Search Results (111)

The recent COVID-19 pandemic has made the public aware of the importance of combating pathogenic microorganisms before they enter the human body. This growing threat from microorganisms prompted us to conduct research into a new type of coating that would be an alternative to the continuous disinfection of touch surfaces. Our goal was to design, synthesise and thoroughly characterise such a coating. In this work, we present a nanocomposite material composed of a thin-layer mesoporous SBA-15 silica matrix containing copper phosphonate groups, which act as catalytic centres responsible for the generation of reactive oxygen species (ROS). In order to verify the structure of the material, including its molecular structure, microscopic observations and Raman spectroscopy were performed. The generation of ROS was confirmed by fluorescence microscopy analysis using a fluorogenic probe. The antimicrobial activity was tested against a wide spectrum of Gram-positive and Gram-negative bacteria, while cytotoxicity was tested on BALB/c3T3 mouse fibroblast cells and HeLa cells. The studies fully confirmed the expected structure of the obtained material, its antimicrobial activity, and the absence of cytotoxicity towards fibroblast cells. The results obtained confirmed the high application potential of the tested nanocomposite coating. Full article

Background: Microsurgery is a highly complex and technically demanding field within reconstructive surgery, with outcomes heavily dependent on meticulous planning, precision, and postoperative monitoring. Over the last five years, artificial intelligence (AI) has emerged as a transformative tool across all phases of microsurgical care, offering new capabilities in imaging analysis, intraoperative decision support, and outcome prediction. Methods: A comprehensive narrative review was conducted to evaluate the peer-reviewed literature published between 2020 and May 2025. Multiple databases, including PubMed, Embase, Cochrane, Scopus, and Web of Science, were searched using combinations of controlled vocabulary and free-text terms relating to AI and microsurgery. Studies were included if they described AI applications during the preoperative, intraoperative, or postoperative phases of microsurgical care in human subjects. Discussion: Using predictive models, AI demonstrated significant utility in preoperative planning through automated perforator mapping, flap design, and individualised risk stratification. AI-enhanced augmented reality and perfusion analysis tools improved precision intraoperatively, while innovative robotic platforms and intraoperative advisors showed early promise. Postoperatively, mobile-based deep learning applications enabled continuous flap monitoring with sensitivities exceeding 90%, and AI models accurately predicted surgical site infections, transfusion needs, and long-term outcomes. Despite these advances, most studies relied on retrospective single-centre data, and large-scale, prospective validation remains limited. Conclusions: AI is poised to enhance microsurgical precision, safety, and efficiency. However, its integration is challenged by data heterogeneity, generalisability concerns, and the need for human oversight in nuanced clinical scenarios. Standardised data collection and multicentre collaboration are vital for robust, equitable AI deployment. With careful validation and implementation, AI holds the potential to redefine microsurgical workflows and improve patient outcomes across diverse clinical settings. Full article

Artificial intelligence (AI) and assistive robotics can transform older-person care by offering new, personalised solutions for an ageing population. This paper outlines recent advances in AI-driven applications and robotic assistance in silver care, emphasising their role in improved healthcare services, quality of life and ageing-in-place and alleviating pressure on healthcare systems. Advances in machine learning, natural language processing and computer vision have enabled more accurate early diagnosis, targeted treatment plans and robust remote monitoring for elderly patients. These innovations support continuous health tracking and timely interventions to improve patient outcomes and extend home-based care. In addition, AI-powered assistive robots with advanced motion control and adaptive response mechanisms are studied to support physical and cognitive health. Among these, companion robots, often enhanced with emotional AI, have shown potential in reducing loneliness and increasing connectedness. The combined goal of these technologies is to offer holistic patient-centred care, which preserves the autonomy and dignity of our seniors. This paper also touches on the technical and ethical challenges of integrating AI/robotics into eldercare, like privacy and accessibility, and alludes to future directions on optimising AI-human interaction, expanding preventive healthcare applications and creating an effective, ethical framework for eldercare in the digital age. Full article

