We studied the expression of connexin 43 (Cx43) and pannexin 1 (PANX1) in different cellular populations of the kidneys of diabetic mice and diabetic and non-diabetic patients, to evaluate their role as potential therapeutic targets in diabetic kidney disease (DKD). A combination of a low dose of streptozotocin and a high-fat diet (HFD) was used to induce a type 2 diabetes model (DM2) in mice. Kidney tissues from diabetic (n = 9) and control patients (n = 11) who underwent nephrectomy were collected. Tissues from mice and humans were processed for double immunofluorescence, using antibodies against Cx43, phosphorylated Cx43 (pCx43) or PANX1 and markers for specific cell populations: endothelium (CD31/PECAM1); pericytes/mesangium (PDGFRB); podocytes (nephrin/synaptopodin); proximal tubules and collecting ducts (aquaporin 2). The results showed a significant decrease in the expression of pCx43 in PDGFRB-immunoreactive mesangium in diabetic patients compared to the control group (p < 0.0001). This contrasted with an increase in pCx43 in pericytes of diabetic mice (p = 0.1). However, we found a general decrease in Cx43 protein expression in diabetic mouse kidneys (p < 0.05). We also found a decrease in the expression of PANX1 in endothelial cells of diabetic patients (p < 0.05) and a significant increase in PANX1 expression in cells expressing PDGFRB (p < 0.05). Expression of PANX1 in endothelium (r = -0.50; p < 0.05) and pCx43 in the mesangium (r = -0.65; p < 0.01) correlated negatively with the percentage of sclerotic glomeruli. The expression and activation of Cx43 and the expression of PANX1 are altered in distinct populations of renal cells during long-term type 2 diabetes mellitus, especially cells of the vascular wall. This may indicate their role in the pathophysiological processes of DKD. Therefore, connexin and pannexin channels could be considered as possible therapeutic targets in the prevention and treatment of diabetic kidney disease.

Purpose: This study aimed to examine the relationships between stoppage time (ST), rally time (RT), and selected performance variables in elite men’s volleyball competitions across three leagues with and without ST restrictions. Methods: A total of 1616 rallies were analysed, including 450 from the 2022 Men’s Volleyball Nations League (VNL), 500 from the 2022 Turkish Efeler League (TL) playoff finals, and 666 from the 2022 Italian Serie A League (IL) playoff finals series. Match videos were analysed using Data Volley software, and technical performance variables, RT, and ST were recorded. Spearman correlation and Kruskal–Wallis H tests were applied to examine relationships and differences among leagues (p < .05). Ethical approval was not required because only publicly available match data were analysed. Results: Significant positive correlations were found between ST and RT in all leagues (ρ = .28–.33, p < .001). Weak to moderate negative correlations were observed between ST and reception percentage in the IL and VNL (ρ = −.08 to −.13, p < .05), while a weak positive correlation was found in the TL (ρ = .10, p = .029). Across all leagues, ST was negatively correlated with first-attack success (ρ = −.15 to −.21, p < .001). Conclusion: ST was significantly shorter in the VNL compared with the TL and IL. Differences were also observed in reception and attack performance variables across leagues. These findings indicate that temporal characteristics of the game are associated with technical performance in elite men’s volleyball.

This study explored the relationships between geological substrate and the structural and compositional attributes of mixed beech ( Fagus sylvatica L.), fir ( Abies alba Mill.), and spruce (Picea abies [L.] Karst.) forests on Mt. Konjuh in northeastern Bosnia and Herzegovina. Research was conducted on 81 experimental plots established across three dominant substrates: limestone, peridotite, and chert. Stand structure, diversity, and spatial organization were assessed using the Shannon diversity index, Pretzsch’s species profile index, Gini coefficient, and the Clark–Evans and Füldner indices. The analyses revealed consistent differences among substrates, suggesting that geological conditions influence forest structure and diversity. Higher diversity and vertical heterogeneity were generally associated with limestone, while stands on peridotite and chert exhibited simpler but more balanced structures. All forest types displayed a reverse J-shaped diameter distribution, indicating uneven-aged composition and ongoing natural regeneration. Spatial patterns showed a tendency toward clustering of beech and spruce and higher species mingling on limestone. Overall, mixed beech–fir–spruce forests on Mt. Konjuh appear to be stable ecosystems whose structure and diversity are shaped by an interplay of geological, edaphic, and ecological factors. The results highlight the relevance of site-specific and adaptive silvicultural approaches that account for local variability in substrate and stand conditions.

