Announcements
Research News
This study identifies and investigates the critical barriers to adopting Agile methodologies within Turkey’s defense industry. Utilizing an extensive literature review and expert consultations, the research identifies key challenges in transitioning to flexible and collaborative methodologies. By applying Interpretive Structural Modeling (ISM) and MICMAC approaches to questionnaire data, the study maps the causal relationships and interdependencies among these barriers, establishing a layered, hierarchical framework. Ultimately, this research provides defense industry managers with a systematic understanding and a comprehensive framework to enhance awareness, mitigate adoption impediments, and lay a solid groundwork for successful Agile transformation.
Date: 17.06.2026 Place: A-212
This study takes a quantitative look at how protein-coding genes replicate across different cell lines using SigProfilerTopography, where we score earlier-replicating genes higher. We originally set out to build a model that could predict replication timing directly, but when that didn't pan out as expected, we shifted our focus to mapping out the actual timing differences between cell lines.
Date: 23.06.2026 / 14:30 Place: A-212
This thesis proposes a self-supervised framework for detecting Primary (P) waves in strong motion accelerograms, an essential task for Earthquake Early Warning systems. Using Variational Autoencoders trained exclusively on P-wave segments sourced from 648 recordings of the Turkish National Strong Motion Network, the model detects P-waves through reconstruction-error-based detection without requiring labeled data. A systematic search across 492 configurations of four VAE architectures reveals that attention mechanisms achieve the best detection performance. The final Attention-VAE model achieves an AUC of 0.998, surpassing supervised baselines and demonstrating potential for real-time deployment.
Date: 12.06.2026 / 14:00 Place: A-212
This thesis develops a regime-aware day-ahead electricity consumption forecasting framework for Türkiye. It aims to predict the next day’s 24-hour national consumption profile using historical load, weather, and calendar variables from 2019–2025. Special attention is given to weekends, public holidays, Ramadan, and Eid periods, since these days create different demand patterns. Several statistical, machine learning, deep learning, hybrid, and meta-learning models are compared. The best result is achieved by a Ridge and LightGBM-based meta-learner, reaching 1.63% MAPE in 2025.
Date: 04.06.2026 / 10:00 Place: A-212
MicroRNAs (miRNAs) regulate gene expression post-transcriptionally through sequence-specific binding to target messenger RNAs (mRNAs). Single nucleotide variants (SNVs) can disrupt miRNA–mRNA interactions and contribute to human disease. We present miRHub, a comprehensive database that integrates miRNA–mRNA duplex information with 3′UTR SNV context using matched The Cancer Genome Atlas expression and genotyping data. The miRHub portal provides integrated views for mRNA expression, miRNA expression, and co-localizing SNVs with the corresponding miRNA–mRNA duplexes. As a use case, we report statistically significant differences in mRNA regulation between different sample types based on SNV-associated duplexes.
Date: 09.06.2026 / 13:00 Place: A-212
