Zejun Wang

Spontaneous imbibition is an important process in enhanced oil recovery, yet its accurate prediction in nanoscale pores remains a significant challenge due to the breakdown of continuum assumptions at the nanoscale. The classical Lucas-Washburn (L-W) equation, which suggests that imbibition length is proportional to the square root of time, fails to capture the complex early-time dynamics in nanopores. Therefore, in this work, coarse-grained molecular dynamics simulations were employed to systematically investigate two-phase imbibition across a range of pore sizes (1.85–7 nm), fluid types (C4–C20 alkanes), and wettability conditions. The results reveal a distinct transition from continuum-dominated to molecularly dominated flow as pore size decreases, with anomalous kinetics that originate from molecular-scale interactions in sub-2-nm pores. A refined imbibition model is proposed that explicitly incorporates an entrance resistance term in addition to the capillary and viscous forces. This new model successfully resolves the overestimated initial velocity by the L-W equation, revealing a dual-regime physics, including an early-time, almost linear regime influenced by entrance resistance, followed by the classical late-time L-W regime. The model demonstrates excellent agreement with molecular dynamics data for mesoscopic nanopores (r ≥ 3.5 nm), validating its physical basis. Although the proposed model is still not predictive due to the implicit inclusion of imbibition rate in the derivision, the findings provide a robust theoretical foundation for describing imbibition in nanopores and highlight the critical need for molecular-scale approaches in extreme nanoconfinement.

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.

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.

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.

This academic paper examines the strategic interactions between Japan, other nations, and the International Atomic Energy Agency (IAEA) regarding Japan's decision to release treated nuclear wastewater from the Fukushima Daiichi Nuclear Power Plant into the sea. It introduces a payoff matrix and time-delay elements in replicator dynamic equations to mirror real-world decision-making delays. The paper analyzes the stability of strategies and conditions for different stable states using characteristic roots of a linearized system and numerical simulations. It concludes that time delays significantly affect decision-making stability and evolution trajectories in nuclear wastewater disposal strategies. The study highlights the importance of efficient wastewater treatment technology, the impact of export tax revenue losses on Japan's strategies, and the role of international cooperation. The novelty of the research lies in integrating time-delay elements from ocean dynamics and governmental decision-making into the game-theoretical model.

On August 24, 2023, Japan controversially decided to discharge nuclear wastewater from the Fukushima Daiichi Nuclear Power Plant into the ocean, sparking intense domestic and global debates. This study uses evolutionary game theory to analyze the strategic dynamics between Japan, other countries, and the Japan Fisheries Association. By incorporating economic, legal, international aid, and environmental factors, the research identifies three evolutionarily stable strategies, analyzing them via numerical simulations. The focus is on Japan's shift from wastewater release to its cessation, exploring the myriad factors influencing this transition and their effects on stakeholders' decisions. Key insights highlight the need for international cooperation, rigorous scientific research, public education, and effective wastewater treatment methods. Offering both a fresh theoretical perspective and practical guidance, this study aims to foster global consensus on nuclear wastewater management, crucial for marine conservation and sustainable development.

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.