Large language model (LLM)–based chatbots are rapidly emerging as a promising vehicle for delivering companionship, psychoeducation, on-demand mental health support, and clinical workflow automation or augmentation. However, inconsistent or inadequate crisis handling, along with well-documented hallucinations, bias, and opaque accountability of LLMs, pose significant challenges for responsible deployment and clinical use of those chatbots. This paper introduces key streams of research on LLM-based Mental Health Chatbots (LLM-MHCs) and critically examines their benefits and risks. Then, we propose a five-dimensional human-centered framework for responsible design of LLM-MHCs that consists of governance, safety, transparency and explainability, empathy, and personalization (G-STEP). Finally, we outline several directions for future research, including conducting longitudinal randomized clinical trials with patients with mental health conditions to rigorously assess the effectiveness and user experience of LLM-MHCs; developing open benchmarks and comprehensive metrics for evaluating their efficacy, reliability, and equity; and supporting continuous human-in-the-loop and responsible deployment of LLM-MHCs.
Rapid urbanization and growing mobility demand are reshaping transportation systems, calling for more advanced intelligence and management capabilities. Artificial intelligence (AI) has emerged as a key enabler for enhancing perception, prediction, and decision-making in transportation. This paper presents a systematic review of AI applications across four major transportation domains: road, rail, air, and maritime systems. Rather than exhaustively surveying all published studies, this review adopts a thematic synthesis approach, organizing representative, recent research by major transportation modes and core AI application scenarios, with an emphasis on influential studies published in leading journals and conferences. The review examines representative applications and key functionalities within each domain, highlighting how AI techniques—such as deep learning, graph-based models, reinforcement learning, and emerging foundation models—are adapted to diverse transportation contexts. Furthermore, this paper analyzes key challenges, including data quality and sparsity, interpretability, uncertainty, and cross-domain generalization, and discusses emerging research directions such as foundation models, physics-informed learning, and continual adaptation. By integrating insights from both methodologies and real-world applications, this review provides insights for advancing intelligent, scalable, and resilient transportation systems.
The accelerated servitization of manufacturing and the rapid advancements in emerging information technologies have enabled flexible process configuration, efficient resource collaboration, and dynamic value chain reconstruction. However, the complex behavioral interactions among users, firms, and platform systems, alongside massive data flows, pose significant challenges to the operational management of service-oriented manufacturing (SOM). This study conducts a systematic literature review of data-driven, behavior-based SOM using a mixed-methods approach that integrates Bibliometric-Systematic Literature Review (B-SLR) and snowballing techniques. Drawing on an in-depth analysis of 367 core publications from 1988 to April 2026, this study develops a lifecycle-based framework comprising four key operational stages, covering product design, production, smart operations and maintenance, and product lifecycle management, including recycling and remanufacturing. The review synthesizes major findings from both academic and practical perspectives and identifies future research directions across four behavioral dimensions, including value co-creation in design, collaborative sharing in production, human–AI interaction in operations, and multi-agent collaboration in lifecycle management. The findings provide practical insights that advance both research and practice in SOM to support the development of resilient, sustainable, and intelligent manufacturing systems.
The functional robustness and structural integrity of real-world networks disproportionately on a subset of influential nodes. Identifying these influential nodes is a central objective in network science, underpinning applications spanning from epidemic control to predicting high-impact journal publications. In this study, cascade mitigation is defined as a pivotal, yet overlooked, canonical problem in network science that has received comparatively limited attention, and a generalized benchmarking framework is established to unify this interdisciplinary field. A decade of progress is synthesized, tracing the evolution from classical structure-centrality-based to artificial intelligence (AI)-based influential-node identification techniques. Beyond providing a systematic survey, nearly 20 distinct methodologies were empirically evaluated across diverse synthetic and real-world data sets using standard spreading models such as the susceptible-infected-recovered, linear threshold, and independent cascade models. This large-scale cross-domain comparison elucidates the performance differences between structural and AI-based strategies. By standardising the evaluation of node influence, this review offers a critical roadmap for researchers and identifies promising avenues for future interdisciplinary innovations.
