The integration of technology in correctional facilities represents a paradigm shift in modern prison rehabilitation approaches globally, offering unprecedented opportunities to enhance inmate reintegration while addressing systemic challenges. This research examines the global phenomenon of digital rehabilitation, using the current state and prospects of technology adoption in Albanian prisons as an illustrative case study. By analysing both opportunities for rehabilitation enhancement and implementation challenges, this study highlights the broader implications of digital tools in correctional settings. Through a comprehensive analysis of recent developments from 2020 to 2025, including international cooperation initiatives, this study reveals significant potential for digital transformation in correctional systems. Key findings indicate that technology-enhanced rehabilitation programs can reduce recidivism rates by up to 23% compared to traditional methods, while digital education platforms show 68% success rates versus 35% for conventional approaches. However, implementation faces substantial barriers globally, including high costs (85% impact), ageing infrastructure (78% impact), and staff training requirements (72% impact). The research demonstrates that strategic   technology adoption, supported by international partnerships and phased implementation approaches, can transform prisons into modern rehabilitation-focused institutions. This study contributes to the growing body of international knowledge on correctional technology, providing insights that are transferable to other transitional contexts and informing global policy decisions.

The first industrial robots appeared in the production processes in the 1960s have continued to be implemented in manufacturing worldwide. The greatest application of industrial robots has been observed in three major industries: the automotive industry, the electrical and electronics industry, and the metal industry. The automotive industry was the first to adopt the most industrial robots extensively, and in recent years the electrical and electronics industry has followed. Together, these two sectors account for more than 60% of the total number of industrial robots deployed worldwide. Industrial robots have primarily been used to perform tasks that are physically demanding and hazardous to workers’ health, including welding operations, which are predominantly carried out in the automotive industry. To date, first-generation industrial robots have been the most widely implemented. These systems are typically enclosed by protective fences to ensure worker safety, occupy substantial floor space, and are relatively complex to reprogram. The development of advanced technologies — such as sensor systems, the Internet of Things (IoT), big data analytics, cloud computing, virtual and augmented reality (AR), artificial intelligence (AI), and advanced safety systems — has significantly contributed to the evolution of robotic technology. The present study presents current trends in the implementation of industrial robots and examines their role in welding processes.

Industry 4.0 marks a new phase of industrial transformation, driven by the integration of advanced technologies such as industrial robotics, the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and cyber‑physical systems (CPS). The Republic of Korea and Singapore are global frontrunners in this domain, ranking first and second worldwide in robot density per 10,000 manufacturing workers. This paper explores how the strategic integration of robotics with key Industry 4.0 technologies contributes to smart manufacturing and enhanced industrial performance. Using a comparative case study approach, the research analyzes national policies, investments in R&D and education, 5G infrastructure, and support for innovation ecosystems that have enabled these countries to develop flexible, automated, and intelligent production systems. Findings indicate that both Korea and Singapore have successfully combined robotics with IoT, big data analytics, and cloud platforms to create efficient and adaptive manufacturing environments. The study emphasizes that robotization alone is not sufficient; its effectiveness depends on alignment with broader digital transformation strategies. Based on longitudinal data from 2013 to 2023, sourced from the International Federation of Robotics (IFR), the OECD, and national innovation agencies, the research highlights how coordinated implementation of Industry 4.0 technologies fosters sustainable and globally competitive manufacturing.

The integration of human-robot collaboration (HRC) into industrial and service environments demands efficient and adaptive robotic systems capable of executing diverse tasks, including pick-and-place operations. This paper investigates the application of Soft Actor-Critic (SAC) and Conservative Q-Learning (CQL)—two deep reinforcement learning (DRL) algorithms—for the learning and optimization of pick-and-place actions within HRC scenarios. By leveraging SAC’s capability to balance exploration and exploitation, the robot autonomously learns to perform pick-and-place tasks while adapting to dynamic environments and human interactions. Moreover, the integration of CQL ensures more stable learning by mitigating Q-value overestimation, which proves particularly advantageous in offline and suboptimal data scenarios. The combined use of CQL and SAC enhances policy robustness, facilitating safer and more efficient decision-making in continually evolving environments. The proposed framework combines simulation-based training with transfer learning techniques, enabling seamless deployment in real-world environments. The critical challenge of trajectory completion is addressed through a meticulously designed reward function that promotes efficiency, precision, and safety. Experimental validation demonstrates a 100 % success rate in simulation and an 80 % success rate on real hardware, confirming the practical viability of the proposed model. This work underscores the pivotal role of DRL in enhancing the functionality of collaborative robotic systems, illustrating its applicability across a range of industrial environments.

