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Item type:Článek, listelement.badge.peer-reviewedpeer-reviewed, listelement.badge.statuspublished Access status: open access , Is last-mile delivery by autonomous robots a reality?: A multi-criteria decision-making approach for prioritization of autonomous vehicle modes(Elsevier BV, 2026-08) Dupljanin, Đorđije; Jovčić, Sara; Dumnić, Slaviša; Švadlenka, Libor; Simic, Vladimir; Dobrodolac, MomčiloThe rapid growth of e-commerce and the resulting surge in last-mile deliveries have intensified urban congestion and logistical inefficiencies. Although recent studies highlight the potential of autonomous vehicles and delivery robots to improve delivery efficiency and sustainability, the practical realization of these systems remains limited. Most existing research focuses on technological development or simulation-based optimization, while comprehensive decision frameworks for prioritizing different autonomous delivery modes under real urban conditions are largely absent. Addressing this gap, this study investigates whether last-mile delivery by autonomous robots is a viable and implementable reality. We propose a multi-criteria decision-making framework that integrates the AROMAN and FullEx techniques to systematically evaluate and prioritize various autonomous vehicle modes for last-mile logistics. The proposed approach incorporates multiple evaluation dimensions – economic, technical, environmental, and operational, to support evidence-based decision-making. The methodology is demonstrated through a case study in the city of Novi Sad, which illustrates the framework's applicability in assessing the readiness and suitability of autonomous delivery solutions in a real-world urban context. Findings from the study contribute to both academic discourse and practical logistics management by offering a structured approach to determine when and how autonomous delivery systems can transition from concept to reality.Item type:Konferenční objekt, listelement.badge.peer-reviewedpeer-reviewed, listelement.badge.statuspostprint Access status: Omezený přístup , Towards Automated Detection of Bookbinding Tool Impressions: A Pilot Study(Springer Nature Switzerland, 2026) Roleček, Jiří; Kopáčik, Ivan; Štursa, Dominik; Rozsíval, Pavel; Doležel, PetrThis research presents a pilot study on the automated recog-nition of historical bookbinding tools using deep learning and synthetic image generation. The work focuses on the documented collection of Czech binder Jenda Rajman (1892–1965), whose complete set of metal stamping tools provides an ideal reference dataset. Each tool leaves a characteristic impression on leather bindings, forming a unique visual signature that can support attribution, documentation, and conservation of historical works. To address the lack of complete photographic docu-mentation, two complementary synthetic data generation pipelines were developed: the Procedural Compositional Synthesis and the Background-Normalized Synthetic Stamping methods. These pipelines produce large, annotated image sets that simulate realistic embossing under diverse surface and lighting conditions. A YOLO-based deep learning architec-ture (YOLO11 family) was trained and evaluated on datasets generated by both pipelines and their combination. Experimental results indicate that the proposed approach provides a promising direction for further research, demonstrating the feasibility of detecting and classifying small, low-contrast blind-stamped motifs and highlighting the potential of syn-thetic data and deep learning for digital analysis of bookbinding heritage.Item type:Konferenční objekt, listelement.badge.peer-reviewedpeer-reviewed, listelement.badge.statuspostprint Access status: Omezený přístup , Classification of Damaged Potatoes Using YOLO11(Springer Nature Switzerland, 2026) Ksiażek, Jakub; Štursa, Dominik; Doležel, PetrPost-harvest sorting of potatoes is a key step in ensuring quality, preventing degradation of stored stocks and reducing subse-quent losses. Traditional manual sorting methods are laborious, subjec-tive and inconsistent, especially in large operations. Automatic detection of potato multi-type defects remains a challenge because of the diversifi-cation in defect size and visual similarity among multi-type defects. This study proposes a comprehensive potato sorting framework based on the YOLO11 object detection family, explicitly targeting real-world conveyor sorting scenarios. A novel mixed-condition dataset was constructed, com-bining both cleaned (washed) and uncleaned (harvest-state) potatoes captured under continuous conveyor motion and fixed industrial light-ing. The dataset includes four practically relevant classes: healthy pota-toes, defective potatoes, stones with soil residues, and plant debris. Unlike previous studies that focus on a single model configuration, all YOLO11 variants were systematically evaluated to analyze the trade-offs between detection accuracy, model complexity, and deployment feasibil-ity. The proposed approach achieves consistently high performance across both clean and unclean test sets, with macro F1-scores exceeding 0.96. The YOLO11-medium model demonstrated the best balance between accuracy and computational e fficiency, reaching F1-scores of 0.9908 on cleaned samples and 0.9752 on unwashed samples. The main contribu-tions of this work are threefold: 1) the creation of a realistic, mixed-condition post-harvest potato dataset reflecting industrial conveyor-line constraints; 2) a comparative analysis of the entire YOLO11 model fam-ily for agricultural object detection under heterogeneous visual conditions; and 3) the demonstration of a deployment ready solution that combines high accuracy with moderate computational requirements. These findings highlight the suitability of YOLO11 based systems for robust, real-time postharvest sorting and contribute to advancing practical deeplearning applications in precision agriculture.Item type:Habilitační práce, Access status: open access , Využití georadarové technologie v diagnostice liniových dopravních staveb(Univerzita Pardubice, 2026) Borecký, Vladislav; Anton, Ondřej; Vacín, Otakar; Štainbruch, JakubHabilitační práce je zaměřena na diagnostiku liniových dopravních staveb pomocí georadarové technologie a její začlenění do stávajících systémů správy této infrastruktury. Cílem práce je představit shrnující pohled na tuto problematiku v kontextu ostatních používaných diagnostických metod v ČR a upozornit na její specifika a limity jejího použití. Práce je dělena do čtyř základních kapitol zabývajících se diagnostikou liniových staveb obecně, technologií GPR, testováním této technologie a jejím využitím v oblasti liniových staveb.Item type:Diplomová práce, Access status: open access , Pracovní spokojenost radiologických asistentů ve vybraném zdravotnickém zařízení(Univerzita Pardubice, 2025) Šerý, Martin; Červenková, Zuzana; Černohorská, IvetaTato diplomová práce se zaměřuje na pracovní spokojenost radiologických asistentů na radiologické klinice ve vybraném zdravotnickém zařízení. Strukturována je do dvou hlavních částí - teoretické a výzkumné. Teoretická část se věnuje vymezení pojmu pracovní spokojenost, jejím faktorům, důsledkům, způsobům měření, vlivu personálního managementu a propojení s profesí radiologického asistenta. Dále se zabývá pracovní motivací, jejími faktory, nástroji a hlavními motivačními teoriemi. Výzkumná část popisuje cíle a výzkumné otázky práce a analyzuje pracovní spokojenost radiologických asistentů pomocí standardizovaného dotazníku McCloskey/Mueller Satisfaction Scale. Součástí je také posouzení vhodnosti tohoto nástroje pro radiologické asistenty z pohledu respondentů.