The sixth generation of mobile networks (6G) has the potential to revolutionize the way we communicate, interact, and use information for machine-to-machine (M2M) communication in Industry 4.0 and Industry 5.0, while also improving coverage in places that were previously considered difficult to access and/or digitally excluded, and supporting more devices and users. The 6G network will have an impact through a combination of many technologies: the Internet of Things (IoT), artificial intelligence/machine learning, virtual and augmented reality, cloud computing, and cyber security. New solutions and architectures and concepts for their use need to be developed to take full advantage of this. This article provides an overview of the challenges in this area and the proposed solutions, taking into account the disruptive technologies that are yet to be developed.

An aging population, the effects of pandemics and civilization-related conditions, and limited leapfrogging in the number of rehabilitation and physiotherapy specialists are driving demand for modern assistive technologies, especially upper and lower limb exoskeletons. Patient-tailored devices are a rapidly developing group of technologies, both from a biomechanics, informatics, and materials engineering perspective. In particular, the technological development of 3D printing, the expanding range of available materials and their properties (including contact with living tissue and bodily fluids), and the possibility of selecting and optimizing them using artificial intelligence (including machine learning) are encouraging the emergence of new concepts, particularly within the Industry 4.0 paradigm. The article provides an overview of what is available in this area, including an assessment of as yet untapped research and industrial and, in part, clinical potential.

The human hand is the most precise and versatile tool that nature has given man, and any deficits in this area affect the functional capabilities and quality of human life. Scientists, engineers and clinicians are constantly looking for solutions in the field of diagnosis, treatment, rehabilitation and care of patients with hand function deficits. One such solution is a hand exoskeleton. In the process of designing and testing the hand exoskeleton, emphasis should be placed on the full usability and comfort of the system; hence, the issues of personalization, matching and testing are crucial for the development of the discussed group of solutions. The aim of this paper is to present the possibilities of personalizing 3D-printed medical devicesbased on our own experience in functional user assessment andthe material selection, design, optimization using artificial intelligence and production and testing of several generations of different upper limb exoskeletons, incorporatingthe considerations of the Medical Device Regulation (MDR), ISO 13485 and ISO 10993 standards.The novelty and possible contribution of the proposed approach consist of the possibilities and limitations of the personalization of the upper limb exoskeleton discussed in the article as well as the directions of further development of significant scientific, technical and clinical importance.

Creative thinking is an inherent process in the creation of innovations. Imagination is employed to seek creative solutions. This article presents research results on the use of inventive methods to develop an eco-friendly product. A household appliance was selected as the object of research. The article deals with issues relating to eco-design, eco-innovation, and inventory. The process of selecting inventive methods was presented. Selected inventive methods used to develop the product concept were briefly characterized. Creativity sessions were conducted using the methods of brainstorming, stimulating, reverse brainstorming, word games, and superpositions. The effect of these activities is the concept for an eco-innovative product. A product design was developed that is highly recyclable and environmentally friendly. An ecological analysis of the designed product, including AI-based (artificial neural networks), was carried out, which showed the legitimacy of the actions taken to develop an environmentally friendly product. The novelty of the proposed approach consists of combining the use of research data, with new methods for their analysis using both traditional and artificial intelligent tools, to create a transparent and scalable product design. To date, this approach is unique and has no equivalent in the literature. Despite higher manufacturing costs, the more environmentally friendly refrigerator is cheaper in operation (consumes less energy) due to the ecological solutions incorporated into its design.

The rapid development of modern information technology (IT), power supply, communication and traffic information systems and so on is resulting in progress in the area of distributed and energy-efficient (if possible, powered by renewable energy sources) smart grid components securely connected to entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence. Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. This article presents and develops the authors’ own concept in this area, which is planned for research in the coming years. The paper aims to develop and initially test a conceptual framework that takes into account the aspects discussed above, emphasising the practical aspects and use cases of the Social Internet of Things (SIoT) and artificial intelligence (AI) in the everyday lives of smart sustainable city (SSC) residents. We present an approach consisting of seven algorithms for the integration of large data sets for machine learning processing to be applied in optimisation in the context of smart cities.

The incidence of myocardial infarction (MI) is growing year on year around the world. It is considered increasingly necessary to detect the risks early, respond through preventive medicines and, only in the most severe cases, control the disease with more effective therapies. The aim of the project was to develop a relatively simple artificial-intelligence tool to assess the likelihood of a heart infarction for preventive medicine purposes. We used binary classification to determine from a wide variety of patient characteristics the likelihood of heart disease and, from a computational point of view, determine what the minimum set of characteristics permits. Factors with the highest positive influence were: cp, restecg and slope, whilst factors with the highest negative influence were sex, exang, oldpeak, ca, and thal. The novelty of the described system lies in the development of the AI for predictive analysis of cardiovascular function, and its future use in a specific patient is the beginning of a new phase in this field of research with a great opportunity to improve pre-clinical care and diagnosis, and accuracy of prediction in preventive medicine.