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Assessment of the Impact of twin transition on the economic performance of the Europen Union food industry.
No full textThe food industry of the European Union is one of the largest and most economically important branches of the manufacturing sector. It creates significant added value, provides a high level of employment, and plays an important role both in the internal market and in international trade. However, the sector is characterised by relatively low productivity, high energy and resource intensity, and increasing environmental regulation. In line with the strategic priorities of the European Union, digitalisation and sustainability-oriented innovations are increasingly seen as interconnected processes, whose interaction is referred to as the concept of the twin transition. Therefore, assessing the impact of the twin transition on the economic performance of the European Union food industry has become an important research issue. The aim of this study is to empirically assess the impact of progress in the twin transition on the economic performance of the European Union food industry. To achieve this aim, the following objectives were set: (1) to analyse issues related to sustainability-oriented innovations and digitalisation; (2) to review European Union policy directions related to the integration of digitalisation and sustainability in the food industry; (3) to discuss the theoretical foundations of the twin transition and its potential impact mechanisms on business performance; (4) to empirically evaluate the relationships between twin transition indicators and economic outcomes using correlation and regression analysis; and (5) to formulate conclusions and recommendations for the application of the twin transition in the EU food industry. The study applies methods of scientific literature analysis and synthesis, statistical data analysis, graphical analysis, time series stationarity and normality testing, correlation analysis, as well as pairwise linear and non–linear regression models. The empirical analysis is based on aggregated data for the European Union food industry. The level of digitalisation is measured using the Digital Economy and Society Index, while progress in sustainability–oriented innovations is assessed using the Eco-Innovation Index. Economic performance indicators include labour productivity, wages, investment, energy intensity, and carbon dioxide emissions intensity. The results of the study show that progress in the twin transition is statistically significantly related to certain economic indicators of the European Union food industry. An increase in digitalisation and sustainability–oriented innovations is associated with productivity growth. At the same time, the empirical results indicate that the impact of the twin transition is not uniform and depends on the applied models and selected variables. This suggests that the economic effects of the twin transition do not always occur directly
Relationship between servant leadership and teacher conformity in educational institutions in the Panevėžys region.
No full textThe modern education system faces the challenge of balancing formal quality of education with the need for authentic, autonomous, and creative educators. To achieve these goals, there is an increasing focus on servant leadership – a model oriented toward follower growth and empowerment. However, conformity – the tendency to align with group norms – naturally exists within organizations and can suppress employee initiative. This study analyzes how leader behavior affects employees' tendency to conform and identifies which specific aspects of leadership can encourage or reduce conformity The object of the work is the relationship between servant leadership and educators' conformity in educational institutions. The aim of the work is to reveal the links between servant leadership and educators' conformity in educational institutions within the Panevėžys region. To achieve this aim, the following objectives were set: to analyze the theoretical foundations of servant leadership and conformity phenomena; to investigate the expression of servant leadership in educational institutions from the educators' perspective; to determine the expression of normative and informational conformity traits among educators; and to identify the links between servant leadership and educators' conformity, assessing the significance of sociodemographic factors. A quantitative research strategy was chosen for the empirical study. An anonymous online survey was conducted involving 383 educators from educational institutions in the Panevėžys region. Data analysis employed descriptive statistics, internal consistency analysis, nonparametric criteria, Spearman's correlation, and hierarchical linear regression. The research results revealed that servant leadership in the surveyed organizations manifests fragmentarily: leaders are favorably evaluated for functional competencies (ethical behavior, empowerment) but lack deep personal qualities (humility, authentic emotional support). The analysis of conformity showed that educators are more characterized by normative conformity, manifested as seeking security and a desire to fit into the group, and informational conformity, associated with adherence to rules. Statistically significant correlations determined that follower empowerment is the most effective instrument for reducing passivity and blind obedience. Meanwhile, emotional support and community building can have an ambiguous effect – strengthening group cohesion but simultaneously increasing defensiveness (a "tribal" mentality). Hierarchical regression analysis confirmed that servant leadership traits have incremental predictive value in explaining normative conformity, whereas informational conformity is more strongly determined by sociodemographic factors (gender, age, work experience, education)
Analysis of strength and stiffness of composite steel-timber floor structures.
