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.

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).

BackgroundDialysis is a very complex treatment that is received by around 3 million people annually. Around 10% of the death cases in the presence of the dialysis machine were due to the technical errors of dialysis devices. One of the ways to maintain dialysis devices is by using machine learning and predictive maintenance in order to reduce the risk of patient's death, costs of repairs and provide a higher quality treatment.ObjectivePrediction of dialysis machine performance status and errors using regression models.MethodThe methodology includes seven steps: data collection, processing, model selection, training, evaluation, fine-tuning, and prediction. After preprocessing 1034 measurements, twelve machine learning models were trained to predict dialysis machine performance, and temperature and conductivity error values.ResultsEach model was trained 100 times on different splits of the dataset (80% training, 10% testing, 10% evaluation). Logistic regression achieved the highest accuracy in predicting dialysis machine performance. For temperature predictions, Lasso regression had the lowest MSE on training data (0.0058), while Linear regression showed the highest R² (0.59). For conductivity predictions, Lasso regression provided the lowest MSE (0.134), with Decision tree achieving the highest R² (0.2036). SVM attained the lowest MSE on testing dataset, with 0.0055 for temperature and 0.1369 for conductivity.ConclusionThe results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automated systems for advanced management of dialysis machines.

Background Poorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. As infant incubators are used as a form of fundamental healthcare support for the most sensitive population, prematurely born infants, special care mus be taken to ensure their proper functioning. This is done through a standardized process of post-market surveillance. Objective To address the issue of faulty infant incubators being undetected and used between yearly post-market surveillance, an automated system based on machine learning was developed for prediction of infant incubator performance status. Methods In total, 1997 samples were collected during the inspection process of infant incubator inspections performed by an ISO 17020 accredited laboratory at various healthcare institutions in Bosnia and Herzegovina. Various machine learning algorithms were considered, including Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) for the development of the automated system. Results The aforementioned algorithms were selected because of their ability to handle large datasets and their potential for achieving high prediction accuracy. The 0.93 AUC of Naïve Bayes indicates that it is overall stronger in predictive capabilities than decision tree and random forest which displayed superior accuracy in comparison to Naïve Bayes. Conclusion The results of this study demonstrate that machine learning algorithms can be effectively used to predict infant incubator performance status on the basis of measurements taken during post-market surveillance. Adoption of these automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of infant incubators that are already being used in healthcare institutions.