The article aims to mathematically model processes that occur in non-metallic heterogeneous materials when active thermography methods were used for deep study control. Currently, the trend in industrial development is using non-metallic heterogeneous mediums as a system of various components as structural materials. Such materials are characterized by improved physical and mechanical properties, which can be adjusted by selecting compositions and the ratio of component phases and macrostructure. At the same time, they are characterized by disadvantages such as variability of volume and time properties and the presence of various defects. Effective control methods are of particular importance to ensure the quality and reliability of products made from materials of this class. In the article, the authors analyzed the capabilities of existing non-destructive testing methods for flaw detection of products made of non-metallic heterogeneous materials. When developing a new and improving an existing measurement method, the problem of establishing a set of radiation parameters was used. This allowed for describing the state of the material with the necessary accuracy and the functional connections of these parameters with the latter’s characteristics.

Integrating service robots into contemporary healthcare systems has significantly advanced the scope and complexity of robotic design, especially regarding the materials used in direct interaction with patients and sterile medical environments. This article investigates the pivotal role of biomaterials in shaping both the structural integrity and functional performance of service robots. A key focus was placed on the selection criteria, biocompatibility, sterilization potential, and adaptability of advanced biomaterials used in components that demand mechanical efficiency and safety. A key focus was also placed on the quantitative selection criteria for these materials, including mechanical strength (e.g., tensile strength of more than 50 MPa for polymeric joints), elasticity (Young’s modulus ranging from 10–1000 MPa depending on the application), and biocompatibility ratings based on the ISO 10993 standard.Particular attention was paid to integrating biocompatible polymers and composites that should withstand repeated sterilization cycles (up to 100 autoclave exposures without structural degradation) while maintaining antimicrobial surfaces and hypoallergenic properties. Additionally, the study explored the application of smart materials (e.g., stimuli-responsive hydrogels and shape-memory alloys), which showed response times under 5 s and deformation recovery rates above 90 %, enabling adaptive robotic behavior in dynamic contexts. The study also outlines current research trends, i.e., using responsive polymers, bioinspired composites, and additive manufacturing techniques that enable personalized robotic solutions. Additive manufacturing techniques were analyzed as enablers of rapid prototyping and patient-specific customization, with the article referencing case studies where 3D-printed biopolymer components reduced development time by 40 % and improved fitting precision in assistive robotic devices by 30 %.Emerging research trends were finally examined through bibliometric data, indicating 3.5 times increase in publications related to “biomaterials in medical robotics” from 2015 to 2024 in Scopus. Overall, the research critically examined the challenges associated with material certification processes, emphasizing that the average duration required to obtain regulatory approval typically spans between 18 and 24 months, posing a significant barrier to the timely deployment of advanced robotic systems in actual environments. By adopting an interdisciplinary perspective that combines materials science and robotics engineering, this study underscores the transformative impact of biomaterials in redefining the capabilities, safety, and personalization of medical service robots. The findings highlight technological advancements and future directions in robotic systems’ sustainable and intelligent deployment.

Industry 4.0 represents a new chapter in the development of manufacturing systems, where digitalization, automation, and the application of advanced technologies become key drivers of competitiveness. The textile industry, traditionally characterized by manual processes, is undergoing a profound transformation through the integration of next-generation robotics. This paper analyzes the significance and impact of robotic implementation within the Industry 4.0 framework on process efficiency, flexibility, and sustainability in textile production. Special attention is given to the application of collaborative and autonomous robots, which enable smart work organization, optimized transport and storage, and adaptive production flow management. The study highlights both the benefits brought by the adoption of advanced robotic systems and the challenges encountered during their implementation, such as the need for digital competencies among the workforce and high investment costs. Through the analysis of current trends and examples of good practice, the paper points to key development directions aimed at enhancing innovation, sustainability, and global competitiveness of the textile sector. The conclusion emphasizes the necessity of a strategic approach and continuous investment in new technologies to ensure a successful transition toward the smart factory of the future.

