Falling is an emergency situation that can result in serious injury or even death, especially in the absence of immediate assistance. Therefore, developing a model that can accurately and promptly detect falls is crucial for enhancing quality of life and safety. In the field of object detection, while YOLOv8 has recently made notable strides in detection accuracy and speed, it still faces challenges in detecting falls due to variations in lighting, occlusions, and complex human postures. To address these issues, this study proposes the SDES-YOLO model, an improvement based on YOLOv8. By incorporating a multi-scale feature extraction pyramid (SDFP), occlusion-aware attention mechanism (SEAM), an edge and spatial information fusion module (ES3), and a WIoU-Shape loss function, the SDES-YOLO model significantly enhances fall detection performance in complex scenarios. With only 2.9M parameters and 7.2 GFLOPs of computation, SDES-YOLO achieves an mAP@0.5 of 85.1%, representing a 3.41% improvement over YOLOv8n, while reducing parameter count and computation by 1.33% and 11.11%, respectively. These results indicate that SDES-YOLO successfully combines efficiency and precision in fall detection. Through these innovations, SDES-YOLO not only improves detection accuracy but also optimizes computational efficiency, making it effective even in resource-constrained environments.

As dynamic systems, social networks exhibit continuous topological changes over time, and are typically modeled as temporal networks. In order to understand their dynamic characteristics, it is essential to investigate temporal community detection (TCD), which poses significant challenges compared to static network analysis. These challenges arise from the need to simultaneously detect community structures and track their evolutionary behaviors. To address these issues, we propose TCDA-NE, a novel TCD algorithm that combines evolutionary clustering with convex non-negative matrix factorization (Convex-NMF). Our method innovatively integrates community structure into network embedding, preserving both microscopic details and community-level information in node representations while effectively capturing the evolutionary dynamics of networks. A distinctive feature of TCDA-NE is its utilization of a common-neighbor similarity matrix, which significantly enhances the algorithm’s ability to identify meaningful community structures in temporal networks. By establishing coherent relationships between node representations and community structures, we optimize both the Convex-NMF-based representation learning model and the evolutionary clustering-based TCD model within a unified framework. We derive the updating rules and provide rigorous theoretical proofs for the algorithm’s validity and convergence. Extensive experiments on synthetic and real-world social networks, including email and phone call networks, demonstrate the superior performance of our model in community detection and tracking temporal network evolution. Notably, TCDA-NE achieves a maximum improvement of up to 0.1 in the normalized mutual information (NMI) index compared to state-of-the-art methods, highlighting its effectiveness in temporal community detection.

Cropland Change Detection (CCD) is a critical technological means for ensuring food security and advancing agricultural sustainability, holding significant application value in remote sensing image interpretation. However, prevalent domain shifts between bi-temporal remote sensing images—caused by land cover phenological variations (e.g., crop growth cycles) and multi-source imaging disparities (e.g., sensor differences, illumination changes, atmospheric interference)—severely degrade the detection accuracy and robustness of conventional methods. To address these challenges, this paper proposes a novel end-to-end domain adaptation framework named MDANet for cropland change detection, which innovatively integrates the long-sequence modeling capability of Mamba with domain-invariant feature learning strategies. First, a Mamba-based domain adaptation method is constructed to conduct cross-domain alignment of bi-temporal images, aiming to eliminate domain shifts caused by imaging and geographical differences. Specifically, a source-target weight adjustment mechanism is designed to adaptively balance the migration degree of image source and target features, and two identity losses are proposed to preserve the geospatial structure and semantic consistency of croplands. Then, a cross-domain feature alignment and fusion mechanism is designed to align transformed features via domain-invariant learning, effectively suppressing pseudo-changes and enhancing discriminability in genuine change regions. Experimental results on JLYHCD, CLCD, and Hi-CNA datasets demonstrate that the proposed method achieves F1-scores of 79.87%, 81.94%, and 87.24%, respectively, outperforming existing advanced change detection approaches and exhibiting superior detection accuracy and cross-domain generalization capability. All codes are available at https://github.com/dawu/MDANet.

The integration of computing, network, and physical devices across cloud and fog platforms has significantly increased the coordination complexity of industrial cyber-physical systems (ICPSs). This complexity stems from managing numerous heterogeneous components, making it increasingly challenging to identify security vulnerabilities. To address these issues, it is crucial to rapidly test high-risk components and support timely recovery of industrial processes. In this article, we propose DomFuzz, a dominator-based directed grey-box fuzzing method that enables precise vulnerability distance calculations for critical infrastructure components. Specifically, to fairly distribute computing resources to testing inputs, we use a dominator-based power schedule for the first time. Additionally, we adjust the transition time between testing phases dynamically to improve vulnerability identification efficiency in ICPSs. Furthermore, we implement a lightweight patching method capable of addressing incomplete call graphs in the presence of proof-of-concept code, effectively resolving the complexity challenges inherent in ICPSs. Our evaluation shows that DomFuzz outperforms AFL, AFLGo, and WindRanger. DomFuzz finds 77.06 percent of the vulnerabilities in LAVA-M, while AFLGo and AFL find only 0.41 percent on average. Remarkably, DomFuzz significantly reduces the time-to-exposure (TTE) of vulnerabilities in ICPSs, identifying vulnerabilities on average 2.09x faster than AFLGo and 1.57x faster than WindRanger.