This study investigates the integration of Artificial Intelligence (AI) in Religious Education (RE), a field traditionally rooted in spiritual formation and human interaction. Amid increasing digital transformation in education, theological institutions are exploring AI tools for teaching, assessment, and pastoral engagement. Using a critical literature review and analysis of institutional case studies, the paper examines the historical development of AI in education, current applications in general and theological contexts, and the ethical challenges it introduces, especially regarding decision making, data privacy, and bias as well as didactically grounded opportunities such as AI-mediated dialogic simulations. The study identifies both the pedagogical advantages of AI, such as personalization and administrative efficiency, and the risks of theological distortion, overreliance, and epistemic conformity. It presents a range of real-world implementations from institutions like Harvard Divinity School and the Oxford Centre for Digital Theology, highlighting best practices and cautionary approaches. The findings suggest that AI can enrich RE when deployed thoughtfully and ethically, but it must not replace the relational and formational aspects central to RE. The paper concludes by recommending policy development, ethical oversight, and interdisciplinary collaboration to guide responsible integration. This research contributes to the growing discourse on how AI can be aligned with the spiritual and intellectual goals of RE in a rapidly evolving digital age. Full article

The International CCN Society has been organizing workshops and conferences for the past two decades to advance our understanding of the biology and pathophysiology of the cellular communication network (CCN) proteins. The 12th CCN Workshop broadened the scope of discussions, introducing topics like CCN-dependent and -independent signaling networks involved in brain development, cellular senescence, efferocytosis, neurobiology, and the application of DNA-fabricated origami structures. This expansion proved fruitful and should continue in future events. Fostering collaborations across various fields has created a dynamic environment for innovative ideas, driving substantial progress to tackle both basic scientific questions and clinically relevant challenges. Three standout presentations sparked significant discussions and highlighted key advancements in these areas. These include the work of Li-Jen Lee (Neurobiology and Cognitive Science Center, National Taiwan University) on the involvement of the CCN2 protein in depressive and aggressive behaviors in mice; the studies of Anna Zampetaki (King’s College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences) and Brahim Chaqour (University of Pennsylvania, Perelman School of Medicine, Dept of Molecular Ophthalmology) on the metabolome and mechanosensing in iPSC-derived human blood vessel organoids and in the microvasculature of genetically modified mice, and the talk of Björn Högberg (Karolinska Institutet, Department of Medical Biochemistry and Biophysics) on the promises of DNA origami. We believe that these examples illustrate better future directions, as they offer an opportune moment to pursue initiatives that broaden the focus of the CCN Workshops and other projects like ARBIOCOM (website link included below) that support collaboration among research societies, educational institutions, and private biomedical industries, all working together to further our understanding of biosignaling and cellular communication networks for the development of new drug discovery methods and disease treatments. Full article

The advent of Artificial Intelligence in the cockpit and the air traffic control centre in the coming decade could mark a step-change improvement in aviation safety, or else could usher in a flush of ‘AI-induced’ accidents. Given that contemporary AI has well-known weaknesses, from data biases and edge or corner effects, to outright ‘hallucinations’, in the mid-term AI will almost certainly be partnered with human expertise, its outputs monitored and tempered by human judgement. This is already enshrined in the EU Act on AI, with adherence to principles of human agency and oversight required in safety-critical domains such as aviation. However, such sound policies and principles are unlikely to be enough. Human interactions with current automation in the cockpit or air traffic control tower require extensive requirements, methods, and validations to ensure a robust (accident-free) partnership. Since AI will inevitably push the boundaries of traditional human-automation interaction, there is a need to revisit Human Factors to meet the challenges of future human-AI interaction design. This paper briefly reviews the types of AI and ‘Intelligent Agents’ along with their associated levels of AI autonomy being considered for future aviation applications. It then reviews the evolution of Human Factors to identify the critical areas where Human Factors can aid future human-AI teaming performance and safety, to generate a detailed requirements set organised for Human AI Teaming design. The resultant requirements set comprises eight Human Factors areas, from Human-Centred Design to Organisational Readiness, and 165 detailed requirements, and has been applied to three AI-based Intelligent Agent prototypes (two cockpit, one air traffic control tower). These early applications suggest that the new requirements set is scalable to different design maturity levels and different levels of AI autonomy, and acceptable as an approach to Human-AI Teaming design teams. Full article

