Scholars have paid so much academic attention to UPB in the past decade. However, there is lacking a quantitative review to uncover the relationship between UPB and its antecedents. To address this, we make a meta-analytic review about UPB. Specifically, we propose a theoretical framework of antecedents of UPB and test it using meta-analysis technology (k = 67, n = 20,957). We found moral disengagement, organizational identification, identification with supervisors, leader UPB, ethical judgments, psychological entitlement, transformational leadership, and job satisfaction (...) are positively related to UPB. Moral identity and taking responsibility are negatively related to UPB. Besides, ethical leadership, abusive supervision, and authoritarian leadership have double-edged sword effects on UPB. For ethical leadership, it increases UPB via LMX, on the other hand, it decreases UPB via moral disengagement. For abusive supervision and authoritarian leadership, they increase UPB via moral disengagement and decrease UPB via LMX meanwhile. We discuss the results of the current meta-analysis and put some future research directions. (shrink)

We often feel survivor guilt when the very circumstances that harm others leave us unscathed. Although survivor guilt is both commonplace and intelligible, it raises a puzzle for the standard philosophical account of guilt, according to which people feel guilt only when they take themselves to be morally blameworthy. The standard account implies that survivor guilt is uniformly unfitting, as people are not blameworthy simply for having fared better than others. In this paper, we offer a rival account of guilt, (...) the relational account of guilt, which makes sense of survivor guilt and other forms of guilt without self-blame while preserving the intelligibility of guilt about culpable wrongdoing. According to this account, guilt involves the feeling of being unable to justify ourselves to others, and we lack self-justification when we (however blamelessly) stand on the positive side of an undesirable asymmetry with them. When someone survives something that those around her do not, the disparity in outcome constitutes an asymmetry that is often undesirable, because it arises from luck or violates a requirement of solidarity. Thus, survivor guilt is often fitting. (shrink)

It has been argued that an advantage of the safety account over the sensitivity account is that the safety account preserves epistemic closure, while the sensitivity account implies epistemic closure failure. However, the argument fails to take the method-relativity of the modal conditions on knowledge, viz., sensitivity and safety, into account. In this paper, I argue that the sensitivity account and the safety account are on a par with respect to epistemic closure once the method-relativity of the modal conditions is (...) taken into account. Therefore, epistemic closure is no longer an arbiter in the debate. (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)

Cognitive neuroscience research has found that individuals from different cultures have different neural responses and emotional perceptions. Differences in executives’ perception of external pay gaps in different cultures can affect their work attitudes and behavior. In this study, we explore the direct relationship between executive compensation fairness and executive innovation motivation. We also investigate the moderating effects of Confucian culture and western culture between executive compensation fairness and executive innovation motivation. Data were collected from the Chinese listed firms from 2011 (...) to 2019 and test the relationship using the fixed-effect panel regression models. The results demonstrate that executive compensation fairness positively influences the executive innovation motivation. This effect is more pronounced in Confucian culture regions, while Western culture weakens this effect. The findings of this study confirmed that executive compensation fairness, provide a cross-cultural comparison for compensation research, validate the findings of cultural neuroscience, and provide useful insights into the research of common prosperity. To improve the corporate compensation structure, it is necessary to consider the relative pay equity with firms in the same region and the influence of corporate culture. (shrink)

Background: Previous studies have found that alexithymia plays an important role in the pathogenesis of Internet addiction. However, the effect of alexithymia on both metacognition and Internet addiction has yet to be examined.Methods: The Toronto Alexithymia Scale, Metacognition Questionnaire, and Internet Addiction Test were used to assess a sample of 356 college students. A parallel mediator effect analysis was applied to test the hypothesis that metacognition mediates the relationship between alexithymia and Internet addiction.Results: The parallel multiple mediator models showed that (...) alexithymia predicted the five dimensions of metacognition and Internet addiction, and that three dimensions—cognitive confidence, positive beliefs about worry, and the need to control thoughts—partially mediated this relationship.Conclusion: Alexithymia could directly and indirectly predict Internet addiction via metacognition. (shrink)

The KNN algorithm is one of the most famous algorithms in machine learning and data mining. It does not preprocess the data before classification, which leads to longer time and more errors. To solve the problems, this paper first proposes a PK-means++ algorithm, which can better ensure the stability of a random experiment. Then, based on it and spherical region division, an improved KNNPK+ is proposed. The algorithm can select the center of the spherical region appropriately and then construct an (...) initial classifier for the training set to improve the accuracy and time of classification. (shrink)