It is well known that with the emergence of Industry 4.0, the focus was placed on the digitalization and automation of industrial processes through technologies such as the Internet of Things (IoT), Big Data, artificial intelligence (AI) and robotics, which led us in the direction of smart production processes with the goal of ‘’smart factories’’. Unlike Industry 4.0, Industry 5.0 emphasizes the importance of humanization of technology, where people and robots work together in a harmonious environment. The paper examines whether advanced robotic technology can be synergistically integrated with human creativity to create more efficient, innovative and sustainable production practices. The paper explores the key elements that enable the integration of robotic technology and human creativity, including collaborative robots (cobots), artificial intelligence that supports creative processes and advanced sensor systems. Collaborative robots, designed to work safely alongside humans, take over routine and physically demanding tasks, freeing up time for workers to focus on creative and strategic activities. AI technologies analytically support human decisions, enabling faster and more informed innovation. Ethical and safety aspects of robotic technology integration are discussed, emphasizing the need for a transparent and responsible approach. The application of robotic technology in industry brings significant benefits, including increased productivity, cost reduction, improved worker safety and more sustainable development. The key to the success of Industry 5.0 is in creating a balanced synergy between technology and human creativity. By harmonizing automation with humanization, industry can achieve new levels of innovation and efficiency, adapting to the dynamic needs of the global marketplace. This approach ensures not only technological progress, but also social responsibility, thus laying the foundations for a sustainable and prosperous future for the industry.

The rapid evolution of Industry 4.0 is fundamentally reshaping the global automotive sector, positioning digitalization, automation, and robotics as core drivers of innovation and competitiveness. This paper examines the implementation and impact of Industry 4.0 technologies in three leading vehicle-producing countries with distinct industrial trajectories - China, India, and the United States. Through a comparative approach, the study explores the relationship between annual vehicle production, the intensity of industrial robot adoption, and the integration of smart manufacturing solutions. Special attention is given to robotics-both industrial and collaborative-as a key enabler of efficiency, flexibility, and innovation in production systems. The analysis also highlights the fundamental components of Industry 4.0, including cyber-physical systems, the Internet of Things (IoT), digital factories, artificial intelligence (AI), and digital twins, which collectively enable the synergy between humans, machines, and data. The paper presents recent trends in robotization and digital integration within automotive manufacturing, accompanied by an overview of national policies and investment priorities. Findings reveal that China leads in absolute vehicle output and robot installations, the United States focuses on highly automated and digitally connected production systems, while India is rapidly developing its capacities through selective and adaptive implementation of Industry 4.0 technologies. The study concludes that differing approaches to digital transformation are shaping unique models of competitiveness, technological sovereignty, and sustainable development in the automotive industry.

Picture this: a world where machines can decode the intricate rhythms of the human body, tracing electrical patterns from the brain and heart to uncover hidden signs of disease. Artificial intelligence has brought this vision closer to reality, transforming electroencephalography (EEG) and electrocardiography (ECG) analysis into a sophisticated fusion of data science and medicine. Yet, the journey is far from complete. Biomedical signals are notoriously complex—drenched in noise, prone to variability, and demanding meticulous preprocessing before they reveal their secrets. This review embarks on a deep dive into the essential preprocessing and feature engineering techniques that refine raw EEG and ECG data, making them suitable for intelligent analysis. From signal filtering to wavelet transformations, each step in the pipeline plays a crucial role in shaping AI’s ability to detect meaningful patterns. Particular attention is given to recurrent neural networks (RNNs), which excel in capturing the temporal dependencies hidden within these signals but come with their own set of computational hurdles. Beyond technical refinement, the discussion extends into the future—how can multimodal AI enhance clinical diagnostics?

The implementation of new Industry 4.0 technologies in robotics (mobile and collaborative robotics) with artificial intelligence (AI) is reshaping maintenance planning in advanced manufacturing. This paper analyzes the application of robotic systems combining collaborative robots (cobots) and autonomous mobile robots (AMRs) as support for predictive maintenance. Predictive maintenance is based on continuous real-time visual monitoring with the goal of managing faults. A mixed-methods approach was used, combining quantitative metrics such as downtime reduction, mean time to repair, and return on investment with qualitative staff assessments. The results of implementing robotic systems to support predictive maintenance indicate a significant reduction in production downtime, increased operational efficiency, and faster resolution of faults in the manufacturing process. In addition to technical efficiency, the study analyzes the economic feasibility, stability, and challenges of implementing AI vision systems within Industry 4.0. Compared to previously published studies in this field, this work is distinguished by the implementation of a cobot and an AMR in a unified system for visual inspection and control, with real-time data used for predictive maintenance. The system is connected to Computerized Maintenance Management Systems software for maintenance planning and monitoring and Enterprise Resource Planning software for real-time business activity planning. The results demonstrate that the integration of advanced robotics, computer vision, and machine learning algorithms enables the transformation of the traditional reactive approach into a proactive asset management model, thereby ensuring a long-term sustainable increase in reliability, safety, and competitiveness of the manufacturing processes.

