Quantifying heat health risks by integrating high-temperature hazards with demographic, economic and environmental components is critical to understanding the comprehensive impacts of urban thermal environments. The objective is to develop a fine-scale methodology for assessing heat hazard, exposure, and vulnerability in urban areas, accounting for both daytime and nighttime conditions, to inform more targeted and effective heat mitigation strategies. We propose a novel framework that combines multiple high-resolution data sources to assess heat health risks at the sub-district level. The framework combines NASA ECOSTRESS land surface temperature data, mobile signaling for population mobility, and 3D urban morphology from street view images via deep learning. The Heat Health Risk Index quantifies heat exposure, accounting for temporal and spatial urban variations. Key findings include: (1) Distinct diurnal migration patterns of heat hazards were observed, shifting from northeastern areas during nighttime to southwestern regions during the daytime; (2) Population and environmental exposure analysis showed higher daytime levels in southern and eastern areas between the 4th and 5th ring roads due to commuter influx, with elevated nighttime risks persisting in densely populated regions; (3) Principal component analysis identified four key vulnerability factors of urban built-up, economic development, education level, and disadvantaged groups. High risks in northeast Fengtai and correlation analysis emphasized the critical role of socioeconomic and environmental factors in mitigating heat risks. This study offers a fine-scale approach to assess heat health risks, providing key insights for urban planning and climate adaptation to mitigate heat-related health impacts.查看详情>>
粮食安全对国家稳定和社会福祉至关重要。随着气候变化的不断加剧，极端天气事件对粮食生产的影响日益严重，给粮食供应带来了巨大挑战。因此，深入研究极端气候对粮食生产的具体影响，并探索有效的应对策略，具有重要的现实意义。基于理论分析，研究利用2007—2020年中国296个城市的面板数据，实证探讨了极端气候对粮食生产保障的影响，并分析了农地流转在其中的调节机制与门槛效应。研究发现：（1）在全国样本中，极端高温、极端低温和极端降水三大极端气候因子均对粮食生产产生显著的负向影响。其中，极端高温和极端低温在1%的显著性水平上对粮食生产产生负向影响，极端降水在5%的显著性水平上对粮食生产产生负向影响。（2）从不同粮食生产区域来看，极端气候对粮食主产区的粮食生产负向影响最大；在产销平衡区，极端高温显著抑制了粮食生产；而在主销区，极端气候对粮食生产的影响并不显著。（3）农地流转能显著调节极端降水对粮食生产的影响，但在调节极端高温和极端低温对粮食生产的影响方面并不显著，尤其是对粮食生产至关重要的粮食主产区与产销平衡区。（4）极端气候对粮食生产的影响存在单一门槛效应。当农地流转水平跨过门槛值10.8905后，极端气候对粮食生产的影响不再显著。因此，为保障粮食生产安全，提升应对极端气候的能力，应积极贯彻落实“藏粮于地”方针，通过农地流转优化种植结构，实现规模经济，从而提高农业生产效率。查看详情>>
Understanding the spatiotemporal dynamics of industrial land is crucial for sustainable land use and industrial transformation. However, data and methodological limitations hinder large-scale, long-term studies of industrial land from a high-granularity, multi-dimensional perspective. This study develops a methodological framework using multi-source data to identify industrial land parcels, enabling a systematic analysis of both external attributes (including quantity scale and spatial pattern) and internal attributes (including functional structure and utilization intensity). The validity of the framework is demonstrated through a case study of the Southern Jiangsu Urban Agglomeration in China from 1990 to 2020. The overall accuracy of industrial land identification reaches 94.70 %, with detailed representations of parcel morphology. Based on this data, the study reveals the nonlinearity of industrial land evolution, as well as the interrelationships and asynchrony among various attributes. Spatial patterns at the parcel level are uncovered, including four types of spatial evolution. Finally, the study identifies potential dimensions and areas for industrial land upgrading and provides specific recommendations for addressing compatibility between different attributes in planning and correcting land misallocation in land supply. The study contributes a new analytical method for advancing industrial land theory and offers support for refined management practices.查看详情>>
Background: Large language model (LLM) artificial intelligence (AI) tools have the potential to streamline health care administration by enhancing efficiency in document drafting, resource allocation, and communication tasks. Despite this potential, the adoption of such tools among hospital administrators remains understudied, particularly at the individual level. Objective: This study aims to explore factors influencing the adoption and use of LLM AI tools among hospital administrators in China, focusing on enablers, barriers, and practical applications in daily administrative tasks. Methods: A multicenter, cross-sectional, descriptive qualitative design was used. Data were collected through semistructured face-to-face interviews with 31 hospital administrators across 3 tertiary hospitals in Beijing, Shenzhen, and Chengdu from June 2024 to August 2024. The Colaizzi method was used for thematic analysis to identify patterns in participants' experiences and Results: Adoption of LLM AI tools was generally low, with significant site-specific variations. Participants with higher technological familiarity and positive early experiences reported more frequent use, while barriers such as mistrust in tool accuracy, limited prompting skills, and insufficient training hindered broader adoption. Tools were primarily used for document drafting, with limited exploration of advanced functionalities. Participants strongly emphasized the need for structured training programs Conclusions: Familiarity with technology, positive early experiences, and openness to innovation may facilitate adoption, while barriers such as limited knowledge, mistrust in tool accuracy, and insufficient prompting skills can hinder broader use. LLM AI tools are now primarily used for basic tasks such as document drafting, with limited application to more advanced functionalities due to a lack of training and confidence. Structured tutorials and institutional support are needed to enhance usability and integration. Targeted training programs, combined with organizational strategies to build trust and improve accessibility, could enhance adoption rates and broaden tool use. Future quantitative investigations should validate the adoption rate and influencing factors.查看详情>>
基层形式主义，是指在政策执行中强调表面形式，忽略工作的实际效果。当前，政府和学界关注的形式主义，主要发生在基层政府的日常活动或一般程序中，如会议、文件、督导检查等。但是，各地的田野调查显示，还有一种普遍发生在政府体制内部运行中的形式主义，其目的是应对考核中有明确数字或数量要求的指标任务，也就是“数据形式主义”。“数据形式主义”的主要表现是，为达到某一量化指标，基层政府通过各种非常规方法，从表面或账面上实现了“数据达标”,但并未产生任何实质性效果。同时，还耗费了政府资源，增加了基层负担，损害了政府的公信力，甚至可能影响上级的政策制定。要遏制“数据形式主义”的蔓延，关键在于厘清基层政府的职责范围和综合运用多样化的考核方法，避免考核结果的“唯数据论”。查看详情>>
基于收集和预处理的22份国家层面政策文本，运用文本挖掘技术提取高频词并构建关键词共现网络，结合PMC指数模型，分析数字医疗政策的主要内容和关键要素，以期从政策发展视角探究我国数字医疗政策的内部一致性及演进特征。研究发现：我国数字医疗政策在整体上保持较强连续性和较高质量，但仍需要进一步优化；不同政策类别在PMC指数上差异揭示了顶层设计、实施路径和执行工具上的不足，需要更高水平的协调与联动；政策质量随时间呈现先升后降的特点，体现在政府统筹的间断性、政策工具系统性不足、治理观念转变不完全性；政策演进呈现从“管理”到“服务”再到“治理”的变化，演进过程政策一致性较为平衡。基于结论，建议优化政策工具组合，增强政策适应性与执行力，加强环境型和需求型工具的综合运用；构建跨部门协同的政策框架，确保政策协调与联动；推动治理导向的政策框架，促进多方协同合作；提升政策时效性与关注度，增强政策的前瞻性与适应性。查看详情>>
