Associations between Self-Esteem, Psychological Stress, and the Risk of Exercise Dependence

Body concerns and stress-related disorders are increasing in the younger population in a wide range of nations. Studies find links between both self-worth, exercise dependence, and self-esteem in relation to stress, but few have considered all three variables in relation to one another. The present study explored whether the co-appearance of high levels of psychological distress, and low levels of self-esteem may be a vulnerability factor for developing exercise dependence by studying the links between self-esteem, psychological stress, and exercise dependence. A standardized cross-sectional questionnaire was completed by 203 regular exercisers attending two gyms (mean age: 35.9 years). The variables self-esteem, psychological distress, and exercise dependence were all significantly correlated with each other, even after weekly exercise amount, age, and gender had been accounted for. Those who exercised for more than 9 h per week had a significantly higher score on stress and exercise dependence symptoms, and a lower score on self-esteem compared with the remaining groups. One could hypothesize that low self-esteem is a vulnerability factor and high psychological stress a maintenance factor for an exercise-dependent person. It is argued that more focus should be directed toward the negative consequences of excessive exercise.

Implicit Beliefs About Emotions in Youth: Associations With Temperamental Negative Emotionality and Depression

Introduction: Individual differences in beliefs about the controllability of emotions are associated with a range of psychosocial outcomes, including depressive symptoms. Less is known, however, about factors contributing to individual differences in these beliefs. The current study examined prospective associations between negative emotionality (NE) and implicit beliefs about emotions, as well as the indirect effect of NE on depressive symptoms through implicit beliefs about emotions. Methods: In a sample of children and adolescents, NE was assessed at baseline, implicit beliefs about emotions were assessed 18 months later, and depressive symptoms were assessed at baseline and 36 months later. Results: NE was associated with implicit beliefs about emotions, and an indirect effect of NE on depressive symptoms through implicit beliefs about emotions was observed. Discussion: NE represents a salient dispositional vulnerability factor contributing to individual differences in implicit beliefs about emotions, with implications for the development of depressive symptoms in youth.

COVID-19-Related Stressors and Chinese Adolescents' Adjustment: The Moderating Role of Coping and Online Learning Satisfaction

The present study aims to examine the main and interactive relations of COVID-19-related stressors, coping, and online learning satisfaction with Chinese adolescents' adjustment during the COVID-19 pandemic. A total of 850 adolescents from three Chinese secondary schools participated in the survey during the pandemic outbreak, and the data were analyzed by hierarchical linear regression. The results show that COVID-19-related stressors were a vulnerability factor in predicting adjustment. Adolescents' adjustment could be attributed to both individual-level (e.g., coping) and class-level (e.g., a class-level indicator of coping) characteristics. Specifically, problem-based coping and online learning satisfaction can promote adolescents' adjustment directly or serve as a buffer against the negative impact of stressors on adjustment, while emotion-based coping is a vulnerability factor in predicting adjustment directly or as a risk factor in strengthening the relation between stressors and adjustment. Compared with male adolescents and adolescents with high socio-economic status, female and impoverished adolescents reported poorer adjustment during the COVID-19 pandemic. These findings enrich our understanding of the impact of the COVID-19 pandemic on adolescents' adjustment and are helpful in improving adolescents' adjustment during the pandemic.

The Unprecedented Rise in Cybercrime and the Role of the Human Vulnerability Factor

This chapter is primarily intended to firstly define and review the literature in cybersecurity and vividly shed light on the mechanisms involved in the social engineering phenomenon. It will discuss the various attempts at network intrusion and the steps typically taken in the implementation of cyber-thefts. The chapter will provide the rationale behind the justification of why humans are considered to be the weakest link in these attacks. The study will also explain the reasons for the rise in cybercrimes and their impact on organizations. In closing, the chapter will put forward some recommendations to serve as preventative measures and solutions to the threats and vulnerabilities posed by cyber-attacks. Finally, measures, such as conducting regular, thorough, and relevant awareness training, frequent drills, and realistic tests, will be addressed with a view to maintaining a steady focus on the overall discipline of the organization, thereby hardening the component of the network that is the softest by nature—the human vulnerability factor.

Analyzing the Impact of Soft Errors in Deep Neural Networks on GPUsfrom Instruction Level

­ Deep Neural Networks (DNNs) used in safety­critical systems cannot compromise their performance due to reliability issues. In particular, soft errors are the worst. Selective software­based protection solutions are among the best techniques to improve the reliability of DNNs efficiently. However, their most significant challenge is precisely hardening portions of the DNN model to avoid performance degradation. In this work, we propose a comprehensive methodology to analyze the reliability of object detection and classification algorithms run on GPUs from the lowest (instruction) evaluation level. The ultimate goal is to avoid the performance penalty of full instruction duplication by confidently identifying the vulnerable instructions. For this purpose, we propose a technique, Instruction Vulnerability Factor (IVF). By applying our methodology on ResNet and YOLO models, we demonstrate that both models’ most vulnerable instructions can be precisely determined. Moreover, we show that YOLO is more sensitive to the changes caused by soft errors than ResNet. Also, ResNet depends on the input image in its reliability, while YOLO tends to be independent.