This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and aims to explore early detection and disease progression in Alzheimer's disease (AD). We employ innovative data preprocessing strategies, including the use of the random forest algorithm to fill missing data and the handling of outliers and invalid data, thereby fully mining and utilizing these limited data resources. Through Spearman correlation coefficient analysis, we identify some features strongly correlated with AD diagnosis. We build and test three machine learning models using these features: random forest, XGBoost, and support vector machine (SVM). Among them, the XGBoost model performs the best in terms of diagnostic performance, achieving an accuracy of 91%. Overall, this study successfully overcomes the challenge of missing data and provides valuable insights into early detection of Alzheimer's disease, demonstrating its unique research value and practical significance.

In this study, considering the delays for a susceptible individual becoming an alcoholic and the relapse of a recovered individual back into being an alcoholic, we formulate an epidemic model for alcoholism with distributed delays and relapse. The basic reproduction number $R_0$ is calculated, and the threshold property of $R_0$ is established. By analyzing the characteristic equation, we demonstrate the local asymptotic stability of the different equilibria under various conditions: when $R_0<1$, the alcoholism-free equilibrium is locally asymptotically stable; when $R_0>1$, the alcoholism equilibrium exists and is locally asymptotically stable. Furthermore, we demonstrate the global asymptotic stability at each equilibrium using a suitable Lyapunov function. Specifically, when $R_0<1$, the alcoholism-free equilibrium is globally asymptotically stable; when $R_0>1$, the alcoholism equilibrium is globally asymptotically stable. The sensitivity analysis of $R_0$ shows that reducing exposure is more effective than treatment in controlling alcoholism. Interestingly, we found that extending the latency delay $h_1$ and relapse delay $h_2$ also effectively contribute to the control of the spread of alcoholism. Numerical simulations are also provided to support our theoretical results.

This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and aims to explore early detection and disease progression in Alzheimer's disease (AD). We employ innovative data preprocessing strategies, including the use of the random forest algorithm to fill missing data and the handling of outliers and invalid data, thereby fully mining and utilizing these limited data resources. Through Spearman correlation coefficient analysis, we identify some features strongly correlated with AD diagnosis. We build and test three machine learning models using these features: random forest, XGBoost, and support vector machine (SVM). Among them, the XGBoost model performs the best in terms of diagnostic performance, achieving an accuracy of 91%. Overall, this study successfully overcomes the challenge of missing data and provides valuable insights into early detection of Alzheimer's disease, demonstrating its unique research value and practical significance.

On August 24, 2023, Japan controversially decided to discharge nuclear wastewater from the Fukushima Daiichi Nuclear Power Plant into the ocean, initiating intense domestic and global debates. This study employs a mixed-method approach, integrating quantitative evolutionary game theory and qualitative data analysis to explore the strategic dynamics among Japan, other nations, and the Japan Fisheries Association regarding this decision. The data includes international environmental reports and economic statistics, served as the basis for simulating decision-making processes under various legal, economic, and environmental pressures. The evolutionary game theory model is used to predict and analyze three evolutionarily stable strategies (ESS), detailing the transition from the initiation to cessation of wastewater discharge. These strategies highlight the necessity for international cooperation, rigorous scientific research, public education, and effective wastewater treatment methods. This study aims to provide both a theoretical framework and practical guidance to foster a global consensus on nuclear wastewater management, which is vital for marine conservation and sustainable development.

On August 24, 2023, Japan commenced the ocean discharge of treated nuclear-contaminated water from the Fukushima Daiichi Nuclear Power Plant, sparking widespread international concern. This study develops a tripartite evolutionary game model incorporating bounded rationality and time-delay effects to analyze strategic interactions among Japan, other countries, and the International Atomic Energy Agency (IAEA). Through the construction of payoff matrices and replicator dynamic equations with delay terms, the model identifies three evolutionarily stable strategies \(\gamma _4(0,0,1)\), \(\gamma _6(1,0,1)\), and \(\gamma _8(1,1,1)\) under specific cost and benefit conditions. Numerical simulations reveal that time delays significantly influence convergence speed and trajectory complexity, particularly for Japan and the IAEA. Parametric analyses show that Japan’s decision to discharge is sensitive to storage costs (\(C_{SJ}\)), discharge costs (\(C_{DJ}\)), export tax revenue loss (\(T_{RJ}\)), and reputational damage (\(I_J\)), while other countries’ responses are shaped by litigation compensation (\(C_{LC}\)) and aid to Japan (\(C_{HJ}\)). The model also aligns with real-world developments such as China’s conditional partial lifting of its seafood import ban in June 2025, exemplifying the stabilizing role of credible international oversight and transparency. This research contributes a novel delayed-replication framework to evolutionary game theory and offers actionable insights for managing transboundary environmental conflicts.

This study investigates the potential impact of "LK-99," a novel material developed by a Korean research team, on the power equipment industry. Using evolutionary game theory, the interactions between governmental subsidies and technology adoption by power companies are modeled. A key innovation of this research is the introduction of sensitivity analyses concerning time delays and initial subsidy amounts, which significantly influence the strategic decisions of both government and corporate entities. The findings indicate that these factors are critical in determining the rate of technology adoption and the efficiency of the market as a whole. Due to existing data limitations, the study offers a broad overview of likely trends and recommends the inclusion of real-world data for more precise modeling once the material demonstrates room-temperature superconducting characteristics. The research contributes foundational insights valuable for future policy design and has significant implications for advancing the understanding of technology adoption and market dynamics.