Peng Wang

Background Ethical issues in nursing care for older adults in community setting are becoming more visible as population aging accelerates. However, existing studies are fragmented and lack a comprehensive synthesis to inform ethical practice and governance. Aims To identify major ethical issues, their impacts, and related strategies in nursing care for older adults in community setting, providing evidence for ethical practice, education, and policy development. Methods A scoping review was conducted following JBI methodology. PubMed, Web of Science, Scopus, CINAHL, Ovid-Embase, PhilPapers, and PsycINFO were searched up to 8 February 2026. Two reviewers independently screened, extracted, and synthesized data. Results Fourteen studies were included in this review. Evidence was synthesized at three levels including nursing staff, older adults, and the care system, and categorized into three domains: ethical challenges, ethical impacts, and improvement strategies. Ethical challenges mainly arise from three aspects: role ambiguity and competence limitations among nursing staff under contextual constraints, restrictions on older adults’ autonomy and opportunities to participate in decision-making, and structural conditions related to resource allocation and governance mechanisms. These ethical issues manifested as increased moral distress and emotional strain among nurses, diminished trust in nurses and reduced care adherence among older adults, and systemic pressures on the quality, equity, and sustainability of community nursing services. To address these challenges, the literature proposed strategies including strengthening ethics education and reflective practice, promoting supportive communication and shared decision-making, and improving governance structures and resource support. Conclusions Ethical issues in community nursing for older adults demonstrate multilevel characteristics embedded within practice contexts and institutional conditions. Strengthening ethical competence, promoting supportive communication and participatory decision-making, and advancing institutionalized ethical governance are essential for fostering more equitable and sustainable community nursing practice. Future research should expand geographical representation and further strengthen the evidence base for ethical governance in community nursing through high-quality empirical studies.

Due to the complexity of the network structure, log analysis is usually necessary for the maintenance of network-based distributed systems since logs record rich information about the system behaviors. In recent years, numerous works have been proposed for log analysis; however, they ignore temporal relationships between logs. In this paper, we target on the problem of mining informative patterns from temporal log data. We propose an approach to discover sequential patterns from event sequences with temporal regularities. Discovered patterns are useful for engineers to understand the behaviors of a network-based distributed system. To solve the well-known problem of pattern explosion, we resort to the minimum description length principle and take a step forward in summarizing the temporal relationships between adjacent events of a pattern. Experiments on real log datasets prove the efficiency and effectiveness of our method.

ObjectivesAimed to analyze the mediating effect of executive function between physical activity level and trait anxiety in college students.MethodsThe International Physical Activity Questionnaire, State-Trait Anxiety Inventory, Stroop task, 1-back task, and More-odd shifting task were used to analyze 248 college students.ResultsTrait anxiety were significantly correlated with shifting function (r = 0.182, P = 0.004) and inhibition function (r = 0.163, P = 0.010) and not with working memory (r = 0.056, P = 0.385). Vigorous physical activity (VPA) was most highly correlated with inhibition function (Beta = −0.144, P = 0.024) and working memory (Beta = −0.208, P = 0.001), and light physical activity (LPA) was most highly correlated with shifting function (Beta = −0.211, P = 0.001). Physical activity had a 72.31% association with trait anxiety (B = −0.195), with 11.79% mediated by inhibition function (B = −0.023) and 15.90% by shifting function (B = −0.031).ConclusionCollege students' physical activity promotes both inhibition and shifting functions, which in turn affect trait anxiety. VPA had a direct effect, while the effect of moderate physical activity (MPA) and LPA was completely mediated exclusively through executive functions, and the mediating effect of shifting function was the highest. It is recommended that college workers should motivate students with high trait anxiety to engage in more VPA and pay attention to changes in their inhibition and shifting functions.

Entrepreneurship is a sustainable development tool that supports the alleviation of poverty and unemployment. Focusing on the promotion of entrepreneurial intention under the background of entrepreneurship education, this study used a structural equation model to examine the role of entrepreneurship policy, entrepreneurial practice, and entrepreneurial spirit on the EI of 384 college students from 22 universities in Guangdong Province. The test results show that there are significant positive correlations between EPo and EI; EPo and EPr; EPo and ES; and EPr and EI. They also support the hypothesis that EPr enhances the positive effect that EPo has on EI. This study puts forth measures to improve EI and makes contributions to future research on EE.

Unethical research practices are prevalent in China, but little research has focused on the causes of these practices. Drawing on the criminology literature on organisational deviance, as well as the concept of cengceng jiama, which illustrates the increase of pressure in the process of policy implementation within a top-down bureaucratic hierarchy, this article develops an institutional analysis of research misconduct in Chinese universities. It examines both universities and the policy environment of Chinese universities as contexts for research misconduct. Specifically, this article focuses on China’s Double First-Class University Initiative and its impact on elite universities that respond to the policy by generating new incentive structures to promote research quality and productivity as well as granting faculties and departments greater flexibility in terms of setting high promotion criteria concerning research productivity. This generates enormous institutional tensions and strains, encouraging and sometimes even compelling individual researchers who wish to survive to decouple their daily research activities from ethical research norms. This article is written based on empirical data collected from three elite universities as well as a review of policy documents, universities’ internal documents, and news articles.

