Coal demand,market forces,and U.S. coal mine closures

Economic transitions have the potential to displace workers and cause social unrest. Coal mine closures and the resulting employment losses in rural areas have become salient issues. Using data on coal mine and power plant operation, we model closure as a function of expected profits, which allows us to compare the effects on mine closure of specific demand and supply shocks to expected mine profits. Increasing costs of production have had a large impact on closures, but lower natural gas prices and lower electricity demand have played more recently important roles.     

地方政府融入“双循环”新发展格局的探索与实践——以涪陵区为例

构建“双循环”新发展格局是党中央基于“两个大局”，着眼于我国进入新发展阶段而作出的重大战略部署。三重逻辑是“双循环”新发展格局的生成逻辑，即历史逻辑是其形成的必然性，理论逻辑是其形成的合理性，实践逻辑是其形成的可行性。涪陵区作为西部地区一个重要节点城市，要深度融入“双循环”新发展格局，就要找准涪陵区在全国构建新发展格局中的位置，分析其优势和短板，找准结合点和着力点。即：一是要发挥自身优势，将制造业作为提升融入“双循环”的关键着力点；二是要抓发展机遇，以成渝双城经济圈建设为契机融入“双循环”的关键节点；三是要优化功能布局，以“一城三区两带”协调发展作为融入“双循环”的主要动力源。     

Explainable deep learning powered building risk assessment model for proactive hurricane response

Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities’ preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building-level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine-grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model-agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep-learning models in integrating multi-sourced features and accurately predicting buildings’ risks of weather extremes for property owners and households. The AI-powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long-term climate adaptation.     

实现人工智能与高校思想政治教育融合发展的创新思考

在"万物互联"的"后机器学习"时代,人工智能以增强人类认识与改造客观世界能力的生命样态而存在,搭建起了主流意识形态直接起作用的媒体环境.新时代高校思想政治教育的转型创新,必然要主动契合这一智慧生态语境的现实变化,全面剖析人工智能融入教育生态的合理性,理性研判拟人智慧融入日常生活场景和思想教育活动的时代意义与技术困境;高度重视"思想政治教育+"话语生态的智慧化发展趋势,充分观照人工智能生态与思想政治教育深度融合所体现的学理意味,科学重构传统思想政治教育的灌输场景、主体关系、传播层次与伦理关怀,确保实现马克思主义理论育人与"智慧育人"科学性和时代性的协调统一.