J Huang

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death, and their function is related to their types. According to the classification scheme by Zhou and Doctor (2003), the apoptosis proteins are categorized into the following four types: (1) cytoplasmic protein; (2) plasma membrane-bound protein; (3) mitochondrial inner and outer proteins; (4) other proteins. A powerful learning machine, the Support Vector Machine, is applied for predicting the type of a given apoptosis protein by incorporating the sqrt-amino acid composition effect. High success rates were obtained by the re-substitute test (98/98 = 100 %) and the jackknife test (89/98 = 90.8%).

As a worldwide epidemic in the digital age, cyberbullying is a pertinent but understudied concern—especially from the perspective of language. Elucidating the linguistic features of cyberbullying is critical both to preventing it and to cultivating ethical and responsible digital citizens. In this study, a mixed-method approach integrating lexical feature analysis, sentiment polarity analysis, and semantic network analysis was adopted to develop a deeper understanding of cyberbullying language. Five cyberbullying cases on Chinese social media were analyzed to uncover explicit and implicit linguistic features. Results indicated that cyberbullying comments had significantly different linguistic profiles than non-bullying comments and that explicit and implicit bullying were distinct. The content of cases further suggested that cyberbullying language varied in the use of words, types of cyberbullying, and sentiment polarity. These findings offer useful insight for designing automatic cyberbullying detection tools for Chinese social networking platforms. Implications also offer guidance for regulating cyberbullying and fostering ethical and responsible digital citizens.

Understanding the influencing factors of cyberbullying is key to effectively curbing cyberbullying. Among the various factors, this study focused on the personal level of individual students and categorized the influencing factors of cyberbullying among college students into five sublevels, i.e., background, Internet use and social network habits, personality, emotion, and literacy related to digital citizenship. Then a questionnaire survey was applied to 947 Chinese college students. The results show that cyberbullying among Chinese college students are generally at a low level. There are many factors influence cyberbullying. Specifically, at the personal background level, gender has a significant impact on cyberbullying and being cyberbullied. In terms of personal Internet use and social network habits, students’ average daily online time has no significant correlation with cyberbullying and being cyberbullied; however, the proportion of online non-learning time has a significantly positive correlation with cyberbullying, and the proportion of online learning/work time has a significant impact on being cyberbullied. At the personality level, the Big Five personality traits have varying degrees of correlation with and influence on cyberbullying and being cyberbullied. At the personal emotions level, students’ life satisfaction has a significantly negative correlation with cyberbullying and being cyberbullied while it only has a significant impact on being cyberbullied; the personal stress and empathetic concern aspects of empathy have a significantly positive correlation with cyberbullying and being cyberbullied among female students. At the literacy related to digital citizenship level, students’ understanding of and compliance with Internet etiquette have significantly negative impacts on cyberbullying; the ability to communicate and collaborate online and Internet addiction have significantly positive impacts on cyberbullying and being cyberbullied; the understanding of and compliance with relevant digital laws and regulations have significantly negative correlations with cyberbullying and being cyberbullied. Overall, college students’ digital citizenship level has a significantly negative correlation with cyberbullying but no significant correlation with being cyberbullied. Finally, analysis and suggestions were provided according to these statistical results and the effects of these factors on cyberbullying and being cyberbullied among college students, so as to help solve this problem and provide a new perspective for research in this field.

For understanding and controlling spreading in complex networks, identifying the most influential nodes, which can be applied to disease control, viral marketing, air traffic control, and many other fields, is of great importance. By taking the effect of the spreading rate on information entropy into account, we proposed an improved information entropy method. Compared to the benchmark methods in the six different empirical networks, the IIE method has been found with a better performance on Kendall’s Tau and imprecision function under the Susceptible Infected Recovered model. Especially in the Facebook network, Kendall’s Tau can grow by 120% as compared with the original IE method. And, there is also an equally good performance in the comparative analysis of imprecise functions. The imprecise functions’ value of the IIE method is smaller than the benchmark methods in six networks.

