Araştırmacılar(45)

• Agin, Kaya
• Akar, Alper
• Akar, Fahriye
• Akar, Özlem
• Alkan, Okan
• Aydin, I.
• Aysondu, Mehmet Hanifi
• Balkar, Oğuz Emre
• Bayır, Ismail
• Bayram, Bahri
• Bilgin, Musa
• Ceylan, Yasar
• Cirak, Burcu Bozkurt
• Çatır, F. E. Cimilli
• Çatır, Fulya Esra Cimilli
• Çetin, Murat
• Çomakli, Binali
• Dasci, Mahmut
• Eden, Cigdem
• Elibol, Betul
• Erten, Ömer
• Gezici, Anıl
• Gurgen, Seren Gulsen
• Gül, Emre
• Gülnahar, Murat
• Kayalar, Mehmet
• Kirtiloglu, Yusuf
• Koseoglu, Salih Cihan
• Kumbasaroglu, Hediye
• Kuş, Hüsamettin
• Kürşat Demirci, Oğuz
• Küzeci, Usgur
• Özdemir, Agah Oktay
• Serin, Yunus
• Siyambaş, Yusuf
• Taçoral, Ezgi
• Tan, Mustafa
• Tandiroğlu, Ahmet
• Turan, Metin
• Ulusu, Hayati
• Uzun, Suleyman
• Yalçinyavuz, Azapay Ummuhan
• Yılmaz, Mehmet
• Yildirim, A.
• Yolcu, Halil

• ArticleYayıncıda açık erişim2026/r/ebyu/19026
  Heterogeneous Effects of Green Technology Innovations on Renewable Energy Supply Share: A Dynamic Panel Evidence

  Balkar, Oğuz Emre; Özenoğlu, Yunus Emre; Alkan, Okan

  This study examines the determinants of renewable energy efficiency (REE) in a balanced panel of 17 countries (1995–2021), focusing on green technology innovations (GTI), economic growth (EG), and energy taxation (ET). The analysis accounts for heterogeneity, common shocks, and dynamic adjustment by combining two-way fixed effects with dependence-robust and dynamic common correlated effects (CCE) estimators using Driscoll–Kraay inference. The results show strong persistence in REE and a robust positive association between GTI and REE in the preferred common-factor framework, while the average effects of EG and ET are weaker and model-sensitive. Dumitrescu–Hurlin panel causality tests indicate feedback between REE and EG and bidirectional linkages between REE and ET at the level, whereas innovation dynamics are asymmetric. Overall, the evidences support innovation-centered transition strategies and emphasizes evaluating policy instruments over multi-year horizons given strong persistence and cross-country heterogeneity.

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• ArticleYalnız künye2026/r/ebyu/18199
  Investigation of cutting temperature, cutting force and surface roughness in turning of inconel 625 superalloy under different eco-friendly cooling/lubrication conditions

