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.

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.

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.

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.

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.

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).

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.

PURPOSE OF REVIEW The degree to which computerized methods, such as artificial intelligence (AI), will aid in the assessment of kidney histopathology is undergoing intense study and application; and this is particularly true for interstitial fibrosis, which is often used as a surrogate measure of chronic kidney disease progression, since interobserver variability among human pathologists has been demonstrated in the assessment of interstitial fibrosis and other features. RECENT FINDINGS Computerized assessment of interstitial fibrosis, including with AI, has been assessed alongside pathologists. Computerized methods such as AI have shown direct interstitial fibrosis measurement and indirect assessment through kidney compartment segmentation; however, some studies have shown lack of complete concordance among computerized methods and humans; and studies have still shown the persistent value of human assessment in many circumstances. SUMMARY Computerized methods, including AI, are showing increased application in kidney pathology for a wide variety of clinical and histopathologic parameter assessment, including interstitial fibrosis; however, further studies are needed to characterize the performance of AI and handcrafted methods; and additional work is needed to fully integrate computerized methods into routine pathology practice. Ultimately, humans working with AI ("humans + AI") may provide enhanced analysis for more effective patient care.

Olaparib, the first clinically approved poly (ADP-ribose) polymerase (PARP) inhibitor, may be repurposed for non-oncological conditions such as acute respiratory distress syndrome (ARDS), where PARP-1 inhibition has shown benefits in preclinical models. We investigated the expression and functional status of PARP-1 and the effects of olaparib in peripheral blood mononuclear cells (PBMCs) from ARDS patients and healthy controls. PBMCs from healthy volunteers (N = 8) and ARDS patients (N = 8) were isolated via Ficoll gradient. PARP-1, cleaved PARP (cPARP), and PAR polymers were assessed by Western blotting. Cytokine production was measured in plasma and in PBMC supernatants after 1 h preincubation with olaparib (10 µM) or vehicle, followed by LPS (100 ng/ml) stimulation for 4 h. Cellular bioenergetics were analyzed using Seahorse XFe24 after H2O2 (100 µM, 2 h) with or without olaparib pretreatment. Control PBMCs showed a lymphocyte-predominant population with mostly full-length PARP-1. In ARDS Day 1 samples, PARylated proteins increased and PARP became downregulated. By Day 8, PARylation decreased and full-length PARP-1, as well as cleaved PARP-1 were detectable. Olaparib treatment of the cells did not alter the LPS-induced cytokine responses. Exposure of healthy PBMCs to oxidative stress suppressed cellular bioenergetics, and this effect was attenuated by olaparib. However, in ARDS PBMCs, which were already bioenergetically suppressed, oxidative stress had no further effect, and olaparib was without protective effect. Thus, in PBMCs isolated from ARDS, olaparib’s cytoprotective effect is no longer detectable, likely due to PARP-1 inactivation and degradation. Supplementary Information The online version contains supplementary material available at 10.1186/s12931-026-03623-4.

Background: Australia’s increasingly multicultural landscape has seen a rise in culturally and linguistically diverse populations, many of whom face subtle and systemic forms of discrimination known as “new racism”. Objective: Underpinned by a person-centred and holistic framework, which recognises individuals as experts in their own lived experiences and emphasises strength-based, culturally situated understandings of well-being, this paper reports on a study that explores how culturally and racially marginalised diverse people in Australia cope with the mental health impacts of new racism. Design: A qualitative descriptive approach was employed in this study. Participants: Thirty participants from ten culturally and linguistically diverse communities participated in eight focus groups, providing rich insights into their lived experiences. Methods: Data were collected through semi-structured focus-group interviews conducted between March and June 2025. Data were analysed using Braun and Clarke’ method of thematic analysis. Results: Thematic analysis revealed four key coping strategies: (1) acceptance of immutable identity traits to foster resilience, (2) emotional ventilation within culturally safe spaces, (3) self-growth and empowerment through reflection and adaptive practices, and (4) assertive responses to racism when necessary. While some participants reported psychological distress, many demonstrated resilience and resourcefulness, challenging deficit-based assumptions often found in the existing literature. Findings underscore the importance of culturally responsive mental healthcare, including peer support, emotional safe spaces, and strength-based interventions. Conclusions: This study offers a holistic understanding of how culturally and racially marginalised people cope with new racism and its mental health impacts. The findings highlight the critical need for person-centred, culturally responsive, and equity-focused mental health support, providing actionable guidance for nursing practice and policy development.

The rapid growth of the global population has increased the consumption of chicken eggs, leading to the generation of significant quantities of eggshell waste. The sustainable valorization of this biowaste represents an important environmental and resource management challenge. In this study, CaO was synthesized from waste chicken eggshells via calcination at 800 °C and evaluated as a green precipitating agent for the removal of toxic Pb(II) from aqueous solutions. The effects of key precipitation parameters, including initial pH, stirring speed, contact time, and CaO dosage, were systematically investigated. The results showed that the removal efficiency increased with increasing pH, mixing intensity, contact time, and CaO dosage, reaching a maximum Pb(II) removal of 90% under investigated conditions of initial pH 9, stirring speed of 500 rpm, contact time of 15 min, and CaO dosage of 500 mg. In the presence of the competing ion Fe(III), the removal efficiency further increased to 99.99%, indicating a potential synergistic effect in the precipitation process. FT-IR analysis confirmed the successful formation of CaO and revealed significant spectral changes after Pb(II) precipitation, including shifts and disappearance of characteristic absorption bands, indicating the formation of insoluble hydroxide and carbonate phases. These findings demonstrate that eggshell-derived CaO is an effective and environmentally sustainable material for Pb(II) removal from aqueous media and represents a promising approach for the valorization of eggshell waste.