This paper discusses the responsible use of artificial intelligence (AI) in public health and in medicine, and questions the development of AI ethics in international guidelines from a public health perspective. How can a global ethics approach help conceive responsible AI development and use for improving public health? By analysing key international guidelines in AI ethics (UNESCO, WHO, European High-Level Expert Group on AI) and the available literature, this paper advocates conceiving proper ethical and legal frameworks and implementation tools for AI in public health, based on a pragmatic risk-based approach. It highlights how ethical AI principles meet public health objectives and focuses on their value by addressing the meaning of human-centred innovations, transparency, accountability, diversity, equity, privacy protection, technical robustness, environmental protection, and post-marketing surveillance. It concludes that AI technology can reconcile individual and collective ethical approaches to public health, but requires specific legal frameworks and interdisciplinary efforts. Prospects include the development of supporting data infrastructures, of stakeholders’ involvement to ensure long-term commitment and trust, of the public’s and users’ education, and of international organisations’ capacity to coordinate and monitor AI developments. It formulates a proposal to reflect on an integrated transparent public health functionality in digital applications processing data. Full article

Experts consider tackling companion animal ownership problems, such as delayed veterinary treatment and a lack of appropriate care provision, to be key in striving towards improved animal welfare. Additionally, every year, millions of companion animals are relinquished to rescue centres globally; a process that can be distressing for both people and animals. By adapting traditional shelter model activity, it is possible to develop proactive community interventions to provide support for companion animal owners prior to crisis points and therefore, prevent suffering. This case report shares a novel approach to improving companion animal welfare and reducing avoidable relinquishment in communities. As part of a three-stage process, a mixed-method approach was applied to build an understanding of the needs of owners of potentially vulnerable companion animals in the target community. The research stages revealed that the lack of timely veterinary treatment for pets within the target community represented a welfare concern. Based upon this understanding, a co-creation approach was deployed to design targeted interventions to improve companion animal welfare in the community through the application of human behaviour change theories. The process revealed the operational effectiveness of a co-creation approach to intervention design in the context of improving animal welfare. This novel approach has demonstrated significant value in addressing the needs of pet-owning communities. Full article

This study explores the application of Temporal Fusion Transformers (TFTs) to improve the predictability of hourly potential hydropower production for a small run–of–the–river hydropower plant in Portugal. Accurate hourly power forecasts are essential for optimizing participation in the spot electricity market, where deviations incur penalties. This research introduces the novel application of the TFT, a deep–learning model tailored for time series forecasting and uncovering complex patterns, to predict hydropower production based on meteorological data, historical production records, and plant capacity. Key challenges such as filtering observed hydropower outputs (to remove strong, and unpredictable human influence) and adapting the historical series to installed capacity increases are discussed. An analysis of meteorological information from several sources, including ground information, reanalysis, and forecasting models, was also undertaken. Regarding the latter, precipitation forecasts from the European Centre for Medium–Range Weather Forecasts (ECMWF) proved to be more accurate than those of the Global Forecast System (GFS). When combined with ECMWF data, the TFT model achieved significantly higher accuracy in potential hydropower production predictions. This work provides a framework for integrating advanced machine learning models into operational hydropower scheduling, aiming to reduce classical modeling efforts while maximizing energy production efficiency, reliability, and market performance. Full article

