The COVID-19 pandemic has affected more than 100 million people worldwide, with around 500,000 cases reported daily. This has led to the overwhelming of healthcare systems even in developed countries such as the US, UK, etc. Remote monitoring of COVID-19 patients with non-serious symptoms can help reduce the burden on healthcare facilities and make them available for high risk groups and the seriously affected. The pandemic has accelerated the demand for the remote patient monitoring (RPM) technologies, and the market is expected to reach 2.14 billion in 2027 from the value of 786.4 million in 2019. In RPM programs, there are two types of sensors that can be used: wearable and contactless. The former, which is currently more widely used, is not only more obtrusive and uncomfortable, but can also lead to cross-infection through patient contact. These two types of technologies are discussed and compared for each vital sign. In the respiratory system, the vital signs are the respiratory rate (RR) and oxygen saturation (SpO2), while for the latter, they are the heart rate/rhythm and the blood pressure (BP). Then, the discussion is broadened to policy level changes needed to expedite the use of such technologies for remote patient monitoring (RPM) in the world. Around 80% of countries' RPM programs are either informal or in a pilot phase, and thus lack policies and an established regulatory framework to implement their programs. The various policies needed to initiate, deliver, and reimburse RPM programs during emergency situations and outbreaks are discussed. Finally, technologies such as contactless systems, robotics, and Internet-of-things (IoT) that will revolutionize healthcare in the future by reducing the interaction between physicians and patients and cross-infection are discussed.

Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out (POCO) around nuclear facilities each year, resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases. The UK Nuclear Decommissioning Authority supports the view that human‐in‐the‐loop (HITL) robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear sites. The authors present a novel implementation of a Cyber‐Physical System (CPS) deployed in an analogue nuclear environment, comprised of a multi‐robot (MR) team coordinated by a HITL operator through a digital twin interface. The development of the CPS created efficient partnerships across systems including robots, digital systems and human. This was presented as a multi‐staged mission within an inspection scenario for the heterogeneous Symbiotic Multi‐Robot Fleet (SMuRF). Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together, where a single robot would face challenges in full characterisation of radiation. Key contributions include the demonstration of symbiotic autonomy and query‐based learning of an autonomous mission supporting scalable autonomy and autonomy as a service. The coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being.

Assessment and feedback (A&F) are two major components of any educational program and must be properly in place to ensure student learning and quality of experience. However, these important components come under severe challenges of meeting student expectations in the large class size context. When the program delivery relates to a transnational educational (TNE) scenario, the additional constraints on staff–student physical interaction, regional time differences and cultural background gaps introduce additional challenges: Conducting proper assessments and provide timely and constructive feedback to the students. In this paper, the authors propose a novel assessment and feedback framework which exploits having a large student number as a positive factor by introducing staff–student partnership to implement efficient assessment and feedback strategies. Authors propose to use students for peer-review, assessment design, evaluation rubric design and tutorial-based feedback. The students also take part in preparing feedback clusters based on which the instructor provides pseudo-personalised video feedback. Through feedback clusters, authors introduce the trade-off between individual feedback and generic feedback. The results of the study are particularly promising in terms of student satisfaction and learning enhancement.

In the rapidly advancing field of wireless localization, achieving accurate indoor tracking is crucial for the next generation of smart factories, automated workflows, and efficient supply chains. The integration of 5G networks within industrial environments offers high connectivity, yet challenges remain in obtaining the fine-grained positioning required for localization applications. This paper presents the development and simulation-based evaluation of the SGNCL algorithm within the 5G New Radio (5GNR) network framework. The proposed algorithm utilizes data integration techniques to effectively mitigate Non-Line of Sight (NLoS) errors, which are prevalent in complex indoor environments with high delay spreads. We describe the algorithm's design, operational principles, and the comprehensive simulation setup used to assess its performance. In comparison to the minimum variance (min-var) anchor set, which exhibited a mean error of 2.5 meters, the SGNCL algorithm achieved a significant improvement, reducing the mean error to 0.86 meters. The results also highlight the algorithm's ability to handle varying delay spreads and sensor densities, ensuring robust localization performance across different scenarios. These findings demonstrate the potential of the SGNCL algorithm to enhance 5G-enabled indoor localization services by addressing NLoS challenges through simulation-based insights.