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.

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.

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.

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.

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.

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 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.

In terms of ichthyology, Bosnia and Herzegovina (BiH) is one of the most interesting parts of Southeast Europe, due to its rich biodiversity and high level of endemism. Despite its relevance, the entire territory has been poorly explored. Here, we provide an updated inventory of the current state of knowledge on fishes, including lampreys, from the freshwaters of BiH by hydrographic basin, with recent distributional data and updated taxonomic status reviewed and compared with previous lists. The checklist was compiled based on the existing scientific and grey literature, technical reports, scientific congresses, academic dissertations, and unpublished/personal observations. In total, 123 species including diadromous and euryhaline fishes have been documented in BiH freshwaters to date. Of these, 110 are primarily freshwater. In comparison to the last published monography (Sofradžija 2009), we present a 9% increase in species number (11 species), resulting mainly from taxonomic re-evaluations of existing taxa on the basis of new information and the adoption of a new changes in the taxonomic status of several species. Among the valid primarily freshwater species, 87 are native and 23 are non-native. A total of 38 endemic species have restricted distribution, and are threatened by numerous anthropogenic pressures. Four species are considered endemic only to BiH: Cobitis herzegoviniensis Buj & Šanda, 2014; Phoxinellus pseudalepidotus Bogutskaya & Zupančič, 2003; Telestes dabar Bogutskaya, Zupančič, Bogut & Naseka, 2012; and T. metohiensis (Steindachner, 1901). In total, 75 genera and 34 families are represented: Leuciscidae is represented by 37 species, the Salmonidae by 13, followed by the Cyprinidae, Cobitidae and Percidae, each with eight species. The native species richness follows a pattern similar to that observed in other southern European countries. A national list of endangered species has not yet been proposed to BiH and management strategies for their protection or conservation are also not implemented. Hopefully, this updated checklist will serve as a basis for future research aimed at understanding the origin and status of conservation of the BiH fishes diversity, and supporting effective management and conservation programmes.

The within-school gap in teaching has long been a primary focus of policymakers, researchers, and practitioners working to ensure equitable student outcomes. However, limited empirical research has examined the factors that can address this gap. This study examined the role of instructional school leadership in explaining variation in within-school creative pedagogies, controlling for various school-level contextual and teacher-related variables. The data come from 17 countries that participated in the 2022 PISA program. The analysis followed three steps. First, variables related to school and teachers were included in the regression. Then, instructional leadership was included in the analysis to examine its association with within-school variation in teachers’ use of creative pedagogies. The regression coefficients from each country were then combined in a meta-analysis to estimate the country-level effects. Across 17 countries, instructional leadership was generally associated with lower within-school variation in teaching quality, though this relationship was statistically significant in only three countries. These results point to a modest but potentially meaningful role for instructional leadership in reducing the gap among teachers in their creativity-oriented teaching practices and, in turn, promoting greater equity in student learning.

KRAS mutations are among the most prevalent oncogenic alterations in colorectal, lung, and pancreatic cancer, yet their detection remains analytically challenging in the presence of an overwhelming wild-type (WT) background. Here, we report a photoelectrochemical (PEC) genotyping platform that integrates clamp-inhibited loop-mediated isothermal amplification (C-LAMP) with enzyme-free singlet oxygen (1O2)-driven PEC transduction for mutation-selective KRAS detection. Locked nucleic acid (LNA) clamp probes selectively suppress WT amplification during isothermal amplification, enriching mutant alleles and enabling single-nucleotide variant (SNV) discrimination with high selectivity. Amplified products are magnetically captured and transduced into photocurrent via visible-light-induced 1O2 redox cycling, eliminating enzymatic reporters and reducing background interference. The C-LAMP/PEC platform achieves a limit of detection of 35 copies µL-1 (58 aM) and a minimum detectable variant allele frequency (VAF) of 4.8% in heterogeneous mutant/WT genomic DNA mixtures. Analytical performance was validated in cancer cell lines and in patient-derived fresh frozen tissues, showing complete concordance with Nanopore sequencing and droplet digital PCR (ddPCR) within the evaluated cohort (n = 16). This work introduces a robust and modular PEC biosensing strategy that combines molecular WT suppession with enzyme-free photoelectrochemistry, offering an economically competitive and instrumentation-simplified approach for clinically relevant KRAS mutation analysis toward decentralized testing.

The Open and Reproducible Musculoskeletal Imaging Research (ORMIR) community is a scientific community dedicated to promoting openness and reproducibility in musculoskeletal imaging, image processing, and computational modelling. In this perspective paper, we outline the motivations for conducting transparent research and provide practical guidelines to implement it. We start with defining open and reproducible research and describing the benefits and challenges of working transparently. Next, we redefine the outputs of a computational research study as—ideally—a combination of data, code, and a publication, recommend a folder and file structure that reflects these three study outcomes, and describe how to maintain and update such a structure during the study and at study publication. Finally, we emphasize that working in an open and reproducible manner is a learning process and the best way to acquire the necessary competencies is simply to start. Lay summary: The ORMIR community promotes openness and reproducibility in musculoskeletal imaging research. In this perspective paper, we explain why transparency matters and recommend how to conduct a computational study in an open and reproducible manner focusing on its three outputs: data, code, and publication. Finally, we highlight that the best way to learn these practices is simply to start.

