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This study aims to investigate the mediating effect of anxiety and inhibitory control in the relationship between physical activity and Internet addiction (IA) among adolescents. A total of 951 adolescents from western China completed a self-report survey assessing physical activity, anxiety, inhibitory control, and IA. Descriptive analysis, correlation analysis, and mediation analysis were conducted using SPSS software and the Process plug-in. Controlling for age, gender, and only child status, the findings revealed a negative association between physical activity and anxiety, inhibitory control, and IA. Moreover, anxiety were positively correlated with inhibitory control and IA. Additionally, anxiety exhibited a positive association with inhibitory control. Notably, physical activity directly and negatively predicted IA in adolescents, while also indirectly predicting it through anxiety and inhibitory control. This study contributes to a deeper understanding of the mechanisms that underlie the complex effects of physical activity on IA among adolescents. [ABSTRACT FROM AUTHOR]

In order to solve the problems of the small capacity of structured data and uneven distribution among classes in machine learning tasks, a supervised generation method for structured data called WAGAN and a cyclic sampling method named SACS (Semi-supervised and Active-learning Cyclic Sampling), based on semi-supervised active learning, are proposed. The loss function and neural network structure are optimized, and the quantity and quality of the small sample set are enhanced. To enhance the reliability of generating pseudo-labels, a Semi-supervised Active learning Framework (SAF) is designed. This framework redistributes class labels to samples, which not only enhances the reliability of generated samples but also reduces the influence of noise and uncertainty on the generation of false labels. To mine the diversity information of generated samples, an uncertain sampling strategy based on spatial overlap is designed. This strategy incorporates the idea of spatial overlap and uses global and local sampling methods to calculate the information content of generated samples. Experimental results show that the proposed method performs better than other data enhancement methods on three different datasets. Compared to the original data, the average F 1 m a c r o value of the classification model is improved by 11.5%, 16.1%, and 19.6% relative to compared methods. [ABSTRACT FROM AUTHOR]

Purpose: Our study aimed to explore the current status of patient participation in medication safety from the perspectives of general practitioners (GPs), pharmacists, and outpatients in Beijing, China. Patients and Methods: A qualitative study using semi-structured in-depth individual interviews with GPs, pharmacists, and outpatients. Subjects were identified by purposive sampling until code saturation. Semi-structured qualitative interviews were conducted with GPs, pharmacists, and patients from community health service centers in three urban districts of Beijing, China. The interviews were transcribed verbatim and the text was analysed using thematic analysis techniques including familiarising with data, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report. Results: A total of eight GPs, seven pharmacists, and 18 outpatients were interviewed. Data analysis led to the generation of five key themes: (1) mutual trust between patient and GP, (2) communication with healthcare professionals, (3) acquisition of knowledge about medication safety, (4) implementation of medication self-management at home, and (5) different attitudes toward participation in medication decisions. Patients participated in medication safety in multiple ways. However, insufficient knowledge about medication safety, lack of awareness of the patient's role in ensuring medication safety, shortage of consultation lengths, and being misled by some information were problems with patient participation in medication safety. Conclusion: This exploratory study contributes to our initial understanding of patient participation in medication safety. There were still many issues and barriers in the process of patient participation. Appropriate policies and measures, such as providing various forms of patient education, ensuring sufficient physician-patient communication, giving full play to the role of pharmacists, and making judicious use of digital health tools should be taken to improve medication safety by fully utilising the role of patients. Plain Language Summary: Medication safety is a significant concern around the world. Patient participation in the medication process is effective in reducing the incidence of medication errors and improving medication safety. However, the role of outpatients with chronic conditions in ensuring medication safety is often neglected. This study aims to explore the perspectives and experiences of GPs, pharmacists, and outpatients by qualitative interviews in Beijing, China. The study involved a series of interviews with eight GPs, seven pharmacists, and 18 outpatients living with noncommunicable diseases. The interview revealed five themes: (1) mutual trust between patient and GP, (2) communication with healthcare professionals, (3) acquisition of knowledge about medication safety, (4) implementation of medication self-management at home, and (5) different attitudes toward participation in medication decisions. The findings might help propose suggestions for patient participation in medication safety. Integrating these findings into future studies can help healthcare professionals formulate interventions and better support patients in participating in the medication process. [ABSTRACT FROM AUTHOR]

