Analysis of the experiences in implementing the participatory budgeting in Kaunas district municipality.

The increasing focus on citizen involvement in decision-making is giving rise to a growing number of new forms of citizen participation and tools for building trust and accountability in public authorities and promoting democracy. One of the tools that encourages citizens to take an active part in decisions affecting their local area is the participatory budget. Strengthening democratic processes is crucial in democracies, and participatory budgeting is an excellent tool for citizens to take over the decision-making power of certain decisions. Participatory budgeting is a dynamic process that is usually adapted to the specific needs of local communities (Boc, 2021). The implementation of projects proposed and won by local citizens through the participatory budget process increases their positive experiences, which improves the involvement of local citizens in the decision-making process and promotes trust in local government by sharing responsibility for the allocation of the budget together. Most scholars analyse the benefits of participatory budgeting, its impact on citizens' involvement in decision-making and community development (Brun-Martos & Lapsley, 2017; Schugurensky & Mook, 2024), but there is a lack of in-depth analysis on the experiences of implementing participatory budgeting, i.e. on the benefits obtained and the challenges faced, especially in Lithuania. This final project analyses the experiences of participatory budgeting in Kaunas District Municipality, the analysis of which can significantly contribute to a more effective implementation of participatory budgeting in Lithuania. The research problem is: what are the benefits and challenges of implementing participatory budgeting in Kaunas District Municipality? The object of the study is the benefits and challenges of implementing participatory budgeting. The aim of the project is to examine the experience of implementing participatory budgeting in Kaunas district municipality in order to identify its benefits and challenges. The objectives of the project are as follows: 1) to carry out a theoretical analysis of the benefits and challenges of implementing participatory budgeting; 2) to examine the international and national legal frameworks and good practices of implementing participatory budgeting; 3) to carry out a research on the experiences of implementing participatory budgeting in Kaunas District Municipality. The research was based on the analysis of scientific literature, analysis of secondary data, content analysis of strategic documents and legal acts, theoretical modelling, qualitative research – semi-structured interviews, qualitative content analysis. The research sample consisted of seven informants – local residents involved in the participatory budgeting process, who submitted the project proposal, and Kaunas District Municipality employees directly involved in the participatory budget instrument. The study found that each stage of the participatory budgeting process has its benefits and challenges. The implementation of the participatory budget instrument in Kaunas District Municipality increases the participation of local residents in the decision-making process and promotes participatory democracy. Social justice, trust in government, and dialogue between local residents are enhanced. The surveydata revealed that the main challenge faced is unforeseen technological barriers. Although the implementation of participatory budgeting in the municipality faces some challenges, there is an analysis of the challenges and continuous improvement. After the analysis of the empirical research results, conclusions are drawn and recommendations are made to the Ministry of the Interior of the Republic of Lithuania, the Kaunas District Municipality Administration, the local population of Kaunas District, and the researchers studying the participatory budget. The project consists of three parts: a theoretical analysis of the benefits and challenges of implementing participatory budgeting, an analysis of the international and national legal framework for implementing participatory budgeting, and a study of the experiences of participatory budgeting in Kaunas District Municipality

Training and applying artificial intelligence for the creation of original music.

Current artificial intelligence (AI) tools for music creation are pre-trained using general datasets. As a result, AI models assimilate general principles of music creation, while the unique style of individual artists is not highlighted. This can lead to stylistic homogenization of compositions and raise copyright concerns. This issue is particularly relevant for creators seeking to integrate AI tools into their creative process while maintaining a distinctive sound. This study aims to investigate how to personalize the training of AI models with limited resources by using personal creative content, as well as how to creatively adapt existing tools trained on general data. This dual perspective seeks to provide creators with methods to effectively integrate AI tools into the creative process without losing originality and artistic authenticity. Research objective – the use of AI tools in the music creation process and the possibilities of training generative AI (GAI) with limited resources. Project goal – to explore the potential of AI in original music creation and to develop a simplified GAI training methodology for music creators without programming experience. Tasks: 1. Examine the possibilities offered by pre-trained AI for original music creation. 2. Analyze available GAI training options, considering existing resources (financial, technological, and knowledge-based). 3. Define the concept of original music in the context of AI. 4. Apply the analyzed pre-trained AI capabilities and identified tools to original music creation to test AI’s creative potential in practice. 5. Implement the analyzed GAI training options and tools for original music creation to test the creative possibilities of trained GAI in practice. An experiment was conducted using "Magenta" models ("MelodyRNN" and "MusicVAE") trained on personal compositions, as well as a Markov chain model through "Max for Live." Additionally, pre-trained AI tools such as "Suno" and "Udio" were employed, with their generated ideas creatively integrated into compositions. The results demonstrated that personalized AI models can generate new musical ideas while preserving the artist's style, however, they are less flexible than the most advanced AI music generators and pose technical challenges. Pre-trained models provide interesting interpretations, but their use limits control and raises copyright issues. This work highlights the potential and limitations of AI in the creative process, opening opportunities for new experiments, rhythms, and melodies while preserving artistic authenticity

