Majority world successes and European and American failure to contain COVID‐19: Cultural collectivism and global leadership

United Nations and World Health Organization data show a positive correlation, r = .53, p < .0001, N = 189, between COVID‐19 infection rates and the human development index (HDI). Less wealthy, less educated countries with lower life spans were also more successful in maintaining lower fatality rates, r = .46, p < .0001, N = 189 whereas 9 of the top‐10 countries in the world in per capita fatalities due to COVID‐19 were Western societies high in HDI. Similar positive correlations were found between COVID‐19 infection and fatality rates and a smaller sample of 76 countries measured on Schwartz intellectual autonomy (or individualism), and negative correlations of similar magnitude were found for embeddedness (or collectivism). East Asia was a global leader in preventing the spread of COVID‐19 because of a vigilant public concerned for public safety and compliant with public safety measures. African Union leaders coordinated their responses, and bought into a continent‐wide African Medical Supplies Platform that prevented panicked competition for scare supplies. Western global media and scholars have not paid attention to the successes of East Asia, Africa, and the South Pacific in fighting the pandemic. It is worth asking why this should be the case; understand the weaknesses of extreme individualism in fighting a pandemic requiring coordinated and unified public response, and consider the lessons for global scholars from the pandemic for doing research in the future.     

Selfish power and unselfish status in Chinese work situations

The present research explored the selfishness of power and status as well as the mechanism in Chinese work situations. By using experimental and survey methods, two studies were designed to investigate the relationships among power, status, and self‐interested behaviour (SIB) as well as the mediating effect of perspective‐taking (PT). Results of the two studies consistently indicated that power increases whereas status reduces SIB. These opposing behavioural effects are partially attributed to the fact that power reduces whereas status increases PT. In addition, our results showed an interaction between power and status on SIB in Study 1 (interaction as a mental function), and a positive correlation between perceived power and status in Study 2 (correlation as a mental perception). Through the discussion from the perspective of the self/other‐orientation trait, as well as the stable effects of power and status on SIB in the two studies, we conclude that power is selfish and status is unselfish in Chinese work situations.     

Multilevel metric rank match for person re-identification

Metric learning is one of the important ways to improve the person re-identification (ReID) accurate, of which triplet loss is the most effect metric learning method. However, triplet loss only ranks the extracted feature at the end of the network, in this paper, we propose a multilevel metric rank match (MMRM) method, which ranks the extracted feature on multilevel of the network. At each rank level, the extracted features are ranked to find the hard sample pairs and the backward dissemination triplet loss. Each rank level has different penalize value to adjust the network, in which the value is bigger with the deeper level of the whole network. Experiment results on CUHK03, Market1501 and DukeMTMC datasets indicate that The MMRM algorithm can outperform the previous state-of-the-arts.     

De l’estime de soi et du sentiment d’auto-efficacité à la motivation au travail : le rôle médiateur du sens du travail chez les alternants en France

The purpose of this study was to test the links between self-esteem, the feeling of self-efficacy and the different forms of motivation. It also aimed to understand the mediating effect of the meaning of work between the variables. French work-integrated learning students (n = 471) completed a self-administered online questionnaire. The results showed significant links between self-esteem, perceived self-efficacy, work meaning, and different dimensions of motivation (extrinsic, intrinsic, and amotivation). In addition, the meaning of work played a mediating role in the relationship between self-esteem, perceived self-efficacy, and dimensions of motivation. This study showed the central mechanism of work meaning in work motivation.     

Detecting anonymous attacks in wireless communication medium using adaptive grasshopper optimization algorithm

Intrusion Detection Systems (IDSs) is a system that monitors network traffic for suspicious activity and issues alert when such activity is revealed. Moreover, the existing IDSs-based methods are based on outdated attacks that unable to identify modern attacks or malicious trends. For this reason, in this study we developed a new multi-swarm adaptive grasshopper optimization algorithm to utilize adaptation mechanism in a group of swarms based on fuzzy logic to protect against sophisticated attacks. The proposed (MSAGOA) technique has the capability of global optimization and rapid convergence that are used to attain optimal feature subsets to identify attack types on IDS datasets. In the MSAGOA technique, learning engine as Extreme learning Machine, Naive Bayes, Random Forest and Decision Tree is applied as a fitness function to select the highly discriminating features and to maximize classification performance. Afterward, select the best classifier which works as a fitness function in our approach to measure the performance in terms of accuracy, detection rate, and false alarm rate. The simulations are performed on three IDS datasets such as NSL-KDD, AWID-ATK-R, and NGIDS-DS. The experimental results demonstrated that MSAGOA method has performed better and obtained high detection rate of 99.86%, accuracy of 99.89% in NSL-KDD and high detection rate of 98.73%, accuracy of 99.67% in AWID-ATK-R and detection rate of 89.50%, accuracy of 90.23% in NGIDS-DS. In addition, the performance is compared with several other existing techniques to show the efficacy of the proposed approach.     

Research in counselling and psychotherapy Post‐COVID‐19

The COVID‐19 pandemic brings to light many areas the field of counselling and psychotherapy may need to address in future research. We outline several issues stemming from or exacerbated by the pandemic and offer suggestions for future research to address the mental health needs of those impacted. Our suggestions focus on five domains: (a) the health and well‐being of helping professionals, (b) the infodemic, (c) discrimination and minority stress, (d) spiritual and existential dynamics in mental health and (e) couple and family stress and resilience. We aim to provide a multi‐systemic perspective of mental health and well‐being in the time of COVID‐19, as well as encourage current and future studies to incorporate these suggestions to advance the health and well‐being of our communities through evidence‐based treatment approaches.     

双眼视差的神经机制与知觉学习效应

双眼瞳距使得空间某物体在左右眼视网膜的成像存在微小位置差异, 这种差异被称为双眼视差(binocular disparity), 是立体视知觉的重要信息来源。对双眼视差的心理物理学研究始于18世纪初, 迄今已有接近两百年的历史。近年来, 双眼视差研究主要集中在两方面。其一是用电生理、脑成像技术考察双眼视差在视觉背、腹侧通路的模块化表征, 其脑区表征反映出视觉系统的层级式、平行式加工规律。其二是应用知觉学习范式研究双眼视差的可塑性。未来研究应综合脑成像和神经调控技术考察双眼视差的神经机制及其学习效应, 包括双眼视差与多种深度线索间的信息整合和交互作用。应用方向上, 可结合虚拟现实等技术优化训练范式, 实现立体视力的康复和增强。     

象似性和手语学习经验对中国手语-汉语跨模态语义启动效应的影响

采用启动条件下的词汇判断任务,考察熟练手语使用者和无手语经验成年听人的跨模态语义启动效应。结果发现：1)在象似词条件下,两组被试判断汉语语义相关词的反应时均快于语义无关词,说明手语象似词和汉语词之间存在跨模态语义启动效应。2)在非象似词条件下,仅手语熟练被试判断汉语语义相关词的反应时快于语义无关词,无手语经验被试判断汉语语义相关词和无关词的速度没有差异。这是由于前者心理词库中的手语词和口语词共享语义表征,而后者主要依赖手语象似词的视觉模拟性。整个研究表明,中国手语和汉语间存在跨模态语义启动效应,但该效应受到手语词象似性和手语学习经历的调节。     

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client’s needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.