The joint multifractal analysis is usually conducted in two different variables for their cross-correlations but rarely used for two records of one variable collected at two different places. It is important for the detection of change in multifractality in space. Besides, the cross-correlations in two analyzed series make the analysis of sources of joint multifractality difficult. There are few studies on the source of joint multifractality. We focus on the two issues for two level records at pairs of adjacent sites (...) along one river and carry out an extension of our previous work which is about the single multifractality of one record with the same data set. The data set is collected from 10 observation stations of a northern China river and contains about two million high-frequency river level records. Results of joint multifractal analysis via multifractal detrended cross-correlation analysis show that the change in joint multifractality at pairs of adjacent sites caused by weak cross-correlations can be detected by comparing the single generalized Hurst exponent with the joint scaling exponent function and reveal the effects of human activities on joint multifractality. This analysis provides an approach for detecting the change in multifractality. Following the idea of our previous work, two robust hypothesis tests via a set of pairs of surrogate series are proposed for the source testing of joint multifractality. The analysis of the effects of cross-correlations is carried out via a proposed simultaneously half-shifting technique which can both minimize the cross-correlations between original series and make full use of records. Results of source analysis show not only the effects of autocorrelations in series and probability distribution of river levels but also the effects of cross-correlations between series. (shrink)

The energy hole is a severe problem for underwater acoustic distributed networks in that it affects the normal operations of the network and shortens the network’s life span. To deal with this problem, a loop-free routing control technique is proposed in this paper. The classical shortest-path routing control method is used to generate multiple disjointed routing tables. The residual energy of the nodes and the changing information of the uplink/downlink matrix are added to the data frames for distribution. The source (...) node specifies the routing path to transmit the data frames based on the changing information, and the relay nodes route the data frames strictly according to the routing path designated by the source node. Besides, the energy consumption of the relay node is saved by replying to the pseudo-ACK frame. Simulation experiments are implemented in four typical scenarios, and the results reflect that the proposed technique could extend the network’s life span by approximately 10% when compared to other mature techniques. Besides, it has no other negative effects on the normal operations of the network. (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)

Because they are key components of aircraft, improving the safety, reliability and economy of engines is crucial. To ensure flight safety and reduce the cost of maintenance during aircraft engine operation, a prognostics and health management system that focuses on fault diagnosis, health assessment, and life prediction is introduced to solve the problems. Predicting the remaining useful life is the most important information for making decisions about aircraft engine operation and maintenance, and it relies largely on the selection of performance (...) degradation features. The choice of such features is highly significant, but there are some weaknesses in the current algorithm for RUL prediction, notably, the inability to obtain tendencies from the data. Especially with aircraft engines, extracting useful degradation features from multisensor data with complex correlations is a key technical problem that has hindered the implementation of degradation assessment. To solve these problems, deep learning has been proposed in recent years to exploit multiple layers of nonlinear information processing for unsupervised self-learning of features. This paper presents a deep learning approach to predict the RUL of an aircraft engine based on a stacked sparse autoencoder and logistic regression. The stacked sparse autoencoder is used to automatically extract performance degradation features from multiple sensors on the aircraft engine and to fuse multiple features through multilayer self-learning. Logistic regression is used to predict the remaining useful life. However, the hyperparameters of the deep learning, which significantly impact the feature extraction and prediction performance, are determined based on expert experience in most cases. The grid search method is introduced in this paper to optimize the hyperparameters of the proposed aircraft engine RUL prediction model. An application of this method of predicting the RUL of an aircraft engine with a benchmark dataset is employed to demonstrate the effectiveness of the proposed approach. (shrink)

Against the scourge of the COVID-19 pandemic, college students’ learning engagement has become a key issue in universities and society. Guided by the theories of existential positive psychology and social perception, we explored the positive effect of a growth mindset on learning engagement during the COVID-19 pandemic. A total of 1,040 college students from universities in Henan Province of China effectively completed online questionnaires. The results showed that growth mindset was positively related to learning engagement and negatively associated with perceived (...) COVID-19 event strength and perceived stress; perceived COVID-19 event strength was positively related to perceived stress, while perceived COVID-19 event strength and perceived stress were negatively associated with learning engagement. Growth mindset affected learning engagement through three indirect paths: the mediating role of perceived COVID-19 event strength, the mediating role of perceived stress, and the serial mediating role of both perceived COVID-19 event strength and perceived stress. The results indicated that the growth mindset could contribute to college students’ learning engagement through the roles of perceived COVID-19 event strength and perceived stress during the COVID-19 pandemic. This study advances the understanding of the mechanism underlying the relationship between growth mindset and college students’ learning engagement during the COVID-19 pandemic. Furthermore, the findings of the study have important implications for promoting college students’ learning engagement during the pandemic. (shrink)

