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Kaukazo regiono energijos perėjimas: politiniai ir socialiniai klausimai ES partnerystės kontekste.
No full textThe thesis provides a comprehensive exploration of the political, and socio-economic dimensions of energy transition, emphasizing its global and regional implications. The study structured into three main sections: political dimensions and strategic principles of energy transition, political agreements and legal frameworks in energy transition and a comparative analysis of social and political issues related to energy transition. The relevance of the topic is the high demand for renewable energy sources to address climate change and cut greenhouse gas emissions globally. The novelty of this study lies in its comparative approach and emphasis on lesser-studied contexts like the Caucasus, which remain underrepresented in energy transition literature. It focuses on political and socio-economic aspects of energy transition in the Caucasus regions, in the context of the European Union partnership, to analyze the political, social, and legal dimensions of the energy transition and to explore how governance models, international agreements, and regional cooperation in Caucasus Region. The thesis seeks an answer to the research question: How do political, legal, and socio-economic factors along with EU partnership, influence the effectiveness of energy transition strategies in Caucasus and their integration with global goals? In order to get answer, the tasks of the thesis are examining governance models and political strategies that facilitate the energy transition, evaluating the role of international agreements and partnerships in driving energy reforms, analyzing the socio-economic factors influencing energy policy development and implementation, comparing political and social challenges across different dimensions to derive practical insights and recommendations for the energy transition in the Caucasus Countries and analyzing the role of regional cooperation and partnerships with EU in fostering sustainable energy goals in the Caucasus Region. The thesis begins by examining governance models, identifying the structures and frameworks necessary for effective energy transitions. These governance models serve as a foundation for managing the shift from traditional energy systems to sustainable alternatives. The text delves into the political drivers that facilitate energy transition, such as governmental policies, international initiatives, and public support. So, this thesis presents the barriers, including political instability, economic challenges, and obstacle in regulation structures, that prevent progress in this domain. The thesis further explores the socio-economic factors that shape energy transition, illustrating how economic growth, employment opportunities, and societal acceptance play a pivotal role. For achieving energy transition goals, regional cooperation and partnerships are emphasized as critical with a focus on cross-border collaboration and shared responsibilities. The chapter concludes by synthesizing insights into the political and strategic principles essential for driving energy transition efforts. The second section focuses on political agreements and legal frameworks that underpin energy transition. It discusses the influence of international agreements on global energy policies, emphasizing their role in shaping commitments and actions toward sustainable energy. The European Green Deal is presented as a key driver of energy transition within the European Union, showcasing its policies, targets, and impact. The Energy Community Treaty is explored as a mechanism for fostering European integration through shared energy policies. Additionally, the text addresses the specific legal frameworks governing energy transition in the Caucasus region, offering insights into how localized policies contribute to broader global goals. This section concludes with a summary of the key findings. The final section provides a comparative analysis of social and political issues related to energy transition. It outlines the methodology and research design employed for the analysis, offering transparency and rigor to the study. The results present a nuanced understanding of the diverse challenges and opportunities faced by different regions and societies during energy transition. The considered comparative approach the document identifies patterns, variations, and lessons that can inform future energy policies and strategies. In essence, the thesis presents political, legal, and socio-economic factors that are a multi-faceted process. It emphasizes the importance of international collaboration, innovative governance, and inclusive policies to achieve transition toward a sustainable energy future within its complexities
Integration of artificial intelligence into project management performance domains.
