Based on the observation of the geochemical characteristics of 19 hot springs in the Litang Fault Zone (LFZ) from 2010 to 2019, the major elements, trace elements, and stable isotopes were investigated, and a conceptual model of ground fluid circulation in the LFZ was established. The main hydrochemical type of hot spring water samples is HCO₃⁻-Na⁺. The δ²H values range from −157.6‰ to −123.4‰ and δ¹⁸O values range from −24.5‰ to −15.4‰. The hot spring water in the Litang fault zone is mainly recharged by infiltrating precipitation, with a recharge elevation of 4062~6018 m. Hydrochemical types of Litang hot springs are mainly controlled by the circulation of groundwater in a deep fault system, and are related to the rock lithology of thermal reservoir and water–rock reaction areas. Hot springs in the Litang fault zone attribute to three different heat sources, belonging to three geothermal systems. The flow direction of groundwater in the LFZ is roughly from northwest to southeast along the Litang fault. The deeper the circulation depth of hot spring water on the fault, the higher the thermal reservoir temperature and the stronger the seismic activity of the segment, which is closely related to the increase in pore fluid pressure, rock weakening, and deep fluid upwelling. This study is helpful for further study on regional hydrogeological environments and provides a scientific basis for revealing geothermal fluid movement in fault zones.

With the extensive use of digital signage, precise site selection is an urgent issue for digital signage enterprises and management agencies. This research aims to provide an accurate digital signage site-selection model that integrates the spatial characteristics of geographical location and multisource factor data and combines empirical location models with machine learning methods to recommend locations for digital signage. The outdoor commercial digital signage within the Sixth Ring Road area in Beijing was selected as an example and was combined with population census, average house prices, social network check-in data, the centrality of traffic networks, and point of interest (POI) facilities data as research data. The data were divided into 100–1000 m grids for digital signage site-selection modelling. The empirical approach of the improved Huff model was used to calculate the spatial accessibility of digital signage, and machine learning approaches such as back propagation neural network (BP neural networks) were used to calculate the potential location of digital signage. The site of digital signage to be deployed was obtained by overlay analysis. The result shows that the proposed method has a higher true positive rate and a lower false positive rate than the other three site selection models, which indicates that this method has higher accuracy for site selection. The site results show that areas suitable for digital signage are mainly distributed in Sanlitun, Wangfujing, Financial Street, Beijing West Railway Station, and along the main road network within the Sixth Ring Road. The research provides a reference for integrating geographical features and content data into the site-selection algorithm. It can effectively improve the accuracy and scientific nature of digital signage layouts and the efficiency of digital signage to a certain extent.

Extreme rainfall events are significant manifestations of climate change, causing substantial impacts on urban infrastructure and public life. This study takes the extreme rainfall event in Beijing in 2023 as the background and utilizes data from Sina Weibo. Based on large language models and prompt engineering, disaster information is extracted, and a multi-factor coupled disaster multi-sentiment classification model, Bert-BiLSTM, is designed. A disaster analysis framework focusing on three dimensions of theme, location and sentiment is constructed. The results indicate that during the pre-disaster stage, themes are concentrated on warnings and prevention, shifting to specific events and rescue actions during the disaster, and post-disaster, they express gratitude to rescue personnel and highlight social cohesion. In terms of spatial location, the disaster shows significant clustering, predominantly occurring in Mentougou and Fangshan. There is a clear difference in emotional expression between official media and the public; official media primarily focuses on neutral reporting and fact dissemination, while public sentiment is even richer. At the same time, there are also variations in sentiment expressions across different affected regions. This study provides new perspectives and methods for analyzing extreme rainfall events on social media by revealing the evolution of disaster themes, the spatial distribution of disasters, and the temporal and spatial changes in sentiment. These insights can support risk assessment, resource allocation, and public opinion guidance in disaster emergency management, thereby enhancing the precision and effectiveness of disaster response strategies.

为了评估弥勒—富源断裂带不同段落的地震风险，明确未来滇东地区地震重点监测区域，探讨温泉流体地球化学特征及其形成原因，对2016年—2020年期间，在滇东地区采集的6个温泉水化样品和3个气体样品进行主量元素、微量元素、氢氧同位素、二氧化硅浓度和锶同位素含量的测试分析，结果表明：（1）弥勒—富源断裂带温泉水的补给来源以大气降水为主，温泉的补给高程约为2167~2383 m。（2）通过SiO2温标计算得到的弥勒—富源断裂带温泉水循环深度为552~828 m，硅焓模型计算的结果为929~1089 m，与川滇菱形块体东边界断裂带上的温泉相比，循环深度相对较浅。（3）弥勒—富源断裂带温泉气体主要为壳源，受幔源成分影响较小。断裂带的南端弥勒地区地震相对频繁，中小地震较多，与地下水作用造成岩石弱化，断裂强度相对较小有关。

Based on Weibo big data, BERTopic, and dual-channel sentiment analysis model, a dynamic analysis framework of public perception and emotion evolution is constructed from the perspective of disaster chain and public response. The results show that (1) Due to the trust of information sources by the public, the efficiency of early warning information reaching the public and attracting attention is relatively high. Social media activity on related topics peaked several times in response to reports of major hazards, such as railway suspensions, passengers trapped in trains, and severe flooding in Miyun District, Beijing. (2) The evolution of topics of public attention strongly corresponds to the disaster process: From early warning and emergency risk avoidance, gradually move to disaster report and rescue coordination, and finally focus on the criticism of infrastructure vulnerability. (3) The emotional response presents phased characteristics. At the initial stage of the rainstorm red warning issued by the meteorological department, anxiety is dominant; after the release of rescue information, emotion rises briefly, and reflection and attribution tendencies generally appear after the disaster. (4) Elderly populations and those in remote areas exhibit characteristics of vulnerability, information isolation, and high dependency in response to disasters.