New technologies in distributed energy systems offer solutions for managing demand and generation variability in the grid. Trade between small grids allows cost reduction and system flexibility. This thesis proposes Multi-Agent Reinforcement Learning model for short-term energy trading among peers. It integrates short-term forecasts for load, generation, and price, leading to more accurate decision-making. Real-world data simulations demonstrate improved efficiency and stability compared to rule-based agents. This approach empowers prosumers to respond effectively to dynamic energy markets, enhancing grid reliability, energy efficiency, and sustainability.

Date: 07.09.2023 / 11:30 Place: -

Despite the high mortality and morbidity rates associated with alcohol use disorder (AUD) worldwide, the incomplete understanding of its complex molecular mechanism is the biggest limitation in providing successful treatments to patients. Monozygotic twin studies have found that AUD is about 50-60% heritable, and they have also revealed that environmental factors and epigenetic mechanisms play a role in the etiology of this disorder. In this study, we investigated excessive alcohol consumption related DNA methylation changes in four brain regions and blood in wistar rats to contribute to understanding to the understanding of the etiology of AUD.

Date: 04.09.2023 / 09:30 Place: B-116

This study explores the transformative potential of virtual reality in laparoscopic surgical training. Traditional box trainer implemented as a VR simulation, eliminating the need for physical tools and improved with incorporating feedback from surgeons and participant questionnaires. Results revealed that the VR version, equipped with tutorials, haptic feedback, and assisted grabbing physics, was more usable and accepted. Kinematic analysis showed similarities to physical training, and physiological monitoring revealed increased heart rate and reduced heart rate variability during tasks. This multimodal approach highlights VR's potential to enhance laparoscopic surgical skill development, providing an immersive and realistic training experience.

Date: 01.09.2023 / 10:00 Place: A-212

As number of proteins increases, computational methods play vital role for accurate function annotation. In spite of these methods, there are issues to compete which are reliable datasets and fair evaluation of performances. To address them, a fair comparison tool, ProFAB, Open Protein Functional Annotation Benchmark is developed. It aids computational and experimental researchers with its easy access to datasets and machine learning algorithms for protein function prediction using Gene Ontology terms and Enzyme Commission numbers.

Date: 21.08.2023 / 11:00 Place: A-212

This thesis presents a novel method that actively blends game levels using advanced procedural generation techniques and maintains the playability of the blended game. It introduces a structured approach for blending additional game elements like game physics, objects, and rules. The research tests various procedural generation techniques against each other, evaluating the novelty, diversity, and complexity of the levels these networks create.

Date: 24.07.2023 / 15:00 Place: A-212

This study proposes a model to explain dominant word orders emergence and distributions. It integrates preferences observed in newly emerged small deaf communities and explores how new behaviours are adopted through iterated learning. The model examines the transmission of word order preferences through generations, simulates reproduction and community development, and considers possible innate biases. It analyses the effects of language pressures, and network structures on generations. Various scenarios with different parameters are presented, including community size, bias distribution, communication networks, and pressure effects. Results are provided to explain the outcomes.

Date: 21.07.2023 / 13:00 Place: A-212

In this study, the performance of machine learning methods in credit risk scoring for "Buy Now Pay Later" customers is compared with the performance of standard credit risk models. Both traditional credit risk models and machine learning algorithms are evaluated using a real dataset. The comparison of models is conducted through variable selection, model training, and performance metrics. The results summarize to what extent machine learning methods outperform traditional models in credit risk assessment for "Buy Now Pay Later" customers. It is expected that this study will provide practical recommendations to improve risk assessment processes for financial institutions and credit providers.

Date: 14.07.2023 / 13:30 Place: A-212

This thesis compares the performance of univariate and multivariate energy consumption forecasts using deep learning techniques. The study finds that the univariate model outperforms the multivariate models for two of the three data sets tested. Among all the model architectures, LSTM outperforms all the univariate experiments, while TFT performs best among the multivariate experiments. The results suggest that univariate models are superior in forecasting energy consumption despite being less complex and requiring significantly less training time, cost, and resources.

Date: 13.08.2023 / 13:15 Place: A-212

Understanding biological phenomena is not always easy. Some phenomena cannot be easily transferred due to their complexity. Various visualization methods are used to speed up the process. These include new technologies such as Virtual and Mixed Reality. Such new technologies offer new approaches in the field of education by giving users completely new interactive experiences. In this study, we interactively simulated the defense of white blood cells, one of the body's defense mechanisms, in a 3D environment. We developed the application on 3 different platforms and obtained data about the platforms that users would prefer for such an application.

Date: 18.07.2023 / 10:30 Place: A-212

This thesis proposes automatic evaluation techniques to assess mobile health applications as alternatives to manual methods. It utilizes large language models to classify applications' employed Persuasive System Design (PSD) principles from collected user reviews and application descriptions. Mobile Application Rating Scale (MARS) scores are predicted with regression models trained on classification probabilities that are enriched with additional descriptive data. The study’s proposed techniques outperform baseline models, while feature importance scores show that PSD principles have significant contributions to quality predictions of mobile applications.

Date: 20.06.2023 / 14:00 Place: A-212