Different types of buildings in different climate zones have their own design specifications and specific user populations. Generally speaking, these populations have similar sensory feedbacks in their perception of environmental thermal comfort. Existing thermal comfort models do not incorporate personal thermal comfort models for specific populations. In terms of an algorithm, the existing work constructs machine learning models based on an established human thermal comfort database with variables such as indoor temperature, clothing insulation, et al., and has achieved satisfactory classification results. More importantly, such thermal comfort models often lack scientific interpretability. Therefore, this study selected a specific population as the research object, adopted the 0-order Takagi–Sugeno–Kang (TSK) fuzzy classifier as the base training unit, and constructed a shared feature-guided new TSK fuzzy classification algorithm with extra feature compensation (SFG-TFC) to explore the perception features of the population in the thermal environment of buildings and to improve the classification performance and interpretability of the model. First, the shared features of subdatasets collected in different time periods were extracted. Second, the extra features of each subdataset were independently trained, and the rule outputs corresponding to the key shared features were reprojected into the corresponding fuzzy classifiers. This strategy not only highlights the guiding role of shared features but also considers the important compensation effect of extra features; thereby, improving the classification performance of the entire classification model. Finally, the least learning machine (LLM) was used to solve the parameters of the “then” part of each basic training unit, and these output weights were integrated to enhance the generalization performance of the model. The experimental results demonstrate that SFG-TFC has better classification performance and interpretability than the classic nonfuzzy algorithms support vector machine (SVM) and deep belief network (DBN), the 0-order TSK, and the multilevel optimization and fuzzy approximation algorithm QI-TSK.

The extreme thermal environment during the underground coal gasification (UCG) process poses a severe threat to the stability of the gasification cavity and the integrity of the surrounding rock. This paper aims to reveal the thermo-mechanical response characteristics and damage evolution mechanism of sandstone under true triaxial unloading conditions following exposure to high temperatures. Sandstone specimens were thermally pre-treated at five temperature gradients (25 °C, 200 °C, 400 °C, 600 °C, and 800 °C) and subsequently subjected to true triaxial loading and unloading experiments. The effects of varying temperatures on the strength, deformation parameters, dilation angle evolution, and macroscopic failure modes of the sandstone were systematically analyzed. The results indicate a significant critical transition point in the mechanical behavior of the sandstone at 400 °C. Below this threshold, thermal-induced microcrack closure leads to an increase in peak strength (with the peak strength at 800 °C increasing by approximately 67% compared to room temperature). Conversely, above 400 °C, thermal damage to the mineral grains intensifies, causing the crack propagation pattern to transition from brittle shear to a complex tension-shear splitting mode, accompanied by severe dilatancy (with a generalized Poisson’s ratio exceeding 0.8). Based on these findings, this study proposes a stage-wise damage evolution model alongside a targeted zonal support strategy, recommending the application of high-prestressed support in high-temperature zones above 400 °C to suppress tensile failure. Ultimately, this research provides a crucial theoretical basis for evaluating the long-term stability of high-temperature underground engineering projects and ensuring operational safety.

The personal thermal comfort model is used to design and control the thermal environment and predict the thermal comfort responses of individuals rather than reflect the average response of the population. Previous individual thermal comfort models were mainly focused on a single material environment. However, the channels for individual thermal comfort were various in real life. Therefore, a new personal thermal comfort evaluation method is constructed by means of a reliable decision-based fuzzy classification model from two views. In this study, a two-view thermal comfort fuzzy classification model was constructed using the interpretable zero-order Takagi–Sugeno–Kang (TSK) fuzzy classifier as the basic training subblock, and it is the first time an optimized machine learning algorithm to study the interpretable thermal comfort model is used. The relevant information (including basic information, sampling conditions, physiological parameters, physical environment, environmental perception, and self-assessment parameters) was obtained from 157 subjects in experimental chambers with two different materials. This proposed method has the following features: (1) The training samples in the input layer contain the feature data under experimental conditions with two different materials. The training models constructed from the training samples under these two conditions complement and restrict each other and improve the accuracy of the whole model training. (2) In the rule layer of the training unit, interpretable fuzzy rules are designed to solve the existing layers with the design of short rules. The output of the intermediate layer of the fuzzy classifier and the fuzzy rules are difficult to explain, which is problematic. (3) Better decision-making knowledge information is obtained in both the rule layer of the single-view training model and in the two-view fusion model. In addition, the feature mapping space is generated according to the degree of contribution of the decision-making information from the two single training views, which not only preserves the feature information of the source training samples to a large extent but also improves the training accuracy of the model and enhances the generalization performance of the training model. Experimental results indicated that TMV-TSK-FC has better classification performance and generalization performance than several related state-of-the-art non-fuzzy classifiers applied in this study. Significantly, compared with the single view fuzzy classifier, the training accuracies and testing accuracies of TMV-TSK-FC are improved by 3–11% and 2–9%, respectively. In addition, the experimental results also showed good semantic interpretability of TMV-TSK-FC.

Heatwaves are intensifying across China under global warming. Although previous SSP-RCP studies project more frequent and intense events, systematic evaluations of exposure mechanisms among the elderly in China remain limited. The purpose of the paper is to reveal the spatiotemporal dynamics and inequality of heatwave exposure among China’s urban elderly and to disentangle the driving influences of climate change, ageing, and urbanization. Historical and future heatwaves across China are identified and analyzed, exposure inequality is evaluated using the Gini coefficient, and the relative contributions of key drivers are quantified through factor separation. Results showed that heatwave frequency and duration increased from 2000 to 2019, with high-risk provinces clustering in the Yangtze River Basin, North China Plain, and Sichuan Basin. Future projections indicate substantial growth in elderly exposure to heatwaves, while under the SSP3-70 scenario, inter-provincial inequality in exposure tends to alleviate rather than intensify. Climate change was identified as the dominant driver, while ageing amplified risks and urbanization partly mitigated growth. These findings highlighted the urgent need for place-based adaptation and health protection strategies, aligned with climate mitigation, demographic transition, and sustainable urban planning.