A BSTRACT Repaired TOF (rTOF) is a common condition in adult congenital heart disease care. The aim of this article is to investigate how inflammatory markers and volume overload affect rTOF, and how these factors correlate with echocardiographic findings in the right heart cavities. This study included 32 adult patients (mean age, 27.44 ± 6.22 years) who had undergone surgical correction of TOF during infancy. All participants underwent transthoracic echocardiography, and laboratory assessments included measurements of N-terminal pro-B-type natriuretic peptide (NT-proBNP; reference range ≤125 pg/mL) and high-sensitivity C-reactive protein (hs-CRP; reference range ≤2.0 mg/L). Significant correlations were found between NT-proBNP levels and right ventricular (RV) fractional area change (FAC) (r = −0.55, P = 0.001), tricuspid annular plane systolic excursion (TAPSE) (r = −0.35, P = 0.04), RV basal diameter (r = 0.47, P = 0.006), and right atrium (RA) area (r = 0.50, P = 0.003). Similarly, hs-CRP levels showed strong associations with RV FAC (r = −0.58, P < 0.001), TAPSE (r = −0.45, P = 0.009), RV basal diameter (r = 0.43, P = 0.01), and RA area (r = 0.43, P = 0.01). NT-proBNP had a significant impact ( P = 0.017; EXP B = 0.982) on RV FAC. A decrease of 10 units in NT-proBNP was associated with a 17% reduction likelihood of having RV FAC <35%. The proinflammatory response (hs-CRP) and volume load (NT-proBNP) are directly correlated with echocardiographic parameters of the right heart cavities in patients with rTOF, suggesting that patients may benefit from treatment with anti-inflammatory drugs, as well as medications targeting diastolic dysfunction.

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.