J Li

The complexity of today’s organizational environment increasingly requires leaders to think in a dynamic and flexible way to resolve contradictory issues. This study explored and compared the effects of servant leadership and authoritarian leadership on employees’ work behavior from the perspectives of ambidextrous leadership theory and social exchange theory, and further examined the mediating role of psychological empowerment. In this study, 315 employees from state-owned communication companies in Shandong and Zhejiang Provinces in China were selected as subjects, and path analysis was used to test the hypotheses. The results showed that servant leadership positively predicted organizational citizenship behavior and task performance. While authoritarian leadership negatively predicted organizational citizenship behavior and positively predicted task performance, psychological empowerment mediated the relationship between the two leadership styles and organizational citizenship behavior and task performance. Moreover, psychological empowerment and organizational citizenship behavior played a multiple mediating role between the two leadership styles and task performance. The theoretical implications of these findings for advancing the ambidextrous leadership theory in Chinese organizational contexts and practical approaches for corporate managers to effectively use ambidextrous leadership style were discussed.

One of the most efficient predator-prey models with spatial effects is the one with ratio-dependent functional response. However, there is a need to further explore the effects of spatial motion on the dynamic behavior of population. In this work, we study a ratio-dependent predator-prey model with diffusion terms. The aim of this work is to investigate the changes in predator’s distribution in space as the prey populations change their mobility. We observe that the frequency diffusion of the prey gives rise to the sparse density of the predator. Moreover, we also observe that the increasing rate of the conversion into predator biomass induces pattern transitions of the predator. Specifically speaking, Turing pattern of the predator populations goes gradually from a spotted pattern to a black-eye pattern, with the intermediate state being the mixture of spot and stripe pattern. The simulation results and analysis of this work illustrate that the diffusion rate and the real intrinsic factor influence the persistence of the predator-prey system mutually.

Configural face processing precedes featural face processing under the face-attended condition, but their temporal sequence in the absence of attention is unclear. The present study investigated this issue by recording visual mismatch negativity, which indicates the automatic processing of visual information under unattended conditions. Participants performed a central cross size change detection task, in which random sequences of faces were presented peripherally, in an oddball paradigm. In Experiment 1, configural and featural faces were presented infrequently among original faces. In Experiment 2, configural faces were presented infrequently among featural faces, or vice versa. The occipital-temporal vMMN emerged in the 200–360 ms latency range for configural, but not featural, face information. More specifically, configural face information elicited a substantial vMMN component in the 200–360 ms range in Experiment 1. This result was replicated in the 320–360 ms range in Experiment 2, especially in the right hemisphere. These results suggest that configural, but not featural, face information is associated with automatic processing and provides new electrophysiological evidence for the different mechanisms underlying configural and featural face processing under unattended conditions.

Individuals with high risk of internet gaming disorder showed abnormal psychological performances in response inhibition, impulse control, and emotion regulation, and are considered the high-risk stage of internet gaming disorder. The identification of this population mainly relies on clinical scales, which are less accurate. This study aimed to explore whether these performances have highly accurate for discriminating HIGD from low-risk ones. Eye tracking based anti-saccade task, Barratt impulsiveness scale, and Wong and Law emotional intelligence scale were used to evaluate psychological performances in 57 individuals with HIGD and 52 matched low risk of internet gaming disorder. HIGD group showed significantly increased BIS total, attention, motor, and non-planning scores, but significantly decreased WLEIS emotion regulation score and correct rate of eye tracking anti-saccade task compared with LIGD group. BIS total score was negatively correlated with the WLEIS total and WLEIS emotion regulation scores. A combination of the WLEIS emotion regulation score and the correct rate of anti-saccade task could discriminate HIGD from LIGD with 91.23% sensitivity, 82.69% specificity, and 87.16% accuracy. Participants with higher gaming hours daily were 40 times more likely to be high risk than their counterparts. Hence, psychological performances were worse in HIGD. A combination of abnormal emotion regulation and response inhibition might be a potential marker to identify HIGD individuals.

In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression, K-Nearest Neighbor, eXtreme Gradient Boosting, and Gated Recurrent Unit as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation.

