Digitální repozitář Univerzity Hradec Králové

Repozitář uchovává a zpřístupňuje publikační činnost a kvalifikační práce Univerzity 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

Nedávné příspěvky

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.
Ethical and governance challenges of large language models in power and energy systems: a review and framework for responsible AI deployment
(Elsevier, 2026) Sulaima, Mohamad Fani; Arora, Sunny; Krejcar, Ondřej; Herout, Lukas; Namazi, Hamidreza
Large Language Models (LLMs) are increasingly used for forecasting, monitoring, diagnostics, predictive maintenance, and decision support in power and energy systems, yet their deployment in safety-critical infrastructures raises concerns regarding hallucination, bias, explainability, cybersecurity, accountability, and human oversight. This structured scoping and critical review synthesizes a 126-source evidence corpus and compares emerging LLM paradigms, including domain-adapted models, retrieval-augmented generation, multimodal and reasoning models, agentic systems, and MCP-enabled architectures. Particular attention is given to power-system-specific requirements such as SCADA/EMS/DMS integration, topology awareness, state estimation, power-flow and voltage constraints, N-1 security, protection coordination, and operator authority. Quantitative publication trends and comparative evidence analysis are used to identify methodological and deployment gaps in the literature. Building on these findings, a multi-layer governance framework is proposed that integrates data and model governance, deterministic engineering verification, cybersecurity safeguards, human authorization, auditability, and regulatory oversight. A distribution-network fault-diagnosis and service-restoration workflow, together with a staged validation protocol, illustrates practical implementation. The review concludes with measurable research priorities for trustworthy LLM-enabled energy systems. © 2026 The Authors.
Biomimetic climbing robot design inspired by geckos and cats for rough wall applications
(MacMillan, 2026) Jodah, Mohammed K.; Rashid, Mofeed Turky; Abdulaali, Ali Hassan; Alhasnawi, Bilal Naji Hussain; Dočekalová, Alžběta; Bureš, Vladimír
This research presents the design of a quadrupedal robot capable of climbing rough surfaces. The robot's shape, body, arms, and locomotion mechanism were inspired by the gecko, while its limbs were inspired by the limbs cats used to climb trees and grip rough surfaces. The robot has three degrees of freedom. Its grasping limbs were designed in an arched shape, with each limb equipped with seven claws so that each hook moves independently on the wall without overlapping, The arms were designed in a parallelogram shape and at the same level as the robot's body to ensure stability during climbing and maintain the robot's center of gravity close to the wall surface, enhancing gripping force and reducing the risk of slipping or falling. These features enable the robot to grasp, cling, and climb rough surfaces with greater ease. The motion algorithm applied to the climbing robot was inspired by the gecko algorithm, and a kinematic model was developed to calculate the displacement and position of the robot arm's end-receiver. The robot's body was designed using SolidWorks, and the robot's body was fabricated and implemented using a 3D printer. Some system kinematics calculations were performed using MATLAB. Several climbing experiments were conducted on rough surfaces at 75 degrees and 85 degrees inclinations. In 15 trials on a 75 degrees slope, the robot achieved a success rate of 93% demonstrating reliable performance. The robot's climbing speed ranged from 20 to 60 cm/min. The experiments demonstrated the robot's ability to climb and navigate on rough surfaces and uneven terrain efficiently and with stable performance.
Cybersecurity challenges in integrated energy systems with cyber-physical integration: A review
(ELSEVIER, 2026) Sheta, Ahmed N.; Sedhom, Bishoy E.; Eladl, Abdelfattah A.; Bureš, Vladimír; El-Afifi, Magda I.
Integrated modern energy systems employ advanced information, communication, and control technologies to optimize the coordination of electricity, heat, and gas subsystems, forming interconnected energy cyber-physical systems (IECPS). While this deep integration enhances operational efficiency, it amplifies cybersecurity risks due to the interdependence of cyber and physical components. This paper comprehensively reviews IECPS cybersecurity challenges, focusing on cyber-physical coupling models, security performance assessment, attack methodologies, defense mechanisms, and recovery strategies. The analysis identifies vulnerabilities inherent in coupling models, evaluates security metrics, and classifies attack vectors alongside tailored mitigation approaches. Effective recovery strategies, such as self-healing systems and smart redundancy, are explored to minimize disruptions and bolster resilience. The study underscores critical research gaps, including insufficient multi-timescale modeling and control-level defense frameworks, while advocating for advanced solutions like AIdriven detection, quantum-secure protocols, and blockchain-based integrity verification. By synthesizing cuttingedge research, this work provides a roadmap for developing adaptive, standardized cybersecurity frameworks to safeguard IECPS against evolving threats, ensuring secure and resilient operation in smart energy infrastructures.