Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article The Synergistic Engine of Sustainable Entrepreneurship: Fueling AI-Driven Circular Transformation and Social Entrepreneurial Orientation with Knowledge Integration and Digital Capabilities(Elsevier B.V., 2026) Shah, Syed Haider Ali; Murad, Majid; Wang, MansiArticle The Integration of 21st Century Skills into Secondary School English Classes and the Challenges Faced by Teachers(Asya Publishing and Consultancy, 2024) Deneme-Gençoğlu, Selma; Bolat, YelizArticle Strategic Modeling of Hybrid Smart Micro Energy Communities: A Decision-Oriented Approach(MDPI, 2026) Perez-Sanchez, Modesto; Coronado-Hernandez, Oscar E.; McNabola, Aonghus; Erdfarb, Alex; Ramos, Helena M.; Demircan, Isil; Koca, KemalHybrid renewable energy systems are increasingly important for enabling sustainable and resilient energy supply in rural smart communities, yet existing tools often lack the ability to integrate environmental variability, multi-technology interactions, and economic-environmental assessment in a unified framework. This study presents Hybrid Smart Micro Energy Community (HySMEC), a novel modeling approach that combines high-resolution meteorological data, technology-specific generation models, detailed demand characterization, and financial analysis to evaluate hybrid configurations of hydropower, solar PV, wind, battery storage, and grid interaction. Hourly simulations capture seasonal dynamics and system behavior under realistic technical efficiencies, investment costs, and emission factors, enabling a transparent assessment of energy flows, self-consumption, and grid dependence. The results show that hybrid systems can achieve competitive economic performance, low Levelized Costs of Energy, and significant CO2 emission reductions across diverse rural community profiles, even when space or demand constraints are present. The analysis confirms the technical feasibility and environmental benefits of integrating multiple renewable sources with storage, highlighting the importance of self-consumption ratios in improving system profitability. Overall, HySMEC provides a robust and scalable tool to support data-driven design and optimization of distributed energy systems, offering valuable insights for researchers, planners, and decision-makers involved in sustainable rural energy development.Article RCE-IFE: Recursive Cluster Elimination with Intra-Cluster Feature Elimination(PeerJ Inc., 2025) Kuzudisli, Cihan; Bakir-Gungor, Burcu; Qaqish, Bahjat; Yousef, MalikConference Object Sensorless Position and Speed Control of IPMSM with Sliding Mode Observer and Voltage Signal Injection(Institute of Electrical and Electronics Engineers Inc., 2021) Tekgun, Burak; Ablay, Gunyaz; Ates, ErtugrulArticle Raster Orientation Effects on the Adhesion of iCVD-Deposited PSA Thin Films on FDM-Printed PLA(MDPI, 2026) Yilmaz, Kurtulus; Gursoy, Mehmet; Gunes, Aydin; Karaman, MustafaThe adhesion performance of pressure-sensitive adhesive (PSA) thin films on additively manufactured polymers is strongly governed by surface anisotropy induced during printing. In this study, PSA thin films based on 2-ethylhexyl acrylate (EHA) and acrylic acid (AA) were deposited by initiated chemical vapor deposition (iCVD) onto fused deposition modeling (FDM) printed PLA substrates with different raster orientations (0 degrees, 30 degrees, 60 degrees, and 90 degrees). The deposited films exhibited high optical transparency on glass, and thicknesses consistent with the targeted deposition. Adhesion performance was evaluated using tensile and three-point bending tests, revealing a strong dependence on raster orientation. The 0 degrees raster orientation yielded the highest shear adhesion strengths, reaching 12.03 N/cm2 under tensile loading and 4.59 N/cm2 under bending, along with the largest failure displacements. In contrast, specimens printed at 90 degrees exhibited an approximately 47% reduction in tensile shear adhesion strength and limited deformation prior to failure. SEM analysis showed that raster alignment parallel to the loading direction promoted extensive adhesive deformation and PSA fibrillation, whereas higher raster angles resulted in predominantly interfacial debonding. These results demonstrate that raster orientation is a critical design parameter for tuning PSA adhesion on FDM-printed PLA substrates without modifying adhesive chemistry.Article Optimization of Precision Machine Part Manufacturing by Integration of Grey-Taguchi Method with Principal Component Analysis(Yildiz Technical University, 2026) Kapan Ulusoy, Selda; Şenyiğit, Ercan; Erol, KübraArticle Network Anomaly Detection Using Deep Autoencoder and Parallel Artificial Bee Colony Algorithm-Trained Neural Network(PeerJ Inc., 2024) Dedeturk, Bilge Kagan; Bakir-Gungor, Burcu; Hacılar, Hilal; Gungor, Vehbi CagriArticle Machine Learning and Scenario-Based Forecasting of Türkiye’s Renewable Energy Transition toward Net-Zero 2053(Elsevier Ltd, 2026) Sutcu, Muhammed; Yildiz, Baris; Sahin, Nurettin; Almomany, Abedalmuhdi; Gulbahar, Ibrahim TumayConference Object Low-Cost SERS Substrates via AuNP-Decorated Carbonized Polyimide Films for Fluorescence-Quenched Raman Sensing(Institute of Electrical and Electronics Engineers Inc., 2025) Tuzel, D.; Cinar, K.; Bek, A.; Khan, G.A.; Kina, S.; Genc, S.Conference Object Impact of Gene Duplicate Handling Strategies on Classification Performance and Feature Selection in Gene Expression Data(Institute of Electrical and Electronics Engineers Inc., 2025) Kuzudisli, Cihan; Qaqish, Bahjat; Gungor, Burcu Bakir; Yousef, MalikArticle GraphUnet-SS: A Novel Deep Learning Model for Protein Secondary Structure Prediction Based on U-Net Architecture(Elsevier Ltd, 2026) Aydin, Zafer; Görmez, Yasin; Sabzekar, MostafaArticle Fine-Tuning Large Language Models for Turkish Flutter Code Generation(Sakarya University, 2025) Uluırmak, Buğra Alperen; Kurban, RifatThe rapid advancement of large language