Research on internal waves in the coastal ocean is one of the most important tasks both in physical oceanography and ocean monitoring network. Currently, how to quickly and accurately detect the ocean internal waves from the huge ocean surface is still a challenging issue. In this paper, we model the ocean internal wave detection as a task of region classification for texture images and then propose a rapid internal waves detection method based on a deep learning framework. In the proposed method, two models have been trained: one is the deep feature representation model, which combines principal component analysis, binary hashing, and block-wise histograms and can extract more distinguishing features than handcraft feature. Moreover, because the filter learning in PCANet does not require regularized parameters and numerical optimization solver, the training process of the representation model is very fast. The other one is a classification model based on a linear support vector machine. The object proposal method has been applied to get the possible candidates when analyzing a captured image, which dramatically decreases the searching time. Experiment results on the data set captured by unmanned aerial vehicles verify the speed ability and effectiveness of the proposed method.

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.

Previous studies have examined the outcome evaluation related to the self and other, and recent research has explored the outcome evaluation of the self and other with pro-social implications. However, the evaluation processing of outcomes in the group in need remains unclear. This study has examined the neural mechanisms of evaluative processing by gambling for the self and charity, respectively. At the behavioral level, when participants make decisions for themselves, they made riskier decisions following the gain than loss in small outcomes and engage in more risky behaviors following the loss than gain in large outcomes. However, magnitude and valence did not affect the next risky behavior when participants made decisions for the charity. At the neurophysiological level, the results found that the FRN was larger for the charity outcome than for the self-outcome. For FRN, the valence difference of small outcomes was smaller than that of large outcomes. The P3 response was larger for the self-outcome than for the charity outcome. Meanwhile, compared with the small outcome, the self-charity discrepancies have a significant difference in large outcomes. In addition, the FRN amplitude for self in large outcomes was negatively correlated with the upcoming risky choices, regardless of outcome valence. The behavioral results suggest that people are more likely to optimize strategies for themselves than for the charity. The ERP findings indicated that people focus more on charity outcome than self-outcome in the early stage. In the middle and late stages, people turn attention to their outcomes, and the difference between self’s and charity’s outcome varies with the magnitude. Specifically, it is only in large outcomes that people engage more emotional attention or motivation in their outcomes, but self and charity outcomes had a similar emotional engagement in small outcomes.

Previous studies suggested that facial attractiveness perception can be increased with facial skin homogeneity improving; and human’s facial change detection increases along with facial skin homogeneity increases. However, it’s unknown whether a face can be perceived prettier than it did before while still being considered as physically the same. It is possible that these two kinds of cognitive-aesthetic processing may have separate mathematical functions in psychophysical studies. In other words, human’s facial attractiveness differentiation may be more sensitive than facial change detection. In this current study, we explored the above questions. Using three types of psychophysical techniques to manipulate facial skin homogeneity, we measured how participants’ sensitivity to facial skin homogeneity and attractiveness change. Results showed a linear function curve for facial physical change detection and a logarithmic function curve was drawn in the forced-choice technique, which was the most sensitive one, indicating that participants can judge a face prettier than before without being aware of it has physically changed. Besides, two linear function curves were shown in the same/different technique and a rating technique. Taken together, this current study revealed that facial attractiveness can be enhanced and discriminated by improving facial skin homogeneity, without being realized by people with conscious awareness that the face has been changed.

As a branch of statistical latent variable modeling, multidimensional item response theory plays an important role in psychometrics. Multidimensional graded response model is a key model for the development of multidimensional computerized adaptive testing with graded-response data and multiple traits. This paper explores how to automatically identify the item-trait patterns of replenished items based on the MGRM in MCAT. The problem is solved by developing an exploratory pattern recognition method for graded-response items based on the least absolute shrinkage and selection operator, which is named LPRM-GR and facilitates the subsequent parameter estimation of replenished items and helps maintaining the effectiveness of item replenishment in MCAT. In conjunction with the proposed approach, the regular BIC and weighted BIC are applied, respectively, to select the optimal item-trait patterns. Simulation for evaluating the LPRM-GR in pattern recognition accuracy of replenished items and the corresponding item estimation accuracy is conducted under multiple conditions across different numbers with respect to dimensionality, response-category numbers, latent trait correlation, stopping rules, and item selection criteria. Results show that the proposed method with the two types of BIC both have good performance in pattern recognition for item replenishment in the two- to four-dimensional MCAT with the MGRM, for which the weighted BIC is generally superior to the regular BIC. The proposed method has relatively high accuracy and efficiency in identifying the patterns of graded-response items, and has the advantages of easy implementation and practical feasibility.

