Nantong University

Following PRISMA-ScR guidelines, this systematic scoping review searched PubMed and Web of Science and identified 72 relevant studies, mapping AI applications in social support for persons with disabilities through the lens of participation, autonomy, and environmental fit across five areas—"Mobility and Navigation Assistance" (n = 20, 41.7%), "Communication and Information Accessibility" (n = 14, 29.2%), "Smart Assistance for Daily Living" (n = 7, 14.6%), "Education and Vocational Empowerment" (n = 5, 8.3%), and "Mental Health Support and Social Inclusion" (n = 3, 6.3%)—and identifying challenges including data privacy (58.3%), inadequate training datasets (25.0%), high implementation costs (25.0%), algorithmic biases (20.8%), and limited real-world evidence of benefits (20.

Following PRISMA-ScR guidelines, this systematic scoping review searched PubMed and Web of Science and identified 72 relevant studies, mapping AI applications in social support for persons with disabilities through the lens of participation, autonomy, and environmental fit across five areas—"Mobility and Navigation Assistance" (n = 20, 41.7%), "Communication and Information Accessibility" (n = 14, 29.2%), "Smart Assistance for Daily Living" (n = 7, 14.6%), "Education and Vocational Empowerment" (n = 5, 8.3%), and "Mental Health Support and Social Inclusion" (n = 3, 6.3%)—and identifying challenges including data privacy (58.3%), inadequate training datasets (25.0%), high implementation costs (25.0%), algorithmic biases (20.8%), and limited real-world evidence of benefits (20.

The loaded text contains only the paper title, "RaFT-DM: A Residue-Aware Fusion Transformer With Domain-Wise Memory for Accurate Multi-Label Protein Function Prediction," together with IEEE Xplore navigation, account, copyright, and front-end script template content, with no abstract, methods, experiments, or results, so it can only be confirmed that the work proposes a method named RaFT-DM that combines a residue-aware fusion transformer with domain-wise memory for multi-label protein function prediction, while its specific approach and findings cannot be summarized.

The loaded text contains only the paper title, "RaFT-DM: A Residue-Aware Fusion Transformer With Domain-Wise Memory for Accurate Multi-Label Protein Function Prediction," together with IEEE Xplore navigation, account, copyright, and front-end script template content, with no abstract, methods, experiments, or results, so it can only be confirmed that the work proposes a method named RaFT-DM that combines a residue-aware fusion transformer with domain-wise memory for multi-label protein function prediction, while its specific approach and findings cannot be summarized.

Using an 18-keyword query, this study retrieved 743 Bilibili reproductive-health videos published between September 2016 and June 2024, covering 486.8 million views, 12.3 million likes, and roughly 760,000 textual entries, described engagement metrics with descriptive and Pearson-correlation analyses, and applied a three-class sentiment classifier powered by Qwen2.5-32B to comments and danmaku, finding that adolescent engagement centres on basic contraception while niche or contentious topics (sterilisation surgery 3.0%, fertility-awareness-based methods 2.3%) attract disproportionately high interaction, only 17% of uploaders had confirmed medical training, and negative sentiment predominated for induced abortion (52.2%) and novel contraceptives (52.

Using an 18-keyword query, this study retrieved 743 Bilibili reproductive-health videos published between September 2016 and June 2024, covering 486.8 million views, 12.3 million likes, and roughly 760,000 textual entries, described engagement metrics with descriptive and Pearson-correlation analyses, and applied a three-class sentiment classifier powered by Qwen2.5-32B to comments and danmaku, finding that adolescent engagement centres on basic contraception while niche or contentious topics (sterilisation surgery 3.0%, fertility-awareness-based methods 2.3%) attract disproportionately high interaction, only 17% of uploaders had confirmed medical training, and negative sentiment predominated for induced abortion (52.2%) and novel contraceptives (52.