This study examines factors impacting organizational commitment of 214 employees working at a Chinese state-owned steel company. Ethical behavior of peers and ethical behavior of successful managers had a significant impact on organizational commitment. The four facets of job satisfaction (pay, coworker, supervision, and work itself) had a significant impact on organizational commitment. Respondent’s age also significantly impacted organizational commitment. Perceptions of ethical behavior of successful managers, satisfaction with work, and gender were significantly correlated with social desirability bias.

Due to the complexity and versatility of network security alarm data, a cloud-based network security data extraction method is proposed to address the inability to effectively understand the network security situation. The information properties of the situation are generated by creating a set of spatial characteristics classification of network security knowledge, which is then used to analyze and optimize the processing of hybrid network security situation information using cloud computing technology and co-filtering technology. Knowledge and information about the security situation (...) of a hybrid network has been analyzed using cloud computing strategy. The simulation results show that a cyber security crash occurs in window 20, after which the protection index drops to window 500. The increase in the security index of 500 windows is consistent with the effectiveness of the concept of this document method, indicating that this document method can sense changes in the network security situation. Starting from the first attacked window, the defense index began to decrease. In order to simulate the added network defense, the network security events in the 295th time window were reduced in the original data, and the defense index increased significantly in the corresponding time period, which is consistent with the method perception results, which further verifies the effectiveness and reliability of this method on the network security event perception. This method provides high-precision knowledge of network security situations and improves the security and stability of cloud-based networks. (shrink)

The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for (...) strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification. (shrink)

In recent years, deep learning as the requirement of higher education for students has attracted the attention of many scholars, and previous studies focused on defining deep learning as the deep processing of knowledge of the brain, however, in the process of knowledge processing, the brain not only involves the deep processing of information but also participates in learning consciously and emotionally. Therefore, this research proposed a four-factor model hypothesis for deep learning that includes deep learning investment, deep cognitive-emotional experience, (...) deep information processing, and deep learning meta-cognitive. In addition, the research proposed teachers’ emotional support perceived by students has an effect on the four factors of deep learning. Through SPSS 26 and AMOS 24, this research has verified the four-factor model of deep learning applying exploratory factor analysis and confirmatory factor analysis and verified that the perceived teacher emotional support has an impact on the four factors of students’ deep learning using the SEM. (shrink)

Sensitivity account of knowledge states that if one knows that _p_ (via method M), then were _p_ false, one would not believe that _p_ via M. This account has been highly controversial. However, even its critics tend to agree that the account enjoys an important advantage of solving the Gettier problem—that is, it explains why Gettierized beliefs are not knowledge. In this paper, I argue that this purported advantage of sensitivity is merely illusory. The account cannot, in principle, solve the (...) Gettier problem. Moreover, another formulation of sensitivity—which is fully in line with Nozick’s original account—is not unscathed either. (shrink)

The COVID-19 pandemic has had a substantial impact on the retail industry around the globe, including in the vast market of India. The response to the pandemic required stores to close and develop new ways to approach shoppers more efficiently. The worldwide usage of social media enabled the growth of social commerce. Influencers on s-commerce platforms use live broadcasting on their channels to promote endorsed products. The features of s-commerce influencers enhance users' trust in the online community and s-commerce intention, (...) impacting their online purchasing intentions. In this study, we collected data from 379 Indian consumers to test the measurement and structural model using Partial Least Square-Structural Equation Modeling to verify our conceptual framework. We found that trust in the online community and s-commerce intention are antecedents of online purchase intentions. Additionally, the results demonstrate that trust in Indian social media influencers and s-commerce intentions are vital for boosting consumers' purchase intention, verifying the hypothesized mediating effect of these factors. Based on these results, we suggest several managerial actions that could enhance the value of s-commerce for franchises, executives, e-retailers, and e-marketplaces. (shrink)