The large scale, time varying, and diversification of physically coupled networked infrastructures such as power grid and transportation system lead to the complexity of their controller design, implementation, and expansion. For tackling these challenges, we suggest an online distributed reinforcement learning control algorithm with the one-layer neural network for each subsystem or called agents to adapt the variation of the networked infrastructures. Each controller includes a critic network and action network for approximating strategy utility function and desired control law, respectively. For avoiding a large number of trials and improving the stability, the training of action network introduces supervised learning mechanisms into reduction of long-term cost. The stability of the control system with learning algorithm is analyzed; the upper bound of the tracking error and neural network weights are also estimated. The effectiveness of our proposed controller is illustrated in the simulation; the results indicate the stability under communication delay and disturbances as well.

This paper proposes a constrained solution update strategy for multiobjective evolutionary algorithm based on decomposition, in which each agent aims to optimize one decomposed subproblem. Different from the existing approaches that assign one solution to each agent, our approach allocates the closest solutions to each agent and thus the number of solutions in an agent may be zero and no less than one. Regarding the agent with no solution, it will be assigned one solution in priority, once offspring are generated closest to its subproblem. To keep the same population size, the agent with the largest number of solutions will remove one solution showing the worst convergence. This improves diversity for one agent, while the convergence of other agents is not lowered. On the agent with no less than one solution, offspring assigned to this agent are only allowed to update its original solutions. Thus, the convergence of this agent is enhanced, while the diversity of other agents will not be affected. After a period of evolution, our approach may gradually reach a stable status for solution assignment; i.e., each agent is only assigned with one solution. When compared to six competitive multiobjective evolutionary algorithms with different population selection or update strategies, the experiments validated the advantages of our approach on tackling two sets of test problems.

BackgroundPsychological resilience may reduce the impact of psychological distress to some extent. We aimed to investigate the mental health status of the public during the outbreak of coronavirus disease 2019 and explore the level and related factors of anxiety and depression.MethodsFrom February 8 to March 9, 2020, 3,180 public completed the Zung’s Self-Rating Anxiety Scale for anxiety, Zung’s Self-Rating Depression Scale for depression, the Connor–Davidson resilience scale for psychological resilience, and the Simplified Coping Style Questionnaire for the attitudes and coping styles.ResultsThe number of people with depressive symptoms was 1,303. The number of people with anxiety symptoms was 1,184. The depressed group and anxiety group had less education, more unmarried and younger age, as well as had significant different in SDS total score, SAS total score, CD-RISC total score, and SCSQ score. The binary logistic regression showed that female, strength, and the subscales of active coping style in SCSQ remained protective factors and passive coping style and higher SAS score were risk factors for depression. Optimism in CD-RISC was a protective factor, and passive coping styles and higher SDS score were risk factors for anxiety.LimitationsThis study adopted a cross-sectional design and used self-report questionnaires.ConclusionThe mental health of the public, especially females, the younger and less educational populations, and unmarried individuals, should be given more attention. Individuals with high level of mental resilience and active coping styles would have lower levels of anxiety and depression during the outbreak of COVID-19.

Evidence accumulation has been the core component in recent development of perceptual and value-based decision-making theories. Most studies have focused on the evaluation of evidence between alternative options. What remains largely unknown is the process that prepares evidence: how may the decision-maker sample different sources of information sequentially, if they can only sample one source at a time? Here we propose a normative framework in prescribing how different sources of information should be sampled proactively to facilitate the decision process: beliefs for different noisy sources are updated in a Bayesian manner and participants can proactively allocate resource for sampling (i.e. saccades) among different sources to maximize the information gain in such process. We show that our framework can account for human participants’ actual choice and saccade behaviors in a two-alternative value-based decision-making task. Moreover, our framework makes novel predictions about the empirical eye movement patterns.

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose “The lockdown: One month in Wuhan” YouTube documentary, and applied emotion analysis as well as a machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust, joy, and anticipation are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing.

The virtual brand community has become an important marketing tool for companies. A successful brand community marketing strategy should attract a large number of consumers. Although past studies have revealed consumer motivations for participating in virtual brand communities, they fail to answer an important question: Why is it so easy for some virtual brand communities to attract users while others have such difficulty? In this study, product characteristics are hypothesized to be important factors that determine consumer motivation to participate in brand communities. Product characteristics can directly affect how actively consumers participate in brand communities. The results of questionnaires show that product complexity, product symbolism, and product satisfaction have a positive influence on consumers’ willingness to participate in brand communities. Notably, the duration of product use has a regulating effect on the influence of product satisfaction and product symbolism. A long period of product use weakens the influence of product satisfaction on consumers’ willingness to participate in brand communities. On the contrary, a long period of product use strengthens the influence of product symbolism on consumers’ willingness to participate in brand communities. This study enriches the literature on brand community participation and has implications for companies that aim to utilize brand communities for marketing.