Digital Repository of the University of Hradec Králové

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Fakulta informatiky a managementu
Fakulta informatiky a managementu
Filozofická fakulta
Filozofická fakulta
Pedagogická fakulta
Pedagogická fakulta
Přírodovědecká fakulta
Přírodovědecká fakulta
Univerzita Hradec Králové

Recent Submissions

In silico studies into the therapeutic potential of organophosphorus compounds against Alzheimer's disease
(KeAi publishing, 2026) de Castro, Alexandre A.; Silva, Henrique F.; Militani, Isabela A.; Mesquita, A.P.L.; da Cunha, Elaine F.F.; Homem-de-Mello, Paula; de Castro Ramalho, Teodorico
Background Alzheimer’s disease (AD) is the most prevalent form of dementia in the elderly and is characterized by a progressive decline in cognitive function. Therapeutic strategies for AD have largely been guided by the cholinergic hypothesis, which has driven the investigation of acetylcholinesterase inhibitors, including trichlorfon, as potential therapeutic agents. Objectives This study aims to propose novel organophosphorus (OP) compounds as Hss AChE inhibitors, based on the cholinergic hypothesis. Methods A diverse array of OP compounds was systematically evaluated using advanced computational approaches, along with rigorous statistical analyses. The most promising candidates were further investigated through drug-likeness assessments, binding interaction analyses, and mechanistic simulations. Results In particular, inhibitors 12 , 13 , 20 , and 28 emerged as promising candidates for the treatment of AD. However, following a detailed safety analysis, inhibitor 12 was identified as the least toxic inhibitor, exhibiting favorable interactions with the molecular target. Molecular dynamics simulations indicated that inhibitor 12 is more energetically stabilized in the Hss AChE active site than the reference compound trichlorfon. QM/MM calculations further revealed a lower energy barrier for the Hss AChE inhibition mechanism. Conclusions These findings may facilitate the development of more effective and safer therapeutic options for the treatment of AD derived from this class of compounds.
Error-Based Metrics for Fingerprint Vector Similarity Assessment in Clustering-Based Fingerprinting Systems
(Institute of Electrical and Electronics Engineers Inc., 2026) Yaro, Abdulmalik Shehu; Malý, Filip; Pražák, Pavel
In clustering-based fingerprinting systems, theaccuracy of similarity determination between fingerprint vectors directly affects how well the vectors are clustered in the database. The metric used to assess this similarity is known as the similarity metric. Magnitude-aware metrics such as Euclidean and Manhattan distances are the most commonly used. Despite their popularity, magnitude-aware metrics have several limitations, including sensitivity to differences in vector magnitudes, vulnerability to outliers, and constraints imposed by geometric assumptions. As an alternative, this paper investigates two error-based metrics, mean absolute error (MAE) and mean squared error (MSE), for fingerprint vector similarity determination. The MAE-based metric is proportional to Manhattan distance, and the MSE-based metric is proportional to squared Euclidean distance for a fixed vector dimension. However, their interpretation as average prediction errors offers a different perspective that emphasizes robustness and interpretability. The investigation was conducted using k-means and k-medoids clustering algorithms across four publicly available fingerprint databases with varying properties: SEUG_IndoorLoc, MSI_IndoorLoc, JKKNIUIndoorLoc, and PIEP_UM. The magnitude-aware metrics considered for comparison are Euclidean distance, Manhattan, Minkowski L3, Chebyshev, and Canberra distance. Similarity determination performance was evaluated using mean silhouette scores obtained from 50 independent iterations. Results indicate that the MSE-based metric, despite satisfying only three of the four metric axioms, consistently produces clusters with the highest silhouette scores across all databases considered. The MAE-based metric, while satisfying all four metrics, axioms generally underperform compared to the magnitude-aware metrics considered. These findings indicate that the MSE-based metric is a viable alternative for clustering fingerprint vectors with both k-means and k-medoids algorithms.
Impact of Distance Metric Selection on Local Outlier Factor-Based Outlier Detection in Time-Series Received Signal Strength Observations
(John Wiley and Sons Ltd, 2026) Yaro, Abdulmalik Shehu
