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.

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.

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 textile industry is the second most environmentally polluting industry in the world after the petroleum industry. Approximately 25% of global chemicals are used in textile production, around 10% of the world's CO2 emissions are generated by the textile industry, and it consumes more water than any other industry. Annually, about 92 million tons of textile waste are produced, with only an estimated 10-30% of the waste being recycled. Textile waste can be reused or recycled using various methods for thermal insulation materials. The aim of this study is to analyze and compare recycled thermal insulation materials with conventional thermal insulation materials based on economic and environmental criteria. By using the DEXi software tool, a multicriteria analysis was conducted between recycled textile and conventional thermal insulation materials based on economic and environmental criteria. In terms of economic and environmental criteria, conventional thermal insulation materials have an advantage over thermal insulation materials made from recycled textiles. In conclusion, the decision on which option is better depends on the specific requirements of the project. If sustainability is a key priority, based on all available information, thermal insulation made from recycled textiles is the better choice. However, if priorities are focused on heat insulation, moisture resistance, and durability, EPS is currently the better option. According to the planned scenario, there is a possibility that the efficiency of both thermal insulation materials can be balanced through additional research and improvement.

Red mud, a by-product of alumina production from bauxite, and tionite, a residue from titanium dioxide manufacturing from ilmenite, represent two large-scale industrial wastes with significant environmental impact and underutilized resource potential. Both residues contain high concentrations of iron and titanium-bearing phases, yet their stability and complex mineralogy hinder direct valorization. In this work, a carbothermal reduction strategy was applied to simultaneously address the removal of iron and the structural modification of titanium phase. Calcium oxide was introduced as a fluxing agent, not only to enhance reduction kinetics but also to promote the formation of calcium titanate, thereby destabilizing the rutile-rich tionite structure. The primary objective was the selective reduction and separation of iron, while preparing a CaO-modified titanium-bearing slag suitable for subsequent leaching. This approach highlights a dual benefit: mitigating the environmental burden of industrial residues and providing a pathway toward resource recovery of iron and titanium.

This study aims to identify the impact of macroeconomic and market variables on the Bosnian and Herzegovinian stock market. Two stock market indices on the capital market of Bosnia and Herzegovina are considered: the SASX-30 and BIRS. At the same time, macroeconomic and market variables include turnover (TRN), market capitalization (MC), exports (EXP), imports (IMP), gross domestic product (GDP), consumer price index (CPI), and foreign direct investment (FDI). The study adopts panel regression analysis for the period from 2013 to 2022. The results of the panel regression analysis showed that export and import values have the strongest influence on the movement of returns for the SASX-30 and BIRS stock market indices in the capital market of Bosnia and Herzegovina. On the other hand, the variables achieved the lowest significance: turnover, market capitalization, foreign direct investments, and consumer price index.

The primary objective of this paper is to identify factors that have a statistically significant impact on the exposure of banks in Bosnia and Herzegovina to interest rate risk and its effect on business profitability indicators. Therefore, this paper investigates the impact of interest rate risk on the performance of banks in Bosnia and Herzegovina between 2014 and 2024. The least squares panel regression model predicts the return on assets and the return on equity of banks in Bosnia and Herzegovina as a function of interest rate risk, which is indexed by the ratio of loans to assets, the average lending ratio, and the risk of interest rate diversification. Empirical findings in the paper revealed that the independent variable, average lending ratio as a measure of interest rate risk based on the method of least squares, has a significantly positive influence on the first and second dependent variables, that is, ROA and ROE, with a significance of less than 5%. The correlation between the risk of interest diversification and the first indicator of profitability (ROA) is positive with a significance of less than 5%, while the correlation between the risk of diversification of interest and the second indicator (ROE) is positive but with a significance of more than 5%. On the other hand, a negative correlation was recorded between the loan-to-assets ratio and the ROA indicator, with a significance greater than 5%. Also, a positive correlation was achieved between the loan-to-assets ratio and the ROA indicator with a probability greater than 5%.

Aware of the negative impact that agricultural production has on the environment, especially on the soil, alternative systems of agricultural production have been developed. It combines traditional knowledge characteristic of conservation agriculture and organic agriculture, but also modern technologies important for urban and precision agriculture, in order to manage the land in the best possible way. The biggest consequences of conventional agricultural production on the soil are: decrease in the content of organic matter, deterioration of the structure, soil compaction and contamination, salinization, alkalization, acidification. The aim of the paper is to present different concepts (forms) of agricultural production systems that can contribute to the preservation of soil and the environment, some of them, Urban Agriculture and Precision Agriculture. What they all have in common is that they are in line with the objectives of Good Agricultural Practice (GAP). Contemporary domestic and foreign literature was used in the research, which deals with the issue of sustainable land management primarily from an agronomic point of view, but also considers the economic, social and ecological importance. The results show that when deciding which system of plant production is the most acceptable for a certain area, several factors must be taken into account, such as: production characteristics of the soil, reef, ecological conditions, intensity and type of plant production, economic strength of the farm, even the educational structure of the holder of the farm and the mentality of the population in the rural environment. Farmers willingness to switch from convectional production to systems that will improve soil properties is conditioned by their recognition that they will realize financial benefits. The speed of recognition will depend on the institutional measures taken through education, financial support for the transition to some of the mentioned systems and appropriate legal regulations that will support all of the above.

This paper emphasizes different aspects of the activity of Serbian choral societies in Bosnia and Herzegovina in the period since the foundation of the first Lower Tuzla Serbian Church Choir in March 1886 until the beginning of the First World War. The aim of the paper is to comprehensively and systematically present the available data. It was made on the basis of archival material and previous research by scholars from Bosnia and Herzegovina and Serbia. The activities of the choral societies are presented chronologically, pointing out the choirmasters active in this period, the repertoire of the societies, the most important performances, anthems and flags.