Chainsaw felling and processing work is conducted in various natural conditions and requires significant physical effort from the workers, movement in severe weather and environmental conditions, and has a high risk of injury. The aim of this study was to determine the physiological workload of chainsaw operators through continuous heart rate measurement during the entire working day. The research was carried out during the summer of 2024, encompassing different parts of the Federation of Bosnia and Herzegovina. Heart rate was measured using a Polar H10 Heart Rate Monitor Chest Strap with continuous data logging and storage of heart rate readings. A time study was performed based on recordings conducted simultaneously with the recording of heart rate, with the aim of determining the duration of individual work operations and identifying the work operation with the highest negative impact on the worker. The average working heart rate during productive work time for subject 1 was 104 bpm, 83 bpm for subject 2, 109 bpm for subject 3, 94 bpm for subject 4 and 129 bpm for subject 5. The results of the Kruskal-Wallis test showed a statistically significant difference in average heart rate in relation to the time study element. The heart rate reserve (%HRR) for the whole study time was estimated at 41.05 % for subject 1; 22.69% for subject 2; 44.50% for subject 3; 24.04% for subject 4, and 45.78% for subject 5. The results of the study showed that the %HRR of chainsaw operators during felling and processing exceeded the value of 40% for 3 out of 5 subjects, which corresponds to hard work and may have negative consequences for operators´ health.

With the growth of data volume and increased query complexity, the need for the application of various optimisation techniques that enable faster execution and more efficient use of resources is increasingly becoming evident. Research shows that indexing, query execution optimisation, and the use of caching significantly reduce processing time and increase system responsiveness. Given that databases are constantly growing in size due to the need to store and analyse data, efficient database architecture and organisation are imperative to the business environment. This paper deals with the topic of analysing databases with large data sets and how to retrieve them most efficiently, using web applications, which are today the most common UI for databases.

C-reactive protein structurally belongs to the pentraxin family, calcium-binding proteins with immune defense properties. In the serum of healthy adults and adolescents, there is less than 5 mg of C-reactive protein. Its concentration is increased in inflammatory diseases where values up to 500 mg/l can be found. The main role of C-reactive protein is complement activation and prevention of inflammation. It binds to bacteria or damaged cells and thus helps the activation of the classic complement pathway, opsonization and phagocytosis. Binding depends on calcium. Antibiotics are products of the metabolism of bacteria, fungi and molds, rarely higher plants, which in small concentrations prevent the growth and development of microorganisms or kill them. They belong to the group of antimicrobial drugs, which are used to treat and prevent bacterial infections. Cephalosporins are beta-lactam antibiotics with the same mechanism of action as penicillin, which means that they block the synthesis of the bacterial cell

Atrial fibrillation (AF) is the most common persistent cardiac arrhythmia in clinical practice and a significant, often underdiagnosed risk factor for stroke. The electrocardiogram (ECG) is the primary method for its detection, typically manifesting as irregular $\mathbf{R R}$ intervals and the absence of P-waves. Numerical ECG parameters enable quantitative analysis of these changes and provide a foundation for the development of automated detection systems. This study examines the association between atrial fibrillation and numerical ECG parameters using the ECG-ViEW II database. From 12-lead ECG recordings, key temporal and morphological parameters were extracted, and descriptive statistics were calculated to form the final dataset. Descriptive statistical analysis, inferential tests, and graphical visualizations were applied to compare AF and non-AF groups. The results indicate that parameters describing RR-interval variability show a strong association with atrial fibrillation, confirming their potential for application in automated systems for early AF detection.

The recent rise of large language models (LLMs) and other generative artificial intelligence (AI) tools like agents represent only the visible tip of the iceberg in artificial intelligence. Beneath the surface lies a vast foundation of advanced mathematics, statistics, signal processing techniques, countless smaller/domain-specific models, optimization algorithms, distributed computing infrastructure, specialized engineering tools, and domain knowledge that make these headline-grabbing AI systems possible. This paper uses the iceberg metaphor to illuminate these hidden layers and surveys the technical background, including the mathematical and statistical underpinnings of AI, but also limitations of LLMs in many engineering applications such as power system fault detection, simulation-based optimization, and time-series forecasting. By exploring these “submerged” components of the AI iceberg, this paper provides a comprehensive perspective on the true breadth of modern AI, bridging the gap between public perceptions of AI and the complex reality beneath with multiple supporting case studies. The findings contribute to the existing body of knowledge by underscoring that meaningful progress in AI requires not only visible breakthroughs in models and interfaces, but also continuous advances in the less glamorous but critical supporting layers and applications of the AI stack.

