The Present Situation and Future Prospect of Online Fitness in the Post-Epidemic Era

After the COVID-19 epidemic broke out around the world, large-scale home isolations have restricted the activities of ordinary residents. Therefore, online activities have become more frequent and online fitness have ushered in a new round of large-scale rise. The concept of national fitness has gradually rooted in the hearts of the people. The 14th five-year Plan of the people\u27s Republic of China for National Economic and Social Development and the outline of long-term goals for 2035 further make it clear that it is necessary to create new advantages in the digital economy and promote the digital transformation of the industry. Under the background of the epidemic, the online fitness industry promotes the digital transformation of China\u27s sports industry and provides new ideas and directions for it. Combined with the current social background, this study was to expound the development status of online fitness in the post-epidemic era and put forward prospects and suggestions for the future development of online fitness. This study took the global online fitness market as the research object and mainly used the literature method and data analysis method with two main purposes: (1) Investigating the current status of online fitness development and relevant national policies on national fitness and the promotion of industrial digital transformation through online literature platforms such as CNKI and Wanfang platform and online news platforms such as Xinhuanet, and serve as the research background of this article . (2) Collecting relevant second-hand data from the global online fitness market through online database platforms such as the Sports Information Network and the China Economic and Social Big Data Research Platform and analyzing relevant data on the online fitness market before and after the outbreak of the new crown epidemic. The findings showed the necessity of digital transformation in the sports industry. The vigorous development of emerging digital industries such as artificial intelligence, big data, and cloud computing has brought human society into a new era of digitalization. In the context of digitalization, the digital transformation of all walks of life has become an inevitable trend for the survival and development of the industry. In 2020, the global digital economy will reach US$32.6 trillion, a nominal year-on-year increase of 3.0$6.04 billion. In 2021, the scale of the global online fitness industry reached US$10.71 billion, an increase of 77.33%. Although the epidemic has restricted residents’ outing activities, more and more people have begun to choose online sports, including the use of smart wearable devices, online app guidance and recording, etc., which has brought online fitness models. In the context of the epidemic, the online fitness model has greatly promoted the implementation of the national fitness policy and is also an important path for the digital transformation of the sports industry. It is suggested that the digital transformation of the sports industry is an important direction for the future development of the sports industry. In the context of technological development and support and the new crown pneumonia epidemic, online fitness, an emerging fitness model, has emerged and has become an important fitness model, and at the same time has promoted the digital transformation of the sports industry. Online fitness should focus on the development of personalized customization of fitness courses and programs to meet the individual needs of users; strengthen the association with social platforms to increase user stickiness; dig deep into user data, identify user pain points, and then take advantage of their products make improvements with services

Gender Differences in Depressive Symptoms Among HIV-Positive Concordant and Discordant Heterosexual Couples in China.

HIV seropositive individuals and their heterosexual partners/spouses, either seropositive or seronegative, are facing several mental health challenges. The objective of this study was to examine gender differences in depressive symptoms among HIV-positive concordant and HIV-discordant couples. We identified heterosexual couples from participants of a randomized controlled trial conducted in Anhui province, China. A total of 265 couples, comprising 129 HIV+ male/HIV- female couples, 98 HIV- male/HIV+ female couples, and 38 HIV-positive concordant couples, were included in the analyses. We collected data using the computer-assisted personal interview method. We used a linear mixed-effects regression model to assess whether gender differences in depressive symptoms varied across couple types. HIV-positive women reported a significantly higher level of depressive symptoms than their partners/spouses. HIV-positive women with HIV-positive partners had higher depressive symptoms than those with HIV-negative partners, whereas HIV-positive men reported similar levels of depressive symptoms regardless of their partners' serostatus. Among the concordant couples, those with the highest annual family income showed the greatest gender differences in depressive symptoms. We suggest that family interventions should be gender- and couple-type specific and that mental health counseling is warranted not only for HIV-positive women but also for HIV-negative women in an HIV-affected relationship

CeNiAsO: an antiferromagnetic dense Kondo lattice

A cerium containing pnictide, CeNiAsO, crystallized in the ZrCuSiAs type structure, has been investigated by measuring transport and magnetic properties, as well as specific heat. We found that CeNiAsO is an antiferromagnetic dense Kondo lattice metallic compound with Kondo scale $T_K \sim$ 15 K and shows an enhanced Sommerfeld coefficient of $\gamma_0 \sim$ 203 mJ/mol$\cdot$K$^{2}$. While no superconductivity can been observed down to 30 mK, Ce ions exhibit two successive antiferromagnetic (AFM) transitions. We propose that the magnetic moment of Ce ion could align in the G type AFM order below the first transition at $T_{N1}$=9.3 K, and it might be modified into the C type AFM order below a lower transition at $T_{N2}$=7.3 K. Our results indicate that the 3$d-4f$ interlayer Kondo interactions play an important role in Ni-based Ce-containing pnictide.Comment: 13 pages, 5 figures, to appear in J. Phys.: Condens. Matte

Discovering Cancer Subtypes via an Accurate Fusion Strategy on Multiple Profile Data

Discovering cancer subtypes is useful for guiding clinical treatment of multiple cancers. Progressive profile technologies for tissue have accumulated diverse types of data. Based on these types of expression data, various computational methods have been proposed to predict cancer subtypes. It is crucial to study how to better integrate these multiple profiles of data. In this paper, we collect multiple profiles of data for five cancers on The Cancer Genome Atlas (TCGA). Then, we construct three similarity kernels for all patients of the same cancer by gene expression, miRNA expression and isoform expression data. We also propose a novel unsupervised multiple kernel fusion method, Similarity Kernel Fusion (SKF), in order to integrate three similarity kernels into one combined kernel. Finally, we make use of spectral clustering on the integrated kernel to predict cancer subtypes. In the experimental results, the P-values from the Cox regression model and survival curve analysis can be used to evaluate the performance of predicted subtypes on three datasets. Our kernel fusion method, SKF, has outstanding performance compared with single kernel and other multiple kernel fusion strategies. It demonstrates that our method can accurately identify more accurate subtypes on various kinds of cancers. Our cancer subtype prediction method can identify essential genes and biomarkers for disease diagnosis and prognosis, and we also discuss the possible side effects of therapies and treatment

SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions

With the rapid development of Large Language Models (LLMs), increasing attention has been paid to their safety concerns. Consequently, evaluating the safety of LLMs has become an essential task for facilitating the broad applications of LLMs. Nevertheless, the absence of comprehensive safety evaluation benchmarks poses a significant impediment to effectively assess and enhance the safety of LLMs. In this work, we present SafetyBench, a comprehensive benchmark for evaluating the safety of LLMs, which comprises 11,435 diverse multiple choice questions spanning across 7 distinct categories of safety concerns. Notably, SafetyBench also incorporates both Chinese and English data, facilitating the evaluation in both languages. Our extensive tests over 25 popular Chinese and English LLMs in both zero-shot and few-shot settings reveal a substantial performance advantage for GPT-4 over its counterparts, and there is still significant room for improving the safety of current LLMs. We believe SafetyBench will enable fast and comprehensive evaluation of LLMs' safety, and foster the development of safer LLMs. Data and evaluation guidelines are available at https://github.com/thu-coai/SafetyBench. Submission entrance and leaderboard are available at https://llmbench.ai/safety.Comment: 15 page

Discovering Cancer Subtypes via an Accurate Fusion Strategy on Multiple Profile Data

Discovering cancer subtypes is useful for guiding clinical treatment of multiple cancers. Progressive profile technologies for tissue have accumulated diverse types of data. Based on these types of expression data, various computational methods have been proposed to predict cancer subtypes. It is crucial to study how to better integrate these multiple profiles of data. In this paper, we collect multiple profiles of data for five cancers on The Cancer Genome Atlas (TCGA). Then, we construct three similarity kernels for all patients of the same cancer by gene expression, miRNA expression and isoform expression data. We also propose a novel unsupervised multiple kernel fusion method, Similarity Kernel Fusion (SKF), in order to integrate three similarity kernels into one combined kernel. Finally, we make use of spectral clustering on the integrated kernel to predict cancer subtypes. In the experimental results, the P-values from the Cox regression model and survival curve analysis can be used to evaluate the performance of predicted subtypes on three datasets. Our kernel fusion method, SKF, has outstanding performance compared with single kernel and other multiple kernel fusion strategies. It demonstrates that our method can accurately identify more accurate subtypes on various kinds of cancers. Our cancer subtype prediction method can identify essential genes and biomarkers for disease diagnosis and prognosis, and we also discuss the possible side effects of therapies and treatment

Thorium-doping induced superconductivity up to 56 K in Gd1-xThxFeAsO

Following the discovery of superconductivity in an iron-based arsenide LaO1-xFxFeAs with a superconducting transition temperature (Tc) of 26 K[1], Tc was pushed up surprisingly to above 40 K by either applying pressure[2] or replacing La with Sm[3], Ce[4], Nd[5] and Pr[6]. The maximum Tc has climbed to 55 K, observed in SmO1-xFxFeAs[7, 8] and SmFeAsO1-x[9]. The value of Tc was found to increase with decreasing lattice parameters in LnFeAsO1-xFx (Ln stands for the lanthanide elements) at an apparently optimal doping level. However, the F- doping in GdFeAsO is particularly difficult[10,11] due to the lattice mismatch between the Gd2O2 layers and Fe2As2 layers. Here we report observation of superconductivity with Tc as high as 56 K by the Th4+ substitution for Gd3+ in GdFeAsO. The incorporation of relatively large Th4+ ions relaxes the lattice mismatch, hence induces the high temperature superconductivity.Comment: 4 pages, 3 figure

Research progress of hydrogels as delivery systems and scaffolds in the treatment of secondary spinal cord injury

Secondary spinal cord injury (SSCI) is the second stage of spinal cord injury (SCI) and involves vasculature derangement, immune response, inflammatory response, and glial scar formation. Bioactive additives, such as drugs and cells, have been widely used to inhibit the progression of secondary spinal cord injury. However, the delivery and long-term retention of these additives remain a problem to be solved. In recent years, hydrogels have attracted much attention as a popular delivery system for loading cells and drugs for secondary spinal cord injury therapy. After implantation into the site of spinal cord injury, hydrogels can deliver bioactive additives in situ and induce the unidirectional growth of nerve cells as scaffolds. In addition, physical and chemical methods can endow hydrogels with new functions. In this review, we summarize the current state of various hydrogel delivery systems for secondary spinal cord injury treatment. Moreover, functional modifications of these hydrogels for better therapeutic effects are also discussed to provide a comprehensive insight into the application of hydrogels in the treatment of secondary spinal cord injury

CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models

In this paper, we present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters. Our CharacterGLM is designed for generating Character-based Dialogues (CharacterDial), which aims to equip a conversational AI system with character customization for satisfying people's inherent social desires and emotional needs. On top of CharacterGLM, we can customize various AI characters or social agents by configuring their attributes (identities, interests, viewpoints, experiences, achievements, social relationships, etc.) and behaviors (linguistic features, emotional expressions, interaction patterns, etc.). Our model outperforms most mainstream close-source large langauge models, including the GPT series, especially in terms of consistency, human-likeness, and engagement according to manual evaluations. We will release our 6B version of CharacterGLM and a subset of training data to facilitate further research development in the direction of character-based dialogue generation.Comment: Work in progres