  Siyambas, Yusuf

  Inconel 625 superalloy is widely used in aerospace and energy industries; however, its low thermal conductivity, severe work-hardening tendency, and strong chemical affinity with cutting tools make it difficult to machine. This study systematically investigated the machinability of Inconel 625 under fifteen environmentally friendly cooling/lubrication conditions, including dry machining, high-pressure air cooling (HPAC), vortex air cooling, minimum quantity lubrication (MQL), and hybrid strategies. Machining performance was evaluated using cutting temperature, cutting force, surface roughness, tool wear, total machining cost, and carbon emission. Hybrid cooling/lubrication conditions significantly improved machining performance, reducing cutting temperature by up to 56.04%, cutting force by 11.48%, and surface roughness by 74.08%. Dry machining exhibited the lowest machining cost and carbon emission because no auxiliary cooling/lubrication equipment was employed. Grey Relational Analysis (GRA), integrating technical, economic, and environmental performance indicators, identified the HPAC (14 bar) + MQL (0.90 ml/min) condition as the optimum sustainable machining condition among the tested alternatives. SEM and EDS analyses confirmed that hybrid cooling/lubrication effectively suppressed built-up edge formation and adhesive wear. The proposed experimental framework provides a practical approach for selecting sustainable cooling/lubrication strategies for machining Inconel 625 by simultaneously considering machining performance, economic efficiency, and environmental impact. Le superalliage Inconel 625 est largement utilis & eacute; dans les secteurs de l'a & eacute;rospatiale et de l'& eacute;nergie. Cependant, sa faible conductivit & eacute; thermique, sa forte tendance & agrave; l'& eacute;crouissage et sa forte affinit & eacute; chimique avec les outils de coupe le rendent difficile & agrave; usiner. Cette & eacute;tude a examin & eacute; syst & eacute;matiquement l'usinabilit & eacute; de l'Inconel 625 sous quinze conditions de refroidissement/lubrification respectueuses de l'environnement, notamment l'usinage & agrave; sec, le refroidissement par air haute pression (HPAC), le refroidissement par air vortex, la lubrification minimale (MQL) et des strat & eacute;gies hybrides. On a & eacute;valu & eacute; les performances d'usinage en utilisant la temp & eacute;rature de coupe, la force de coupe, la rugosit & eacute; de surface, l'usure de l'outil, le co & ucirc;t total d'usinage et les & eacute;missions de carbone. Les conditions de refroidissement/lubrification hybrides ont am & eacute;lior & eacute; significativement les performances d'usinage, r & eacute;duisant la temp & eacute;rature de coupe jusqu'& agrave; 56.04%, la force de coupe de 11.48% et la rugosit & eacute; de surface de 74.08%. L'usinage & agrave; sec pr & eacute;sentait le co & ucirc;t d'usinage et les & eacute;missions de carbone les plus faibles, puisque l'on a utilis & eacute; aucun & eacute;quipement auxiliaire de refroidissement/lubrification. L'analyse relationnelle grise (GRA), int & eacute;grant des indicateurs de performance techniques, & eacute;conomiques et environnementaux, a identifi & eacute; la condition HPAC (14 bars) + MQL (0.90 ml/min) comme la condition d'usinage durable optimale parmi les choix & eacute;valu & eacute;s. Les analyses de MEB et d'EDS ont confirm & eacute; que le refroidissement/lubrification hybride supprimait efficacement la formation de bordures renforc & eacute;es et l'usure adh & eacute;sive. Le cadre exp & eacute;rimental propos & eacute; offre une approche pratique pour la s & eacute;lection de strat & eacute;gies de refroidissement/lubrification durables pour l'usinage de l'Inconel 625, en consid & eacute;rant simultan & eacute;ment les performances d'usinage, l'efficacit & eacute; & eacute;conomique et l'impact environnemental.

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• ArticleYalnız künye2026/r/ebyu/12237
  Prediction of dimensional deviation and quality tolerance class in the production of AlSi10Mg alloy parts with laser powder bed fusion using machine learning algorithms

  Siyambaş, Yusuf; Turgut, Yakup; Akdulum, Aslan

  Metal-based workpieces can be produced additively with the selective laser melting (SLM) technique, which is one of the laser powder bed fusion (LPBF) methods. However, due to complex production parameters, the dimensional deviation and quality tolerance class of the produced parts cannot be predicted. This situation causes the need for secondary operations such as turning, milling, grinding, or polishing after production and even increases the amount of scrap. These processes are quite costly as they require extra time, labor, and machinery. This study aims to clarify complex manufacturing parameters, predict dimensional deviation with a high success rate before production, and categorize part quality tolerance classes with high accuracy. To examine the effect of manufacturing variables using dimensional accuracy, parts were produced at various laser power and scanning speeds, and features like length, width, thickness, and radius were measured. Dimensional changes were more significant at extreme parameter values and minimized at medium values. Additionally, extensive datasets were generated using manufacturing parameters and physics knowledge, and various machine learning models were applied and analyzed in detail. As a result of exploratory data analysis, dimensional deviation is estimated with a high success rate, while the part quality tolerance class can be categorized with a high precision rate. With the proposed method, proactive measures can be taken for possible errors before production. By effectively determining production parameters during the process planning phase, the part qualification process will become more efficient. Therefore, advantages such as reducing the cost of scrap parts, the absence of secondary processes, and saving cost and time will be achieved, and production comparable to traditional production methods will be possible.

