negativity of event-related potentials is a biomarker reflecting the preattentional change detection under non-attentional conditions. This study was performed to explore whether high self-related information could elicit MMN in the visual channel, indicating the automatic processing of self-related information at the preattentional stage. Thirty-five participants were recruited and asked to list 25 city names including the birthplace. According to the difference of relevance reported from the participants, we divided names of the different cities into high, medium, and low self-related (...) information. Visual MMN was elicited by high self-related information but not by medium self-related information, with an occipital–temporal scalp distribution, indicating that, under non-attentional condition, high self-related information can be effectively processed automatically in the preattentional stage compared with low self-related information. These data provided new electrophysiological evidence for self-related information processing. (shrink)

Business Ethics, the Environment &Responsibility, Volume 31, Issue 4, Page 1040-1052, October 2022.

: Governance means the policy and strategy adopted in the process of running a country. In the long history of more than two thousand years, Confucianism had become the cultural core and political doctrine of the imperial autocracy, and the theory of “governance of the whole country” contained in it had become an important category in traditional Chinese culture. We should dig deep into the rich connotation of Confucian governance thought, and build a national governance system that suits society, the (...) overall situation and future through innovative development and creative transformation, so as to let excellent traditional Chinese culture return to open a new road and make the past serve the present, thus providing important theoretical guidance and historical reference for the great rejuvenation of the Chinese nation. Resumo: Governança significa a política e a estratégia adotadas no processo de gestão de um país. Na longa história de mais de dois mil anos, o confucionismo tornou-se o núcleo cultural e a doutrina política da autocracia imperial, e a teoria da “governança de todo o país” contida nele tornou-se uma categoria importante na cultura tradicional chinesa. Deve-se aprofundar na rica conotação do pensamento de governança confucionista e construir um sistema de governança nacional que se adapte à sociedade, à situação geral e ao futuro, por meio de desenvolvimento inovador e transformação criativa, de modo a permitir que a excelente cultura tradicional chinesa retorne à base, para abrir um novo caminho e fazer o passado servir ao presente, fornecendo, assim, importante orientação teórica e referência histórica para o grande rejuvenescimento da nação chinesa. (shrink)

Previous studies have found the effect of cognitive skills on reading ability, but the role of different reading-related skills in acquisition of Chinese as a second/foreign language remains unexplored. Prior meta-analyses on the relationship between cognitive skills and reading have been conducted primarily in native English-speaking or Mandarin-speaking children. The purpose of the present meta-analysis was to examine the relationship between Chinese reading-related skills and Chinese word reading of CSL/CFL learners. A search of English and Chinese databases yielded 42 effect (...) sizes, comprising 1103 subjects met the criteria for meta-analysis and were included in the final meta-analysis. Results revealed a moderate relationship between phonological awareness, morphological awareness, orthographic awareness, rapid automatized naming and Chinese word reading in CSL/CFL learners. In addition, a moderating effect of length of study on the relationship between phonological awareness and Chinese word reading : phonological awareness and Chinese word reading correlated more strongly for beginning learners than for advanced learners. These results suggest importance of cognitive factors in the acquisition of Chinese word reading as a second language. Results also shed light on the impact of length of study on the influence from phonological awareness to the sensitive period of phonological learning for CSL/CFL learners. (shrink)

Governments have adopted strict home quarantine measures during the COVID-19 pandemic. A monotonous, barren, and under-stimulating environment can cause state boredom, and people often deal with boredom via novelty-seeking behavior. Novelty-seeking behavior can be divided into “novelty input” and “novelty output.” The former refers to obtaining novel information such as browsing the Web; the latter refers to engaging in creative behavior such as literary creation. This study explores the relationship between two types of novelty-seeking behavior and individual state boredom during (...) home quarantine, along with the moderation effect of trait creativity. The study sample consists of 582 Chinese college students who were quarantined at home during the COVID-19 pandemic. Participants completed the Multidimensional State Boredom Scale, the Williams Creativity Aptitude Test, and self-compiled questionnaires of novelty input and novelty output. The results show that there is no significant relationship between novelty input or novelty output and boredom during the COVID-19 quarantine. Trait creativity is found to negatively moderate the relationship between the two means of novelty seeking and boredom. Specifically, novelty output negatively predicts the state boredom of individuals with high creativity, while novelty input positively predicts the state boredom of individuals with low creativity. Our findings suggest that different novelty-seeking behaviors may have different effects on the boredom level of individuals with high versus low creativity during quarantine. During a quarantine period, individuals should avoid excessively engaging in novelty input behaviors aimed at escaping boring situations. (shrink)

