The study of urban functional zoning is not only important for analyzing urban spatial structure but also for optimizing urban management and promoting scientific urban planning. Different areas undertaking different urban functions correspond to different traffic patterns and specific cycles. Here, a method named Urban Functional Zoning based on the Spatial Specificity (UFZ-SS) is proposed. The core of this method is to obtain urban spatial zoning through the specific cycles of traffic flows. First, UFZ-SS uses the Ensemble Empirical Modal Decomposition (EEMD) method to extract the specific periodic signal characteristics of traffic flows. Second, UFZ-SS calculates the contribution of online car-hailing traffic of different cycles in each zone. Then, the Gaussian Mixture Model (GMM) is utilized to classify all spatial zones into different spatial partitions based on the contribution of each periodic signal. Finally, this study validates UFZ-SS with the online car-hailing traffic volume in northeast Chengdu, China. The results show that the periodic characteristics of traffic can be effectively extracted and analyzed by the EEMD method, and highly distinct and accurate urban spatial partitioning results can be derived by spatial clustering based on the measures of specific cycles. Moreover, with the assistance of Point of Interest (POI) data, we verify the functional zones and structural patterns, which further demonstrates the validity and rationality of urban functional zones identified by UFZ-SS. This study provides a new potential perspective for the identification of urban functional zones, which may lead to a better understanding of the urban spatial structure and even urban planning.

Rolling bearings are widely used in coal mine equipment, real-time monitoring of bearing status and intelligent diagnosis of bearing fault is very important to the safe and efficient mining of coal mine. In order to solve the problem of insufficient representation ability about fault diagnosis model, a multi-scale segmentation attention-based residual network is proposed for the fault diagnosis of rolling bearings, which can fully and accurately extract vibration signal features to realize intelligent diagnosis. For time-frequency images of vibration signals, residual networks was used for feature extraction. Furthermore, the pyramid split attention mechanism was combined to optimize the feature selection to construct the intelligent diagnosis model for bearing. The proposed method has been applied to the task of fault diagnosis on SKF6205 bearing, and the experimental results show its superior diagnostic performance.

Accurate assessment of the cooling effect from urban green space (UGS) is largely hindered by insufficient field samples or consideration of the internal and surrounding three-dimensional (3D) structure. This study developed a transferable modeling-optimization framework that integrated a multi-temporal sampling strategy, multimodal 3D environmental reconstruction, and Bayesian-based optimization. First, the potential influencing factors of the cooling effect were quantified from three aspects of inner 2D/3D structure, surrounding building ventilation, and background meteorology through fusing field measurements, multi-spectral UAV images, and Sentinel-2 images. Then, a generalized additive mixed-effects model was used to explore cooling-related patterns of UGS, and a Bayesian network was further applied to identify potential optimized configurations. The results suggest the following: (1) The adopted multi-temporal sampling strategy enhances the stability of detected cooling signals and minimizes spatial interference among neighboring UGS patches and water bodies. (2) Temporal changes in the cooling effect are mainly driven by average air temperature and maximum wind speed, while the spatial variation by the UGS inner characteristics of area and shape index and surrounding ventilation. (3) The “win–win” situation of cooling intensity and range occurred in UGSs with larger areas, higher shape regularity, and medium ventilation. This approach is useful for model-based planning of climate-responsive green infrastructure and city-scale ventilation systems in heat-vulnerable environments.

Land surface temperature (LST) is a critical indicator of the earth’s surface environment, which has significant implications for research on the ecological environment and climate change. The influence of terrain on LST is complex due to its rugged and varied surface topography. The relationship between traditional terrain features and LST has been comprehensively discussed in the literature; however, terrain blockage has received less attention and could influence LST by hindering the redistribution of heat energy in mountain regions. Here, we investigate the influence of terrain blockage on the spatiotemporal variation in LST in mountain regions. We first propose a terrain feature framework to characterize the effect of terrain blockage from the perspective of heat energy redistribution and then adopt a random forest model to analyze the relationship between terrain blockage features and LST over a whole year. The results show that terrain blockage significantly influences the spatial heterogeneity of LST, which can be effectively simulated based on terrain blockage features, with a mean deviation of less than 0.15 K. Terrain blockage has a more pronounced influence on LST during the four months from June to September. This influence is also more evident during nighttime than daytime. Regarding LST in mountain regions, local terrain blockage features have a greater influence than global terrain blockage features. In spatial terms, the influence of terrain blockage on LST is uniform. Moreover, the diurnal variation in LST can also be effectively simulated based on terrain blockage. The contribution of this study lies in the finding that terrain blockage can influence the spatiotemporal variation in LST through the process of heat energy redistribution. The terrain blockage features proposed in this study may be useful for other studies of the ecological environment in mountain regions.