Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Shooting a Water Slug Into an Air Column with and without Vent(Amer Soc Mechanical Engineers, 2025-07-20) Bozkus, Zafer; Dincer, Ali Ersin; Tijsseling, Arris S.; van de Ven, FonsCompressed air is used to shoot a single water slug into an upward sloping pipe with elbow and orifice at its upper end. The experiment concerns a 12 m long pipe of 0.1 m diameter connected to a 0.5 m3 air vessel. The 10 to 50 kg heavy slugs are initially at rest in the lower part of the system. Because the upper end is closed by a flange with orifice, the water slug is expected not to hit the upstream elbow. It causes - like a piston - a fast compression of the air column ahead of it. Sometimes the slug bounces back and forth, which results in a pressure oscillation of serious amplitude. Numerical simulations based on an elementary mathematical model are normally used to interpret the pressure measurements, not all of which are fully understood. Lessons learned are summarised, and suggestions for improved experiments and enhanced simulations are given. The research is of importance, for example, for steam lines where liquid condensates may collect in lower parts after power failure. Start-up of the system will then lead to rapid slug acceleration and potentially damaging impact on elbows, orifices, and machinery.Book Part Design and Analysis of a Solar-Assisted Combined Cooling, Heating, and Power System for Smart Cities: Case Study From Doha(IGI Global, 2025-08-22) Akroot, A.; Almohammedi, A.A.; Talal, W.The rising demand for sustainable and energy-efficient solutions in urban areas has driven interest in renewable systems for smart cities. This chapter presents a solar-assisted combined cooling, heating, and power (SA-CCHP) system designed for Doha, Qatar, where high solar radiation and cooling needs prevail. Powered solely by a parabolic trough collector (PTC) field, the system delivers net power from 1200 kW in winter to 195 kW in summer, with cooling loads of ~2100-3400 kW and heating loads of ~90)00-14500 kW. Increasing the superheating degree at the ORC turbine inlet enhances power and heating but reduces cooling, while raising the pressure ratio (A) from 0.5 to 0.8 boosts net output and efficiency, cutting CO2 emissions from 0.22 to 0.13 kg/kWh. Overall energy efficiency rises from 85% to 90% and exergy efficiency from 76% to 78.5%, while costs decline from $40/hr to $36/hr, confirming both environmental and economic viability. The study demonstrates the feasibility of solar-powered CCHP systems as scalable models for achieving clean energy goals in smart cities. © 2026, IGI Global Scientific Publishing. All rights reserved.Conference Object Fully Flexible, Low-Cost, Environmentally Friendly Yarn-Based Inp/Ag Nw Photodetectors for UV-Visible Light Detection(SPIE, 2025-08-01) Savaş, M.; Akrema, A.; Ocal, S.K.; Erdem, T.We report the fabrication and investigate of a novel photodetector using a heterostructure of InP quantum dots (QDs) and silver nanowires (Ag NWs) incorporated into yarn. This device is simple, scalable, low-cost, flexible, and functions under ambient conditions. Ag NWs and red-emitting InP QDs were separately synthesized via chemical methods and mixed in a specific ratio to coat functional yarns, which were then knitted into fabrics. The photodetector benefits from the excellent electrical conductivity of Ag NWs and the strong optical absorption of InP QDs. It shows enhanced photoelectric response in both UV and visible regions. At 405 nm illumination, the device achieves a photoresponsivity of 5.8 mA W-1 and a detectivity of 2 × 1010 Jones-values comparable to or exceeding those of similar devices. The enhanced performance is attributed to efficient charge transfer enabled by favorable band alignment between Ag NWs and InP QDs, along with synergistic effects from nanostructure dimensionality and quantum confinement. The device's combination of flexibility, sensitivity, and cost-efficiency makes it a strong candidate for wearable UV-visible photodetectors. © 2025 SPIE. All rights reserved.Article Citation - WoS: 2Citation - Scopus: 2Numerical Analysis and Experimental Comparison of Stress and Stiffness Parameters of Steel Reinforced Geopolymer Concrete Columns(Elsevier Sci Ltd, 2026-01) Ozbayrak, Ahmet; Kucukgoncu, Hurmet; Aslanbay, Huseyin Hilmi; Aslanbay, Yuksel Gul; Altun, FatihDespite extensive research, Geopolymer concrete (GPC) lacks reinforced concrete construction and design specifications. Developing such specifications requires comprehensive studies to promote the use of GPC, which is known for its superior performance and environmental benefits compared to ordinary Portland cement concrete (OPC). This study numerically investigated and compared the behavior and strength of fly ash-based geopolymer-reinforced concrete columns with the experimental results. Comparisons with OPC were made based on existing specifications. Herein, FEM analyses were conducted on 16 GPC and 4 OPC columns under eccentric axial compressive loads. Parameters such as eccentricity, reinforcement ratio, curing method, and activation solution ratios were varied. According to average numerical results, the