Dynamic vision sensor is a kind of bioinspired sensor. It has the characteristics of fast response, large dynamic range, and asynchronous output event stream. These characteristics make it have advantages that traditional image sensors do not have in the field of tracking. The output form of the dynamic vision sensor is asynchronous event stream, and the object information needs to be provided by the relevant event cluster. This article proposes a method based on the event correlation index to obtain the (...) object’s position, contour, and other information and is compatible with traditional tracking methods. Experiments show that this method can obtain the position information of the moving object and its continuous motion trajectory and analyze the influence of the parameters on the tracking effect. This method will have broad application prospects in security, transportation, etc. (shrink)

This research examines the knowledge constructed in political ironic discourses, which is associated with different models of practicing self-censorship, taking a case study of the 2017 Hong Kong Chief Executive Election via social media Weibo. Critical discourse analysis, the verbal irony principle and semi-structured interviews were employed to compare participants from mainland China and Hong Kong, including opinion leaders and casual users. This research suggests a three-stage analytical framework that clearly emphasizes the act of rhetorical discourse and the practice of (...) self-censorship. As such, it reveals citizens’ intrinsic motivation related to self-efficacy, resistance or empowerment. This research argues that dissidents’ shared knowledge and ideology are structurally represented in the ‘binary opposition’ mode of rhetorical discourse, concerning the discursive construction of Hong Kong identity. The proliferation of rhetorical discourse, evolving shoulder to shoulder with Internet self-censorship, results from the complexity of politics, history and digital technology development within China. (shrink)

Here we present and develop the hypothesis that the derepression of endogenous retrotransposable elements (RTEs) – “genomic parasites” – is an important and hitherto under‐unexplored molecular aging process that can potentially occur in most tissues. We further envision that the activation and continued presence of retrotransposition contribute to age‐associated tissue degeneration and pathology. Chromatin is a complex and dynamic structure that needs to be maintained in a functional state throughout our lifetime. Studies of diverse species have revealed that chromatin undergoes (...) extensive rearrangements during aging. Cellular senescence, an important component of mammalian aging, has recently been associated with decreased heterochromatinization of normally silenced regions of the genome. These changes lead to the expression of RTEs, culminating in their transposition. RTEs are common in all kingdoms of life, and comprise close to 50% of mammalian genomes. They are tightly controlled, as their activity is highly destabilizing and mutagenic to their resident genomes. (shrink)

Dual organizational identification (DOI) is generally considered beneficial to multinational corporations (MNCs) and their employees. However, this study challenges this consensus by exploring the potential negative impacts of DOI in the ethical controversy context when MNCs and host countries have conflicting views on a business decision and both feel that they are ethically correct. Integrating role identity theory, we propose that the DOI of host country nationals (HCNs) may create conflict in their work-related perceptions and behaviors amid an ethical controversy. (...) In Study 1, we interviewed 51 HCNs from MNCs’ Chinese subsidiaries and found that HCNs’ DOI caused their role conflict during ethical controversies, which in turn affected their perceptions (i.e., perceived isolation, ethical pressure) and behaviors (i.e., procrastination, exit, voice, headquarters (HQ)–subsidiary cooperation). We also found that the job level of HCNs played a key role in moderating the above relationship. In Study 2, we tested the theoretical framework developed in Study 1 using a multi-wave survey from a sample of 300 HCNs working in MNCs’ Chinese subsidiaries. The results generally validated the Study 1 model. Theoretical and practical implications are discussed. (shrink)

Based on event history analysis, this study examined the survival distribution of the duration of online public opinions related to major health emergencies and its influencing factors. We analyzed the data of such emergencies that took place in China during a period of 10 years. The results of the Kaplan-Meier method and Cox proportional hazards regression analysis showed that the average duration of online public opinions regarding health emergencies is 43 days, and the median is 19 days, which dispels the (...) myth of the “Seven-day Law of Propagation.” Furthermore, the duration of online public opinions can be divided into three stages: the rapid decline stage, the slowdown stage, and the disappearing stage. In addition, the type of event, and the volume of both social media discussion and traditional media coverage all had significant impacts on the duration. Our findings provide practical implications for the carrying out of targeted and stage-based governance of public opinions. (shrink)

