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.

Production CRM systems increasingly use large language models, yet typical Retrieval-Augmented Generation (RAG) implementations suffer from knowledge staleness due to 5–10 min batch processing cycles. This paper presents a streaming RAG architecture for business CRM applications that provides real-time knowledge updates with average document-to-query propagation latency of 3.1 s and strong retrieval quality. The event-driven system uses Apache Kafka for document ingestion, Rust microservices for embedding generation, PostgreSQL with pgvector for vector storage, and GPT-4 for response generation. On 62 insurance policy documents from 20 users and 102 test queries, mean document-to-query propagation latency was $3.1 ~\mathrm{s}, 75-150 \times$ faster than batch processing, with retrieval quality metrics of Precision@5 = 0.398, MRR $=0.938$, and NDCG${@} 10=0.942$ consistent with values reported in prior literature. Additional load testing with simulated users verified production-grade performance stability (P95 latency $<10.33 ~\mathrm{s}$), suggesting that streaming designs may mitigate the knowledge-currency vs. system performance trade-off in production CRM applications.

This paper benchmarks LLM-generated synthetic data for fine-tuning RoBERTa-base on two GLUE tasks (SST2 sentiment classification and MRPC paraphrase detection) under a low-resource setting with 1,000 real training examples per task. Real-only, synthetic-only, and hybrid (1 k real + 1 k synthetic) regimes are compared using data from eleven contemporary LLMs. Results show that synthetic-only training remains below real-only baselines, but hybrid training consistently improves performance: on SST-2, the best hybrid configuration nearly matches doubling the real data, while on MRPC gains are smaller but positive. LLM-generated text is most effective as a supplement rather than a replacement for human-labeled data.

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.

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.

Background/Objectives: COVID-19 severity is influenced by a complex interplay between host, viral, and environmental factors. Emerging evidence suggests that Neanderthal-derived genetic variants may influence the progression and severity of SARS-CoV-2 infection. This study aimed to evaluate the association between selected Neanderthal-derived variants and COVID-19 severity in the population of the Republic of Srpska, considering relevant clinical, sociodemographic, and lifestyle factors. Methods: This multicentric cross-sectional study included 402 participants, classified as healthy or SARS-CoV-2-positive individuals. A total of 378 COVID-19-positive participants were further stratified according to disease severity and hospitalization status. All individuals were genotyped for the Neanderthal-derived OAS3 rs1156361 (C/T) and LZTFL1 rs35044562 (A/G) variants. Detailed sociodemographic, clinical, and lifestyle data were also collected. Results: A higher frequency of the LZTFL1 rs35044562 AG genotype was observed among hospitalized patients compared with non-hospitalized individuals (36.8% vs. 20.9%; p = 0.005), while the AA genotype was more prevalent among non-hospitalized patients (77.3% vs. 63.2%, p = 0.015). Multivariable logistic analysis showed that carriers of the LZTFL1 AG genotype had a higher chance of hospitalization compared to AA carriers (adjusted OR = 1.372, 95% CI = 0.763-6.383, and p = 0.021). Hospitalized patients more frequently carried the combined CT (OAS3) and AG (LZTFL1) genotypes, supporting a potential synergistic effect. Several sociodemographic factors, including age, sex, education, employment, and urban residence, were also associated with COVID-19 severity, while no significant associations were observed in allele-based analyses. Conclusions:LZTFL1 gene polymorphisms may influence COVID-19 severity, with heterozygote-specific and combined risk effects observed. These preliminary findings are exploratory and require validation in larger cohorts, but may guide future studies and targeted interventions in high-risk groups.

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.

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.

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.

This article examines the use of collaborative online international learning to support educator and educational leadership preparation. As part of a university partnership, the authors piloted virtual exchanges in 2021 and 2022 between university students in the United States (U.S.) and Bosnia and Herzegovina (B.H.). The pilot included 18 U.S. doctoral leadership students and 22 B.H. bachelor's, master's and doctoral students in religious pedagogy and theology. Qualitative case study methods were used to examine two COILs. The authors analyzed curricular and instructional materials, student reflections, faculty notes and correspondence and publicly available B.H. media accounts. Reported learning emphasized reflection focused on cultural attitudes, knowledge and skills; intercultural and interlinguistic awareness; intercultural team functioning and educational leadership, system and policy comparisons. Supports for and challenges to reported learning were structural, curricular and instructional in nature. Little research exists on the use of virtual exchange for educational leadership preparation. This study offers early lessons for using virtual technologies to incorporate an international and intercultural dimension into educational leadership preparation.

Chimeric Antigen Receptors (CAR) T-cell therapy is a ground-breaking discovery in immunotherapy, mainly known for its exceptional results in treating haematological malignancies. The latest research has revealed that the potential of CAR T-cell therapy extends far beyond its current capabilities and could represent a novel therapeutic approach for treating various cancers. This review aims to summarize the latest innovations in CAR T-cell therapy applied in cancer treatment, including multiple myeloma, osteosarcoma, glioblastoma, melanoma and various childhood malignancies. However, several challenges limit success of CAR T-cell therapy, including the antigen escape phenomenon, 'on-target off-tumour' toxicity, penetration into solid tumour tissue, alongside the cost-effectiveness concerns. The improvement of cancer immunotherapies currently available requires an increase in the effectiveness of CAR T-cells in managing refractory and solid cancers. This could be achieved by using CAR T-cells to target various antigens, enhancing their local delivery and tumour infiltration capabilities and utilizing CAR T-cells in combination with checkpoint blockade and immunotherapy, such as PD-1 blockade and CD19 CAR T-cell combined therapy. Although CAR T-cell treatment offers a lot of promise, its cost needs to be taken into account, especially in healthcare systems with limited funding. More importantly, frameworks for Health Technology Assessment (HTA) must adapt to incorporate ethical, sociological and psychological aspects. Reducing CAR T-cell toxicity is also essential, as it remains among biggest obstacles to their widespread application in clinical practice. Future research should therefore focus on enhancing our understanding of CAR T-cell therapy and expanding the application of immunotherapy in treatment.

Neural activity reorganizes profoundly after birth, transitioning from highly synchronous population events to sparse, decorrelated firing in the mature brain. Although inhibitory maturation and shifts in excitation-inhibition balance have been implicated in this process, how individual neurons implement the transition remains unclear because rapid brain growth has prevented long-term, same-neuron mapping. Here, we introduce growth-adaptive spring electronics that provide depth-wise compliance during tissue expansion, maintaining a stable electrode-tissue interface over weeks of neonatal development. We developed a vision-language model-assisted spike processing pipeline for the developing brain that probabilistically matches units across days using high-density waveform spatial footprints, despite developmental changes in the neonatal brain. Together, these innovations enable spike-resolved mapping of the same neurons in rat visual cortex and medial prefrontal cortex from postnatal day 10 to 45. Using population coupling to quantify each neuron’s coordination with local population activity, we show that developmental decorrelation is driven primarily by a distinct subset of neurons that progressively shifts from strong to weak coupling during postnatal weeks 3 to 5, whereas other neurons remain stably weakly or strongly coupled throughout development. These results resolve population-level desynchronization into identifiable neuron-specific trajectories. This framework enables direct tests in neurodevelopmental disorder models, including schizophrenia and autism, of whether altered maturation reflects global circuit imbalance or selective disruption and mistiming of specific developmental programs.