Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Prosocial Behavior as Bridge and Buffer: Exploring Its Dual Role between Stress and Mental Health in Emerging Adults(Springer, 2026-03-16) Coskun, Muhammet; Arslan, Gokmen; Genc, Emel; Caprara, Gian VittorioThe present study investigated the dual role of prosocial behavior among emerging adults in a non-Western cultural con-text. Within this scope, first, adaptation and validation of the Adult Prosocialness Behavior Scale (PBS) was carried out for Turkish population. Then, it tested both mediating and moderating functions of prosocial behavior in the links from stress to subjective wellbeing (SWB) and quality of life. To this end, a sample of 419 emerging adults (41.3% males and 58.7% females) completed an online survey. Results revealed Turkish version of the APBS to be a reliable and valid mea-surement tool for assessing prosocial behavior. Regarding the main hypotheses, serial multiple mediation analysis revealed that perceived stress predicted quality of life through the sequential pathway of prosocial behavior and SWB. Additionally, the moderated-mediation analysis confirmed the moderating role of prosocial behavior in the relationship between stress. Nevertheless, moderated mediation index did not reveal a significant conditional indirect effect of prosocial behavior for the indirect path from stress on quality of life through SWB. Overall, findings suggest that prosocial behavior plays a dual role as both a 'bridge' (mediator) and a 'buffer' (moderator) in the relationship between stress and mental health.Article Sensitive Hybrid Plasmonic Refractive Index Sensor Based on Ag Cross-Grating Nanoantenna and Au Quantum Dot upon SiO2 Nanowire(IOP Publishing Ltd, 2026-04-03) Sanli, Atif Kerem; Kilic, Veli Tayfun; Tabaru, Timucin EmreThis study presents a distinctive hybrid plasmonic sensor architecture combining a silver (Ag) cross-shaped nanoantenna with a gold (Au) quantum dot (QD) for enhanced refractive index sensing applications. The structure consists of a silicon dioxide (SiO2) substrate and a cylindrically shaped SiO2 wire on it, topped with a silicon nitride (Si3N4) dielectric layer and an Ag cross grating, with an Au QD positioned at the center. Using free and open source 3D Finite-Difference Time-Domain (FDTD) simulations, exceptional electric field enhancement at the resonant wavelength of approximately 639-667 nm is demonstrated. The optimized structure achieves remarkable quality factors (Q-factors) exceeding 267 for biological media, representing among the highest reported values for plasmonic sensing structures. Unlike conventional red-shift sensors, our design exhibits a distinctive blue-shift sensing behavior arising from hybrid plasmonic mode coupling, achieving sensitivities ranging from 190 to 344 nm RIU-1 for various analytes, including water, blood, PDMS, body fat, ethanol, and glass. The ultrasharp resonances (FWHM similar to 2.3 nm) combined with intense field enhancement make this design highly suitable for biosensing applications.Article Parametric Study on the Behavior of CFRP-Strengthened Reinforced Concrete Deep Beams with Cut Circular Web Openings in Shear Spans(Nature Portfolio, 2026-02-17) Yagmur, ErenWeb openings in reinforced concrete deep beams are often necessary for functional purposes but substantially reduce structural performance. Carbon fiber-reinforced polymer (CFRP) strengthening is commonly employed to mitigate these effects. Previous studies typically examined openings in regions without stirrups or assumed closed stirrup configurations, overlooking the frequent stirrup damage that occurs in practice due to the high shear reinforcement in deep beams. In this study, three specimens from a prior experimental program were modeled in ABAQUS, and the numerical results were validated against experimental data. Openings of varying diameters were introduced by cutting reinforcements, and the beams were subsequently strengthened with CFRP laminates, and a parametric study was conducted. Results showed that increasing opening diameter markedly reduces load-carrying capacity and energy absoption, while thicker CFRP laminates partially restore performance. For example, a 300 mm opening in a 500 mm high unstrengthened beam reduced load capacity by 56% and energy absorption by 87%. Even when the opening diameter was less than one-third of the beam height, 1.8 mm CFRP laminates provided only limited improvement. Deep beam performance was strongly influenced by web opening size, and the effectiveness of CFRP strengthening was limited when stirrup integrity was compromised.Article Feasibility Analysis of Granitic Rocks for Use in the Dimension Stone Industry(Univ Zagreb, FAC Mining, Geology & Petroleum Engineering, 2026) Koken, EkinThis study presents two objective evaluation tools for assessing the feasibility of granitic rocks in dimension stone applications. The developed methods integrate fundamental physical and mechanical properties, including dry density (rho d), effective porosity (ne), P-wave velocity (Vp), uniaxial compressive strength (UCS), and Böhme abrasion value (BAV). Feasibility analyses based on the conditional formatting (CF) and ranking method RM reveal that the adopted input parameters are essential for determining the suitability of granitic rocks as dimension stones. The strong relationship between the CF and RM results highlights their consistency and broad applicability. Both methods exhibit good agreement with the recommendations of the American standard for granitic rocks. Consequently, the suggested methods provide practical guidance for selecting suitable rock exposures in field studies, also offering a time- and energy-efficient decision-making framework for the dimension stone industry. To comprehensively evaluate the strengths and limitations of the proposed approaches, it is recommended that these tools be applied to a wider range of dimension stone types and geological settings.Article Views on Climate Change, Climate Action and Mental Health, in Young People with and without Existing Depression Symptoms: A Qualitative Study(Elsevier, 2026-01) Kaya, M. Siyabend; Hawkins, Ed; McCabe, CiaraBackground: Youth mental health is in crisis. Climate change has the potential to tip more young