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Comparative analysis of external ventilated thermal insulation systems with frame consoles used in modernization of apartment building facade.
No full textWith the rapid expansion of modernization in Lithuania’s multi-apartment residential buildings, the selection of a high-performance facade system has become particularly important. Therefore, to determine the most effective facade system alternative, a comparative analysis of external ventilated thermal insulation facade systems used in the modernization of multi-apartment buildings is conducted. The aim of this study is to perform a comparative analysis of external ventilated thermal insulation facade systems with structural frame brackets used in the renovation (modernization) of multi-apartment residential buildings by applying the TOPSIS method. Tasks: 1. To analyse foreign and Lithuanian scientists’ publications and research related to facade systems used in the modernization of multi-apartment buildings. 2. To describe the calculation procedures, the TOPSIS method, and the expert survey evaluation methodologies used in the study. 3. To perform a comparative analysis of external ventilated thermal insulation facade systems with structural frame brackets used in the modernization of multi-apartment building facades by applying the TOPSIS method. The first part of the Master’s thesis provides a comprehensive review of the scientific literature. The second part delineates the calculation procedures, the TOPSIS method, and the methodology applied for expert survey evaluations. In the research section of the Master’s thesis, a comparative analysis was undertaken of ventilated thermal insulation systems incorporating structural frame brackets, as implemented in the modernization of multi-apartment buildings. Four facade systems featuring different types of frame brackets (stainless steel, basalt fiber composite, galvanized steel, and aluminum alloy) were examined. These alternatives were evaluated in terms of the energy performance classes “A” and “B” of the modernized building. The Master’s thesis consists of summaries in Lithuanian and English, an introduction, three chapters, conclusions, a list of references, and appendices. Its total length is 66 pages, and the list of literature and information sources comprises 65 entries
Entrepreneurial competence expression in the implementation of AI solutions in Start-up organizations: experiences of founders and managers.
No full textThe development of artificial intelligence (AI) technologies is fundamentally changing organizational operating models, innovation processes, and the competitive environment. Although startups are described in the literature as particularly dynamic and innovation-driven organizations, there is still a lack of integrated studies in scientific research that comprehensively analyze the expression of entrepreneurial competencies when implementing AI solutions in startup activities. This paper addresses this research gap by seeking to reveal how the entrepreneurial competencies of startup founders and managers change in the context of AI implementation. The aim of the study is to analyze and empirically evaluate the expression of entrepreneurial competencies in a startup organization when implementing AI solutions, based on the experiences of founders and managers. The theoretical part of the study examines the specifics of a startup as an innovation-based organization, the impact of AI technologies on organizational processes, and traditional entrepreneurial competencies identified based on the EntreComp system. The analysis showed that when implementing AI solutions, traditional entrepreneurial competencies—such as opportunity recognition, problem solving, creativity, planning, collaboration, and learning—take on new forms supplemented by technology. At the same time, competencies specific to the AI environment emerge, related to flexibility, technological creativity, and digital literacy. The empirical part consists of qualitative research – semi-structured interviews with eight founders and managers of Lithuanian start-ups. The research identified the main motives for implementing DI: process automation and improvement of customer experience. It was also found that traditional entrepreneurial competencies dominated before the implementation of AI, but in the AI environment, they are transforming into digital and hybrid forms, in which human-AI interaction, the ability to make technology-based decisions, critically assess the possibilities and limitations of AI, and manage technological risks become particularly important. The results of the study revealed that the implementation of AI is changing the structure of entrepreneurial competencies: traditional competencies do not disappear, but their expression moves to a digital environment that requires a broader understanding of technology, faster adaptation, and interdisciplinary thinking. In addition, founders and managers emphasize the need for new competencies—data literacy, AI solution evaluation skills, and certain personal qualities. The study suggests that entrepreneurial competencies in the context of AI are becoming hybrid in nature, combining traditional entrepreneurial knowledge and digital competencies, and that the successful implementation of AI in a startup organization depends on the ability to balance technology and human factors. The results of the study complement the theoretical discourse on the transformation of entrepreneurial competencies in the context of AI and provide practical insights for startup managers who are planning or implementing AI solutions in their organizations
Success factors for implementing a continuous improvement culture in public sector organisations.
