This critical review looks at the assessment of the application of artificial intelligence in handling legal documents with specific reference to medical negligence cases with a view of identifying its transformative ...This critical review looks at the assessment of the application of artificial intelligence in handling legal documents with specific reference to medical negligence cases with a view of identifying its transformative potentialities, issues and ethical concerns. The review consolidates findings that show the impact of AI in improving the efficiency, accuracy and justice delivery in the legal profession. The studies show increased efficiency in speed of document review and enhancement of the accuracy of the reviewed documents, with time efficiency estimates of 60% reduction of time. However, the review also outlines some of the problems that continue to characterize AI, such as data quality problems, biased algorithms and the problem of the opaque decision-making system. This paper assesses ethical issues related to patient autonomy, justice and non-malignant suffering, with particular focus on patient privacy and fair process, and on potential unfairness to patients. This paper’s review of AI innovations finds that regulations lag behind AI developments, leading to unsettled issues regarding legal responsibility for AI and user control over AI-generated results and findings in legal proceedings. Some of the future avenues that are presented in the study are the future of XAI for legal purposes, utilizing federated learning for resolving privacy issues, and the need to foster adaptive regulation. Finally, the review advocates for Legal Subject Matter Experts to collaborate with legal informatics experts, ethicists, and policy makers to develop the best solutions to implement AI in medical negligence claims. It reasons that there is great potential for AI to have a deep impact on the practice of law but when done, it must do so in a way that respects justice and on the Rights of Individuals.展开更多
Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel...Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.展开更多
介绍了“地球系统与全球变化”重点专项项目“中国极端天气气候事件的形成机理及其预测和归因”2025年度的主要成果。1)发展了群发性极端温度事件的检测识别方法并构建了数据集,揭示了群发性极端温度事件、暖季极端高温-降水复合事件的...介绍了“地球系统与全球变化”重点专项项目“中国极端天气气候事件的形成机理及其预测和归因”2025年度的主要成果。1)发展了群发性极端温度事件的检测识别方法并构建了数据集,揭示了群发性极端温度事件、暖季极端高温-降水复合事件的变化特征及北美-东亚冬季极端低温的空间复合特征,并开展了极端温度变化的归因研究。2)阐明了东亚冬季气温反相事件、2022年夏季长江流域极端高温等典型极端事件的环流特征及动力学机理。3)提出了MJO(Madden-Julian Oscillation)遥相关的动力学新机制,发现夏季MJO在印度洋停留时间3倍增长并加剧了极端气候事件风险;揭示了印度洋快速增暖、春季重新发展增强的La Ni a对中国极端气候的影响,探讨了不同海盆海温异常对夏季高温干旱复合事件的影响,发现华北秋季群发性极端降水增强与关键区北极海冰减少存在密切联系。4)探讨了陆面蒸散发与干旱变化机理、高温干旱复合极端事件的形成机理、积雪与土壤湿度的气候反馈效应以及陆气耦合对极端气候和大尺度环流的影响。5)建立了干旱、极端高温、暴雨-热浪复合极端事件、极端低温次季节-年际预测的物理统计预测模型,发展了极端温度次季节反转的预测方法,在一定程度上改善了中国极端天气气候事件的预测水平。展开更多
文摘This critical review looks at the assessment of the application of artificial intelligence in handling legal documents with specific reference to medical negligence cases with a view of identifying its transformative potentialities, issues and ethical concerns. The review consolidates findings that show the impact of AI in improving the efficiency, accuracy and justice delivery in the legal profession. The studies show increased efficiency in speed of document review and enhancement of the accuracy of the reviewed documents, with time efficiency estimates of 60% reduction of time. However, the review also outlines some of the problems that continue to characterize AI, such as data quality problems, biased algorithms and the problem of the opaque decision-making system. This paper assesses ethical issues related to patient autonomy, justice and non-malignant suffering, with particular focus on patient privacy and fair process, and on potential unfairness to patients. This paper’s review of AI innovations finds that regulations lag behind AI developments, leading to unsettled issues regarding legal responsibility for AI and user control over AI-generated results and findings in legal proceedings. Some of the future avenues that are presented in the study are the future of XAI for legal purposes, utilizing federated learning for resolving privacy issues, and the need to foster adaptive regulation. Finally, the review advocates for Legal Subject Matter Experts to collaborate with legal informatics experts, ethicists, and policy makers to develop the best solutions to implement AI in medical negligence claims. It reasons that there is great potential for AI to have a deep impact on the practice of law but when done, it must do so in a way that respects justice and on the Rights of Individuals.
文摘Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.
文摘介绍了“地球系统与全球变化”重点专项项目“中国极端天气气候事件的形成机理及其预测和归因”2025年度的主要成果。1)发展了群发性极端温度事件的检测识别方法并构建了数据集,揭示了群发性极端温度事件、暖季极端高温-降水复合事件的变化特征及北美-东亚冬季极端低温的空间复合特征,并开展了极端温度变化的归因研究。2)阐明了东亚冬季气温反相事件、2022年夏季长江流域极端高温等典型极端事件的环流特征及动力学机理。3)提出了MJO(Madden-Julian Oscillation)遥相关的动力学新机制,发现夏季MJO在印度洋停留时间3倍增长并加剧了极端气候事件风险;揭示了印度洋快速增暖、春季重新发展增强的La Ni a对中国极端气候的影响,探讨了不同海盆海温异常对夏季高温干旱复合事件的影响,发现华北秋季群发性极端降水增强与关键区北极海冰减少存在密切联系。4)探讨了陆面蒸散发与干旱变化机理、高温干旱复合极端事件的形成机理、积雪与土壤湿度的气候反馈效应以及陆气耦合对极端气候和大尺度环流的影响。5)建立了干旱、极端高温、暴雨-热浪复合极端事件、极端低温次季节-年际预测的物理统计预测模型,发展了极端温度次季节反转的预测方法,在一定程度上改善了中国极端天气气候事件的预测水平。