No full textThe final thesis aims at describing and analysing the following research issues related with artificial intelligence integration into project management performance domains. Project management in this thesis is based on PMBOK 7th version, which focuses on performance domains rather than the traditional process management model. The study argues that artificial intelligence creates benefits in all performance domains, it can improve communication management and collaboration, create greater stakeholder satisfaction and a more supportive and inclusive environment, automate tasks and increase process efficiency, improve decision-making and develop strategies to manage uncertainty. Artificial intelligence can not only improve processes in performance domains, but can also help maintain competitiveness by adding value to organisations and communities. However, the perceived lack of information and practical guidance on the application of artificial intelligence tools and its benefits in specific performance domains, both in Lithuania and globally, complicates the integration of artificial intelligence in project management, and the aim of this study is to determine the influence of the integration of artificial intelligence on the processes of project performance domains. The object of this research. Integrating artificial intelligence into project management performance domains. Research tasks: 1. to uncover problematic aspects of artificial intelligence integration into project management performance domains; 2. to analyse the theoretical solutions for the integration of artificial intelligence into project management performance domains; 3. to justify the methodological approach of the integration of artificial intelligence into project management performance domains; 4. based on the results of empirical research, propose solutions for the integration of artificial intelligence into project management performance domains. Research methods: analysis of scientific literature, semi-structured interviews, qualitative content analysis. Research results. After conducting the analysis of research data, it was determined that 12 artificial intelligence tools provide practical benefits in the performance domains of project management: ChatGPT, Deepl, Project Admin, Smartsheet, Microsoft Project, Canva, Teams, Copilot, internal ChatGPT, Perplexity, Napkin, and Dimensions. The results suggests that artificial intelligence tools can improve and provide benefits in all performance domains. The results of the empirical study confirmed the barriers to artificial intelligence integration outlined in the theoretical model. The main artificial intelligence tool used by respondents in project management, which has the widest applicability in each performance domain, is ChatGPT, and it is therefore recommended that artificial intelligence integration should start with it. In order to ensure successful integration of artificial intelligence in the performance domains of project management, it is suggested that a process inventory is carried out and that a technical assessment is carried out to identify which processes are worthy of artificial intelligence implementation. The study notes that a strong emphasis needs to be placed on human resources, that is change management, the adaptation period and the provision of training. The practical implications of this work are useful for the integration of artificial intelligence tools in project management activities, as the recommendations generated by the study are based on the experience of eleven project management experts with very different projects and sectors, and the practical implications of this work are revealed through the universal applicability of the recommendations
Dirbtinio intelekto adaptacija Lietuvos kariuomenėje.
No full textArtificial Intelligence as a topic raises many questions, especially when it is considered to be adopted in our defence sector. In the past 5 years, Artificial Intelligence has been described as an application, autonomous system, or course of action that helps to achieve a specific goal with the analysis of the environment. Following this idea to the defence sector, artificial intelligence’s ability to process the existing data, analyse patterns, and present autonomous decisions influences defence system activities, from collecting information to building strategy to enhancing training and other potential changes. More industrialized and advanced economy countries are already working on Artificial Intelligence technology development for defence use. Considering Lithuania’s situation, new system usage is not so broadly discussed. Additionally, together with a military situation in Europe and around the world, developing NATO objectives, EU legislations towards AI development, and ethical considerations, the Artificial Intelligence adaptation process is unpredictable, especially when the Lithuanian military encounters new challenges. This study aims to understand the adaptation situation in the Lithuanian Armed Forces by assessing the theoretical approach and current accessibility to Artificial Intelligence technologies. The master thesis conducted an empirical study and implemented Q methodology, to analyse the experience, current review, and availability of Artificial intelligence usage in the Lithuanian Armed Forces. With the empirical research, there were collected 4 interviews, with the following 82 responses from Q methodology target groups. The literature analysis part discussed the concept of social innovation, principal system functioning, its application from the different country’s perspectives, and potential growth shortly. For this research part, there were chosen both foreign and Lithuanian scholars that were freely available from the research results, followed by different institutional legislation analyses that would affect both military activities and Artificial Intelligence usage. In the empirical part, semi-structured interviews of the respondents' opinions on Artificial intelligence, and their effect on the army and individual employees. Q methodology analysis, on the other hand, outlined the civilian and military personnel's