江苏省苏州中学

Loneliness and social isolation have been a rising public health concern among older adults in recent years, especially after the COVID-19 pandemic. However, how the distinct pathways affect health and the effectiveness of existing interventions still need clearer review. By collecting and comparing data from large-scale studies and meta-analyses, the review systematically analyzes different impacts of loneliness and social isolation on cognitive function, biological health, frailty and mortality. In addition, it evaluates the effectiveness of technology-based and social interventions through systematic reviews and experiments on older adults' reflections to interventions. The results show that social isolation is a more robust predictor of dementia and mortality, while loneliness is more strongly connected to cognitive decline and frailty progression. The COVID-19 pandemic significantly intensified both conditions, which the prevalence rates remain above the pre-pandemic level. For intervention strategies, both technology-based and social intervention show positive effects on reducing loneliness and social isolation, but the evidence is limited due to the heterogeneity and methodological weaknesses. Specifically, information and communication technology (ICT) and videoconferencing demonstrate the most significant effect among technology intervention, while structured group-based intervention is more effective than other social interventions. Though most research show significant results in their studies, there are limitations in standardized measurements. The need for consistent outcome measurement across studies can improve the research validity, while with larger-scale randomized controlled trials can increase the reliability of studies. At the same time, the results of intervention effectiveness help build efficient policy interventions.

RNA viruses are very dangerous to global health. Because of their tendency to either undergo high rates of mutation or quickly evolve as the case of influenza, COVID-19, and Ebola depicts. Their etiology is not only the solution to a core mystery in the life sciences, but it also applies to prevention of the disease by viruses and to drug development. This paper compares three classical origin theories, including the Escape Hypothesis (fragments escape from cellular genetic material), the Regressive Hypothesis (degeneration of complex parasitic cells) and the Coevolution Hypothesis. By analyzing empirical data, logical and limitation of both theories, the paper shows the pluralistic nature of the origin of viruses: retroviruses have the Escape Hypothesis, giant viruses have the Regressive Hypothesis, and the majority of RNA viruses have the Coevolution Hypothesis. The Coevolution Hypothesis, which is backed by a set of Endogenous Viral Elements (EVEs) in the genomes of its hosts and a strong level of conservation of replicase genes in lineages of ancient RNA viruses, is the most clear umbrella explanatory force on the overall diversity of RNA viruses. The study contributes to the existing body of knowledge on viral evolution by confirming the complementary character of the three hypotheses. It offers theory foundations to antiviral guidelines. They are the development of broad-spectrum antiviral drugs against conserved viral replicase proteins, and predicting the risks of zoonotic infections depending on lineage evolution.

This dissertation investigates and evaluates the accuracy and potential of data mining techniques for identifying traffic accident hotspots, which is a fundamental step of road accident analysis, identifying the accident-prone regions to bolster further accident research, and the proposal of prevention methods. In the discussion, a hotspot analysis based on data mining techniques is conducted to research the accidents that occurred in Birmingham, UK, in 2017. The identification result is compared with that of Kernel Density Estimation (KDE), a classical identification method, to assess the ability and accuracy of data mining’s identification. The finding underscores that data mining is a potentially effective method to identify the accident hotspot with an accurate estimation.

With the rapid growth of the aging population in the world, elderly care systems are facing increasing pressure and challenges in healthcare services, daily assistance, and emotional support. Artificial intelligence (AI) has been widely integrated into elderly care robots to enhance autonomy, perception, interaction, and decision-making capabilities. This paper reviews recent advances in AI for elderly care robots, focusing on perception, natural language processing, machine learning, affective computing, and decision-making. In addition, it examines key studies, compares primary technical approaches, and identifies key challenges, including privacy risks, ethical issues, system reliability, and human-robot trust. Based on recent studies, this study explains how AI improves elderly care quality and identifies gaps in real-world deployment and long-term performance. The results reveal that AI improves personalization and autonomy, while major challenges remain in safety validation, ethical governance, and multimodal interaction performance, which need to be addressed to enable scalable and sustainable elderly care solutions.

For the whole world, poverty problem has persisted for a long time. To study the impact of tourism polices on poverty alleviation, this dissertation employs a literature review and case analysis approach. On the basis of reviewing relevant research literature, choosing Guizhou province in China as a case study, conducting in-depth study. The result revealed that national tourism poverty alleviation policy for Guizhou province effectively reduced the gap between urban and rural disposable income in Guizhou province. For urban residents, tourism policies have significant improvement in net income from individual operations and net property income growth. For rural residents, tourism policies have improvement in per capita disposable income of permanent rural residents, wage income, net business income, net property income and net transfer income. Baiyun District has the smallest rural-urban gap. However, because poverty alleviation is the result of multidimensional and multifaceted collaborative efforts, the promulgation of tourism policies may be just one of the factors. In addition to this, factors such as relevant economic policies and technological advancements also exert an influence. As a result, poverty alleviation efforts should be tailored to local conditions and areas with different geographical conditions should employ different approaches.