Green human resource management (GHRM) has become an indispensable strategy in green management. As the first step of GHRM, green recruitment and selection (GRS) has been emphasised as the important and probably the only practice to help the firm obtain green human capital directly. However, research has shown that not many firms implement GRS in practice, and there is a lack of studies exploring the barriers to GRS. This study fills this gap by examining the barriers to GRS across different industries in China. It adopted a qualitative and inductive approach, which is relatively rare in the GHRM field. Based on interviews with senior managers, a number of barriers were revealed in relation to the two components of GRS: paperless recruitment and green candidates. More specifically, two new factors that have not been reported in the existing literature were identified: the alignment between the job profile, green practices, and profit orientation. These two factors have both theoretical and practical implications. [ABSTRACT FROM AUTHOR]

With the great development of urban transportation systems, immediate urban traffic information has become an essential resource for the public. Traffic estimation is to predict current or future traffic situation (traffic speed and/or volume) in a road or a region of a city, and can benefit our daily life from many aspects, such as routing planning and traffic management. Existing works focus on estimating future traffic for individual road segments from a perspective of fine-grained level. This paper presents a new approach to estimating future traffic from a perspective of coarse-grained level, by which we estimate the traffic situation of a region, instead of an individual road segment. We propose a new concept about regional traffic named Ω -region, which aims to reflect the traffic situation of a region precisely. Two challenges in the regional traffic estimation problem are how to partition the road network into reasonable regions and how to estimate the regional traffic effectively. To address these challenges, first we define reasonable regions Ω -regions with traffic situations so that the all the road segment in the region has similar traffic. Then, we propose a three-phase partition method to divide the road network into Ω -regions based on historical trajectory data. Thirdly, we propose an effective linear-based model to estimate regional traffic. Experimental results on real-world dataset show that our proposed method achieves high performance., Author(s): Minghe Yu (corresponding author) [1]; Jianhua Feng [2]; Tianmiao Zhang [3]; Tiancheng Zhang [3] 1. Introduction With the great development of urban transportation systems, immediate urban traffic information has [...]

Background: Single‐cell sequencing (SCS) marks the advent of a transformative period in biomedical studies, enabling unprecedented insight into the cellular intricacies of health and disease. Methods: By dissecting the genetic, epigenetic and proteomic landscapes at the single‐cell level, SCS transcends traditional bulk sequencing methodologies, illuminating the heterogeneity and dynamics of individual cells. Results: This analytical leap facilitates a deeper understanding of disease mechanisms, offers novel diagnostic and therapeutic targets and underpins the development of precision medicine across diverse fields such as neurology, oncology and immunology. Conclusions: Despite its profound potential, SCS encounters challenges, including complex sample preparation, sophisticated data analysis and cost considerations. Nevertheless, ongoing advancements promise to overcome these barriers, integrating SCS with other omics data and leveraging machine learning to enhance biological understanding and clinical application. With the advancement of SCS technologies, personalised healthcare might be fundamentally altered, facilitating tailored and efficacious treatment strategies. [ABSTRACT FROM AUTHOR]