Norway spruce ( Picea abies (L.) Karsten) is one of the most economically important conifer species in Europe. Efficient utilisation and processing of its wood require detailed knowledge of its technical properties, as well as the most common wood defects that substantially affect both properties and utilisation. Given the crucial role of wood defects in the roundwood classification system, the primary objective of this study was to identify defects in Norway spruce and to analyse the influence of forest assortment characteristics (diameter and position along the stem) and tree attributes (diameter at breast height and position within the stand) on the size of wood defects. The research was conducted in Bosnia and Herzegovina, within a forest compartment of an uneven-aged, mixed beech and silver fir stand with spruce. Trees were felled and processed into assortments using a chainsaw, predominantly applying the cut-to-length method. After measuring the assortment dimensions, the occurrence of defects was assessed, and their sizes were determined. The analysis showed that, following knots, the most common wood defect was rot, followed by pith eccentricity, compression wood, scars, mechanical damage, and resin pockets. Statistically significant differences were found in the size of knots, ellipticity, and taper among different diameter classes of assortments (p<0.05), as well as assortment positions along the stem (p=0.0000). Also, a statistically significant difference was observed in the size of the knots and ellipticity in relation to both diameter at the breast height and tree position within the stand (p<0.05). Overall, the findings align with previous studies, confirming the higher quality of the lower stem section, as reflected in smaller defect sizes critical for roundwood quality classification, such as knots, rot, ellipticity, and taper.

Despite widespread discussion of digital transformation, many organizations struggle to assess their digital capability and define improvement priorities. We developed a transparent self-assessment tool, implemented as a Streamlit web application, based on 25 items grouped into five dimensions. Dimension averages are combined into a weighted overall score (1-5 scale) using explicit and visible scoring rules. The tool was evaluated through a single organizational case study (XL Labs) and a pilot expert review (N = 3), providing preliminary, non-generalizable evidence. In the case study, the organization achieved a score of 3.41 / 5.00, corresponding to the Intermediate maturity category, which remained stable under one-at-a-time sensitivity analysis (±0. 20 per dimension).

This paper presents the design and implementation of a prototype chatbot system based on the Retrieval-Augmented Generation (RAG) architecture, applied in a scientific research institute to improve knowledge access. The system combines semantic search over a vector knowledge base with response generation using large language models, enabling contextually relevant institutional information. A case study was conducted to evaluate the prototype in a real-world environment. Results indicate improved factual grounding compared to an LLM-only baseline within the evaluated dataset, although the evaluation was limited to a small set of queries and a single institutional document collection.

Assessing organizational readiness for highperformance computing (HPC) adoption requires evaluation beyond hardware benchmarking, encompassing workforce capabilities, software maturity, data interoperability, and regulatory compliance. This paper presents a modular, rule-based decision-support framework implemented in Python that evaluates HPC maturity across five integrated dimensions and generates a phased migration roadmap through a weighted scoring model and recommendation engine. The framework employs a formally defined aggregation formula with configurable dimension weights, and its outputs are validated through a basic sensitivity analysis demonstrating score stability under weight variation. Demonstrated on two simulated organizational profiles-a mid-sized research institute and a public administration body-the framework identified critical gaps in workforce readiness and governance compliance, highlighting the role of non-technical factors in HPC transition planning and the practical value of transparent, reproducible maturity assessment for early-stage decision-making.

This paper presents TutorMe, an AI-assisted elearning chatbot platform designed to support students with lesson explanations, question answering, quiz generation, and learning-material navigation using curriculum-aligned, digitized resources. TutorMe is implemented as a modular web application integrated with a domain-restricted knowledge base built from structured PDF learning materials. The contribution is engineering-focused: we describe a reproducible design for a knowledge-base-grounded tutoring assistant, document key configuration choices (prompting strategy, retrieval behavior, and platform settings), and report a pilot offline evaluation using a rubric for manual assessment of correctness and groundedness. In an internal test set of 120 questions spanning biology, chemistry, physics, and mathematics at three difficulty levels, manual review showed that 80.0 % of answers were fully correct. Biology exhibited the lowest accuracy (60%) due to terminology imprecisions, while mathematics achieved 93.3%. We discuss limitations including hallucination risk, curriculum drift, privacy, and the need for teacher oversight, and outline steps toward deployment-grade validation.

Artificial Intelligence (AI) is becoming an important part of modern educational reforms, introducing innovative approaches and learning methods [2]. This study explores the application of artificial intelligence in the education system, examining whether a tool such as ChatGPT can generate pedagogically relevant and curriculum-aligned teaching materials. The research methodology is based on the analysis of the role of AI in education, focusing on the evaluation of the quality, accuracy, and pedagogical value of the content generated by ChatGPT-5. The study combines international research on the use of generative AI in schools with an analysis of materials created for teaching biology and mathematics in the sixth grade of primary school. The analysis included simple, detailed, and curriculum-aligned prompts to examine how different prompt types affect cognitive complexity, language clarity, and alignment with learning outcomes. The results show that all generated materials were factually accurate but differed in educational value. Tasks created using detailed and curriculum-aligned prompts demonstrated higher pedagogical relevance and contributed to deeper understanding and the development of critical thinking skills among students. The research confirms that thoughtful and responsible use of artificial intelligence can provide significant support to teachers in creating quality and educationally meaningful teaching materials.