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• ArticleYalnız künye2026/r/ebyu/11482
  Removal of Malachite Green from Aqueous Solutions by Using Bi4V2O11 Nanosheet

  Yavuz, Emre; Dundar, Ozan Ali; Yavuz, Sevtap Caglar; Yilmaz, Erkan; Ozdokur, Kemal Volkan; Cirak, Burcu Bozkurt; Caglar, Bulent; Cirak, Cagri; Arar, Özgür

  Introduction: Transition metal oxides are highly effective for dye removal due to their adaptable surface chemistry, availability, and robust mechanical and thermal properties. Among them, Bi4V2O11 nanosheets offer potential due to their morphology and high surface area. To synthesize Bi4V2O11 nanosheets and evaluate their performance in removing malachite green, focusing on the impact of adsorbent amount, contact time, and interfering ions on dye removal efficiency. This study aims to evaluate the performance of Bi4V2O11 nanosheets in removing malachite green dye from aqueous solutions and to examine the effects of adsorbent dosage, contact time, and the presence of coexisting ions on the sorption process. Methods: Bi4V2O11 nanosheets were synthesized and used to remove the malachite green dye. Batch experiments were conducted to study the effects of adsorbent dose, contact time, and coexisting ions (Na+, Ca2+, and tannic acid). Equilibrium data were fitted to the Langmuir isotherm model to determine the maximum sorption capacity. Results: Malachite green sorption using Bi4V2O11 nanosheets reached equilibrium within 240 minutes, demonstrating efficient dye removal under the tested conditions. The sorption process conformed to the Langmuir isotherm model, indicating monolayer adsorption with a maximum capacity of 47.27 mg/g. While the presence of Na+ and tannic acid had negligible effects on dye removal efficiency, the coexistence of Ca2+ significantly reduced the removal efficiency from 95% to 58%, highlighting the potential impact of divalent ions on the adsorption process. Discussion: Bi4V2O11 nanosheets are effective adsorbents for malachite green removal, demonstrating high sorption capacity and resilience to Na+ and tannic acid interference. However, the presence of Ca2+ significantly reduces removal efficiency, highlighting the need for further optimization in practical applications. Conclusion: Bi4V2O11 nanosheets offer high sorption capacity and structural stability, environment friendly synthesis procedure, making them feasible for small-scale or pre-treatment applications. 2026, Bentham Science Publishers

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• ArticleAçık Erişim2026/r/ebyu/11209
  National-Scale Estimation of Brake-Wear Particulate Emissions From On-Road Vehicles in Türkiye: A 2024 Inventory with Uncertainty and Mitigation Scenarios

  Demirci, Oğuz Kürşat

  This research article develops a screening-level national inventory of airborne brake-wear particulate emissions from on-road vehicles in Türkiye for the year 2024. The scope is limited to brake wear only and does not include tyre wear, road wear, resuspension, or concentration and exposure modelling. The inventory combines TurkStat vehicle-kilometre statistics with EMEP/EEA Tier-2 brake-wear emission factors, including passenger-car powertrain differentiation to reflect regenerative-braking effects. Under nominal baseline assumptions, annual emissions are estimated at 5.67 kt•y-1 for PM10 and 2.26 kt•y-1 for PM2.5. Monte Carlo framework that propagates bounded uncertainty in total activity, vehicle-category composition, passenger-car powertrain shares, guidebook-anchored emission factors, and particle-size fractions, the national totals are characterised by a median (95% interval) of 5.67 (4.87-7.19) kt•y-1 for PM10 and 2.26 (1.87-2.90) kt•y-1 for PM2.5 Passenger cars account for approximately 45% of national PM10 emissions, followed by light commercial vehicles and tractor units. Illustrative, assumptiondependent scenario analysis indicates that PM10 could decrease to 4.95 kt•y-1 under partial uptake of low-emission brake technologies, to 5.28 kt•y-1 with greater use of auxiliary braking in heavy-duty vehicles, and to 5.47 kt•y-1 under a passenger-car fleet share of 10% HEV and 10% BEV. These scenario outcomes should be interpreted as indicative screening-level projections rather than empirical or policyevaluation results. Overall, the study provides a transparent brake-wear-only national baseline and a comparative framework for examining how selected assumptions influence estimated PM emissions in Türkiye.

