Abstract Sustainable development demands research into safe, renewable energy sources. Wood briquettes offer numerous advantages, but they can contain heavy metal(oid)s, posing environmental challenges, particularly in the ash produced during combustion. This study examines the concentrations of heavy metal(oid)s (Cd, Cr, Cu, Fe, Mn, Ni, Pb, Co, Zn, and As) in wood briquettes and their residual ash. Samples were prepared via wet digestion using 65% nitric acid (HNO3) in polytetrafluoroethylene vessels, followed by analysis using flame and graphite furnace atomic absorption spectrometry. The results showed that arsenic (As) had the lowest concentration in wood briquettes, while iron (Fe) was the highest. In the ash, chromium (Cr) was detected at the lowest concentration (0.80 mg/kg), while iron (Fe) reached 5830 mg/kg. Heavy metal concentrations in wood briquettes often exceeded permissible limits, and the concentrations in ash were significantly higher, making some ash samples unsuitable for agricultural use. The ash content ranged from 0.70% to 2.34%. This study provides valuable quantitative data on heavy metal(oid)s before and after combustion, highlighting their potential environmental impact and emphasizing the need for careful management of wood briquette ash.

The growing global demand for effective and safe therapeutics has accelerated advances in biomaterials for drug delivery applications. Biomaterials, including polymers, metals, ceramics, and composites, play a central role in modern medical devices and therapeutic systems by enabling controlled interactions with biological environments. Initially defined as inert materials interfacing with biological systems, biomaterials are now rationally engineered to treat, replace, or evaluate tissue and organ functions. Recent progress in regenerative medicine, nanotechnology, and precision healthcare has expanded their use in drug delivery, where tunable physicochemical properties—such as degradation kinetics, surface chemistry, and mechanical stability—allow controlled release, protection of labile therapeutics, and enhanced accumulation at target sites. Polymer-based biomaterials enable sustained drug release through diffusion-controlled, degradation-mediated, or stimulus-responsive mechanisms, thereby extending therapeutic exposure and reducing systemic dosing frequency compared with conventional formulations. Nanostructured carriers, including liposomes, micelles, and dendrimers, further enhance drug delivery by improving solubility, cellular uptake, and site-specific targeting via size control, surface functionalization, and ligand-mediated interactions. Despite these advances, clinical translation remains limited by challenges related to immune–biomaterial interactions, batch-to-batch variability, long-term biodegradation behavior, and the scalability of manufacturing under regulatory constraints. Future biomaterial development must therefore emphasize precision fabrication, good manufacturing practice–compatible production, and biologically informed design strategies that account for patient-specific variability. This review provides a focused overview of biomaterial-based drug delivery systems, summarizes recent technological advances, and critically discusses mechanistic and translational challenges, including immune compatibility, degradation control, and regulatory compliance, with particular emphasis on their implications for personalized drug delivery.

Malware detection using deep learning faces challenges in model selection for practical deployment. We systematically compare five transfer learning architectures (VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB0) on the MaleBin RGB malware dataset ($\text{1 2, 0 0 0 +}$ images through March 2025). Experiments on NVIDIA A100 GPU evaluated accuracy, efficiency, and deployment suitability. DenseNet121 achieved highest accuracy ($91.20 \%, 8 \mathrm{M}$ parameters), MobileNetV2 provided optimal edge deployment (90.39 %, 3.5 M parameters), while ResNet50 and EfficientNetB0 unexpectedly underperformed $(77.34 \%, 71.16 \%)$. Directions for practitioners are to deploy DenseNet121 for cloud environments, prioritizing accuracy, and MobileNetV2 for resource-constrained edge devices.

Orthogonal signal generators are crucial for synchronization in single-phase systems, where accurate estimation of phase, frequency and amplitude is the focal point. Conventional generators are sensitive to a DC-offset in the input signal, which can degrade performance. This paper presents a modified Kalman-based generator with an additional feedback loop for DC elimination. A state-space model of proposed generator is developed, and parameters are calculated using a continuous Kalman estimator. The performance is validated in MATLAB/Simulink environment under several tests to determine performance of the presented orthogonal signal generator. Simulation results show that the generator is accurately tracking the input signal while generating its quadrature components demonstrating robust performance suitable for synchronization loop applications.

