RESEARCH ON THE RELATIONSHIP BETWEEN SPORTS SCIENTIFIC TRAINING AND ATHLETES’ MENTAL HEALTH

ABSTRACT Introduction: Regarding sports mental fatigue research, foreign countries mainly discuss the theoretical model of mental exhaustion. Among them, Silva's theoretical model believes that sports mental fatigue is a negative training stress response. Domestic research mainly analyzes and discusses the concept, causes, and monitoring of mental fatigue. Objective: This study explores the relationship between sports fatigue and mental health of elite athletes through investigation and analysis; analyzes whether social support plays a moderating role in training stress and how aspect support plays an important role. Methods: Based on the stress theory and the negative training stress response model theory, the paper used the Mental Health Inventory (PHI), Athlete Exercise Fatigue Questionnaire, Social Support Rating Scale, and Perceived Social Support Scale to analyze 163 outstanding athletes above the first level. Carry out investigation, use SPSS10.0 software to carry out reliability analysis, Pearson correlation analysis, and multiple linear stepwise regression analysis. Results: The mental health level of elite athletes is closely related to the degree of sports fatigue, and the correlation coefficients between most factors have reached a significant level. The physical (emotional) exhaustion in sports fatigue is an important predictor of the mental health of elite athletes; age, sports grade, economic conditions, perceived family support, and mental health are important predictors of sports fatigue for elite athletes; social support is an important external “buffer” in the process of training stress, in which family support and emotional support play a major regulatory role. Conclusions: The research results can provide references for maintaining and promoting athletes’ physical and mental health, provide some useful references for mental health education of sports teams, and provide empirical data for sports psychology and health psychology. Level of evidence II; Therapeutic studies - investigation of treatment results.

CBM-I training and its effect on interpretations of intent, facial expressions, attention and aggressive behavior

There is abundant evidence suggesting that attention and interpretation biases are powerful precursors of aggression. However, little is known how these biases may interact with one another in the development and maintenance of aggression. Using cognitive bias modification of interpretation (CBM-I), the present study examined whether training more pro-social or hostile intent attributions would affect attention bias, interpretation bias of facial expressions, aggression and mood. University students (17–48 years) were assigned to either a positive training (n = 40), negative training (n = 40), or control training (n = 40). Results showed that the positive training successfully changed measures of intent attributions in a pro-social direction compared to the control training. The negative training changed measures of intent attributions in a hostile direction but not more so than the control training. We found no generalization of the training effects to relevant other outcomes. Possible explanations underlying these findings are discussed.

Automatic Genre Categorization of Emails into predefined categories using machine learning

In today’s dynamic world, there is a need for fast, efficient, and reliable means of communication. To meet these requirements email system was developed and it got popular with the invention of WWW. Now, the Email system has been used extensively for official, business, and personal communication. On average individual users receive 50-60 mails each day. It is becoming a burden to easily manage emails. So there is a need for effective and reliable means to organize the mails for easy and fast retrieval. An efficient approach is proposed in this paper to classify the mails based on the predefined genres. It has been observed in the proposed research that the classification of emails greatly improves efficiency and saves time and effort to manage them. The results obtained in this paper are very encouraging. Over 90 % of emails are categorized correctly. Email genres are predefined and corresponding keyword lists are generated. Frequency tf-idf of the keywords in the email decides the genre of mail. SVM is used as a multiclass classifier. In this paper need for negative training data has been removed as the proposed classifier works on the principle of one class against the rest.

Multiview Self-Supervised Segmentation for OARs Delineation in Radiotherapy

Radiotherapy has become a common treatment option for head and neck (H&N) cancer, and organs at risk (OARs) need to be delineated to implement a high conformal dose distribution. Manual drawing of OARs is time consuming and inaccurate, so automatic drawing based on deep learning models has been proposed to accurately delineate the OARs. However, state-of-the-art performance usually requires a decent amount of delineation, but collecting pixel-level manual delineations is labor intensive and may not be necessary for representation learning. Encouraged by the recent progress in self-supervised learning, this study proposes and evaluates a novel multiview contrastive representation learning to boost the models from unlabelled data. The proposed learning architecture leverages three views of CTs (coronal, sagittal, and transverse plane) to collect positive and negative training samples. Specifically, a CT in 3D is first projected into three 2D views (coronal, sagittal, and transverse planes), then a convolutional neural network takes 3 views as inputs and outputs three individual representations in latent space, and finally, a contrastive loss is used to pull representation of different views of the same image closer (“positive pairs”) and push representations of views from different images (“negative pairs”) apart. To evaluate performance, we collected 220 CT images in H&N cancer patients. The experiment demonstrates that our method significantly improves quantitative performance over the state-of-the-art (from 83% to 86% in absolute Dice scores). Thus, our method provides a powerful and principled means to deal with the label-scarce problem.

Artificial Intelligence in Education

Information technology, through networking, knowledge-based systems and artificial intelligence, interactive multimedia, and other technologies, plays an increasingly important role, which will even increase in the future, in the way that education is taught and delivered to the student. For this reason, we decided to present some ideas for such learning-training environments in education in this chapter. Like many researchers in other countries, we are also developing a user-friendly general system, designed particularly for solving problems. It is based on experience-based intelligent tutoring systems, and intended primarily for executing better lessons and for students’ self-learning. Like all powerful tools, experience-based AI design approaches must be applied carefully. Without a carefully designed experience and extensive testing, these systems could easily result in unwanted outcomes (such as negative training or increased phobia anxiety). Despite the promise of the early efforts, the best approaches to designing these experiences are still topics of research and debate. Any technology as powerful as AI provokes many general social and ethical questions in all of us. Does AI make killing by remote control too consequence-free? Do AI models systematize existing biases? What will AI do when it enters education? We will try to provide an answer to this question in the following chapter.

