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Meteoroids entering the Earth's atmosphere lose most of their mass during atmospheric passage and considerably disturb the background ionosphere. To elucidate better the effects of meteoroids on the near-Earth space environment, it is important to simultaneously observe various meteoroids and their related phenomena in the Earth's atmosphere. This study briefly describes a newly developed Meteor and ionospheric Irregularity Observation System (MIOS), which consists of a phased-array radar, a bi-static all-sky radar, and a multi-station optical imaging and spectroscopy subsystem. The MIOS can capture the processes of ablation and evaporation of meteoroids, creating luminous and ionization trails, and measure the properties of both the meteor trail and its corresponding meteoroid within a large field of view. Based on the MIOS, some observational modes and data processing methods have been developed, where the physical and chemical properties of meteoroids (including their velocity, mass, composition, and source region), meteor plasma head and trail irregularity and their structural evolution, and the instantaneous neutral wind can be obtained. Using the MIOS measurements, a preliminary study of some of the characteristics of meteoroids, meteor plasma head, meteor plasma trail field–aligned and non-field–aligned irregularities, meteor flare and dust trail irregularity, and ionospheric irregularity is presented.

Action recognition in martial arts can offer valuable insights for technicians, athletes, and coaches. Accurate action recognition can enhance performance analysis, inform training strategies, and improve decision-making processes by providing detailed evaluations of technique execution, movement patterns, and match dynamics. This can lead to more effective coaching, better athlete preparation, and a deeper understanding of competitive outcomes. Existing methods in human action recognition often struggle with challenges such as background clutter, occlusion, and variations in appearance and speed, particularly in dynamic combat scenarios. In this study, we proposed a novel Spatio-Temporal Hierarchical Keypoint Aggregation (ST-HKA) framework for martial arts combat action recognition and localization. The ST-HKA model effectively leverages a deep learning-based approach that treats human skeleton keypoints as 3D point clouds. Unlike conventional methods that use graph convolutional networks or appearance-based techniques, our approach adopts a point cloud paradigm to treat human keypoints as a 3D point cloud, significantly improving scalability and robustness against occlusion and variations in appearance. Additionally, we incorporate a weakly supervised spatio-temporal action localization mechanism using a Context-Aware Pooling Mechanism. The proposed model was evaluated on both the Kinetics Human-Action and Taekwondo datasets, demonstrating superior performance in recognizing complex martial arts actions. The ST-HKA model achieves a Top-1 Accuracy of 88.6% and an F1-score of 83.9% on the Kinetics dataset, and 88.7% accuracy and an F1-score of 84.4% on the Taekwondo dataset. The proposed model also exhibits higher precision in detecting precise temporal boundaries, as reflected by its strong performance in action localization tasks. These results highlight the effectiveness of ST-HKA in handling complex martial arts actions with high accuracy and robustness.

With the global trend of aging, lacking of interpersonal communication and spiritual support and companionship have had a great impact on the mental health of older people living alone. This study examines the multifaceted impacts of engaging in tai chi, ba duan jin, and walking on the mental wellbeing of older people residing alone in urban areas. Additionally, this research aims to explore the association between tai chi, ba duan jin, and walking, and the mental health status of urban older people living alone, by considering the mediating influence of social participation and the moderating influence of the exercise environment. To do so, 1,027 older people living alone in six Chinese cities were investigated using the Physical Activity Rating Scale (PARS-3), the Geriatric Health Questionnaire (GHQ-12), the Social Participation Indicator System Scale, and the Exercise Environment Scale. SPSS 25.0 was utilized for conducting mathematical statistical analysis, specifically for doing linear regression analysis. Additionally, AMOS was employed to develop the study model. We found that a significant negative correlation between tai chi, ba duan jin, and walking and mental health status; among these, tai chi had the greatest impact on the mental health status of urban older people living alone. Social participation mediated the relationship between tai chi, ba duan jin, walking, and mental health status, and the exercise environment had a moderating effect on this relationship. The findings of this study indicate that tai chi, ba duan jin, and walking have a positive impact on the mental health of urban older people living alone, which can be influenced by the mediating efficacy of social participation and the moderating effect of the exercise environment.

