The goal of this special session is to address recent advancements and challenges in cyber-physical-social systems (CPSS) for the medical domain. With the proliferation of sensor informatics, the Internet of Things (IoT), big data analytics, and artificial intelligence, there has been significant development in systems aimed at enhancing diagnostic accuracy, biomarker discovery, therapeutic reliability, and rehabilitation processes. These cyber-physical-social systems are crucial for healthcare, representing an integrated network of medical devices that are increasingly being utilized in hospitals to deliver continuous, high-quality care. However, the design and implementation of these systems face several challenges, such as interoperability issues, security and privacy concerns, and the need for high-assurance software. This session aims to serve as a platform for presenting and discussing original research findings and innovative techniques in the field of cyber-physical-social systems in medicine, thereby fostering further advancements and applications in this critical area of healthcare.

This special session focuses on the integration of emerging robotics technologies with Metaverse and Artificial Intelligence (AI) to drive innovation in rehabilitation and construction domains. It aims to provide a multidisciplinary forum for cutting-edge research on intelligent rehabilitation systems, construction automation, perception systems, and human-robot collaboration. Key topics include rehabilitation robots, mobile construction robotics, digital twin systems, and AI-driven multimodal perception and autonomous task execution. In addition, this session welcomes exploratory work related to the application of Metaverse and Virtual Reality (VR) technologies in engineering education. This includes virtual simulation training, remote teaching systems, and the development of intelligent digital learning environments, serving as an extended application of robotics in education and public engagement.

Unmanned systems have received much attention in the research community, covering a wide range of vehicular systems, from aerial vehicles, to ground robots, to underwater systems. The recent trend of studies attempts to investigate the autonomy of unmanned systems, including developing automatic machine systems and increasing the level of intelligence by incorporating machine learning and artificial intelligence. Another trend focuses on cooperation and coordination among unmanned systems, often under the theme of multi-agent systems and their related topics. From the systematic perspective and the scope of unmanned systems, there exists a need for a comprehensive understanding of how unmanned systems interact with their environment, with each other, with humans, and so on. In this session, we would like to collect and investigate the recent advances in the ongoing research of Interactive Unmanned Systems and Intelligent Applications. We also welcome high-quality papers in all theoretical and practical aspects of unmanned intelligent systems.

The advent of Industry 6.0 marks a transformative era in smart manufacturing/remanufacturing, where artificial intelligence (AI), digital twins, predictive analytics, advanced robotics, and smart automation converge to create manufacturing/remanufacturing systems that are not only highly efficient but also adaptive, resilient, and capable of self-optimization. Unlike previous industrial revolutions, Industry 6.0 emphasizes seamless integration between humans and intelligent machines, enabling real-time decision-making, autonomous production planning, and predictive maintenance. This convergence facilitates proactive problem detection, dynamic scheduling, and resource optimization, while simultaneously enhancing sustainability, reducing downtime, and improving product quality. This special issue seeks to explore cutting-edge research, novel methodologies, and practical applications that push the boundaries of intelligent operation, maintenance, and scheduling in industrial processes. Contributions may include, but are not limited to, the development of AI-driven algorithms for predictive and prescriptive maintenance, digital twin-based simulation and optimization of production systems, human–AI collaborative decision-making frameworks, energy-efficient and sustainable manufacturing solutions, and case studies showcasing real-world implementation of Industry 6.0 technologies. By bringing together insights from academia and industry, this special issue aims to highlight the next generation of smart manufacturing/remanufacturing practices, providing a roadmap toward fully autonomous, resilient, and adaptive industrial systems.

This special session aims to bring together innovative research at the intersection of machine intelligence, domain adaptation, and trustworthy decision-making across diverse scientific and healthcare domains. The focus is on developing self-supervised, generative, and explainable AI models that enhance robustness, interpretability, and performance in real-world settings; from detecting solar panel defects and designing therapeutic proteins to diagnosing medical disorders and predicting non-communicable diseases. By showcasing works on advanced architectures such as diffusion models, TabNet variants, and explainable intrusion detection systems, the session seeks to foster cross-domain collaboration and highlight how intelligent, transparent systems can accelerate discoveries and improve decision support in critical infrastructure and healthcare.

Visual-cognitive computing and social vision play a significant role in humanmachine and social interaction applications. With the development of artificial intelligence and data acquiring technologies in visual processing in recent years, it is possible to collect an abundance of multimodal data with various sensors and channels. Thus, there is a strong need for effective visual computing and social signal processing methods to tackle and extract the expert knowledge from the available information for human-machine and social interaction applications. Although current research in this field has shown promising results, accurately understanding the various kinds of signals remains a highly challenging problem. This special session welcomes articles with multidisciplinary methods for above issues.