No full textThe use of composite structures of timber (CLT) and steel beams in modern construction is becoming increasingly popular, but the methods of connecting elements and calculation methodologies have not been sufficiently investigated, and the influence of shear connections on the strength and stiffness of composite floors has not been fully evaluated. The final master‘s project analyses the composite structure of timber (CLT) and steel beams. A literature analysis of shear connectors was performed, in which the characteristics and types of different joints were analysed. Based on scientific publications, an analytical study was conducted to evaluate the accuracy and reliability of the load – bearing capacity calculation methods presented in current standards and by scientist. The calculated bearing capacity of the connections was compared with the results of experimental studies, and it was determined which methodology provides the most accurate results. In order to evaluate the effect of shear connectors on the structural behaviour of timber and steel composite floors, three composite beams with different stiffness of shear connectors were designed. The effect of connection stiffness on the strength and stiffness of the composite floor was evaluated using the engineering finite element calculation software DLUBAL RFEM 5
Sustainability-driven fractionation of hemp seed hulls into novel ingredients by the integrated high-pressure, ultrasound, and enzyme-assisted extractions /
No full textSupercritical CO2 (SFE-CO2), pressurised liquid (PLE), ultrasound (UAE), and enzyme-assisted (EAE) extractions were applied for hemp seed hulls. Conventional and UAE with hexane and SFE-CO2 recovered non-polar constituents. Consecutive PLE with acetone, ethanol, and water efficiently isolated higher polarity fractions. The antioxidant amounts after SFE-CO2 and PLE were up to 16.5 mg GAE/g DW, 35.9 mg TE/g DW, and 77.4 mg TE/g DW for the TPC, ABTS, and ORAC assays, respectively. UAE with acetone before PLE increased the antioxidant potential of extracts. EAE at optimized parameters yielded 4.3–21.0 g/100 g DW of water-soluble substances, containing 17.3–50.1 mg GLU/g DW of reducing sugars. Enzymatic treatment increased the recovery of soluble constituents, the yield of sugars, and the antioxidant characteristics of supernatants and solid residues. The results on bioactivity indicate that all fractions can serve as sources of antioxidants and other nutrients, with potential applications in foods and nutraceuticals
CausaOne-sign: Causal explainable one-shot signature verification with lightweight cross-modality fusion /
No full textBackground: Offline handwritten signature verification remains a difficult biometrics problem due to large intra-writer variability; skilled forgers; the limited number of reference samples available; and the black-box nature of many current deep learning based decision-making methodologies. Objective: To develop an interpretable, efficient one-shot learning framework that can perform offline signature verification for individuals who have never been seen before using as few reference signatures as possible. Materials and Methods: The proposed CausaOne-Sign model uses stroke aware graph encoding, transformer based reasoning, and prototypical embeddings, along with a causal attribution model to provide an explanation of how signature verification works. Experiments have been conducted using CEDAR, SigComp2011 UTSig, and BHSig260 datasets. Results: CausaOne-Sign achieved up to 97.4% accuracy and 99.1% area under the curve (AUC), with low ERR (1.8%), outperforming or matching state-of-the-art methods. Conclusion CausaOne-Sign offers a robust, interpretable, and resource-efficient solution for OSV, suitable for forensic and mobile applications
Resilience on the menu: a migrant woman's path to inclusive entrepreneurship /
No full textThis teaching case presents the entrepreneurial journey of Ms Shumaila Arif, a Pakistani mother of three, human resource management professional, and migrant student entrepreneur, who relocated to Lithuania in 2023 to pursue a Master’s degree at Vilnius University. While studying and living in Vilnius, Arif identified an unmet need in the local food ecosystem: the absence of an authentic halal dining space that could serve as both a culinary experience and a bridge among diverse communities, including locals, international students, and migrants. Motivated by this gap, she founded Dhaba & Delights in 2024, a business enterprise that rapidly transformed from a small restaurant into a space for inclusion, dialogue, and social impact. The case illustrates how food has become a symbolic and practical tool for advancing diversity, equity, and inclusion (DEI), demonstrating how migrant entrepreneurship can foster belonging and cultural understanding in increasingly multicultural European societies. Unfortunately, this journey was marked by significant adversity. Financial instability, the dissolution of a business partnership during a harsh winter, and personal challenges, such as her child’s injury, created emotional and economic strain. Instead of halting her progress, these challenges became catalysts for resilience, innovation, and purpose-driven leadership. The case invites students to analyse inclusive entrepreneurship within an SME context, with a focus on migrant identity, gender, and inclusive leadership. It demonstrates how food-based businesses can foster social integration and how resilience becomes part of an entrepreneur's strategic practice. The case also links migrant women’s entrepreneurship to broader DEI objectives and to the Sustainable Development Goals, which promote reduced inequalities and inclusive economic growth
Numerical investigation of a multi-year sand-based thermal energy storage system for building space heating application /