Detailed determination of delamination parameters in a multilayer structure using asymmetric lamb wave mode /

A signal-processing algorithm for the detailed determination of delamination in multilayer structures is proposed in this work. The algorithm is based on calculating the phase velocity of the Lamb wave A0 mode and estimating this velocity dispersion. Both simulation and experimental studies were conducted to validate the proposed technique. The delamination having a diameter of 81 mm on the segment of a wind turbine blade (WTB) was used for verification of the proposed technique. Four cases were used in the simulation study: defect-free, delamination between the first and second layers, delamination between the second and third layers, and defect (hole). The calculated phase velocity variation in the A0 mode was used to determine the location and edge coordinates of the delaminations and defects. It has been found that in order to estimate the depth at which the delamination is, it is appropriate to calculate the phase velocity dispersion curves. The difference in the reconstructed phase velocity dispersion curves between the layers simulated at different depths is estimated to be about 60 m/s. The phase velocity values were compared with the delamination of the second and third layers and a hole drilled at the corresponding depth. The obtained simulation results confirmed that the drilled hole can be used as a defect corresponding to delamination. The WTB sample with a drilled hole of 81 mm was used in the experimental study. Using the proposed algorithm, detailed defect parameters were obtained. The results obtained using simulated and experimental signals indicated that the proposed new algorithm is suitable for the determination of delamination parameters in a multilayer structure

Application of the simulation model in determining the most suitable evacuation route from the construction site.

In the Master's thesis, a study was carried out in which a simulation game was developed to compare evacuation routes. The game is a tool for collecting data on human behaviour and at the same time a tool for occupational safety training. The study develops a methodology for using panoramic photographs to create a realistic game environment. The panoramic photos were converted into the game development engine “CoSpaces Edu”. The study collected data on the evacuation route choices of respondents in a simulation game. The analysis of the collected data led to the determination of the evacuation time and the preferred evacuation route for people

Strengthening of notched wooden beams at the support using plywood and wood screws.

The aim of this final work is to evaluate, by theoretical calculations and experiments, the increase in shear strength of wooden beams notched at the support, when the beams are reinforced with plywood and wood screws. To compare theoretical and experimental values, to analyze the failure of reinforced and unreinforced beams. To check the reliability of calculation methodologies used in laws and scientific works

Evaluation of micro-level factors impacting household consumption expenditure.

Relevance of the topic. Household consumption, together with investment, government expenditure and net exports, is used in calculating the most important indicator reflecting a country’s economic welfare – gross domestic product. Analysis of household consumption expenditure and its structure can be one of the indicators that helps to assess not only the country’s economic situation but also the population’s quality of life, since a higher level of consumption is considered to correspond to a higher standard of living. Object of the study – total household consumption expenditure and its structural components. Aim of the study – to assess the impact of micro-level factors on total household consumption expenditure and its structure. Methods – scientific literature review, statistical tests, machine learning models. Tasks of the study: 1. to review the factors determining changes in consumption expenditure on the basis of a scientific literature analysis; 2. describe the data used in the study, their preparation steps, and the modeling methods applied; 3. to perform feature selection in order to identify the variables affecting household consumption expenditure; 4. to build a model to determine how consumption expenditure and its structure depend on various household characteristics in Lithuania. The project consists of three main stages. A scientific literature review identified the main consumption related factors and their impact and also discussed previous research and methods applied. The methodological part described the data used, their characteristics and provided a literature analysis of the methods applied. The empirical part presented the research decisions, the best models obtained, their results and interpretations. Based on SHAP analysis applied to the extreme gradient boosting model, it was found that the growth of total consumption expenditure of Lithuanian households is most closely related to a larger household size. Age and consumption expenditure are linked by a non linear relationship. Expenditure increases if households belong to a higher income quintile, have larger usable floor space, raise children, have higher education or work in higher-skilled occupations. Similar conclusions were drawn when analysing the categories of food and non-alcoholic beverages, information and communication services, and insurance and financial services expenditure. In addition, it was established that expenditure on information and communication rises if a person does not live in the countryside, is an employee and does not live in a single-family house, while expenditure on insurance and financial services increases if the person’s main income is not a pension, does not live alone, and is a homeowner with an outstanding mortgage

Investigation of necessity of cross-border electricity interconnections in Baltic sea region to achieve decarbonisation objectives.