The process dissociation procedure (PDP) of implicit sequence learning states that the correct inclusion-task response contains the incorrect exclusion-task response. However, there has been no research to test the hypothesis. The current study used a single variable (Stimulus Onset Asynchrony SOA: 850 ms vs. 1350 ms) between-subjects design, with pre-task resting-state fMRI, to test and improve the classical PDP to the mutually exclusive theory (MET). (1) Behavioral data and neuroimaging data demonstrated that the classical PDP has not been validated. In the SOA = 850 ms group, the correct inclusion-task response was at chance, but the incorrect exclusion-task response occurred greater than chance. In the SOA = 850 ms group, the two responses were not correlated, but in the SOA = 1,350 ms group and putting the two groups together, the two responses were in contrast to each other. In each group, brain areas whose amplitude of low frequency fluctuations (ALFFs) in the resting-state related to the two responses were either completely different or opposite to one another. However, the results were perfectly consistent with the MET proposed by the present study which suggests that the correct inclusion-task response is equal to the correct exclusion-task response is equal toC+A1, and the incorrect exclusion-task response is equal toA2.Cdenotes the controlled response andA1andA2denote two different automatic responses. (2) The improved PDP was proposed to categorize the 12 kinds of triplets as delineating four knowledge types, namely non-acquisition of knowledge, uncontrollable knowledge, half-controllable knowledge, and controllable knowledge with the MET. ALFFs in the resting-state could predict the four knowledge types of the improved PDP among two groups. The participants’ control of the four knowledge types (degree of consciousness) gradually improved. Correspondingly, the brain areas in the resting-state positively related to the four knowledge types, gradually changed from the sensory and motor network to the somatic sensorimotor network, and then to the implicit learning network, and then to the consciousness network. The brain areas in the resting-state negatively related to the four knowledge types gradually changed from the consciousness network to the sensory and motor network. As SOA increased, the brain areas associated with almost all the four knowledge types changed. (3) The inhomogeneous hypothesis of the MET is best suited to interpret behavioral and neuroimaging data; it states that the same components among the four knowledge types are not homogeneous, and the same knowledge types are not homogeneous between the two SOA groups.

Currently, little is known about the mechanism of how university students’ attitudes toward entrepreneurship education (ATEE) affect entrepreneurial self-efficacy (ESE) in the post-pandemic entrepreneurial environment. Based on the existing research, this study explores the relationship between ATEE, the post-pandemic entrepreneurship environment, and ESE through a questionnaire survey. A total of 910 university students from three universities in Zhejiang Province, China participated, with an effective rate of 92.9%. The data collection focused on the period from August to December 2020. In this study, Stepwise Multiple regression analysis was used to analyze university students' ATEE and its impact on ESE, as well as the moderating effect of the post-pandemic entrepreneurial environment.

In this study, we explore the psychological mechanisms underlying the relation between college students’ post-traumatic growth and their entrepreneurial intentions in the post-COVID-19 era. Using the post-traumatic growth, entrepreneurial self-efficacy, prosocial tendency, and entrepreneurial intention scales, we tested 690 valid samples of Chinese undergraduates. The results revealed that post-traumatic growth of college students in the post-COVID-19 era will have a significant and positive effect on their entrepreneurial intentions. Additionally, the results indicated that students’ entrepreneurial self-efficacy and prosocial tendencies play a partial mediation role between post-traumatic growth and entrepreneurial intentions in the post-COVID-19 era and that there is a chain mediating effect between students’ entrepreneurial self-efficacy and prosocial tendencies. This study provides valuable insights into the influence of post-traumatic growth on entrepreneurial intentions among college students in the post-COVID-19 era and suggests that colleges and universities can improve students’ entrepreneurial intentions by adopting measures to foster their post-traumatic growth, entrepreneurial self-efficacy, and prosocial tendencies.