Implementing a cognitive Sense-Think-Act-Learn (STAL) architecture for automated cultural heritage visualization, this paper illustrates a multi-agent system. The system consists of three specialized agents: an Exhibition Curator Agent that applies Large Language Models (LLMs) to classify and generate exhibitions, a Conversational Guide Agent that provides interactive visitor engagement, and an Image Acquisition Agent that performs automated visual content enrichment. Evaluated on a dataset including 6,398 historical events across multiple civilizations and epochs, the system effectively automates the curation of the data by adopting a hybrid approach via deterministic classification algorithms as well as LLM-based analysis. The architecture allows adaptive learning based on administrative feedback, improving classification accuracy over time. This work provides a concrete framework to leverage generative $A I$ in cultural heritage digitization while also addressing issues related to scale, multilingual content, and domain-specific curation needs.

This paper explores the application of FPGA (Field Programmable Gate Array) technology based on the Basys 3 board in video game development. Given that the hardware of most classic games is no longer functional, the programmable nature of FPGA allows for precise replication, allowing modern devices to run these games and provide an authentic gaming experience. The paper presents the design and emulation of a video game on the Basys 3 FPGA development system. It also examines the use of open-source tools for the development of FPGA applications, comparing them with commercial alternatives. The Basys 3 system was prepared for game emulation by implementing a RISC-V processor, a VGA controller, and memory in Verilog. The game was initially developed in $\mathbf{C}$ for Windows and later ported to the FPGA environment, with particular attention paid to memory management to ensure proper image display via the VGA controller. The results demonstrate that FPGA systems are viable platforms for video game emulation and complex application development, with open-source tools proving efficient and effective.

This paper presents a novel UART-based debugging interface for resource-constrained RISC-V soft-core implementations on FPGAs. Unlike traditional JTAG-based approaches that require dedicated hardware and tools, our design leverages the ubiquitous UART peripheral to provide comprehensive debug capabilities through a dual-mode architecture. The interface operates in standard ASCII mode for command-line interaction and switches to a binary protocol for advanced operations including bulk instruction memory programming (IMPR), singleinstruction hot-patching (IMWR), and direct memory/CSR bus read/write operations (BUSR/BUSW). A key innovation is the bus mastering mechanism that enables real-time memory inspection and modification without permanent CPU halting, facilitating live debugging and in-field firmware updates. The FSM-based protocol incorporates checksum verification and timeout recovery for robust operation. Implemented on an 80 MHz RISC-V based SoC for WireGuard VPN acceleration, the interface consumes less than 2% additional FPGA resources while providing functionality comparable to more complex debug modules. Experimental results demonstrate successful hot-patching of running programs, sub-second firmware updates, and effective production diagnostics without requiring specialized JTAG hardware or tools.

The development of reliable Decision Support Systems (DSS) for talent identification requires a rigorous analytical framework capable of processing high-dimensional educational data. This paper presents the mathematical formulation of the machine learning pipeline utilized for classifying student potential, focusing on the algebraic structure of data representation and the optimization of predictive algorithms. We formally define the mapping of unstructured textual attributes into sparse vector spaces using One-Hot Encoding and analyze the dimensionality reduction effects. The study details the training dynamics of classification models, specifically examining the cost function minimization in Decision Trees via the Gini Impurity index and the stochastic aggregation mechanisms within Random Forest ensembles. Furthermore, to address the challenge of class imbalance, we provide a formal definition of performance metrics, including the harmonic mean of precision and recall and the arithmetic mean of indicator functions for Global Top-K Accuracy. By establishing these mathematical foundations, the paper demonstrates how formal optimization directly correlates with the discriminative power and stability of AI-driven educational assessments.

Online Analytical Processing (OLAP) technology facilitates efficient multidimensional data analysis, providing users with valuable insights for decision-making processes. Previous studies have explored the implementation of OLAP technology across various domains; however, a limited number of investigations have compared the Multidimensional Analysis Project and Pentaho on the same database or within a single study. This research contributes to existing literature by evaluating the performance and flexibility of these two tools using Microsoft SQL Server as a benchmark dataset, which represents the database of a specific blog application or an application based on user interactions with diverse posts. The manuscript details the modeling and implementation processes for OLAP cubes in both systems, emphasizing fundamental aspects of OLAP technology, key functionalities, performance metrics, and adaptability characteristics. Furthermore, a comparative analysis between Microsoft's solution and Pentaho was conducted, highlighting their respective advantages and limitations within the context of data analytics.