Due to received signal strength (RSS) variability in indoor environments, multiple observations are often collected sequentially over time to reduce short-term fluctuations. However, these time-series observations may contain local outliers caused by measurement disturbances and environmental variability. The Local Outlier Factor (LOF) algorithm can detect such outliers, but its performance depends on the distance metric, neighbourhood size, and LOF threshold. Existing studies provide limited comparative evaluation of distance metrics for LOF-based outlier detection in time-series RSS data. This study addresses this gap by systematically evaluating four distance metrics, namely absolute, squared, relative, and Canberra, within the LOF framework for time-series RSS outlier detection. Two RSS time-series datasets were evaluated using neighbourhood sizes of k=10,20, and 30 and LOF thresholds of T=1.0,1.5, and 2.0. Performance was assessed using Receiver Operating Characteristic Area Under the Curve (ROC-AUC), Precision-Recall Area Under the Curve (PR-AUC), Precision, Recall, and F1 score, with physically defined RSS bounds used to establish reference outliers. Dataset #1 contained 14 reference outliers, while Dataset #2 contained 175. The results show that distance metric selection affects threshold-based classification performance, although its effect varies across datasets and parameter settings. For Dataset #1, squared distance achieved perfect Precision, Recall, and F1 score at k = 10 and T = 1.5. For Dataset #2, squared distance achieved the highest F1 score of 0.2927 at k = 30 and T = 1.5, while Canberra distance achieved the highest ROC-AUC of 0.5812. The findings demonstrate that distance metric selection is an important consideration when configuring LOF for RSS outlier detection.
COMPETITIVENESS OF THE POLISH BEEF MARKET-ECONOMIC AND ENVIRONMENTAL ASPECTS
(FUNDACJA EKONOMISTOW SRODOWISKA & ZASOBOW NATURALNYCH, 2026) Kowalska, Anna Sylwia; Olszanska, Anna; Soukal, Ivan
The paper discusses economic and environmental aspects of the competitiveness of the beef market from the Polish perspective. Features of the growth in this market after the economic transformation are presented, with emphasis on progress in the specialisation and quality of production. We analysed market trends, production quality improvements, and export dynamics to show Poland's role as a major beef producer and exporter within the EU. The aim of the paper is to outline the changes that have taken place in Polish trade with EU countries and with third countries, as well as the competitiveness of the beef sector on international markets. The analysis is based on Eurostat data from the years 2003-2022, analysed via import-export coverage ratio, market concentration measures, and analysis of comparative advantages. The results show that Poland is in a strong position in the EU market, although potential risk areas were found. The future of the Polish beef market will depend on its ability to adapt to global market trends, changes in consumer preferences, and challenges related to sustainable production.
Trustworthy large language models (LLMs) for power-system operations: From "Grid Copilots" to verifiable decision support
(Elsevier, 2026) Sulaima, Mohamad Fani; Arora, Jyotsna; Herout, Lukas; Galvez, Ignacio Javier Perez; Krejcar, Ondřej; Namazi, Hamidreza
Large language models (LLMs) are increasingly proposed as "Grid copilots" for alarm interpretation, procedural retrieval, incident summarization, scenario analysis, and tool-mediated power-system studies. This review combines a PRISMA 2020-informed systematic identification/screening component with structured qualitative synthesis across power systems, LLM agents, trustworthy AI, and safety-critical decision support. The documented review pathway identified 312 records, removed 48 duplicates, screened 264 records, and assessed 213 full-text articles; after incorporation of additional recent and methodological literature, the current manuscript draws on a total evidence base of 199 references. The synthesis shows that recent tool-using agents can execute power-flow/OPF and contingency workflows, but operational trust remains conditional on authenticated state/topology, valid models and constraints, solver/tool correctness, uncertainty calibration, deadline compliance, human authority, cybersecurity, and reconstructable audit evidence. We therefore present VDS-LLM-Grid as an evidence-informed conceptual reference architecture-not a validated deployment-that integrates six assurance layers: input/state grounding, constraint-aware reasoning, independent verification, calibrated uncertainty with abstention, human oversight, and governance/auditability within a crosscutting cybersecurity boundary. The paper defines conditional "verifiable decision support," mathematical systemlevel metrics, deadline-aware routing, and a study-level evidence map that prioritizes empirical end-to-end validation, constraint verification, calibration, latency, cybersecurity, robustness, and operator evaluation.