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• ArticleYayıncıda açık erişim2026/r/ebyu/11206
  Prediction of quality class in face milling of S355J2 steel using meta model based on cutting temperature and surface roughness data

  Akdulum, Aslan; Siyambaş, Yusuf; Ayhan, Emre; Çakıroğlu, Ramazan; Mavi, Ahmet; Erturun, İbrahim

  In this study, the effects of cutting parameters on surface roughness and cutting temperature in the face milling process of S355J2 steel were experimentally investigated under different cooling strategies (dry, wet, and air-assisted). The experimental data obtained were analyzed by establishing regression models for the prediction of both cutting temperature and surface quality, and the workpiece quality class was estimated by classification algorithms. Initially, various regression and classification algorithms were evaluated as benchmarks, and then the most successful models were subjected to hyperparameter optimization with the Optuna algorithm. According to the experimental findings, the lowest surface roughness was obtained under air cooling conditions, showing a significant improvement of 19.81 % and 20.21 % compared to dry and wet machining, respectively. Conversely, the lowest cutting temperature was observed in wet cutting, which was 78.84 % lower than dry cutting and 62.92 % lower than air-assisted machining. In modeling studies, NGBoost for cutting temperature and LightGBM for surface roughness algorithms showed the highest performance, achieving R2 values of 0.816 and 0.698, respectively, after hyperparameter optimization. The highest success in quality class prediction was achieved with the Extra Trees algorithm, which reached an accuracy of 73.85 % as a benchmark model. The combination of these algorithms created a metamodel structure that excelled in quality classification, achieving a final accuracy rate of 92.3 %. Additionally, the transparency of model decisions is increased with LIME analysis, and the effects of basic cutting parameters on the outputs are visualized. As a result, it is shown that the proposed approach can be used as an effective tool for the optimization of milling processes and the development of automatic quality control systems in the production line.

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• ArticleYayıncıda açık erişim2026/r/ebyu/11143
  Multidimensional Sustainability Performance Measurement in the Banking Industry: A Data-Driven Grey Decision-Support Framework

  Işık, Özcan; Shabir, Mohsin; Oğuz, Alparslan; Alkan, Okan

  Given the critical role that banks play in sustainable development through their financial intermediation and capital allocation functions, assessing their sustainability performance has become an increasingly important research issue. In response, this research introduces a data-driven grey decision-support framework for assessing multidimensional sustainability performance in the banking industry. Sustainability indicators are systematically identified through text-mining-based content analysis of banks' sustainability reports and annual activity reports. The empirical analysis covers nine banks continuously included in the BIST Sustainability Index over the 2020–2024 period. To reflect temporal variability in sustainability disclosures, the assessment is formulated under grey interval numbers. Within this decision-support framework, Grey LODECI is employed to identify criterion importance, while Grey SWI is utilized to rank bank performance in terms of sustainability. This manuscript also offers useful implications for bank managers, regulators, investors, and sustainability rating agencies by supporting more comprehensive sustainability benchmarking and more informed strategic and regulatory decision-making.