Introduction: Recombinant human collagen, developed through advanced recombinant DNA technology, has emerged as a cutting-edge biomaterial with diverse applications in medicine. It addresses significant limitations of animal-derived collagens, such as immunogenicity and the risk of zoonotic diseases. Objective: This review evaluates the clinical applications, benefits, and challenges associated with recombinant human collagen, focusing on its potential to transform medical and surgical practices. Methods: A comprehensive search was conducted in MEDLINE, PubMed, and Ovid databases using keywords such as “Recombinant Human Collagen”, “Collagen-Based Biomaterials”, “Clinical Applications”, “Tissue Repair”, and “Wound Healing”. Relevant studies, including clinical trials and diagnostic applications, were analyzed and classified according to the Oxford Centre for Evidence-Based Medicine evidence hierarchy. Findings: Recombinant human collagen demonstrates superior mechanical properties and controlled degradation rates compared to traditional collagen sources. Clinical studies highlight its effectiveness in accelerating wound closure, promoting dermal regeneration, and minimizing scarring, making it particularly valuable in chronic wound management and surgical interventions. In tissue engineering, recombinant human collagen scaffolds have shown potential for regenerating cartilage, bone, and cardiovascular tissues by supporting cell proliferation, differentiation, and matrix deposition. Additionally, its adaptability for forming hydrogels and matrices enhances its suitability for drug delivery systems, enabling controlled and sustained release of therapeutic agents. Conclusion: Recombinant human collagen represents a transformative advancement in clinical practice, providing a safer and more effective alternative to traditional collagen sources. Its demonstrated success in wound healing, tissue engineering, and drug delivery highlights its potential to significantly improve patient outcomes. However, challenges such as high production costs, regulatory complexities, and long-term biocompatibility remain barriers to widespread clinical adoption. Further research and collaboration between biotechnology developers and regulatory authorities are essential to fully realize its clinical potential. Full article

This paper supports the idea of agroecology playing an integral role in development ‘smart cities’ and its application in different settlement contexts in South Africa. As alluded to in the People-Centered Smart Cities framework, the application of the smart cities approach can be extended to various settlement contexts. This paper promotes ‘the smart city’ concept in different contexts, including rural and small settlement environments, incorporating agroecology, a paradigm which guides us towards building sustainable and equitable urban environments. This approach can significantly contribute to the improved and more resilient design and development of human settlements. The Preferred Reporting Items for Reviews and Meta-analysis were employed to analyze primary and secondary data sources, thereby formulating descriptive and analytical themes around agroecology and smart cities. This paper utilized 54 articles, offering a robust foundation for the paper’s analysis and discussions. Additionally, the paper underscores the adherence to policy and legislative spaces for smart city strategy-led budgeting. It advocates for robust financial policies and long-term development financial strategies aligned with several the Sustainable Development Goals, but especially SGD 11, which is to create inclusive, safe, resilient, and sustainable cities and habitats. The construction of smart campuses, smart rural settlements, and smart school programs is demonstrated by the Centre for Ecological Intelligence at the University of Johannesburg’s food systems hub, the Phumulani rural agrivillage, and the Eastern Cape and Tshwane food security school programs. These showcase projects act as compelling models illustrating how the principles of smart cities can be applied to diverse settlement contexts. Full article

In practice, the structural analysis and design of pedestrian systems subjected to human-induced vibrations is often based on simplified biodynamic models that can be used in place of even more complex computational strategies to describe Human-Structure Interaction (HSI) phenomena. Among various walking features, the vertical reaction force that a pedestrian transfers to the supporting structure during motion is a key input for design, but results from the combination of multiple influencing parameters and dynamic interactions. Robust and practical strategies to support a realistic HSI description and analysis have hence been the object of several studies. Following earlier research efforts, this paper focuses on the optimised calibration of the input parameters for the consolidated Spring-Mass-Damper (SMD) biodynamic model, which reduces a single pedestrian to an equivalent SDOF (with body mass m, spring stiffness k, and viscous damping coefficient c) and is often used for vibration serviceability purposes. In the present study, this calibration process is carried out with smartphone-based acquisitions and experimental records from the Centre of Mass (CoM) of each pedestrian to possibly replace more complex laboratory configurations and devices. To verify the potential and accuracy of such a smartphone-based approach, different pedestrians/volunteers and substructures (i.e., a rigid concrete slab or a timber floor prototype) are taken into account, and a total of 145 original gaits are post-processed for SMD modelling purposes. The analysis of the experimental results shows a rather close match with previous findings in terms of key pedestrian parameters. This outcome poses the basis for a more generalised application of the smartphone-based strategy to a multitude of similar applications and configurations of practical interest. The validity of calibration output and its possible sensitivity are further assessed in terms of expected effects on substructures, with a critical discussion of the most important results. Full article