In order to find out what the development of cross-border interconnections should look like in the light of decarbonisation targets and countries' emission reduction strategies, a study on the development of cross-border interconnections was carried out. This work analysed documents and scientific articles dealing with the transition to 100% RES production and the need for cross-border interconnection development. The paper analyses the energy systems of 9 countries in the Baltic Sea region (Lithuania, Latvia, Estonia, Poland, Germany, Denmark, Sweden, Finland and Norway)

Multi class fashion detection using deep learning and GAN based data synthesis.

This study addresses the problem of clothing classification in the unbalanced DeepFashion dataset. It reviews existing methods for clothing classification and presents an improved pipeline that combines minority classes expansion with generative adversarial networks (GANs) and a YOLO11 classifier. The improvement is highlighted by a comparison of balancing strategies and other model variants. For the experiments, the original dataset of 289 222 garment images assigned with 46 labels was cleaned. The smallest classes were merged or discarded, leaving 24 classes. Every image was cropped to its bounding box and resized to 256x256 px. Two balancing routes were examined: standard augmentations and synthetic data generation using StyleGAN2-ADA and the newer version – StyleGAN3. StyleGAN3 converged faster and produced fewer artifacts than StyleGAN2-ADA. The classification stage evaluated a linear YOLO11 model alongside hierarchical variants. On the raw, imbalanced data the linear baseline achieved 76.15% top-1, 92.59% top-3 and 96.60% top-5 accuracy. Expanding minority classes with StyleGAN3 generated images raised performance to 77.53% top-1, 93.48% top-3 and 97.40% top-5. This GAN-balanced method outperformed both the augmentation-only and hierarchical YOLO11 methods, and surpassed the previously reported results on DeepFashion (91.99% top-3, 96.44% top-5). Results indicate that class distribution highly affects accuracy. Expanding smaller classes data with high quality GAN images delivers the best results while keeping the network simple. The project design section details the data preparation and modelling choices, while the experiments section presents the experiment results

Advancing fractal dimension techniques to enhance motor imagery tasks using EEG for brain–computer interface applications /

The ongoing exploration of brain–computer interfaces (BCIs) provides deeper insights into the workings of the human brain. Motor imagery (MI) tasks, such as imagining movements of the tongue, left and right hands, or feet, can be identified through the analysis of electroencephalography (EEG) signals. The development of BCI systems opens up opportunities for their application in assistive devices, neurorehabilitation, and brain stimulation and brain feedback technologies, potentially helping patients to regain the ability to eat and drink without external help, move, or even speak. In this context, the accurate recognition and deciphering of a patient’s imagined intentions is critical for the development of effective BCI systems. Therefore, to distinguish motor tasks in a manner differing from the commonly used methods in this context, we propose a fractal dimension (FD)-based approach, which effectively captures the self-similarity and complexity of EEG signals. For this purpose, all four classes provided in the BCI Competition IV 2a dataset are utilized with nine different combinations of seven FD methods: Katz, Petrosian, Higuchi, box-counting, MFDFA, DFA, and correlation dimension. The resulting features are then used to train five machine learning models: linear, Gaussian, polynomial support vector machine, regression tree, and stochastic gradient descent. As a result, the proposed method obtained top-tier results, achieving 79.2% accuracy when using the Katz vs. box-counting vs. correlation dimension FD combination (KFD vs. BCFD vs. CDFD) classified by LinearSVM, thus outperforming the state-of-the-art TWSB method (achieving 79.1% accuracy). These results demonstrate that fractal dimension features can be applied to achieve higher classification accuracy for online/offline MI-BCIs, when compared to traditional methods. The application of these findings is expected to facilitate the enhancement of motor imagery brain–computer interface systems, which is a key issue faced by neuroscientists

Automatinio krepšinio metimų atpažinimo ir sekimo iš vaizdo įrašų metodų tyrimas.

This research focuses on the development of a system capable of detecting a basketball backboard, rim, net, and ball from a single static video stream. The system operates by processing frames extracted from the video, with the aim of achieving accurate and efficient object detection in real time. Different versions and configurations of YOLO models were tested and compared based on precision, inference speed, and model size. The ultimate objective is to identify the most effective setup to support a reliable shot recognition system