Training matters! Narrative from a Black scientist

As STEM (Science, Technology, Engineering, and Math) professionals, we are tasked with increasing our understanding of the universe and generating discoveries that advance our society. An essential aspect is the training of the next generation of scientists, including concerted efforts to increase diversity within the scientific field. Despite these efforts, there remains disproportional underrepresentation of Black scientists in STEM. Further, efforts to recruit and hire Black faculty and researchers have been largely unsuccessful, in part due to a lack of minority candidates. Several factors contribute to this including access to opportunities, negative training experiences, lack of effective mentoring, and other more lucrative career options. This is a narrative of a Black male scientist to illustrate some of the issues in retaining Black students in STEM and to highlight the impact of toxic training environments that exists at many institutions. To increase Black participation in STEM careers, we must first acknowledge, then address, the problems that exist within our STEM training environments in hopes to inspire and retain Black students at every level of training.

Recovery-Stress States of Professional Ballet Dancers During Different Phases of a Ballet Season

Ballet dancers are exposed to chronic high training and performance demands that are associated with overtraining syndrome and injury. Balancing high training loads with recovery to reduce the risk of negative training adaptations is critical. Moreover, the recovery-stress states of professional ballet dancers during training phases of a season are largely unknown. Professional dancers (n = 27) from one classical ballet company in South Africa were monitored for two 8-week phases of a ballet season. A recovery-stress questionnaire for Athletes (RESTQ-76 Sport) was completed weekly during the rehearsal phase (P1) and the performance phase (P2), which took place at the start and the end of the ballet season, respectively. Comparisons were calculated between phases, sexes, and levels of performance with a mixed-model ANOVA and between demographic variables with a one-way ANOVA. The performance phase was signified by lower total recovery (TR, p < 0.01) and higher total stress (TS, p < 0.01) for the group. Female dancers had significantly lower recovery scores than male dancers during P2 (p < 0.01). No differences between levels of performance were found. Subscales previously associated with overreaching and injury were identified in certain groups during P2. In conclusion, P2 was a critical period where dancers, especially females, experienced high stress and low recovery. This could increase the risk for injury and negative training adaptations.

The impact of different negative training data on regulatory sequence predictions

Regulatory regions, like promoters and enhancers, cover an estimated 5–15% of the human genome. Changes to these sequences are thought to underlie much of human phenotypic variation and a substantial proportion of genetic causes of disease. However, our understanding of their functional encoding in DNA is still very limited. Applying machine or deep learning methods can shed light on this encoding and gapped k-mer support vector machines (gkm-SVMs) or convolutional neural networks (CNNs) are commonly trained on putative regulatory sequences. Here, we investigate the impact of negative sequence selection on model performance. By training gkm-SVM and CNN models on open chromatin data and corresponding negative training dataset, both learners and two approaches for negative training data are compared. Negative sets use either genomic background sequences or sequence shuffles of the positive sequences. Model performance was evaluated on three different tasks: predicting elements active in a cell-type, predicting cell-type specific elements, and predicting elements' relative activity as measured from independent experimental data. Our results indicate strong effects of the negative training data, with genomic backgrounds showing overall best results. Specifically, models trained on highly shuffled sequences perform worse on the complex tasks of tissue-specific activity and quantitative activity prediction, and seem to learn features of artificial sequences rather than regulatory activity. Further, we observe that insufficient matching of genomic background sequences results in model biases. While CNNs achieved and exceeded the performance of gkm-SVMs for larger training datasets, gkm-SVMs gave robust and best results for typical training dataset sizes without the need of hyperparameter optimization.

Inducing Affective Learning Biases with Cognitive Training and Prefrontal tDCS: A Proof-of-Concept Study

Background Cognitive models of mood disorders emphasize a causal role of negative affective biases in depression. Computational work suggests that these biases may stem from a belief that negative events have a higher information content than positive events, resulting in preferential processing of and learning from negative outcomes. Learning biases therefore represent a promising target for therapeutic interventions. In this proof-of-concept study in healthy volunteers, we assessed the malleability of biased reinforcement learning using a novel cognitive training paradigm and concurrent transcranial direct current stimulation (tDCS). Methods In two studies, young healthy adults completed two sessions of negative (n = 20) or positive (n = 20) training designed to selectively increase learning from loss or win outcomes, respectively. During training active or sham tDCS was applied bilaterally to dorsolateral prefrontal cortex. Analyses tested for changes both in learning rates and win- and loss-driven behaviour. Potential positive/negative emotional transfer of win/loss learning was assessed by a facial emotion recognition task and mood questionnaires. Results Negative and positive training increased learning rates for losses and wins, respectively. With negative training, there was also a trend for win (but not loss) learning rates to decrease over successive task blocks. After negative training, there was evidence for near transfer in the form of an increase in loss-driven choices when participants performed a similar (untrained) task. There was no change in far transfer measures of emotional face processing or mood. tDCS had no effect on any aspect of behaviour. Discussion and Conclusions Negative training induced a mild negative bias in healthy adults as reflected in loss-driven choice behaviour. Prefrontal tDCS had no effect. Further research is needed to assess if this training procedure can be adapted to enhance learning from positive outcomes and whether effects translate to affective disorders.