No full textPublic sector organisations are increasingly facing pressure to improve operational efficiency and ensure high-quality service delivery. As a result, growing attention is being paid to the adaptation of management practices traditionally applied in the private sector to the public sector context. One of the most commonly adopted approaches is the Lean system, an integral component of which is Kaizen, or a continuous improvement culture. The core principle of this culture lies in the systematic and incremental improvement of processes through the active involvement of employees at all organisational levels. A successfully implemented continuous improvement culture enables organisations to enhance overall performance by increasing efficiency, reducing errors, accelerating decision-making processes, and improving service quality, among other benefits (Carnerud et al., 2018). However, despite the well-documented advantages, empirical evidence indicates that many organisations encounter significant challenges when attempting to implement and sustain a continuous improvement culture (Bader et al., 2024; Costa et al., 2019; Sanchez-Ruiz et al., 2020). The success of implementation of a continuous improvement culture is influenced by a wide range of contextual factors, including organisational size and sector, employee behaviour, national cultural traditions, as well as the broader economic and political environment (Aamer et al., 2022; Janjić et al., 2020; Suarez-Barraza et al., 2011). Scholarly interest in a continuous improvement culture within public sector organisations began to intensify only after 2010, with most empirical studies focusing on single cases, individual organisations, or specific tools rather than systemic analyses (Suarez-Barraza & Miguel-Davila, 2013). Research further demonstrates that the outcomes of continuous improvement initiatives are strongly dependent on national, cultural, and organisational contexts (Aamer et al., 2022; Brunet & New, 2003; Janjić et al., 2020; Suarez-Barraza et al., 2011). In the context of Lithuania, the need to examine the success factors of continuous improvement culture implementation in the public sector arises from the specific characteristics of public sector organisations and the contemporary challenges of public governance. This underscores the importance of empirically investigating the determinants of successful continuous improvement culture implementation in Lithuanian public sector organisations. Object of the research. Factors influencing the implementation of a continuous improvement culture in public sector organisations. Aim of the research. To identify the factors determining the successful implementation of a continuous improvement culture in the public sector. Research results. The findings indicate that organisational factors exert the strongest influence on the implementation of a continuous improvement culture in public sector organisations. These include clearly defined guidelines and procedures, the provision of adequate resources to employees, clearly articulated continuous improvement goals, training, performance measurement, communication, and employee incentives. Additionally, positive relationships were identified between the intensity of continuous improvement culture manifestation and other factors such as openness to change, leadership, feedback, perceived value of employee involvement, and collaboration
Research of steel columns filled with rubberized concrete.
No full textAs the requirements to reduce construction waste and increase the use of recycled materials in the construction sector increase, ways to integrate secondary raw materials into construction structures are increasingly sought. One of the promising directions is the mixing of recycled tire rubber waste into concrete. These mixtures are becoming relevant in the design of structures, especially column systems, where the strength of the structure is important. This master's thesis examines the suitability of steel columns filled with concrete with rubber additives for their application in building load-bearing systems and evaluating their mechanical properties. The scope of the project analyzes the influence of rubber on concrete, changes in the behavior of steel profiles, and changes in the bearing capacity and plasticity of columns by comparing different concrete mixtures (NC, RuC5, RuC15). Analytical calculations performed according to EC4 methodologies and numerical analysis using finite element software allow us to evaluate the behavior of columns, the resistance of different profiles (circular and square) and the effect of rubber additives on the performance of the structure
Skatinamuoju mokymusi grįsto neuroninio tinklo kūrimas optimaliam traukinio galios valdymui.
No full textThis study presents a reinforcement learning approach for reducing energy consumption in freight train operations while still meeting scheduled arrival times. A simplified physical simulation, combined with a learned power control policy, allows the system to select engine power levels based on the remaining travel time, speed limits, train mass, and the upcoming terrain along the route. The model is trained through repeated interaction with the simulated environment, where it learns to balance punctuality and energy consumption based on the reward function designed for this task. The results show that the trained model can learn energy-efficient driving behaviour and outperform basic control strategies in terms of energy consumption. Despite relying on simplified models and route-specific training, the work demonstrates that reinforcement learning is a practical and effective tool for supporting train operators in executing energy-efficient train power control
Salt-free pickling with sulfonic acid as an approach to cleaner leather processing /
No full textRecently, increasing attention has been paid to the application of sulfonic acids as alternative materials for the pickling process. The aim of the present study was to investigate the action of pickling with p-toluenesulfonic acid monohydrate on derma’s collagen and the influence of this action on subsequent processes and properties of chromed and crust leather. The application of p-toluenesulfonic acid monohydrate in the pickling process led to a similar effect on collagen compared with conventional process. The solutions after experimental pickling contained lower amounts of total dissolved solids, total suspended solids, and chlorides. The chrome tanning process is improved after the pickling with p-toluenesulfonic acid monohydrate: the exhaustion of chromium compounds reaches 98%, while after conventional pickling, it is only 68.7%; accordingly, lower amounts of basic chromium sulfate can be used for chrome tanning to achieve the same chromium content in the wet blue leather. The crust leather produced after experimental pickling has properties close to the conventional one
Dynamic CO2 emission differences between E10 and E85 fuels based on speed–acceleration mapping /
