New doctoral dissertation

June 12, 2024
VILNIUS TECH Library invites you to follow the published new dissertations. The dissertation „Research on the Connections between Road Transport Companies’ Technological Development and Engineering Competencies of Logistics Specialists“ prepared by VILNIUS TECH, Kristina Vaičiūtė. The dissertation was prepared in 2019–2024. Scientific Consultant – Prof. Dr Gintautas Bureika.

The dissertation wias defended at the public meeting of the Dissertation Defense Council of the Scientific Field of Transport Engineering in the SRA-I Meeting Hall of Vilnius Gediminas Technical University at 10 a.m. on 12 June 2024.

The dissertation explores the link between the technological development of road transport companies and the engineering competencies of logistics specialists and its influence on the efficiency of the companies’ operations. The purpose of the dissertation is to create a methodology for evaluating the interface between the engineering competencies of logistics specialists and the technological development of a transport company. The dissertation consists of an introduction, three chapters, general conclusions, a reference list of used literature, and a list of author’s publications on the dissertation’s topic. The introduction section includes the problem and the relevance of the work, presents the purpose and tasks of the work, describes the object and methodology of the research, defines the scientific innovation, practical significance of the work results, and defensive statements. The introduction closes with a list of the author’s scientific publications on the dissertation’s subject. The first chapter presents the potential of the vehicle fleet, provides an analysis of the criteria of technological development and engineering competencies of logistics specialists, and examines the components of the research objects. It systematizes engineering qualifications provided by different higher education institutions. The chapter ends with conclusions and formulated dissertation tasks. The second chapter presents the evaluation algorithm of the potential of the vehicle fleet, the criteria for the assessment of technological development, and the engineering competencies of logistics specialists that have been selected. It presents the evaluation methodology of the interface of technological development and logistics specialists’ competencies, determines the evaluation criteria and the significance, and compiles the interfaces’ evaluation algorithm. It presents the selected most suitable multi-criteria evaluation methods for the study. The third chapter applies Delphi, Pontriagin, AHP, and SAW evaluation methods to evaluate the synergy between the technological development of a transport company and the engineering competencies of logistics specialists. The concordance of the opinions of the expert group is determined by the ranks and the concordance coefficient. In total, 14 scientific articles have been published on the subject of the dissertation: four in scientific journals included in the Clarivate Analytics Web of Science database with a citation index, four in scientific journals included in the Scopus Journal Metrics database with a citation index, one in other international data in database publications (Scopus), four in peer-reviewed publications of “ISI Proceedings” conferences, and one in conference proceedings of the Scopus database. The results of research conducted on the subject of the dissertation were publicized at eight international conferences: two at scientific conferences abroad and six at conferences in Lithuania.

Doctoral dissertation readers can search via VILNIUS TECH Virtual Library.

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New doctoral dissertation
New doctoral dissertation
VILNIUS TECH Library invites you to follow the published new dissertations. The dissertation „Research and application of machine learning methods for migraine attack prediction“ prepared at VILNIUS TECH by Viroslava Kapustynska. The dissertation was prepared in 2021–2026. Scientific consultant – Prof. Dr Šarūnas Paulikas. The dissertation was defended at the public meeting of the Dissertation Defense Council of the Scientific Field of Electrical and Electronic Engineering in the Aula Doctoralis Meeting Hall of Vilnius Gediminas Technical University at 2 p.m. on 9 June 2026. Migraine is a complex neurological disorder characterized by strong inter- and intra-individual variability, which makes early forecasting difficult using only clinical observations. Wearable biosensors combined with machine learning offer new opportunities to detect subtle physiological changes that may precede migraine attacks and to develop individualized prediction models. This dissertation investigates migraine analysis and next-day prediction using physiological recordings collected under real-life monitoring conditions. Data were obtained with the Empatica Embrace Plus wearable device and include electrodermal activity, pulse rate, skin temperature, and movement-related signals. The analysis focuses on nocturnal recordings, since the night period provides a more stable physiological context with fewer external disturbances. Nights were standardized using sleep-based contextual selection and consistent night-level rules. The experimental framework is organized in two stages. In the first stage, a window-level binary classification task is used as an exploratory methodological analysis to examine how design choices influence model performance. Night recordings are segmented into analysis frames ranging from 5 to 120 minutes, statistical features are extracted, and the influence of signal preprocessing and feature representation is evaluated across several classifier families, including Random Forest, XGBoost, histogram-based gradient boosting, support vector machines, and k-nearest neighbors. In the second stage, the research evaluates next-day migraine prediction based on whole-night recordings. This stage refines the experimental methodology to obtain more reliable estimates of predictive performance under a stricter validation framework. The analysis focuses on the effect of temporal aggregation while comparing the same classifier families under consistent evaluation conditions. The results demonstrate considerable variability across participants in achievable prediction performance and optimal modeling configurations. Shorter analysis frames generally preserve informative short-term physiological changes, whereas longer windows tend to smooth these variations. Signal preprocessing shows a window-dependent effect and does not consistently improve performance. Overall, the results highlight the importance of temporal resolution, rigorous validation, and individualized modeling for wearable-based migraine prediction systems. Doctoral dissertation readers can search via VILNIUS TECH Virtual Library.
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