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.

Immunotherapy with immune checkpoint inhibitors (ICI) has become a transformative pillar in cancer treatment, offering significant improvements in survival and reducing treatment-related side effects compared to traditional therapies. In gynecologic cancers, ICIs have transformed the treatment of endometrial (EC) and cervical cancers, whereas they have not demonstrated clinical benefit in ovarian cancer. This review examines the current state of ICI advancements in EC. Given the unique immunological characteristics of EC, a comprehensive understanding of advancements is crucial for optimizing decision-making and patient outcomes. While ICIs have demonstrated robust and durable efficacy in dMMR/MSI-H EC, the magnitude of benefit in pMMR disease remains modest. Additionally, we examine promising future directions, including personalized immunotherapy approaches and novel combination therapies (e.g. antibody-drug conjugates, PARP inhibitors, antiangiogenic drugs).

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.

Deep learning has become increasingly significant in clinical medicine, including breast cancer detection, offering significant potential to improve patient outcomes. However, recurrent architectures like LSTM (Long Short-Term Memory) and BiLSTM (Bidirectional Long Short-Term Memory) remain underutilized for breast cancer prediction using structured tabular data, primarily due to the absence of explicit temporal dependencies, which are unsuitable for sequence-based modeling. This work presents a novel approach that redefines how LSTM architecture can be applied to the publicly available non-sequential Wisconsin Diagnostic Breast Cancer (WDBC), consisting of 569 samples and 30 features. The flat tabular input is reshaped into a fixed-length 3D tensor using a sliding window approach to adapt the data for sequence modeling. This transformation enables the model to leverage LSTM's sequential processing capabilities in a fundamentally new way, capturing implicit feature interactions across structured attributes without temporal context. Furthermore, Bayesian hyperparameter optimization techniques are applied to enhance the model's performance. The proposed model is evaluated against standard LSTM and state-of-the-art tabular Transformer architectures (FT-Transformer and SAINT). Results show that BiLSTM achieves the best overall performance (AUC 0.9985, accuracy 0.9824, RMSE 0.0964), while the LSTM baseline also surpasses both Transformerbased tabular models (AUC 0.9958, accuracy 0.9719). Performance gains are consistent across seven evaluation metrics, with statistical significance confirmed via paired t-tests $({p}<0.05)$. These findings demonstrate that, when appropriately adapted, recurrent architectures can outperform even advanced self-attention models in structured clinical prediction tasks.

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.

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

Pigmentation is a key functional trait influencing camouflage, predator-prey interactions and energetic efficiency in marine organisms, yet its physiological and ecological consequences remain poorly understood in deep-sea sharks. Here, we describe a deep-sea shark (Heptranchias perlo) exhibiting a mosaic pigmentation disorder characterized by the coexistence of hypermelanotic, hypopigmented and amelanotic regions, indicating disruption of normal melanophore distribution and regulation. Histological examination revealed no structural or inflammatory abnormalities, supporting a non-pathological origin of the pigmentation anomaly. In contrast, condition indices indicated pronounced energetic depletion, with reduced condition factor and hepatosomatic index, while lipid extraction and Fourier-transform infrared and ultraviolet-visible spectroscopy revealed substantial depletion and altered composition of hepatic lipid reserves consistent with chronic negative energy balance relative to phenotypically normal conspecifics. We propose that disruption of countershading in hexanchiform sharks may reduce camouflage efficiency and increase energetic costs, contributing to the observed physiological compromise in sharks. Despite being based on a single individual, this integrative analysis links pigmentation anomalies to functional and energetic consequences, and underscores the need to move beyond descriptive accounts toward mechanistic assessments of coloration in marine predators, particularly in deep-sea elasmobranchs that are inherently rarely encountered.