Value co-creation goes through the stage of co-production, customer experience, service-dominant logic, and service ecosystem. The integration of science and technology has become a key factor to the process of VCC. The rise and application of artificial intelligence technology has added a new driving force to VCC and began to affect its original practical logic. Based on the consumer perspective, this study uses Q-methodology to measure consumer cognitive attitude toward the use of AI technology in VCC, aiming to explore the key factors that affect VCC. The study found that content quality, information security, efficiency concern, and degree of manipulation have become the important concerns of consumers for VCC under AI integration. Moreover, their different statements have their specific focus and direction. The study demonstrates and analyzes the importance of the four factors and proposes the combination of human and non-human actors to shape the new model of VCC in the future, which is worth further deepening and exploring for academia and enterprises.

Precise global solar radiation data are indispensable to the design, planning, operation, and management of solar radiation utilization equipment. Some examples prove that the uncertainty of the prediction of solar radiation provides more value than deterministic ones in the management of power systems. This study appraises the potential of random forest, V-support vector regression, and a resilient backpropagation artificial neural network for daily global solar radiation point prediction from average relative humidity, daily average temperature, and daily sunshine duration. To acquire more accurate predictions of DGSR and examine the influence of historical DGSR on the performance of point prediction models, two different model inputs are considered: three meteorological variables and the lags of DGSR and three meteorological variables. Then, two interval prediction methods are developed by introducing the KDE to out-of-bag, introducing kernel density estimation to split conformal based on the three machine learning models. The two methods for interval prediction are denoted as OOB-KDE and SC-KDE. The mean absolute error, mean relative error, and Kendall rank correlation are used to assess the point prediction models. The performance of interval prediction methods is evaluated by the prediction interval coverage probability, prediction interval normalized average width, and coverage width criteria. The following conclusions are drawn from this study. First, the V-SVR model performs best with the lowest mean absolute error of 0.016 and mean relative error of 0.001. Second, the lags of DGSR improve the prediction accuracy by about 30%. Third, the OOB-KDE and SC-KDE methods improved the quality of the prediction interval. OOB-KDE improved CWC by 81%, and SC-KDE improved CWC by 99.99%. Fourth, the best interval prediction result is obtained using the SC-KDE method using the V-SVR model. The average difference between its PICP and prediction interval nominal coverage is only 3% of the PINC, and its PINAW is less than 0.007.

Scholars are the main force behind academic entrepreneurship. The method of how to stimulate scholars’ academic entrepreneurial intention and how to further promote social and economic development are important questions for the academic community. Research on the “net effect” of the factors affecting academic entrepreneurial intention has achieved some theoretical results. However, the results that affect academic entrepreneurial intention are complex and not influenced by a single factor, but rather by the interaction between various factors. Therefore, this study used a fuzzy set qualitative comparative analysis research method to explore how various factors can affect scholars’ academic entrepreneurial intention from two dimensions: the Big Five personality traits and academic entrepreneurial motivation. Our findings showed two configurations that affect high academic entrepreneurial intention of university scholars: the openness to experience—ribbon—dominant path, and the ribbon—dominant path. Additionally, two configurations were revealed for the formation of not-high academic entrepreneurial intention: extraversion—conscientiousness—inhibition and extraversion—agreeableness—gold—hindrance paths. Moreover, this study revealed that a causal asymmetry exists between the high and the not-high academic entrepreneurial intention configurations. This study broadens the application of the fuzzy set qualitative comparative analysis method in the research of academic entrepreneurial intention and provides theoretical and practical insights for researchers and practitioners on how to effectively stimulate scholars’ academic entrepreneurial intention.

This study aimed to explore the mediating effects of internet gaming disorder, social network use, and generalized pathological internet use on the association between loneliness and depression. A total of 2211 junior high school students completed questionnaires regarding loneliness, internet gaming disorder, social network use, GPIU, and depression. The results of a structural equation model revealed that the path coefficient of loneliness to depression was significantly positive, loneliness could not predict depression through GPIU directly, but loneliness could predict depression through internet gaming disorder to GPIU, loneliness could predict depression through social network use to GPIU, and loneliness could not predict depression through internet gaming disorder to social network use to GPIU. These results provided significant implications for the prevention and reduction of depression in Chinese junior high school students.

Efficient knowledge sharing is an important support for the continuous innovation and sustainable development of scientific research teams. However, in realistic management situations, the knowledge sharing of scientific research teams always appears to be unsustainable, and the reasons for this are the subject of considerable debate. In this study, an attempt was made to explore the interactive mechanism of knowledge hiding behaviors in scientific research teams between individual and collective knowledge hiding behaviors and its impact on knowledge sharing by adopting grounded theory to comprehensively understand this situation. The results show that knowledge hiding behavior in the scientific research team is a two-phase interactive process and is capable of affecting sustainable knowledge sharing by reducing the supply of knowledge, creating a poor knowledge sharing atmosphere, and forming an interpersonal distrust relationship. This research may provide a strong basis for a deeper understanding of the interaction mechanism of knowledge hiding behavior and its impact on knowledge sharing.