perception towards AI usage and adaptation in the defence sector. The research and the literature analysis showed that the adaptation of Artificial Intelligence would not be a smooth process, as it will be affected by different limitations and potential threats. However, together with the challenges, some benefits can effectively and efficiently improve military functions. The project concludes with conclusions and recommendations referring to the Artificial Intelligence adaptation process that can be reviewed to minimize the resistance and fit the current administrative frameworks. Considering strict resource groups and administrative policies of the military institutions, recommendations were presented as additional activities and processes helping to optimize the service delivery process. In an overall view, the project consists of 101 pages (with annexes), 16 tables, 13 figures, 5 appendices, 155 references, and 79 sources of information
Digitizing buildings sustainability assessment: integrating energy audits, operational energy assessments, and life cycle assessments for enhanced building assessment /
No full textHybrid teaching and learning in higher education: a systematic literature review /
No full textHybrid teaching, which integrates traditional in-person learning based on students’ perspectives where online learning offers a flexible approach to education, combines the benefits of technology with face-to-face interactions. Moreover, teaching and learning in a hybrid way met several challenges for both teachers and learners, including technological problems, time management, communication difficulties, and assessment complexities. This systematic review investigates six main research questions: (1) What pedagogical frameworks are used in hybrid teaching and learning? (2) How can we enhance students’ engagement in hybrid teaching and learning? (3) What is the impact of technological integration on hybrid learning scenarios, both for students and teachers? (4) How do training and support measures influence the willingness and ability of university teachers to implement hybrid teaching formats? (5) How do formative assessment and feedback methods in hybrid learning environments enable teachers to effectively monitor student progress and provide tailored support? (6) How does the implementation of hybrid learning affect student learning outcomes? This study identifies the following key themes: technological integration, pedagogical innovation, faculty support, student engagement, assessment practices, and learning outcomes. Our contribution of this literature review is related to teaching and learning by showing teachers the most appropriate way to avoid the challenges encountered when teaching in a hybrid way. These include strong technology integration, innovative pedagogical strategies, strong academic development and support, active student engagement, effective assessment practices, and positive learning outcomes
Strategic foresight, knowledge management, and open innovation: drivers of new product development success /
No full textTo remain competitive and make effective decisions in increasingly challenging markets, firms must integrate internal and external knowledge by embedding knowledge management strategies and technologies into their operations. This study aims to examine the roles of strategic foresight and knowledge management in promoting open innovation and driving new product development. Grounded in the knowledge-based view (KBV) of the firm, it investigates how strategic foresight influences open innovation processes and how knowledge management catalyzes innovation success. Using structural equation modelling (SEM) on data collected from 298 technology-based firms located in Lithuania (n = 142) and Slovakia (n = 156), the study demonstrates that strategic foresight directly impacts open innovation and significantly improves new product development through open innovation; in addition, knowledge exploration and exploitation are shown to play important roles in open innovation, with balanced effects on new product development outcomes. The study identifies open innovation as a critical mechanism that links strategic foresight and knowledge management to improve new product development, extending the KBV of the firm by highlighting the integration of external knowledge with internal processes, particularly in smaller, emerging economies. Practically, managers are recommended to prioritize foresight and balanced knowledge management practices while leveraging strategic alliances and networks to improve new product development outcomes. This integrated approach highlights the importance of collaborative innovation and external knowledge in achieving competitive advantage in dynamic business environments
Enhancement of ferroelectric properties of Ni-substituted Pb2Fe2O5 thin films synthesized by reactive magnetron sputtering deposition /
No full textLead ferrite Pb2Fe2O5 (PFO) is a potential multiferroic material due its exhibition of ferroelectric and ferromagnetic properties. The effects of the substitution with nickel and synthesis temperature on the structural, morphological, and ferroelectric properties of lead ferrite thin films were investigated through the use of reactive magnetron sputtering deposition. Nickel loading concentrations were systematically varied (3%, 5%, and 10% by wt.%). X-ray diffraction analysis confirmed the formation of Ni-substituted distorted PFO lattices, while scanning electron microscopy and energy-dispersive spectroscopy indicated a uniform elemental distribution and surface morphology. Polarization vs. electrical field (P−E) measurements showed improved remnant polarization (Pr) with increasing Ni content and synthesis temperatures, achieving a maximum Pr of 66.7 µC/cm2 at 5 wt.% The Ni loading and substrate (Pt/Ti/SiO2/Si, Nanoshel Company, Cheshire, UK) temperature were 600 °C. These findings suggest that optimizing the synthesis parameters such as temperature and substitution content is crucial for controlling the ferroelectric properties of PFO thin films
Generatyvinis dirbtinis intelektas problemų sprendimo gebėjimų stiprinimui netechninėse pareigos.