The present study explored the correlation between music preference and mental health of college students to make an empirical contribution to research in this field. The self-reported music preference scale and positive mental health scale of college students were adopted to conduct a questionnaire survey in college students. Common method variance was conducted to test any serious common method bias problem. No serious common method bias problem was observed. The results showed that college students’ preference for pop music, Western classical music, and Chinese traditional music has a significant and positive correlation with their mental health. Furthermore, college students’ preference for heavy music has a significant and inverse correlation with their mental health. This research presents a correlational study; therefore, no causality can be inferred.

The purpose is to provide researchers with reliable Scientific Research Data from the massive amounts of research data to establish a sustainable Scientific Research environment. Specifically, the present work proposes establishing an Intelligent Recommendation System based on Machine Learning algorithm and SRD. Firstly, the IRS is established over ML technology. Then, based on user Psychology and Collaborative Filtering recommendation algorithm, a hybrid algorithm [namely, Content-Based Recommendation-Collaborative Filtering ] is established to improve the utilization efficiency of SRD and Sustainable Development of SR. Consequently, the present work designs literature and SRD-targeted IRS using the hybrid recommendation under the background of SD. The proposed system’s feasibility is analyzed through experiments. Additionally, the system performance is analyzed and verified from accuracy, diversity, coverage, novelty, and recommendation efficiency. The results show that the hybrid algorithm can make up for the shortcomings of a single algorithm and improve the recommendation efficiency. Experiments show that the accuracy of the proposed CBR-CF algorithm is the highest. In particular, the recommendation accuracy for the single-user system can reach 82–93%, and the recall of all recommended algorithms falls between 60 and 91%. The recall of the hybrid algorithm is higher than that of a single algorithm, and the highest recall is 91%. Meanwhile, the hybrid algorithm has comprehensive coverage, good applicability, and diversity. Therefore, SD-oriented SRD-targeted IRS is of great significance to improve the SRD utilization and the accuracy of IRS, and expand the achievement value of SR. The research content provides a reference for establishing a sustainable SR environment and improving SR efficiency.

Previously published studies have reported that 150 min of short-term monocular deprivation temporarily changes perceptual eye dominance. However, the possible mechanisms underlying monocular deprivation-induced perceptual eye dominance plasticity remain unclear. Using a binocular phase and contrast co-measurement task and a multi-pathway contrast-gain control model, we studied the effect of 150 min of monocular pattern deprivation in normal adult subjects. The perceived phase and contrast varied significantly with the interocular contrast ratio, and after MPD, the patched eye became dominant. Most importantly, we focused on the potential mechanisms of the deprivation effect. The data of an averaged subject was best fitted by a model, which assumed a monocular signal enhancement of the PE after the MPD. The present findings might have important implications for investigations of binocular vision in both normal and amblyopic populations.

Knowledge sharing is critical for consulting companies to develop sustainable competitive advantages. While the importance of KS in the information communication technology sector has been proved, the assumed linear relationships in KS mechanisms are confronted with KS dilemmas: consultants’ intention to maximize personal gains from KS resulting in restrained KS efforts, for fear of losing value after sharing knowledge with colleagues. Drawing on motivation theory and goal orientation perspective, this study examines the roles of learning goal orientation and incentive schemes in KS among ICT consultants. The multiple regression analyses of 389 consultants’ responses from 14 Chinese and 8 Korean ICT consulting companies demonstrated an inverted U-shape relationship between LGO and knowledge sharing; incentive schemes moderate this relationship. The findings shed light on the knowledge-sharing dilemma, with theoretical implications to research regarding goal-orientation, knowledge sharing, and managerial practices about the motivation and incentives of ICT consultants.

