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Digital solutions and social media platforms for older adults are widely associated with positive social and health-related outcomes. However, empirical evidence on their sustained use remains limited, particularly from a usability and accessibility perspective. Previous reviews have primarily examined social media use in later life from social or psychological perspectives, whereas this review focuses on digital inclusion and psychosocial well-being from a technological and ICT-oriented perspective. The aim is to examine how technological and design-related factors may help explain gaps between reported benefits and actual usage among older adults. It also seeks to support researchers and designers in understanding why many digital platforms fail to sustain long-term engagement despite reported initial benefits. The review further identifies areas where emerging approaches, such as AI- or VR/AR-supported (XR) systems, could be explored in future research. Following PRISMA 2020 guidelines, studies published between 2005 and 2025 were reviewed to identify key technological and user-related factors that influence online participation among older adults. Findings indicate that barriers related to digital literacy, accessibility, and usability are frequently associated with reduced engagement. At the same time, the potential role of user-friendly and well-designed platforms is often implied rather than empirically examined. Although inclusive and adaptable systems are widely discussed, explicit HCI- and interface-level usability evaluations remain surprisingly rare. None of the included studies examined AI- or XR-related features of social platforms. This indicates that ageing and social media research have yet to empirically address emerging technologies that increasingly shape online interaction. The review underscores the need for accessible and adaptable technological solutions that promote digital engagement and emotional well-being among older users.

Weak structured signal enhancement and detection in multi-sensor environments remains challenging due to severe noise interference and the heterogeneity of sensing modalities, which often renders traditional signal processing and conventional deep learning models ineffective. To address these limitations, this study proposes the Convolutional Attentional weak structured signal enhancement and detection Network (CA-WSDN), an end-to-end framework that integrates multi-scale 1D convolution for hierarchical temporal feature extraction with a SE-style cross-channel attention mechanism for adaptive multi-scale feature enhancement across heterogeneous sensor channels. The multi-scale branches capture transient and long-range temporal patterns, while the attention module dynamically emphasizes informative channels and suppresses noise-dominated features, thereby enhancing weak fault-related components. Experiments on simulated medical-equipment monitoring data under ultra-low SNR conditions (−5 dB to 0 dB) demonstrate the model’s robustness and generalization capability. CA-WSDN achieves an SNRI of 8.12 dB at ultra-low SNR experimental setting SNR and 95.1% diagnostic accuracy, outperforming all baseline algorithms. The results indicate that CA-WSDN provides an effective and scalable solution for weak structured signal enhancement and detection in complex noise-flooded multi-sensor systems, offering strong potential for industrial and medical monitoring applications.

Large language models (LLMs) have shown remarkable capabilities in solving complex tasks. Recent work has explored decomposing such tasks into subtasks with independent contexts. However, some contextually related subtasks may encounter information loss during execution, leading to redundant operations or execution failures. To address this issue, we propose a training-free framework with an interaction mechanism, which enables a subtask to query specific information or trigger certain actions in completed subtasks by sending requests. To implement interaction, we introduce a subtask trajectory memory to enable the resumption of completed subtasks upon receiving interaction requests. Additionally, we propose a new action during execution, which generates a concise and precise description of the execution process and outcomes of a subtask, to assist subsequent subtasks in determining interaction targets and requests. Our framework achieves 46.0% and 52.6% success rates on WebShop and HotpotQA, outperforming the strong baselines by 1.0 and 2.6 absolute percentage points, respectively.