BackgroundPopulation aging is a global trend, and the number of older adults living alone is increasing. Tai chi, a traditional Chinese exercise, has been shown to improve the physical and mental health of older adults.AimTo investigate the effects of tai chi on death anxiety in older adults living alone and the role of social support and psychological capital in this relationship.MethodA cross-sectional study of 493 older adults living alone in four cities in southwestern China. Participants were assessed using questionnaires on tai chi practice, social support, psychological capital, and death anxiety.ResultsTai chi practice significantly reduced death anxiety in older adults living alone. It also positively correlated with social support and psychological capital, both of which negatively correlated with death anxiety. Social support and psychological capital mediated the relationship between tai chi practice and death anxiety, suggesting that tai chi may reduce death anxiety through these factors. These findings encourage older adults living alone to practice tai chi, as it may improve their mental and physical health and reduce their risk of death anxiety.ConclusionTai chi practice may reduce death anxiety in older adults living alone through the chain-mediated effects of social support and psychological capital. This suggests that tai chi may be a beneficial intervention for older adults living alone.

Since the end of 2019, a novel coronavirus pandemic (COVID-19), characterized by solid infectivity, rapid communication and diverse communication routes, has become widespread worldwide. This study investigates the motivations of older adults to exercise and keep fit due to the COVID-19 pandemic. The research is based on a survey of older adults in Chengdu, Sichuan Province, China. It adopts the event strength system theory as a conceptual framework and the stimulus-organism-response (S-O-R) theory for causal inferences. (1) the perception of COVID-19’s novelty had a significant negative impact on older adults” intention to exercise and stay fit, and the perception of COVID-19’s disruptiveness and criticality had a significant positive impact on older adults” intention to exercise and fitness; (2) The perception of COVID-19’s novelty had a significant negative effect on risk cognition, and the disruptive and critical cognitions had a significant positive effect on risk perceptions; (3) risk perceptions had a prominent positive effect on older adults” intention to exercise and fitness; and (4) risk communication played an important moderating role between COVID-19 event strength cognition and older adults’ intention to exercise and fitness. The study revealed that the perception of COVID-19’s seriousness significantly impacted older adults” intentions to exercise and keep fit and that risk perception and communication acted as mediating factors.

This work studies multi-label learning (MLL), where each instance is associated with a subset of positive labels. For each instance, a good multi-label predictor should encourage the predicted positive labels to be close to its ground-truth positive ones. In this work, we propose a new loss, named Groupwise Ranking LosS (GRLS) for multi-label learning. Minimizing GRLS encourages the predicted relevancy scores of the ground-truth positive labels to be higher than that of the negative ones. More importantly, its time complexity is linear with respect to the number of candidate labels, rather than square complexity for some pairwise ranking based methods. We further analyze GRLS in the perspective of label-wise margin and suggest that multi-label predictor is label-wise effective if and only if GRLS is optimal. We also analyze the relations between GRLS and some widely used loss functions for MLL. Finally, we apply GRLS to multi-label learning, and extensive experiments on several benchmark multi-label databases demonstrate the competitive performance of the proposed method to state-of-the-art methods.

In existing visual representation learning tasks, deep convolutional neural networks (CNNs) are often trained on images annotated with single tag, such as ImageNet. However, single tag annotation cannot describe all important contents of one image, and some useful visual information may be wasted during training. In this work, we propose to train CNNs from images annotated with multiple tags, to enhance the quality of visual representation of the trained CNN model. To this end, we build a large-scale multi-label image database with 18M images and 11K categories, dubbed Tencent ML-Images. We efficiently train the ResNet-101 model with multi-label outputs on Tencent ML-Images, taking 90 hours for 60 epochs, based on a large-scale distributed deep learning framework, i.e., TFplus. The good quality of the visual representation of the Tencent ML-Images checkpoint is verified through three transfer learning tasks, including single-label image classification on ImageNet and Caltech-256, object detection on PASCAL VOC 2007, and semantic segmentation on PASCAL VOC 2012. The Tencent ML-Images database, the checkpoints of ResNet-101, and all the training codes have been released at https://github.com/Tencent/tencent-ml-images. It is expected to promote other vision tasks in the research and industry community.