Abstract The Open and Reproducible Musculoskeletal Imaging Research community is a scientific community dedicated to promoting openness and reproducibility in musculoskeletal imaging, image processing, and computational modeling. In this perspective paper, we outline the motivations for conducting transparent research and provide practical guidelines for implementing it. We start by defining open and reproducible research and describing the benefits and challenges of working transparently. Next, we redefine the outputs of a computational research study as—ideally—a combination of data, code, and a publication, recommend a folder and file structure that reflects these three study outcomes, and describe how to maintain and update such a structure during the study and at study publication. Finally, we emphasize that working in an open and reproducible manner is a learning process, and the best way to acquire the necessary competencies is simply to start.

Leptospirosis, a reemerging zoonotic disease caused by pathogenic bacteria of the genus Leptospira, affects a wide range of domestic and wild animals. The only investigation into sheep leptospirosis in Bosnia and Herzegovina was conducted nearly 50 years ago. This study aimed to assess the seroprevalence of leptospirosis and to identify the most common serovars in sheep in Bosnia and Herzegovina, using the microscopic agglutination test (MAT). Leptospirosis seroprevalence was determined to be 2.16% at a cut-off titer of ≥1:100 (55/2542) and 8.10% at a cut-off of ≥1:25 (206/2542), with all positive cases related to a single serovar. The MAT titers were 1:25 and 1:100, with the majority of positive animals having low titer (1:25) (151/206; 73.3%). At a cut-off of ≥1:100, the sera most frequently reacted to Pomona (54.55%) and Hardjo (27.27%), and less commonly to Saxkoebing and Icterohaemorrhagiae (0.2%) (P<0.05). Odds of seropositivity were higher for Pomona and Hardjo than for Saxkoebing and Icterohaemorrhagiae. The results of this study showed for the first time in Bosnia and Herzegovina, the presence of serovar Icterohaemorrhagiae in sheep, with Pomona and Hardjo as the dominant serovars. Although the seroprevalence is low, the potential zoonotic risk requires continuous monitoring and control strategies to prevent the spread of leptospirosis.

Background: Inflammation-driven mechanisms play a central role in adverse outcomes after non-ST-elevation myocardial infarction (NSTEMI), yet simple, widely available biomarkers for early risk stratification remain insufficiently defined. Hemogram-derived indices and iron-related inflammatory markers may provide complementary prognostic information. Objective: To evaluate the prognostic significance of the mean platelet volume-to-monocyte ratio (MMR) and serum ferritin in predicting major adverse cardiovascular events (MACE) in patients with NSTEMI, and to assess the association of angiotensin-converting enzyme (ACE) inhibitor therapy with clinical outcomes. Methods: This prospective cohort study included 170 consecutive NSTEMI patients admitted to the University Clinical Center Tuzla between February 2022 and January 2023. All patients received dual antiplatelet therapy and high-intensity statins. The baseline evaluation included a complete blood count, serum ferritin, and C-reactive protein. MMR was calculated as the ratio of mean platelet volume to absolute monocyte count. Patients were followed for 12 months for the occurrence of MACE, defined as cardiovascular death, non-fatal myocardial infarction, urgent revascularization, stroke, or hospitalization for heart failure. Results: During follow-up, 103 patients (60.6%) experienced MACE. Admission MMR (18.1 ± 11.7 vs 13.2 ± 5.5; P = 0.003) and ferritin levels (284 ± 396 vs 152 ± 109 µg/L; P = 0.001) were significantly higher in patients with events. In multivariable analysis, both MMR (odds ratio [OR] 1.06, 95% confidence interval [CI] 1.02-1.11; P = 0.008) and ferritin (OR 1.28 per 100 µg/L, 95% CI 1.10-1.55; P = 0.003) independently predicted MACE, while ACE inhibitor therapy was associated with a lower risk (OR 0.24, 95% CI 0.08-0.70; P = 0.01). The combined model demonstrated good discriminative performance (AUC 0.72; 95% CI 0.64-0.80). Conclusion and Relevance: Elevated admission MMR and ferritin were independently associated with a higher 1-year risk of MACE in patients with NSTEMI. ACE inhibitor therapy was associated with improved outcomes, although causality cannot be inferred. These findings suggest that readily available inflammatory biomarkers may complement established clinical parameters for early risk stratification and support continued guideline-directed pharmacotherapy in NSTEMI.