In a time marked by climate change and the depletion of natural resources, solar energy is increasingly recognized as a key driver of sustainable development. This paper analyzes the role of solar energy in the energy transition, focusing on its technical capabilities, economic benefits, and potential for job creation. Furthermore, it explores how the synergy between technological advancement and market mechanisms can accelerate the replacement of fossil fuels and contribute to the reduction of greenhouse gas emissions. The technical section outlines recent progress in photovoltaic technologies, improvements in solar panel efficiency, cost reductions in production, and the role of smart grids in integrating renewable sources into the energy system. Through an analysis of market trends, the importance of investments in renewable energy, subsidy mechanisms, tax incentives, and public-private partnerships in expanding solar capacity is emphasized. From an economic perspective, the paper explores the potential of the solar industry to create jobs–ranging from manufacturing and installation to maintenance and research. Utilizing available statistical data and models, the study demonstrates that investments in the solar sector yield multiple benefits: energy sovereignty, reduced dependence on fossil fuel imports, and the creation of new economic opportunities in both rural and urban areas. Case studies from countries that have successfully implemented solar strategies (e.g., Germany, Spain, and India) highlight key success factors and foreseeable obstacles that can be addressed proactively. Special attention is given to the role of public policy and regulatory frameworks that support the faster integration of solar energy, as well as the need for education and workforce retraining in accordance with the demands of the new energy paradigm. The paper concludes that synchronized efforts between technological development and market instruments are essential for accelerating the green transition. Solar energy, as a clean, renewable, and increasingly accessible resource, has the potential not only to replace fossil fuels but also to become a foundation for economic resilience and social equity in a post-carbon society.

Solar energy is increasingly gaining significance as an essential part of sustainable development and the transition to cleaner forms of energy. Its application helps reduce dependence on fossil fuels, contributes to the reduction of harmful gas emissions, and offers numerous economic and ecological benefits. This paper examines the role of solar energy in creating a greener energy system, focusing on the technical, economic, and environmental aspects of its application. The paper begins by reviewing global capacities and current trends in solar energy usage, with special emphasis on technological advancements that enable greater efficiency of solar systems. It then analyzes financial aspects, including initial costs, subsidy opportunities, and the long-term profitability of investments in this technology. The ecological benefits of solar energy are also an important focus, ranging from reduced carbon dioxide emissions to preserving natural resources and improving air quality. One of the key elements of the paper is the evaluation of solar system efficiency, as precise measurement is essential for making informed decisions about their implementation and profitability. In this context, factors such as production capacity, efficiency, economic sustainability, and comparisons with other renewable energy sources are considered. The paper also addresses challenges—barriers related to infrastructure, financing, and regulatory frameworks continue to hinder the broader adoption of solar energy. Solar energy holds significant potential for transforming the energy sector toward a more sustainable model. However, to fully realize this potential, it is essential to continue the development of advanced technologies, ensure better integration of solar systems into existing grids, and enhance institutional and regulatory support. Systematic investment in innovation and strengthening international cooperation can significantly contribute to global efforts in combating climate change and ensuring energy stability.

: It is known that in recent years there have been major changes in all branches of industry, especially in the automotive and electro-electronic industry, because new business methods are on the scene, and production processes are being transformed so that they are flexible. In the automotive and electro-electronic industry, the leading technology is robotic technology, the application of which increases the return on investment. Advanced robotics as the basic technology of Industry 4.0 in the new era of production in the automotive and electro-electronic industry plays a very important role because it enables: mobility, readiness, reliability, adaptability, transformation of production, integration with machines, increase of flexibility, improvement of quality, storage and production systems integrated as Cyber-Physical Systems, workers are freed from routine and repetitive tasks. The paper provides an overview of applied and issued patents in robotic technology, the application of robots in the World and China as the leader in the implementation of robotic technology in the world. An analysis of the implementation of industrial robots, as well as advanced robots in the automotive and electro-electronic industries of China, is given, as well as the forecast of the application in the coming years.