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.

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

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.

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.

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Background: I am approaching this text from one of my articles that was recently published in this same journal, which was “processed” by one of the expert systems. This Artifficial Inteligence (AI) processed article by the system can be an argument that AI can be misused. By comparing the original and the AI copy, the readers can conclude that among those who are challenged by AI, the scientific content can be “converted” from the original into a plagiarized one, thus creating the possibility of endless plagiarism and theft of scientific content that was published in the past in various types of publications, and especially those in world-class journals in the world\'s scientific databases. Objective: The aim of this article was to explain current consequences regarding use AI in science editing of scientific literature. Methods: Author use published articles in most influential indexed databases with topics abot AI which were tolked about influence of AI in preparation articles for publishing in the books, monographs, master and doctor thesis and scientific journals.Results and Discussion: The future of artificial intelligence in education is bright, with the potential to transform the way higher education institutions teach and learn. AI can improve the accuracy and speed of tasks, reducing the risk of human error. Conclusion: The responsible development of AI involves addressing ethical concerns and ensuring accountability and trust. AI systems need to be designed with human oversight and control and it’s essential to address the ethical and practical challenges to ensure that AI benefits all stakeholders in the educational ecosystem, including scientific research and scientific editing..

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Summary This paper presents the influence of equal channel angular extrusion on the microstructure and properties of composites based on the A356.0 aluminium alloy with the addition of 6 wt.% fly ash as reinforcement. The composite was produced using the compo casting method. The microstructure of the composite was analysed using an optical microscope, computer tomography, and scanning electron microscopy. Rosettes and spheroidal particles were observed in the microstructure of the cast sample, and there was a good distribution of fly ash within the matrix. Equal channel angular extrusion was performed in three passes at the same parameters. After each pass, the microstructure was refined, and fly ash was even better mixed into the matrix. Thus, the strength and hardness of the composite increased after each pass. A fully homogeneous material was achieved after the second pass.

Background: Water is essential in air conditioning processes, where it is used for cooling or heating. Its longer retention in the tanks and pipes of air conditioning devices can lead to physical and chemical changes that favor the appearance and reproduction of Legionella spp. This pathogen causes diseases that arise from inhalation of contaminated aerosol. The research was conducted in the Federation of Bosnia and Herzegovina, Bosnia and Herzegovina during 2023-2024. and included 30 public facilities where 54 water samples were collected from air conditioning devices. Objective: The main goal of the research was to determine the presence of Legionella spp. in water from air conditioning devices, and to examine correlation of physical and chemical parameters (temperature, residual chlorine, ph value, iron, zinc and copper) with the pathogen presence. An additional goal of the research was to examine the relationship between the physical and chemical parameters of water. Methods: The research was conducted at the territory of the Federation of Bosnia and Herzegovina (FBiH), Bosnia and Herzegovina (BiH) in the period 2023-2024. The samples were analysed in an authorized laboratory in Sarajevo, using standard microbiological and chemical methods. The water of the air conditioning units was analysed for presence of L. spp., the detection of the bacteria and the determination of the relationship between the examined physical and chemical factors and the settlement of pathogens in the air conditioning units. Results: Legionella spp. was isolated in 7 (13%) of 54 analysed samples. A correlation between higher temperature, lower residual chlorine, and higher concentrations of iron and copper with the bacteria appearance was established. Among the analysed parameters, several significant mutually positive correlations were recorded. Out of a total of 54 analysed samples from air conditioning devices, L. spp. was isolated in seven. The results showed that there is a relation between higher temperature and lower concentration of residual chlorine, as well as higher concentrations of Fe and Cu, with the occurrence of L. spp. in air conditioning devices. No statistically significant association between the presence of bacteria and Zn concentration or water pH was confirmed. By analysing the mutual relations between the tested physical and chemical parameters, the relation of Fe with Zn and Cu, and Zn with Cu and Ph value was determined. The limitation of this study refers to the fact that during the testing the so-called dead pipes in the system was not detected nor present, nor was there a test for the presence of biofilm inside the installation. Conclusion: The results indicate the importance of regular monitoring of water parameters in air conditioning devices in order to reduce the risk of Legionella spp. colonization and to improve preventive measures in public health and air conditioning engineering.