models (LLMs) for code generation has largely centered on English programming queries. This paper focuses on a low-resource language scenario, specifically Turkish, in the context of Flutter mobile app development. Two representative LLMs (a 4B-parameter multilingual model and a 3B code-specialized model) on a new Turkish question-and-answer dataset for Flutter/Dart are fine-tuned in this study. Fine-tuning with parameter-efficient techniques yields dramatic improvements in code generation quality: Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE-L), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Bidirectional Encoder Representations from Transformers Score (BERTScore), and CodeBLEU scores show significant increases. The rate of correct solutions increased from ~30–70% (for base models) to 80–90% after fine-tuning. The performance trade-offs between models are analyzed, revealing that the multilingual model slightly outperforms the code-focused model in accuracy after fine-tuning. However, the code-focused model demonstrates faster inference speeds. These results demonstrate that even with very limited non-English training data, customizing LLMs can bridge the gap in code generation, enabling high-quality assistance for Turkish developers comparable to that for English. The dataset was released on GitHub to facilitate further research in multilingual code generation.Article Follow-up of Health-Related Physical Fitness Elements in Mild Intellectual Disability for Three Years: A Sex Comparison(PeerJ Inc., 2026) Bozdağ, Berkan; Sönmez, Hüseyin Gazi; Prieto-González, Pablo; Karahan, Mustafa; Canli, Umut; Ergin, Murat; Koçak, Çalık VeliArticle Effects of Gelatinization Process on Some Physicochemical Parameters, Pasting Characteristics and Some Nutritional Properties of Pulsed Based Flour Blends(Springer, 2026) Kahraman, Kevser; Yuksel, Ferhat; Karaman, SafaIn this study, a flour mixture was composed by three different flours (wheat flour (WF), cranberry bean flour (CBF) and lentil flour (LF)) depending on a constructed mixture design and some physicochemical parameters, pasting characteristics and some nutritional properties were investigated before and after gelatinization process. The highest total dietary fiber content was determined for the sole cranberry bean flour. After gelatinization of the samples, total dietary fiber levels of the samples increased significantly, and it ranged between 4.70 and 25.16% for uncooked samples and 8.46-29.09% for cooked samples. Resistant starch (RS) content of the samples was also affected by the gelatinization process. Wheat flour showed an increase in the RS content after gelatinization process and similar increment in the RS content was observed for the sole lentil flour. Peak viscosity was the highest for the wheat flour (2318 cP) and lowest for the lentil flour (716.5 cP). Glycemic index of the cooked samples changed significantly, and it ranged between 94.4 and 123.5. This study showed that making flour composite and gelatinization process had a significant effect on the pasting properties and nutritional characteristics of the pulse-based flour mixture.Conference Object Examining Tongue Movement Intentions in EEG with Machine and Deep Learning: An Approach for Dysphagia Rehabilitation(European Signal Processing Conference, EUSIPCO, 2024) Aslan, Sevgi Gökçe; Yılmaz, BülentConference Object Enhancing Fire and Smoke Detection with YOLOv8: A Comparative Study of Self-Supervised Learning and Attention Mechanisms(Institute of Electrical and Electronics Engineers Inc., 2025) Kaya, Umut; Uluirmak, Bugra Alperen; Kurban, RifatArticle Computational Fluid Dynamics for the Optimization of Internal Bioprinting Parameters and Mixing Conditions(AccScience Publishing, 2023) Bartolo, Paulo; Ates, GokhanArticle Comparative Assessment of Smooth and Non-Smooth Optimization Solvers in HANSO Software(Balikesir University, 2022) Tor, Ali HakanArticle BrAIn: A Comprehensive Artificial Intelligence-Based Morphology Analysis System for Brain Organoids and Neuroscience(Wiley, 2026) Polatli, Elifsu; Guner, Huseyin; Bastanlar, Yalin; Karakulah, Gokhan; Evranos, Ali Eren; Kahveci, Burak; Guven, SinanHuman-induced pluripotent stem cells (iPSCs) offer transformative potential for biomedical research, with iPSC-derived organoids providing more physiologically relevant models than traditional 2D cell cultures. Among these, brain organoids (BO) are particularly valuable for drug screening, disease modeling, and investigations into molecular pathways. Accurate representation of brain morphology is critical, as more complex organoid structures better mimic the human brain. Deep learning (DL) and machine learning (ML) approaches have become integral to analyzing organoid morphology, yet tools for comprehensive, time-resolved assessments are scarce. Here, we introduce BrAIn, a DL-based application for analyzing the developmental progression of BOs. BrAIn tracks their evolution from embryoid bodies (EBs) and quantifies parameters including area, Feret diameter, perimeter, roundness, and circularity. It also classifies budding and abnormal morphologies of 3D organoids and detects monolayer neural rosette structures, key features of neuronal differentiation. Designed with accessibility in mind, BrAIn provides a no-code interface, enabling researchers of all technical backgrounds to conduct advanced morphological analyses with ease. Our study demonstrates the application of BrAIn to evaluate the effects of different growth conditions-static, orbital shaker, and microfluidic chip-based-on BO development. Orbital shaker cultures resulted in the largest organoids, while chip-based systems achieved more homogeneous growth. Both conditions produced organoids with greater morphological complexity compared to static culture. BrAIn emerges as a robust, user-friendly tool to quantify BO development and explore how versatile growth conditions influence their morphology and maturation.
