JOURNAL/nrgr/04.03/01300535-202501000-00031/figure1/v/2026-05-04T135417Z/r/image-tiff Early identification and treatment of stroke can greatly improve patient outcomes and quality of life. Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale (CPSS) and the Face Arm Speech Test (FAST) are commonly used for stroke screening, accurate administration is dependent on specialized training. In this study, we proposed a novel multimodal deep learning approach, based on the FAST, for assessing suspected stroke patients exhibiting symptoms such as limb weakness, facial paresis, and speech disorders in acute settings. We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements, facial expressions, and speech tests based on the FAST. We compared the constructed deep learning model, which was designed to process multi-modal datasets, with six prior models that achieved good action classification performance, including the I3D, SlowFast, X3D, TPN, TimeSformer, and MViT. We found that the findings of our deep learning model had a higher clinical value compared with the other approaches. Moreover, the multi-modal model outperformed its single-module variants, highlighting the benefit of utilizing multiple types of patient data, such as action videos and speech audio. These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke, thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Online Scheduling for Partial charging with Multi-Mobile Chargers (OSPM) is critical for Wireless Rechargeable Sensor Networks (WRSNs) performing high-power monitoring tasks with a large number of simultaneous charging requests. However, existing studies for online scheduling assume full charging of sensors, leading to delays and inefficient resource utilization. Partially charging the sensors can improve scheduling efficiency and flexibility, but these studies focus on off-line scheduling, hindering dynamic decision-making. Multi-Agent Reinforcement Learning (MARL) is advantageous in online collaboration. Nevertheless, existing MARL methods assume synchronized actions, while Mobile Chargers (MCs) performing charging tasks asynchronously due to the difference in movement and charging times. On the other hand, hybrid actions are required to capture the simultaneous decision-making of MCs, involving sensor selection (discrete action) and energy allocation (continuous parameter). This introduces a circular dependency between a discrete action and its corresponding continuous parameter due to their interdependence. To deal with the above problems and address OSPM, we propose Asynchronous and Scalable Multi-agent Hybrid Proximal Policy Optimization (ASM-HPPO). The evaluation results not only indicate that our ASM-HPPO has advantages in terms of various performance metrics over existing schemes, but also demonstrate that our methods achieve higher stability and scalability.

Determining whether nodes can be localized, called localizability detection, is essential for wireless sensor networks (WSNs). This step is required for localizing nodes, achieving low-cost deployments, and identifying prerequisites in location-based applications. Centralized graph algorithms are inapplicable to a resource-limited WSN because of their high computation and communication costs, whereas distributed approaches may miss a large number of theoretically localizable nodes in a resource-limited WSN. In this paper, we propose an efficient and effective distributed approach in order to address this problem. Furthermore, we prove the correctness of our algorithm and analyze the reasons our algorithm can find more localizable nodes while requiring fewer known location nodes than existing algorithms, under the same network configurations. The time complexity of our algorithm is linear with respect to the number of nodes in a network. We conduct both simulations and real-world WSN experiments to evaluate our algorithm under various network settings. The results show that our algorithm significantly outperforms the existing algorithms in terms of both the latency and the accuracy of localizability detection.