No full textResidential space heating in Northern Europe requires long-duration thermal storage to align summer solar gains with winter heating demand. This study investigates a compact sand-based seasonal thermal energy storage integrated with flat-plate solar collectors for an A+ class single-family house in Kaunas, Lithuania. An iterative co-design couples collector sizing with the seasonal charging target and a 3D COMSOL Multiphysics model of a 300 m3 sand-filled, phenolic foam-insulated system, with a 1D conjugate model of a copper pipe heat-exchanger network. The system was charged from March to September and discharged from October to February under measured-weather boundary conditions across three consecutive annual cycles. During the first year, the storage supplied the entire winter heating demand, though 35.2% of the input energy was lost through conduction, resulting in an end-of-cycle average sand temperature slightly below the initial state. In subsequent years, both the peak sand temperature and the residual end-of-cycle temperature increased by 3.7 °C and 3.2 °C, respectively, by the third year, indicating cumulative thermal recovery and improved retention. Meanwhile, the peak conductive losses rate decreased by 98 W, and cumulative annual losses decreased by 781.4 kWh in the third year, with an average annual reduction of 4.15%. These results highlight the progressive self-conditioning of the surrounding soil and demonstrate that a low-cost, sand-based storage system can sustain a complete seasonal heating supply with declining losses, offering a robust and scalable approach for residential building heating applications
Exploring determinants of network stochastic dominance ratios: a causal approach using explainable AI /
No full textVarious financial ratios are recognised as elements that determine investment decisions, making it essential to identify what factors influence these ratios. The calculation of a ratio is often depicted as a relationship, often in the form of a fraction or percentage, and demonstrates the frequency with which one item is included inside another. The limitations of causal relationships that are derived from observational data are, however, frequently disregarded. We employ structural causal modelling to ascertain the inherent relationship between performance and risk metrics and the network stochastic dominance ratio, as well as how this causal framework influences investment product selection. The network stochastic dominance ratio is an attractive tool for ranking assets with respect to basic stochastic dominance principles. The findings indicate that the extreme Gradient Boosting (XGBoost) technique outperforms the quantile regression method in predicting the network stochastic dominance ratio. To interpret the significance of features, the Shapley Additive Explanations (SHAP) method is employed. The results substantiate the causal importance of network stochastic dominance ratio elements and show the significance of distributional characteristics (Kurtosis) and risk metrics (Max Drawdown and Expected Shortfall) in determining the stochastic dominance ratio. Our research is essential for linking stochastic dominance theories with empirical validation beyond mere correlations
Virtualios realybės valdymo metodai periferinių įrenginių integracijai ir žmogaus laikysenos analizei.
No full textThis dissertation investigates virtual reality (VR) control methods for peripheral device integration and human posture analysis using off-the-shelf VR controls. The research addresses challenges arising from the use of control inputs from devices not originally designed for VR, including data transmission latency, synchronisation inaccuracies, and visual stuttering, which affect the handling and interpretation of input data in VR systems. Data prediction methods such as interpolation, extrapolation, and filtering techniques are analysed and experimentally evaluated using an experimental virtual rowing system to assess their suitability for controlling motion in VR under controlled conditions. In addition, the dissertation examines control methods based on off-the-shelf VR tracking for human posture monitoring and movement analysis by presenting a processing workflow that includes positional data acquisition, transformation into joint-angle representations, and the application of machine learning methods for exercise detection and movement correctness classification. Random Forest and Convolutional Neural Network models are evaluated using eight predefined upper-body exercises, and the results provide an assessment of the applicability of VR-based control and data processing methods for posture evaluation in controlled experimental environments
HMA-DER: a hierarchical attention and expert routing framework for accurate gastrointestinal disease diagnosis /
No full textObjective: Deep learning is employed increasingly in Gastroenterology (GI) endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection. In the real world, implementation requires high accuracy, therapeutically relevant explanations, strong calibration, domain generalization, and efficiency. Current Convolutional Neural Network (CNN) and transformer models compromise border precision and global context, generate attention maps that fail to align with expert reasoning, deteriorate during cross-center changes, and exhibit inadequate calibration, hence diminishing clinical trust. Methods: HMA-DER is a hierarchical multi-attention architecture that uses dilation-enhanced residual blocks and an explainability-aware Cognitive Alignment Score (CAS) regularizer to directly align attribution maps with reasoning signals from experts. The framework has additions that make it more resilient and a way to test for accuracy, macro-averaged F1 score, Area Under the Receiver Operating Characteristic Curve (AUROC), calibration (Expected Calibration Error (ECE), Brier Score), explainability (CAS, insertion/deletion AUC), cross-dataset transfer, and throughput. Results: HMA-DER gets Dice Similarity Coefficient scores of 89.5% and 86.0% on Kvasir-SEG and CVC-ClinicDB, beating the strongest baseline by +1.9 and +1.7 points. It gets 86.4% and 85.3% macro-F1 and 94.0% and 93.4% AUROC on HyperKvasir and GastroVision, which is better than the baseline by +1.4/+1.6 macro-F1 and +1.2/+1.1 AUROC. Ablation study shows that hierarchical attention gives the highest (+3.0), followed by CAS regularization (+2–3), dilatation (+1.5–2.0), and residual connections (+2–3). Cross-dataset validation demonstrates competitive zero-shot transfer (e.g., KS→CVC Dice 82.7%), whereas multi-dataset training diminishes the domain gap, yielding an 88.1% primary-metric average. HMA-DER’s mixed-precision inference can handle 155 pictures per second, which helps with calibration. Conclusion: HMA-DER strikes a compromise between accuracy, explainability, robustness, and efficiency for the use of reliable GI computer-aided diagnosis in real-world clinical settings