With the advancement of tactile sensors, researchers increasingly integrate tactile perception into robotics, but only for tasks such as object reconstruction, classification, recognition, and grasp state assessment. In this paper, we rethink the relationship between visual and tactile perception and propose a novel robotic grasping detection method based on visual–tactile perception. Initially, we construct a visual–tactile dataset containing the grasp stability for each potential grasping position. Next, we introduce a novel Grasp Stability Prediction Module (GSPM) to generate a grasp stability probability map, providing prior knowledge regarding grasp stability to the grasp detection network for each possible grasp position. Finally, the map is multiplied element-wise with the corresponding colored image and inputted into the grasp detection network. Experimental results demonstrate that our novel visual–tactile fusion method significantly enhances robotic grasping detection accuracy.

This article presents a unified adaptive resilient control framework for heterogeneous connected vehicle platoons subjected to hybrid cyberattacks, actuator saturation, parameter uncertainties, and communication delays. Unlike existing methods that often rely on simplified attack models or predefined parameters, the proposed strategy introduces a two-tiered architecture for comprehensive security and robustness. First, a hybrid attack detection mechanism leveraging a random forest classifier is developed, which operates under a novel denial-of-service (DoS)-first, false-data-injection (FDI)-second sequential logic to accurately identify both DoS and FDI attacks in real-time. Subsequently, an adaptive distributed control protocol, integrated with fuzzy logic systems and self-tuning laws, is designed to ensure platoon stability without prior knowledge of attack patterns or system parameters. The framework is rigorously underpinned by theoretical analysis, providing guarantees for both exponential stability and string stability. Extensive simulations demonstrate that the proposed approach not only maintains internal and string stability under hybrid attacks and system constraints but also exhibits superior transient performance, including faster recovery and enhanced robustness, compared to conventional baseline methods.

Graph neural networks have become a popular technique for collaborative filtering. However, most related work is based on user-item bipartite graphs, which can generate a large amount of noise due to the broad and elusive interests of users. To address this problem, we propose a novel generalized insertion framework (CGCN) that directly captures cliques in the item-item co-occurrence graph and considers them as the basic units of the user’s higher-order semantics. The method inserts the structural information in these item-item co-occurrence graphs as an insertion module into the original user-item bipartite graph propagation process, thus providing additional useful information to learn better feature representations. By utilizing the strong proximity relationships between different items in these cliques, the method is able to discover the user’s potential higher-order semantics. We experimentally evaluate two improved variants of the framework on three commonly used public datasets, and the results show significant performance improvements. The method is able to better discover users’ latent true intentions and achieve better recommender system performance by introducing clique information in the item-item co-occurrence graph.

The intensification of the urban heat island (UHI) effect necessitates precise micro-climatic analysis; however, conventional macro-scale satellite studies often obscure the sub-meter morphological details that govern localized thermal dynamics. To address this gap, this study establishes the micro-neighborhood as the fundamental analytical unit, utilizing it as a spatial bridge to align high-resolution unmanned aerial vehicle (UAV) imagery with satellite-derived thermal patterns. By deploying Vision Foundation Models (VFMs), we achieve automated, high-fidelity segmentation of fine-scale urban elements. We construct a comprehensive morphological framework encompassing composition, dominance, and fragmentation metrics to decouple the non-linear drivers of micro-scale thermal variations via explainable artificial intelligence (XAI). Our findings reveal that building complexes and artificial sports facilities function as primary micro-scale heat sources, where spatial contiguity exacerbates thermal accumulation as intensely as absolute areal coverage. For mitigation, structural fragmentation within vegetation canopies and building layouts offers superior heat-dissipation potential. Given the spatial scarcity of water bodies, strategically tailoring the fine-scale configuration of built and vegetative elements emerges as a highly scalable and viable paradigm for climate-resilient urban micro-domain regulation.