In the context of business systems, efficient data analysis through various Online Analytical Processing (OLAP) models represents a key challenge for performance optimization and timely decision-making. This study examines tabular and multidimensional OLAP models within the SQL Server (MSSQL) and Visual Studio environments to determine which model facilitates more effective data processing in a specific business context. An experimental analysis was conducted using the Stats dataset, where the same business question was addressed through both models, comparing their response times and query execution efficiency. Particular attention was paid to execution speed, data processing methodologies, and resource optimization strategies. Results indicated that the tabular model, which relies on in-memory technology and the Data Analysis Expressions (DAX) language, reduces data processing time by approximately 38 % and offers simpler modeling capabilities, making it suitable for analyses where rapid result retrieval is critical. In contrast, the multidimensional model, utilizing Multidimensional Expressions (MDX), provides advanced analytical features and greater scalability, rendering it more appropriate for complex analyses involving large datasets and predefined aggregations. Based on this comparison, it was concluded that the choice between tabular and multidimensional OLAP models depends on specific analytical requirements. If speed and flexibility are prioritized, the tabular model enables faster execution, whereas the multidimensional model offers enhanced control over analytical processes.

The global transition to renewable energy faces challenges, particularly in integrating variable sources such as wind and solar. Battery Energy Storage Systems (BESS) provide a key solution for grid stabilization and peak load management. Peak shaving stores energy during low-demand periods and releases it during high-demand periods, reducing costs and stabilizing the grid. This research aims to model and analyze optimal BESS operation for peak shaving in industrial environments, highlighting both technical performance and contributions to sustainable energy systems. MATLAB/Simulink simulations evaluate effects on grid dependency, energy efficiency, and economic benefits, showing how BESS with photovoltaic generation can enhance efficiency, reduce grid reliance, and support environmentally friendly energy management.

This study investigates the application of machine learning clustering techniques, specifically Dynamic Time Warping (DTW), to define typical load profiles (TLPs) for industrial facilities. Utilizing $\mathbf{1 5}$-minute smart meter data from a plastics manufacturing plant, the research analyzes total factory consumption alongside individual chiller and compressor loads. Cluster quality is assessed using the Silhouette score, Dunn index, and mean intra-cluster distance. Results indicate that while DTW effectively captures temporal shapes, industrial profiles are highly enterprise-specific and noise-intensive, resulting in fair-to-weak cluster quality. The findings suggest that primary electricity datasets and basic temporal metadata are insufficient for high-quality profiling compared to existing household models. The study concludes that integrating production-related metadata, such as work orders, is essential for improving industrial consumption forecasting and capacity planning.

Robust and secure microgrid (MG) design requires test configurations that accurately reflect real-world systems and comply with established standards. This paper reviews guidelines for military MG design, focusing on standards issued by United States defense and security organizations. It also evaluates widely recognized IEEE and CIGRE test MG configurations for their ability to replicate military power network characteristics. To assess analytical accuracy, power flow studies are performed on a selected test MG using DIgSILENT PowerFactory, ETAP, and OpenDSS. A comparative analysis of results highlights differences in modelling approaches and computational precision among these tools. The findings provide insights into the suitability of existing test configurations and simulation platforms for military MG research, supporting the development of secure, standardcompliant solutions.

Photovoltaic (PV) power plants have become an important source of clean electricity. To keep them running efficiently, it is essential to detect faults and unusual behavior. However, this is not easy because data from PV power plants are noisy and strongly affected by environment, seasonal changes, and weather conditions. Reliable anomaly detection could help with preventing power losses, reducing maintenance costs, and extending the lifetime of the system. This paper presents a two step approach for detecting anomalies in PV plant data. First, K-means clustering is used to group days with similar conditions based on daily energy metrics. Then, the Local Outlier Factor (LOF) algorithm is applied within each cluster to find whether any anomalies are present. For visualization and interpretability, results are presented in a graphic form. Initial application demonstrates the framework's ability to segment seasonal data and identify extreme deviations from normal operating clusters. Although larger datasets are still needed for full validation, the method provides a foundation for season-aware monitoring.