The accurate measurement of human balance is required in numerous analysis and training applications. Force plates are frequently used but are too costly to be suitable for home-based systems such as balance training. A growing body of research and commercial products use Pressure-Sensitive Mats (PSMs) for balance measurement. Low-cost PSMs are constructed with a piezoresistive material and use copper tracks as conductors. However, these lack accuracy, as they often have a low resolution and suffer from noise, non-repeatable effects, and crosstalk. This paper proposes novel algorithms that enable the Centre of Pressure (CoP) to be computed using low-cost PSM designs with significantly higher accuracy than is currently achievable. A mathematical model of a general low-cost PSM was developed and used to select the design of the PSM (track width and placement) that maximises CoP accuracy. These yield new optimal PSM geometries that decrease the mean absolute CoP error from 17.37% to 5.47% for an 8 × 8 sensor layout. Then, knowledge of the footprint was used to further optimise accuracy, showing a decrease in absolute error from 17.37% to 3.93% for an 8 × 8 sensor layout. A third algorithm was derived using models of human movement to further reduce measurement error. Full article

The integration of neurophysiological techniques into Industry 5.0 represents a transformative approach to assessing human factors in real-world operational settings. This study presents a systematic review of existing literature to evaluate the application of neurophysiological methods in assessing cognitive and emotional states, such as workload, stress, attention, and trust, within industrial environments. A total of X peer-reviewed articles published between 2018 and 2024 were analyzed following a structured methodology. The findings reveal that EEG (45%), eye-tracking (30%), EDA (20%), and ECG (15%) are the most frequently adopted techniques for monitoring cognitive and emotional responses. Additionally, 60% of the studies focused on stress and workload assessment, while only 25% examined trust and collaboration in human–robot interaction, highlighting a gap in comprehensive teamwork analysis. Furthermore, only 35% of the studies validated their approaches in real-world industrial settings, emphasizing a significant limitation in ecological validity. The review also identifies that multimodal integration remains underexplored, with just 15% of studies combining multiple neurophysiological signals for a more holistic assessment. These results indicate a growing but still fragmented research landscape, with clear opportunities for expanding real-world applications, improving methodological standardization, and fostering interdisciplinary collaboration. Future research should prioritize validation in dynamic, real-life work environments and explore the synergistic potential of multimodal neurophysiological approaches to enhance human-centred industrial systems. Full article

With the increasing application of robots in human-centred environments, there is increasing motivation for incorporating some degree of human-like social competences. Fields such as psychology and cognitive science not only provide guidance on the types of behaviour that could and should be exhibited by the robots, they may also indicate the manner in which these behaviours can be achieved. The domain of social child–robot interaction (sCRI) provides a number of challenges and opportunities in this regard; the application to an educational context allows child-learning outcomes to be characterised as a result of robot social behaviours. One such social behaviour that is readily (and unconsciously) used by humans is behavioural alignment, in which the behaviours expressed by one person adapts to that of their interaction partner, and vice versa. In this paper, the role that robot non-verbal behavioural alignment for their interaction partner can play in the facilitation of learning outcomes for the child is examined. This behavioural alignment is facilitated by a human memory-inspired learning algorithm that adapts in real-time over the course of an interaction. A large touchscreen is employed as a mediating device between a child and a robot. Collaborative sCRI is emphasised, with the touchscreen providing a common set of interaction affordances for both child and robot. The results show that an adaptive robot is capable of engaging in behavioural alignment, and indicate that this leads to greater learning gains for the children. This study demonstrates the specific contribution that behavioural alignment makes in improving learning outcomes for children when employed by social robot interaction partners in educational contexts. Full article