GPC columns have 7% more moment capacity and 30% more curvature values than OPC. Moreover, GPC columns absorbed more energy than OPC columns. Also, GPC columns have higher axial load and bending moment carrying capacities than OPC for numerical results. Error analysis between FEM and experimental data revealed a strong correlation, with MAPE values of 8.88% (axial load) and 7.20% (moment) for GPC columns, confirming the reliability of the numerical model. ACI 318 and Eurocode 2 specifications were deemed applicable for GPC columns, provided axial loads are limited per TEC 2018.Conference Object Enhancing Complex Disease Group Scoring with Mirgedinet: A Multi-Algorithm Machine Learning Framework Based on the GSM Approach(IEEE, 2025-06-25) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, MalikIntegrating biological prior knowledge for disease gene associations has shown significant promise in discovering new biomarkers with potential translational applications. This work investigates the application of a multi-algorithm machine learning framework based on the Grouping-Scoring-Modeling (G-S-M) approach for improving the prediction of complex diseases. The study identifies the primary gene and miRNA interactions in various complex diseases with the help of miRGediNET, which is a machine-learning based tool that integrates data from three biological databases. Traditional methods have only focused on independence between features; the G-S-M method focuses on aggregating genes based on biological interactions, pinpointing the scoring of gene groups for a disease, and modeling its predictive capability using advanced machine learning algorithms. In this research paper, seven algorithms, including Support Vector Machine, Decision Tree, and CatBoost, were applied to eight datasets extracted from the GEO database. This framework proved very robust in ranking gene clusters, thus predicting critical biomarkers while doing 100-fold randomized cross-validation within the evaluation. The results indicate this approach's high potential for refining disease and supporting research for choosing the best algorithm that can provide biological insights and computational advances.Conference Object High Performance and Resource Efficient Low Density Parity Check Decoder Design(IEEE, 2025-06-25) Unal, BurakLow Density Parity Check (LDPC) codes have gained popularity in communication systems due to their capacity-approaching error correction performance. In this study, a highperformance LDPC decoding algorithm with extremely low resource usage is proposed. Among the hard decision class of LDPC decoders, Gallager B (GaB) provides high-performance hardware due to its computational simplicity. However, GaB suffers from poor error-correction performance. In this study, a new intrinsic computation technique for GaB called Intrinsic Gallager B (IGaB) is introduced to improve error correction performance. Our simulation results show that the IGaB algorithm provides better error correction performance compared with GaB. GaB and IGaB algorithms are implemented on Field Programmable Gate Array (FPGA) to compare hardware performance.Conference Object Exploring Microbiome Signatures in Autism Spectrum Disorder via Grouping-Scoring Based Machine Learning(IEEE, 2025-06-25) Temiz, Mustafa; Ersoz, Nur Sebnem; Yousef, Malik; Bakir-Gungor, BurcuThe rapid increase in omic data production increased the importance of machine learning (ML) methods to analze these data. In particular, the use of metagenomic data in the diagnosis, prognosis and treatment of diseases is becoming widespread. Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that occurs in early childhood and continues lifelong. The aim of this study is to increase ML performance, reduce computational costs and achieve successful classification performance using a small number of metagenomic features. In addition, disease prediction is performed; ASD associated biomarkers are determined using the microBiomeGSM on metagenomic data. Classification is performed at three different taxonomic levels (genus, family and order) using the relative abundance values of species. The best performance metric (0.95 AUC) was obtained at the order taxonomic level using an average of 416 features with microBiomeGSM. The identified ASD-related taxonomic species are presented.Conference Object Citation - WoS: 3Citation - Scopus: 4Fine Tuning DeepSeek and Llama Large Language Models with LoRA(IEEE, 2025-06-25) Uluirmak, Bugra Alperen; Kurban, RifatIn this paper, Low-Rank Adaptation (LoRA) finetuning of two different large language models (DeepSeek R1 Distill 8B and Llama3.1 8B) was performed using the Turkish dataset. Training was performed on Google Colab using A100 40 GB GPU, while the testing phase was carried out on Runpod using L4 24 GB GPU. The 64.6 thousand row dataset was transformed into question-answer pairs from the fields of agriculture, education, law and sustainability. In the testing phase, 40 test questions were asked for each model via Ollama web UI and the results were supported with graphs and detailed tables. It was observed that the performance of the existing language models improved with the fine-tuning method.