No full textGenerative Artificial Intelligence (GenAI) is transforming workplaces by shifting how organizations approach data analysis, insight generation, and problem-solving. While extensively adopted in technical fields such as software engineering and data science, its integration into non-technical roles remains underexplored. This research specifically investigates both the perceptions of non-technical employees regarding GenAI and the measurable effects of its use on their problem-solving capabilities in data-related workplace activities. The object of this research is the application of Generative Artificial Intelligence in supporting problem-solving processes among non-technical employees engaged in data-centric tasks. The aim of the research is to reveal how employees in non-technical roles perceive and apply generative AI tools to enhance their problem-solving capabilities while taking into consideration the organizational and contextual factors that support or prevent their effective use. An explanatory sequential mixed-methods approach was implemented, comprising a quantitative survey of 32 respondents, behavioral experiments involving 8 participants, and semi-structured interviews with 5 managerial representatives at a German manufacturing company. This methodological design enabled triangulation of findings across perceptual, behavioral, and contextual dimensions, strengthening the validity of the results despite the exploratory case-study scope. The main findings reveal significant barriers to effective genAI integration, including fragmented data systems, reliance on IT support for data access, cognitive overload resulting from complex AI outputs, and insufficient training in critical evaluation skills. Although survey responses indicated increased confidence and perceived efficiency when using genAI, particularly in creative, unstructured tasks, behavioral experiments demonstrated no measurable improvement in independent data reasoning or adaptive decision-making. These outcomes were assessed through task-based problem-solving exercises comparing performance with and without genAI assistance, revealing a clear perception-performance gap and highlighting the risk of over-reliance on AI-generated outputs. The study contributes to theoretical understanding by refining the Technology Acceptance Model (TAM), Diffusion of Innovations (DOI) theory. Contrary to TAM expectations, perceived ease of use was not a significant factor influencing adoption; instead, compatibility with existing workflows and trust in GenAI outputs emerged as critical determinants. This deviation is likely due to participants’ relatively high levels of digital familiarity, where functional value and contextual relevance outweighed usability concerns. Additionally, applying a Systems Thinking perspective demonstrated that task complexity moderates the perceived benefits of GenAI, with greater value observed in exploratory problem-solving rather than structured analytical activities. Based on these findings, several practical recommendations are proposed. Organizations should integrate genAI tools directly into established workflows to improve contextual relevance and functionality. Leadership must actively demonstrate responsible genAI use by participating in data driven decision-making processes and communicating clear ethical guidelines. Training programs should extend beyond technical onboarding to include structured critical thinking development, enabling employees to assess AI outputs rigorously. Furthermore, increasing genAI access to relevant organizational data is essential for improving the contextual accuracy of outputs, thereby strengthening user trust and supporting effective application in problem-solving contexts. In conclusion, this research advances the understanding of genAI adoption within non-technical roles, demonstrating that while GenAI tools enhance perceived problem-solving confidence, they do not inherently develop independent reasoning capabilities. Effective capability development requires not only thoughtful technological integration, but also targeted interventions aimed at strengthening critical thinking skills and improving access to high-quality organizational data. Given the exploratory case-study design and limited sample size, these findings should be interpreted within the context of the studied organization, with caution in generalizing to broader industry settings
Least squares progressive iterative approximation for spline curve and surface (re-)construction using adaptive refinement.
No full textLeast squares progressive iterative approximation (LSPIA) method allows to approximate data points with curves and surfaces by employing splines that are fitted to the data. The accuracy of approximations is determined by the amount of control points used in the splines, thus an appropriate selection of control points is needed to acquire the best approximation. The use of THB-splines as the underlying spline type inside the LSPIA method allows the resulting splines to better approximate data points with highly irregular sections by employing local resolution improvements. This decreases the total amount of control points used globally, removes visual artefacts resulting from excessive control points and reduces the amount of resources sed during calculations
Development and analysis of a BitB attack recognition method.
No full textThis paper explores the concept of a BitB (Browser in the Browser) attack and presents a method for real-time detection of such attacks in the user's browser. BitB attacks work by simulating a browser pop-up on a malicious website, tricking users into submitting login credentials. The aim of this project is to develop an effective detection method that identifies and prevents this type of attack. The method proposed in this paper is based on the identification of a login form from a web page view, in order to determine whether the login form is fake. The prototype system implemented consists of a browser plug-in and a separate analysis program. The system analyses the web page image in real time, allowing it to detect attack attempts, block the data sent by the web page, and warn the user. The evaluation of the system has shown that it achieves a high level of accuracy in detecting attacks and is fast enough to be applicable in real-world environments. The results of the work show that this type of approach can be an effective means of protection against BitB attacks