To identify the boundary conditions for proactive employees making whistle-blowing decisions, we developed a cross-level model comprising employee proactive personality and two types of whistle-blowing intentions that incorporates the influences of organizational- and individual-level attributes. Analyses of data collected from 432 Chinese employees in 32 companies indicated that proactive personality was positively related to internal whistle-blowing intention and even more positively related to external whistle-blowing intention when individuals were working in organizations characterized by an instrumental ethical climate and employees with (...) a high level of moral identity. (shrink)

The objective of the study was to compare goal scoring patterns among the “Big Five” European football leagues during the 2009/2010–2018/2019 seasons. A total of 18 pattern dimensions related to the offense pattern, the shooting situation and the scoring time period were evaluated. Kruskal–Wallis analyses revealed significant pattern differences among the five leagues. The Spanish La Liga showed a greater proportion of goals from throw-ins. The English Premier League had a higher tendency to score from corner kicks. The German Bundesliga (...) had the greatest number of goals from counterattacks and indirect free kicks, and the Italian Serie A had the greatest proportion of penalties. Ligue 1’s scoring ability is weaker than that of the other leagues, especially Bundesliga. The Bundesliga had an overwhelming advantage in goals scored on big chances with assists, while the Premier League had an advantage in goals scored with assists that were not from big chances. However, the differences among the five leagues in the mean goals scored in the last 15 min and the goals from elaborate attacks and direct free kicks were not statistically significant. These results provide a valuable addition to the knowledge of different goal patterns of each league and allow us to better understand the differences among the leagues. (shrink)

Due to the currently insufficient consideration of task fitness and task coordination for task allocation in collaborative customized product development, this research was conducted based on the analysis of collaborative customized product development process and task allocation strategy. The definitions and calculation formulas of task fitness and task coordination efficiency were derived, and a multiobjective optimization model of product customization task allocation was constructed. A solution based on adaptive genetic algorithm was proposed, and the feasibility and effectiveness of the task (...) allocation algorithm were tested and verified using a 5-MW wind turbine product development project as example. (shrink)

This review examines global human genetic resources management, focusing on genetic data policies and repositories in high- and middle-low-income countries. A comprehensive search strategy was employed across multiple databases, including official government websites and Google, to gather relevant literature on human genetic resources management policies and genetic resource databases. Documents were screened for relevance, focusing on high-income countries (United States, United Kingdom, Japan) and middle-low-income countries (China, India, Kenya). Data were extracted, coded, and analyzed to identify common themes and differences (...) in genetic resource management practices. High-income countries benefit from robust legal frameworks and advanced technological infrastructures. The United States enforces the Health Insurance Portability and Accountability Act and the Genetic Information Nondiscrimination Act to protect privacy and facilitate data sharing, while Japan relies on the Act on the Protection of Personal Information and ethical guidelines. Additionally, high-income countries host a variety of genetic databases and biobanks that support scientific research. In contrast, middle-low-income countries like China, India, and Kenya are still developing their frameworks. China has regulations such as the Biosecurity Law and the Regulations on the Management of Human Genetic Resources, but still requires more unified standards. India’s policies focus on genetic research and data protection through the Biological Diversity Act, while Kenya seeks to improve data management through the 2019 Data Protection Act. Significant disparities exist in human genetic resources management between high-income and middle-low-income countries. High-income countries have robust systems balancing privacy protection with research facilitation, supported by comprehensive and large-scale databases for scientific research. Middle-low-income countries need to enhance legal frameworks and build population-specific databases. Promoting equitable data sharing and adopting best practices from high-income countries are essential for advancing global scientific discovery and ensuring fair management of genetic resources. (shrink)

The evolution of Web 2.0 and social networks has led to the increased use of enterprise social media platforms, making online interactions more common in organizations. However, few studies have researched online interactions in organizational context. This study addressed this gap using two research phases: a qualitative phase and a quantitative phase. The qualitative study phase identified two dimensions of online interaction: employee–employee online interaction and employee–platform online interaction. The employee–employee online interaction assessed responsiveness and suitability. The employee–platform online interaction (...) assessed usefulness, applicability, and ease of use. The quantitative study phase applied a proposed conceptual framework derived from the qualitative study to create and validate measures for a new online interaction scale. This was done using a systematic scale development process. Measuring online interaction can help drive future quantitative research, providing an instrumental basis for further exploring the scientific management practice elements that govern employee psychology and behavior in cyberspace. (shrink)