With engineering architecture being shifted to meet the requirements of sustainable development, the need for optimized design solutions places precise engineering methods at the core of the contemporary industrial transition toward data-driven strategies. A timely conversion to lightweight components in drivetrain systems has led to the prominent use of high-strength polymer gears, establishing them as a critical point of interest in the field of power transmission. However, as the conversion to polymer gears relies on expensive and time-consuming laboratory testing, there is a standstill in evaluating the structural properties specific to polymer gear design. In addition, one of the major concerns in the development of polymer-based gear drives is linked with their operational performance and dynamic response under fault conditions influenced by surface wear. To address these difficulties, a framework for surface wear prediction is developed, enabling precise design optimization for specific drivetrain requirements. Computations of wear progression over multiple duty cycles are built upon the mathematical background of Archard’s wear theory, while internal changes in gear contact pressure distribution are constructed on Winkler’s surface model. The framework provides an innovative support for polymer gear systems, as it imports the three-dimensional (3D) scanning data of gear geometry, therefore enabling the analysis of actual flank surfaces with designated surface modifications and manufacturing errors. The framework’s effectiveness, confirmed by experimental validation, demonstrates a superior estimation of contact parameters and overall performance compared to traditional design methods, highlighting scalable solutions that contribute to ongoing industrial engineering objectives.

Purpose: This study examines the psychological implications of integrating artificial intelligence (AI) into judicial decision-making in criminal justice, including algorithmically supported risk assessment and sentencing decisions. It analyzes how AI-based decision-support systems influence perceptions of fairness, trust in judicial decisions, and decision confidence, as well as the emotional responses of judges, jurors, defendants, and victims. Methodology: The study employs a theory-driven and interdisciplinary conceptual framework grounded in psychological theories of decision-making, procedural justice, and affective processes. Through a critical integrative synthesis of legal, psychological, and ethical scholarship on algorithmic decision-making, predictive modeling, and risk assessment systems in criminal justice, the study examines their implications for human judgment, responsibility attribution, and judicial experience. Findings: The analysis demonstrates that AI-assisted decision-making can substantially shape psychological perceptions of justice and the legitimacy of judicial processes. Although algorithmic tools are often perceived as consistent and objective, their reliance on historical data may reproduce existing biases, thereby negatively affecting perceived fairness, trust in judicial outcomes, and decision confidence among legal professionals and trial participants. These findings indicate that the psychological impact of artificial intelligence extends beyond technical accuracy and plays a significant role in shaping perceptions of the legitimacy of judicial processes. Unique Contribution to Theory, Practice, and Policy: This study contributes to psychological theory by offering a systematic examination of the cognitive, affective, and evaluative processes associated with algorithmically supported judicial decision-making in criminal justice. In the context of judicial practice, the analysis demonstrates how uncritical reliance on AI systems may diminish judicial autonomy and obscure responsibility attribution in decision-making processes. From a public policy perspective, the findings contribute to the conceptualization of regulatory approaches oriented toward transparency, fairness, and trust in the use of AI in judicial decision-making.

This study explores how the choice of database influences performance, modularity, and extensibility in monolithic and microservices software architectures. These software architectures are tested in combination with relational database MySQL and document-based database MongoDB. Apache JMeter is used for automated load testing. Relational databases consistently deliver better performance for structured transactions, while document-based solutions offer greater flexibility and extensibility in distributed systems. In addition, the analysis shows that the choice of database may have more effect on the total performance of a microservices architecture than the inherent overhead of the architecture itself. The findings highlight the critical trade-offs between performance and flexibility, emphasizing the importance of strategic database selection.

This research presents a comparative study of rule-based and machine learning-based approaches for detecting anomalous authentication activities. Rule-based detectors are evaluated against an unsupervised anomaly detector trained on normal user behavior, using the LANL dataset expanded with realistic synthetic attacks. Thresholds used by all detectors are calibrated on an evaluation set to meet fixed false-positive budgets. Results are reported using eventlevel and burst-level metrics. The results show that rule-based approaches perform strongly on high-rate attacks, while machine learning approaches are effective for low-rate, stealthy activity.

Microservices systems often face performance issues when workloads fluctuate, and services degrade over time. Traditional load balancing methods such as Round Robin or Latency-Aware routing do not adapt to changing conditions, which can lead to higher latency and increased error rates. This paper evaluates adaptive decision-making algorithms for request routing, including Deep Q-Network (DQN), Upper Confidence Bound (UCB), Thompson Sampling, and traditional heuristics. Experiments were executed on a production-scale cloud environment (Runpod, 16 vCPUs, 128 GB RAM) for 4 hours per algorithm with 50 concurrent users, generating more than 600,000 requests per experiment. Results show that contextual bandit algorithms significantly outperform deep reinforcement learning. UCB achieved a 0.097 % error rate and a median latency of 220 ms, compared to DQN which produced an 11.32 % error rate and instability during training. Latency-Aware routing performed well but could not match the adaptability of contextual bandits. These findings demonstrate that simpler learning algorithms such as UCB and Thompson Sampling provide faster adaptation, lower error rates, and better stability than deep RL approaches in microservices routing tasks.

Banking systems nowadays handle millions of transactions every day, where speed matters most when the system must detect fraud. Traditional batch-processing systems introduce delays because data is being processed at scheduled intervals. Event-driven architecture handles each transaction at the moment it appears; therefore, the system can react almost immediately. This paper compares event-driven and batchprocessing architectures using simulated banking transactions. The results show that event-driven processing significantly reduces latency and enables earlier fraud detection, while batch processing still works well for non-critical jobs, such as periodic user profiling.