Background: Cognitive impairment is one of the core symptoms of schizophrenia, which is considered to be significantly correlated to prognosis. In recent years, many studies have suggested that metabolic disorders could be related to a higher risk of cognitive defects in a general setting. However, there has been limited evidence on the association between metabolism and cognitive function in patients with early-stage schizophrenia.Methods: In this study, we recruited 172 patients with early-stage schizophrenia. Relevant metabolic parameters were examined and cognitive function was evaluated by using the MATRICS Consensus Cognitive Battery (MCCB) to investigate the relationship between metabolic disorder and cognitive impairment.Results: Generally, the prevalence of cognitive impairment among patients in our study was 84.7% (144/170), which was much higher than that in the general population. Compared with the general Chinese setting, the study population presented a higher proportion of metabolic disturbance. Patients who had metabolic disturbance showed no significant differences on cognitive function compared with the other patients. Correlation analysis showed that metabolic status was significantly correlated with cognitive function as assessed by the cognitive domain scores (p < 0.05), while such association was not found in further multiple regression analysis.Conclusions: Therefore, there may be no association between metabolic disorder and cognitive impairment in patients with early-stage schizophrenia.Trial Registration: Clinicaltrials.gov, NCT02880462. Registered August 26, 2016.

Face recognition payment, an innovative financial technology service, is a recently developed mode of payment service that has garnered attention in the offline market, particularly in China. However, studies examining the adoption of FRP by consumers are scarce. Therefore, this study proposed a causal model built on the Unified Theory of Acceptance and Use of Technology, and key predictors related to the intention of using FRP were identified. The structural equation model-based results obtained from 305 Chinese participants demonstrated that the intention was most affected by relative advantage. In addition, performance expectancy, effort expectancy, social influence, and perceived risk also had a significant impact. However, trust was found to not significantly affect consumers’ intentions, despite it negatively influencing perceived risk. Thus, the results of this study are expected to provide a set of guidelines for companies regarding the implementation of FRP.

The research aims to explore the influence mechanism of proactive personality on the entrepreneurial intentions of college students. Adopting proactive personality scale, social capital scale, human capital scale, and entrepreneurial intention scale, this research tested valid samples of 300 Chinese college students. The results revealed that proactive personality exerted a significant and positive impact on the entrepreneurial intentions. Social capital played a partial mediating role between the proactive personality and the entrepreneurial intentions. Human capital also played a partial mediating role between proactive personality and entrepreneurial intentions. Social capital and human capital exerted a chain mediation effect between proactive personality and entrepreneurial intentions. The result of this research offers valuable insights to the study of the influence of college students’ proactive personality on entrepreneurial intentions and provides entrepreneurship education management in colleges with specific practical suggestions so as to improve entrepreneurial intentions among college students.

In this paper, we study the synchronization of a class of multiple neural networks with delay and directed disconnected switching topology based on state observer via impulsive coupling control. The coupling topology is connected sequentially, and the controller adjusts the state value through event-triggering strategies. Different from the related works on MNNs, its state in this paper is assumed to be unmeasurable, and the time delay is also unmeasurable. Therefore, the observer does not contain the time-delay term. The impulsive switching controller and observer controller adjust the system through the observed value. By constructing the corresponding augmented matrix, the system can finally achieve quasi-synchronization. Through derivation, we give the sufficient conditions ensuring quasi-synchronization via the event-triggered impulse control mechanism. In addition, numerical simulation examples are given to test our results of the theorem.

We explored whether children could apply linguistic strategies for lying, i.e., manipulating linguistic content of speech to mislead others. We announced a knowledge-test entailing prizes in the classrooms of a primary school and a middle school. Altogether 79 Chinese children (6-18 years) voluntarily participated in the test: listening to a series of animal sounds before guessing the names of the animals. Meanwhile, behind the participants, a video was playing images that ostensibly corresponded to the sounds being played. In fact, this was not necessarily the case, i.e., some items cannot be solved because the sounds played are not from any animal but machine-synthesized. Participants were instructed not to look back at the video. However, 51 children peeked at the video for the unsolvable items, although the peeking behavior decreased with age. Moreover, when explaining how they correctly guessed the unsolvable items, children as young as six years old were able to apply a linguistic strategy (i.e., "capability attribution") for lying. Besides "capability attribution", Children also applied "fortune attribution" and "topic shift" for lying. Finally, "fortune attribution" and "topic shift" increased with age. Therefore, educators need to be aware that children are able to apply verbal strategies for lying that could involve truthful statements (i.e., "topic shift") or statements that are difficult to be proved as untruthful (i.e., "fortune attribution").