The expansion of industrialization and household use of synthetic compounds has generated significant wastewater containing toxic heavy metals. In developing countries, this wastewater is often discharged untreated due to the high cost of advanced treatment technologies. This study used sodium hydroxide as a low-cost, readily available precipitation agent to remove selected metal ions from mono- and binary-component solutions. Unlike most studies focusing on pH and initial ion concentration, this work investigated operational parameters such as stirring speed (0–800 rpm) and time (0–30 min) while keeping pH and concentration constant. Results showed that higher stirring speeds and longer stirring times enhanced metal ion removal, with Pb(II) efficiency increasing from 86.64% at 100 rpm to 94.33% at 800 rpm. In binary mixtures, similar improvements were observed. These findings highlight that simple, low-cost operational adjustments can significantly improve metal removal efficiency, which is particularly relevant for water treatment in resource-limited settings. The two-way ANOVA without replication showed that the type of metal or mixture had a significant effect on removal efficiency, while stirring speed and time within the investigated ranges did not have a statistically significant effect. These results indicate that differences in removal efficiency are primarily due to the metals’ chemical properties rather than the operational parameters.

This study provides useful insight into the current state and recent developments in blue (marine) biotechnology education (BBE) in Europe. A questionnaire assessing awareness and interest in blue biotechnology (BB) was conducted among students in their final year of upper secondary school in a selection of European countries. Results demonstrate that there are great regional differences in students’ recognition of BB; recognition of BB is relatively poor (though biotechnology knowledge is relatively good). This illustrates the lack of early education and ocean literacy. The interest in BB studies varied by country. Non-formal science, technology, engineering, and mathematics (STEM) activities, especially visits to research institutes and private businesses, help raise awareness. The review of education programs conducted in this study revealed that only a small number of programs are dedicated to BB and that it is not well integrated into existing curricula. Early ocean literacy, lifelong learning opportunities, and the inclusion of entrepreneurship and interdisciplinary skills in education are essential for developing a workforce capable of driving the future of the blue bioeconomy within the broader objectives of the EU Blue Economy Strategy and European Union (EU) Bioeconomy Strategy. To advance BBE, we propose the following recommendations: 1) incorporate and increase ocean literacy and BBE at the primary and secondary school levels; 2) support lifelong learning and adaptation to emerging technologies; 3) build collaborative ocean education networks; 4) bridge education communities, research and industry; 5) encourage entrepreneurship; 6) harmonise postgraduate BB programs; 7) establish joint European MSc and PhD degrees; and 8) align funding and BBE activities with national and regional gross domestic product (GDP) contributions for the BB sector.

Tekst teorijski analizira povezanost religijskog, etničkog i nacionalnog identiteta kao jednog od složenih transformativnih procesa u društvenoj teoriji. Religija je bila jedan od određujućih elemenata razvoja etnija i nacija u predmodernim društvima. Suvremeno doba kreacije apstraktnih identiteta i bezličnih organizacija revitaliziralo je oživljavanje identiteta zajednice kroz korištenje simboličkog materijala preuzetog iz religija. U bosanskohercegovačkom društvu konfesija je imala snažnu ulogu u izgradnji identiteta što je uvjetovalo procese sakralizacije nacije i religizacije politike i obratno. Izraženi oblici etnokonfesionalizma u Bosni i Hercegovini u smislu revitaliziranja identiteta zajednice u odnosu na religijski simbolički materijal ne pronalaze svoj materijal samo u religijskim fundusima. Oni svoje izvorište pronalaze i u drugim društvenim i kulturnim resursima kakav je i medijski prostor. Medijizacija religije u ovom smislu označavala bi procese u okviru kojih mediji ne produciraju prikaze vjere karakteristične za određenu religijsku zajednicu već one koji služe određenim društvenim funkcijama. Polazeći od stajališta da se prisutni, medijski kreirani religijski narativi ne odnose na promicanje ideja razumijevanja drugog i drugačijeg, već na jačanje religijske identifikacije stanovništva koja se, opet, posljedično reflektira na etničku, nacionalnu artikulaciju i na taj način produbljuje postojeću društvenu razjedinjenost, istraživanje je usmjereno na analizu načina i formi medijskog izvještavanja o religiji i religijskim temama u Bosni i Hercegovini. Rezultati analize pokazuju da mediji, organizirani u različitim etničkim zajednicama prate obrazac dominantnih tema vezanih za samo jednu religijsku zajednicu, i to najčešće kreirajući emotivan ili senzacionalistički ton, čime se, iskorištavanjem religije, religijskih obreda i običaja pri revizionizmu povijesnih događaja i ratnih stradanja, gradi oštar narativ prema pripadnicima drugih etničkih grupacija. Njihovo djelovanje u okviru realnih društvenih okolnosti i prihvatljivih diskursa može biti i drugačije, ako se u obzir uzme činjenica njihove ne samo mogućnosti promicanja postojećih društvenih stavova, već i kreiranja novih.

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.

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 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.

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 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 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.

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.

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.

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.