No full textThis study compared CO2 emissions during a WLTP (Worldwide Harmonized Light-Duty Vehicles Test Procedure) test performed on a chassis dynamometer for the same flex-fuel vehicle, fuelled sequentially with E10 gasoline and E85 fuel. Based on the test data, a CO2 emissions map was created, describing its dependence on speed and acceleration. The use of a 3D surface enabled the visualisation of the whole dynamics of emissions as a function of engine load in the WLTP cycle, including the identification of distinct emission peaks in areas of high positive acceleration. Analysis of the emission surface enabled the identification of structural differences between the fuels. For E85, more pronounced emission increases are observed in areas of intense acceleration, a consequence of the higher fuel demand resulting from the lower calorific value of bioethanol. In steady-state and moderate-load driving, CO2 emissions for both fuels are similar. The results confirm that the main differences between E10 and E85 are not simply a shift in emission levels per se, but stem from variations in engine load during the dynamic cycle. Although E85 emits measurable CO2 emissions, its carbon is not of fossil origin, highlighting the importance of biofuels in the context of greenhouse gas emission reduction strategies and the pursuit of climate neutrality. The presented methodology, combining chassis dynamometer tests with analysis of the speed-acceleration emission map, provides a tool for clearly identifying emission zones and can serve as a basis for further optimisation of engine control strategies and assessing the impact of fuel composition on emissions under dynamic conditions
Domain-adaptive MRI learning model for precision diagnosis of CNS tumors /
No full textBackground: Diagnosing CNS tumors through MRI is limited by significant variability in scanner hardware, acquisition protocols, and intensity characteristics at clinical centers, resulting in substantial domain shifts that lead to diminished reliability for automated models. Methods: We present a Domain-Adaptive MRI Learning Model (DA-MLM) consisting of an adversarially aligned hybrid 3D CNN-transformer encoder with contrastive regularization and covariance-based feature harmonization. Varying sequence MRI inputs (T1, T1ce, T2, and FLAIR) were inputted to multi-scale convolutional layers followed by global self-attention to effectively capture localized tumor structure and long-range spatial context, with domain adaptation that harmonizes feature distribution across datasets. Results: On the BraTS 2020 dataset, we found DA-MLM achieved 94.8% accuracy, 93.6% macro-F1, and 96.2% AUC, improving upon previously established benchmarks by 2-4%. DA-MLM also attained Dice score segmentation of 93.1% (WT), 91.4% (TC), and 89.5% (ET), improving upon 2-3.5% for CNN and transformer methods. On the REMBRANDT dataset, DA-MLM achieved 92.3% accuracy with segmentation improvements of 3-7% over existing U-Net and expert annotations. Robustness testing indicated 40-60% less degradation under noise, contrast shift, and motion artifacts, and synthetic shifts in scanner location showed negligible performance impairment (<0.06). Cross-domain evaluation also demonstrated 5-11% less degradation than existing methods. Conclusions: In summary, DA-MLM demonstrates improved accuracy, segmentation fidelity, and robustness to perturbations, as well as strong cross-domain generalization indicating the suitability for deployment in multicenter MRI applications where variation in imaging performance is unavoidable
Machine learning applications in the mechanical analysis of nanomaterials and nanostructures /
No full textMachine learning (ML) is increasingly used to address the computational complexity and multiscale nature of mechanical analysis in nanomaterials and nanostructures. Traditional analytical, numerical, and atomistic approaches, such as continuum mechanics, finite element methods, and molecular dynamics (MD), often suffer from high computational cost or limited scalability when applied to nanoscale systems. Recently, ML techniques have been increasingly used to predict mechanical properties, analyze static and dynamic responses, and solve governing equations of nanostructures to improve efficiency and accuracy. This review provides a comprehensive overview of ML applications in the mechanical analysis of nanomaterials and nanostructures, including mechanical property prediction, static response analysis, and vibration analysis. Various ML techniques based on the property or type of the mechanical problem are discussed in detail. The review highlights current trends and provides structured guidance for future research on reliable and physically consistent ML methods for nanoscale mechanical analysis
Delineating the contours of citizen science: development of the ECSA characteristics of citizen science /
No full textBACKGROUND: Citizen science is increasingly recognized as a valuable scientific approach across disciplines, contexts, and research areas. However, its rapid expansion and diverse methodologies make it challenging to establish a single definition or universal criteria for what constitutes citizen science. This paper introduces the ECSA Characteristics of Citizen Science, offering a nuanced exploration of the field to support stakeholders, including policymakers and research funders, in understanding and applying citizen science effectively. METHODS: We developed the ECSA Characteristics through a vignette study, a survey method that captures diverse perspectives on complex topics. We then reviewed the ECSA 10 Principles of Citizen Science, a broad framework for best practices in citizen science, to identify its gaps and limitations, showing how the ECSA Characteristics can help address them. RESULTS: The results highlight the disciplinary distinctions as well as ambiguities surrounding various citizen science practices. Two challenges exist when defining citizen science. A very strict definition could exclude valuable practices, hindering innovation and discouraging public participation. Conversely, a loose definition might make it difficult for specific audiences to apply it effectively in their own contexts. Therefore, it is beneficial to adopt an inclusive approach and language that allows the audience to define its own criteria depending on its needs, intended use and specific circumstances. CONCLUSIONS: The ECSA Characteristics were developed in a spirit of openness; identifying areas with diverse and even conflicting views was central to this practice. We recommend their use as a whole set and contend that no one area or characteristic is more important than the other. They should be considered as a toolkit with examples that can guide efforts towards defining citizen science for a specific context and purpose. They are built on the ECSA 10 Principles, addressing some of their gaps and limitations, while at the same time acknowledging the need to update and improve the 10 Principles based on developments in the field