In the context of the knowledge economy, the role of traditional leadership for enterprises is questioned. Based on contingency theory and the resource-based view, this paper proposes the important role of platform leadership, a new leadership type in line with the context of the times, for a sustainable competitive advantage. We conducted an empirical study to examine and confirm the positive effects of platform leadership on sustainable competitive advantage and ambidextrous learning. We also verified the mediation effect of exploratory and (...) exploitative learning on platform leadership and sustainable competitive advantage. Additionally, relevant discussion and research contributions are put forward. (shrink)

Research on the relationship between emotions and job performance is ubiquitous, yet few scholars have examined the combined effects of different emotions. Drawing on the broaden-and-build theory and conservation of resources theory, we propose that employees’ daily emotion fluctuations will affect their service performance in opposite directions. Furthermore, we propose these effects will be moderated by psychological [i.e., regulatory emotional self-efficacy ] and physiological characteristics of the employees. Based on the experience sampling method, data obtained from 187 frontline employees of (...) 35 bank branches over 18 consecutive days supports our hypotheses. (shrink)

Previous research has shown that the proactive deprioritization of emotional distractors through the provision of information about the distractors or passive habituation of emotional distractors may attenuate emotion-induced blindness (EIB) in the rapid serial visual presentation stream. However, whether prior memory encoding of emotional distractors could bias the EIB effect remains unknown. To address this question, this study employed a three-phase paradigm integrating an item-method direct forgetting (DF) procedure with a classic EIB procedure. Participants completed a memory coding phase to (...) either remember or forget negative pictures, then performed an intermediate phase of the EIB test, and finally finished a recognition test. Critically, the same to-be-forgotten (TBF) and to-be-remembered (TBR) negative pictures in the memory learning phase were used as emotional distractors in the intermediate EIB test. The results replicated the typical DF effect by showing higher recognition accuracies for TBR pictures compared to those for TBF pictures. More importantly, the TBF negative distractors attenuated the EIB effect compared to the TBR negative distractors, but showed a comparable EIB effect as the novel negative distractors. These findings indicate that prior memory encoding manipulations of negative distractors could bias subsequent EIB effects, providing an important approach to modulate the EIB effect. (shrink)

Students with learning disabilities (LDs) suffer from executive function deficits and impaired prospective memory (PM). Yet the specificity of deficits associated with different types of LDs is still unclear. The object of the present research was to compare subgroups of students with different forms of LDs (<25th percentile) on executive function and PM. Students with a mathematics disability (MD,n= 30), reading disability (RD,n= 27), both (RDMD,n= 27), or neither (typically developing, TD,n= 30) were evaluated on a set of executive functioning (...) tasks (e.g., updating, inhibition, and shifting) and on PM. The results showed that students with MDs and RDMDs suffered from PM deficits. Among the subtypes of LDs, the deficit is different. The students with RDMDs showed a wide range of defects in PM, shifting, inhibition, and updating. In comparison, students with MDs experienced deficits in PM and shifting, while students with RDs experienced a deficit only in updating. For the RD group, the RDMD group and the TD group, updating, and shifting significantly predicted PM. For the MD group, only shifting significantly predicted PM performance, but PM deficits were not completely confined to shifting deficits. (shrink)

本书的重点是谈作者对波普尔的看法,主要包括对波普尔思想的理解和对波普尔思想的评论和批评两部分。.

Informed by the poststructuralist theory, this study investigates the case of Ming, a Chinese professor of English, about the impacts of his first language and second language learning experience, and the changes of social contexts on his L1 and L2 identities construction. It was found that being a learner of English as a Foreign Language, Ming’s identities development aligned with the poststructuralist theory, in which it is considered dynamic, fluid and conflicting. Ming negotiated and renegotiated his identities in various social (...) contexts in China and the United States and finally gained acceptance into the L2 academic community. This study not only analyzes Ming’s experience with his language learning and identities, but also unravels that conflicts may be part of the process of identities construction, and encourages learners to be persistent and emotionally resilient, while using certain strategies to retain a stable L1 identity so that they can navigate through the negative encounters during the second language acquisition process to sustain the development of their identities and L2 abilities. (shrink)

本书是一本哲学自选文集,它收录了赵汀阳的《伦理学思路的改变》、《道德的维度或生活的维度》、《法律的道德余地》、《人之常情》等19篇文章。.