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• ArticleYayıncıda açık erişim2026/r/ebyu/10860
  Machinability Assessment of Forged, SLM and Heat-Treated Inconel 718 Under Dry and MQL Conditions Using Machine Learning Models

  Cemaloğlu, Fulya; Özlü, Barış; Demir, Halil; Kara, Fuat

  In this study, the milling performance of Inconel 718 alloys produced by forging (WP1), Inconel 718 produced by Selective Laser Melting (SLM) (WP2), and Inconel 718 (WP3) subjected to heat treatment after SLM, under different cooling/lubrication conditions, was evaluated using experimental and artificial intelligence-based approaches. Microstructural analysis showed a homogeneous fine-grained structure in WP1, while WP2 exhibited dendritic features and porosity. Heat treatment improved the microstructural homogeneity of WP3. The hardness values of WP1, WP2, and WP3 were 457 Hv, 303.33 Hv, and 391 Hv, respectively. Milling experiments yielded cutting forces of 336.5–1185.9 N, surface roughness values of 0.22–1.39 µm, and cutting temperatures of 168–658 °C. Compared with dry machining, MQL reduced average cutting force and cutting temperature by 15.5% and 18.65%, respectively, while improving tool wear and surface integrity. Machine learning models including LR, DTR, SVR, and GPR were developed to predict machining responses. GPR provided the highest prediction accuracy, achieving 98.72% for cutting force and 98.99% for cutting temperature. The results demonstrate that manufacturing route and cooling strategy significantly affect the machinability of Inconel 718 and that machine learning techniques can effectively support machining process optimization.

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• ArticleAçık Erişim2026/r/ebyu/10838
  Scenario-based optimization of humanitarian supply chain distribution channels for a possible earthquake: a case study in Türkiye

  Balkar, Oğuz Emre; Akar, Alper; Akar, Özlem

  Earthquakes represent a serious risk to societies, economies, and critical infrastructure, highlighting the need for effective disaster preparedness and resilient humanitarian supply chains (HSCs). In seismically active regions such as Erzincan Province, Türkiye, HSC distribution networks must remain functional despite significant disruptions. This study proposes a five-stage mixed-method framework to design and optimize HSC distribution channels for a potential major earthquake in the central district of Erzincan. First, buildings and transportation links were evaluated in terms of seismic suitability to pre-select candidate main distribution centers (MDCs), local distribution points (LDPs), and low-risk routes. Proximity analyses were then conducted using Quantum GIS (QGIS) to identify potential LDPs within a 1–5 km service radius for 69 neighborhoods. These locations were further validated and optimized using Greenfield analyses in anyLogistix under a distance–demand cost minimization criterion. Four distribution channel scenarios were subsequently simulated over a two-month post-earthquake period in line with national and international disaster standards. Finally, the Shortest Path Method was applied to assign primary, secondary, and tertiary LDPs to each neighborhood. The results indicate that supplying nine LDPs (comprising schools and healthcare centers) through four MDCs, including a newly designated sports facility, minimizes transportation cost, travel distance, and lead time, while enabling 63.8% of neighborhoods to access their primary LDP within 3 km. The proposed framework offers a holistic approach by explicitly incorporating seismic compliance into the design of HSC distribution channels in earthquake-prone regions.

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• ArticleYalnız künye2026/r/ebyu/10804
  A novel multi-objective optimization model for the time–cost–environmental impact trade-off in construction projects using a strength Pareto-based whale optimization algorithm

  Yılmaz, Mehmet; Dede, Tayfun

  The construction industry, a key contributor to global economic development, significantly impacts environmental pollution through greenhouse gas emissions. While conventional construction project management prioritizes cost and time reduction, environmental considerations are increasingly critical. This study addresses the time-cost-environmental impact trade-off problem by introducing a strength Pareto-based whale optimization algorithm (SP2WOA). The algorithm enhances solution diversity and achieves well-distributed Pareto fronts by integrating strength Pareto operators with the whale optimization algorithm. Its effectiveness is demonstrated through three diverse construction projects. Compared to previous algorithms (non-dominated sorting genetic algorithm-II, opposition-based multi-objective differential evolution, golden ratio-based opposite teaching-learning-based optimization and enhanced multi-objective grasshopper optimization algorithm), SP2WOA achieves superior balances between time, cost and environmental impact, using 33-60% fewer function evaluations. Statistical analysis using Wilcoxon rank tests confirms SP2WOA's superiority in both hypervolume and spread metrics. The results demonstrate that SP2WOA offers a valuable tool for sustainable construction project management.

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