Objective. An important domain of a comprehensive geriatric assessment, in addition to the usual domains, is the assessment of nutritional status. The aim of this paper is to present the development of a new instrument/tool for the assessment of nutritional risk in the elderly. Methods. An international team of experts constructed and validated a new instrument/tool for nutritional risk assessment in older adults in Bosnia and Herzegovina (Nutrition risk assessment in older adults in primary care - NRA). Results. The nutritional risk screening and assessment tool is comprehensive, has acceptable validity and internal consistency, and contains a total of 14 questions. The scoring of each question as well as the final score of the tool is designed in detail, and the maximum number of points is 21. The tool has three categories, and a lower score indicates a nutritional risk. An eight-step approach was used to determine the most appropriate items for nutritional risk screening and malnutrition assessment: correspondence analysis, summary item generation, content validity, internal consistency, construct validity, criterion validity, face validity, and reliability. A kappa coefficient of 0.918 (p<0.001) indicates excellent intra-rater reliability. The Alpha coefficient (0.721) was significant. The time required to complete the assessment ranged from 15 to 22 minutes. Conclusion. Application of this new tool is desirable and feasible in other countries with linguistic validation, and the new NRA tool is available in Serbian and English.

Since rainwater extracts toxic metals from landfills, creating harmful leachate, developing methods to remove these metals is necessary. This work presents a method of toxic metal ions removal from a loam-type soil consisting of washing the soil with a mild washing agent to extract toxic metals in a leachate, and a purification of the leachate by filtering it through a synthesized graphene oxide (GO) membrane. As washing agents, the pure water and a mild solution of HCl (0.01 M) were tested. The GO membrane was synthesized using natural Madagascar graphite. The solution of HCl showed a significantly higher washing efficiency of Zn(II), Cd(II), and Pb(II) cations than pure water due to its acidic nature. An intrinsic GO membrane with an interlayer distance of 0.68- 0.74 nm (before and after filtration) and a thickness of ~0.70 ?m yielded rejections of 99.80%, 96.15%, and 44.00% for Pb(II), Cd(II) and Zn(II) ions, respectively. Molecular dynamics simulation showed that ions are retained in the GO interfaces due to the narrow interlayer distance, leading to membrane fouling. Nevertheless, the high rejections of Pb(II) and Cd(II) support the possibility of purifying landfill soil leachate by the GO membrane

This paper presents new records and noteworthy data on the following taxa in SE Europe and adjacent regions: green alga Cosmarium crenulatum, fungi Calvatia fragilis, Hypoxylon fuscum, Myriostoma coliforme and Zeus olympius, mosses Antitrichia curtipendula, Buxbaumia viridis, Homalothecium meridionale, Physcomitrium arenicola, Sphagnum inundatum and Syntrichia calcicola, monocots Anacamptis ? nicodemi nothosubsp. nicodemi, Ornithogalum montanum and Parapholis incurva and dicots Cardamine waldsteinii and Suaeda salsa.

This paper presents a data mining approach for Audit opinion pre¬diction in Government-owned enterprises within the Federation of Bosnia and Herzegovina using the Decision tree algorithm. A database was constructed from financial statements covering 2004-2019, incorporating indicators from balance sheets, income statements, and cash flow statements, alongside cor¬responding Audit opinions from the state audit body. The study evaluates three Decision tree algorithms (J48, RandomTree, REPTree) on data from 2020-2023, with REPTree achieving 73% classification accuracy through seven predictive rules. The findings demonstrate the potential of data mining techniques for pattern recognition in audit reports, contributing to transparency in financial reporting and supporting regulatory authorities in detecting irregularities within Government-owned enterprises.

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?

Computer aided design (CAD) 3D modelling is one of the engineering tasks which is largely routine tasks with a large amount of repetition of the same operations to get from the initial idea for a new product to a 3D model ready for manufacturing. As with all other forms of routine tasks, artificial intelligence (AI) will certainly play a significant role in the future and it will largely automate such jobs. On the other hand, additive manufacturing (AM) can use AI generated CAD 3D models to produce finial product without the need (or with minimal need) for human labour. The combination of these two technologies will certainly shape the future of product design, development and manufacturing. Overview of the current possibilities of using artificial intelligence (AI) and additive manufacturing (AM) in the field of product development, design and manufacturing is presented in this paper. From the point of view of CAD modelling, special attention is given to the so-called "text to 3D model" systems. The challenges, possibilities and further directions of development of these technologies are shown through two real case studies (design, development and manufacturing of two stool chairs). Stool chairs design was generated with the help of "text to 3D model" AI System in a form of 3D models. The generated 3D models were then manufactured with the help of AM.In the last chapter of the paper a comparative analysis of the time spent by human labour for the development, design and manufacturing of this two stool chairs using conventional methods and using AI and AM is carried out

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.

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.

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.