Background: The brain is a central key organ of the body containing the second highest lipid content only after adipose tissue. Lipids as the main structural components of biological membranes play important roles in a vast number of biological processes within the brain such as energy homeostasis, material transport, signal transduction, neurogenesis and synaptogenesis, providing a balanced cellular environment required for proper functioning of brain cells. Lipids and their metabolism are of great physiological importance in view of the crucial roles of lipids in brain development and function. Astrocytes are the most abundant glial cells in the brain and involved in various processes including metabolic homeostasis, blood brain barrier maintenance, neuronal support and crosstalk. Results: Disturbances in lipid metabolism and astrocytic functions may lead to pathological alterations associated with numerous neurological diseases like Alzheimer's disease (AD) recognised as the most frequent cause of dementia leading to major progressive memory and cognitive deficits as well as glioblastoma (GBM) known as the most aggressive malignant brain tumour with a poor prognosis. Conclusions: Herein, we not only review the level and role of altered lipid metabolism in correlation with astrocytic function and astrocyte‐neuron crosstalk in AD and GBM, but also discuss important lipid‐related metabolites and proteins participating in possible mechanisms of pathologically dysregulated lipid metabolism, offering potential therapeutic targets in targeted molecular therapies for AD and GBM. [ABSTRACT FROM AUTHOR]

Knowledge proficiency refers to the extent to which students master knowledge and reflects their cognitive status. To accurately assess knowledge proficiency, various pedagogical theories have emerged. Bloom's cognitive theory, proposed in 1956 as one of the classic theories, follows the cognitive progression from foundational to advanced levels, categorizing cognition into multiple tiers including "knowing", "understanding", and "application", thereby constructing a hierarchical cognitive structure. This theory is predominantly employed to frame the design of teaching objectives and guide the implementation of teaching activities. Additionally, due to the large number of students in real-world online education systems, the time required to calculate knowledge proficiency is significantly high and unacceptable. To ensure the applicability of this method in large-scale systems, there is a substantial demand for the design of a parallel prediction model to assess knowledge proficiency. The research in this paper is grounded in Bloom's Cognitive theory, and a Bloom Cognitive Diagnosis Parallel Model (BloomCDM) for calculating knowledge proficiency is designed based on this theory. The model is founded on the concept of matrix decomposition. In the theoretical modeling phase, hierarchical and inter-hierarchical assumptions are initially established, leading to the abstraction of the mathematical model. Subsequently, subject features are mapped onto the three-tier cognitive space of "knowing", "understanding", and "applying" to derive the posterior distribution of the target parameters. Upon determining the objective function of the model, both student and topic characteristic parameters are computed to ascertain students' knowledge proficiency. During the modeling process, in order to formalize the mathematical expressions of "understanding" and "application", the notions of "knowledge group" and "higher-order knowledge group" are introduced, along with a parallel method for identifying the structure of higher-order knowledge groups. Finally, the experiments in this paper validate that the model can accurately diagnose students' knowledge proficiency, affirming the scientific and meaningful integration of Bloom's cognitive hierarchy in knowledge proficiency assessment. [ABSTRACT FROM AUTHOR]

Learning patterns are crucial for predicting student dropout in educational settings, providing insights into students' behaviors and motivations. However, existing mainstream dropout prediction models have limitations in effectively mining these learning patterns and cannot mine these learning patterns in large-scale, distributed educational datasets. In this study, we analyze the representations of mainstream models and identify their inability to capture students' distinct learning patterns and personalized variations across courses. Addressing these challenges, our study adopts a federated learning approach, tailoring the analysis to leverage distributed data while maintaining privacy and decentralization. We introduce the Federated Learning Pattern Aware Dropout Prediction Model (FLPADPM), which utilizes a one-dimensional convolutional neural network (CNN) and a bidirectional long short-term memory (LSTM) layer within a federated learning framework. This model is designed to effectively capture nuanced learning patterns and adapt to variations across diverse educational settings. To evaluate the performance of LPADPM, we conduct an empirical evaluation using the KDD Cup 2015 and XuetangX datasets. Our results demonstrate that LPADPM outperforms state-of-the-art models in accurately predicting student dropout behavior. Furthermore, we visualize the representations generated by LPADPM, which confirm its ability to effectively mine learning patterns in different courses. Our results showcase the model's ability to capture and analyze learning patterns across various courses and institutions within a federated learning context. [ABSTRACT FROM AUTHOR]