people into depression and anxiety. Knowing how young people with and without depression symptoms view climate change could guide interventions to mitigate against climate induced mental health issues. Materials and Methods: We carried out in-depth, semi-structured interviews with (N = 27) young people aged 18-25 (M-age = 20.3 years). Participants were grouped as healthy controls (C, N = 16, < 16 score on Mood and Feelings Questionnaire, MFQ) or had high depression symptoms (HD, N = 11, >= 27, MFQ). Using thematic analysis, we explored participants views on climate change, climate action, climate messaging, climate agency and mental health. Results: From the interviews, eight key themes emerged: (1) Negative environmental events - Climate change was understood as ranging from weather changes to natural disasters. (2) Mental health impacts - Most participants reported increased anxiety and depression, with the HD group being more pessimistic about climate change prevention. (3) Benefits of action - Focus on individual efforts. (4) Non-disruptive vs. disruptive actions - Preference for non-disruptive solutions. (5) Hope and Fear in climate messaging - balance is needed. (6) Local and global action - Emphasis on combining both approaches. (7) Leadership - Responsibility placed on politicians, institutions, and environmentalists. (8) Shared responsibility - Families, educators, governments, and celebrities all have a role in climate action. Conclusion: These findings offer valuable insights into the perspectives of young people with and without existing symptoms of depression. Notably, identifying differences-such as varying levels of climate pessimism-based on depression status highlights the importance of climate communication strategies that not only effectively address climate change but also safeguard youth mental health. This is important as those with existing depression symptoms may be more vulnerable to the psychological impacts of climate change.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., 2026-09) Shah, Syed Haider Ali; Murad, Majid; Wang, MansiDrawing on dynamic capability theory, this study examines how AI-driven circular transformation (AIT) and social entrepreneurship orientation (SEO) contribute to sustainable entrepreneurial success (SES). This study further investigates the mediating role of knowledge integration (KNI) and the moderating effect of digital capabilities (DIC) in these relationships. Data were collected from 442 top-level managers working in high-tech manufacturing industries in Guangdong Province, China, and were analyzed using partial least squares structural equation modeling. The empirical findings suggest that both AI-driven circular transformation and SEO have a positive influence on SES. Moreover, KNI is found to significantly mediate the relationships between AIdriven circular transformation, SEO, and SES. Additionally, DIC positively moderate the relationship between KNI and SES. Furthermore, this study offers implications for managers and policymakers seeking to promote sustainable entrepreneurship. The results highlight the importance of integrating AI-enabled circular practices with socially oriented entrepreneurial strategies to enhance long-term entrepreneurial outcomes. Finally, the results suggest that investments in DIC and effective KNI mechanisms can strengthen firms' dynamic capabilities, thereby supporting sustainability-oriented innovation and entrepreneurial success.Article Strategic Modeling of Hybrid Smart Micro Energy Communities: A Decision-Oriented Approach(MDPI, 2026-02-10) 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 Raster Orientation Effects on the Adhesion of iCVD-Deposited PSA Thin Films on FDM-Printed PLA(MDPI, 2026-01-30) 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, Kübra; Ulusoy, Selda KapanDetermining and optimizing the process parameters impacting the outputs at each production stage is necessary to reduce production costs. The Taguchi Method (TM) and the Grey Relational Analysis (GRA) are commonly utilized two techniques for process parameter optimization. In precision machine part manufacturing, Computer Numerical Control (CNC) production is the most critical process. In this study, the objective is to optimize CNC manufacturing parameters using TM, GRA and Principal Component Analysis (PCA) in metal sector. Process parameters like operator experience level (in years), CNC machine brand, CNC machine age, and CNC machine size were determined and optimized based on their degree of impact on the outputs. The experiments were carried out using a four-factor, four-level Taguchi orthogonal array (L16), and Analysis of Variance (ANOVA) was conducted aiming to determine the effects of these process parameters on production time, dimension conformity, and surface roughness performance factors. Selection of these input parameters and performance factors in the study is to provide a solution to a problem in the company from which the data are obtained with scientific methods and to contribute to the literature. Utilizing TM, the optimal values of process parameters are determined as ten years for operator experience, as Mazak for CNC machine brand, as two years for machine age, and as 500x550x550 for machine size. Utilizing the combination of GRA and PCA optimal parameter values are determined as ten years for operator experience, as Yuntes for CNC machine brand, as two years for machine age, and as 700x450x500 for machine size. A sensitivity analysis was performed using 21 different weight sets for performance factors (production time, dimension conformity, and surface roughness). Compared to the initial CNC production process parameters, 45%, 95%, and 504% improvements were obtained in production time, dimension conformity, and surface roughness process parameters. Companies, especially operating in the metal sector, can benefit from managerial practices by considering the ranking of parameters affecting CNC production according to the results obtained from this study.Article Citation - Scopus: 1Machine Learning and Scenario-Based Forecasting of Türkiye’s Renewable Energy Transition toward Net-Zero 2053(Elsevier Ltd, 2026-05) Sutcu, Muhammed; Yildiz, Baris; Sahin, Nurettin; Almomany, Abedalmuhdi; Gulbahar, Ibrahim TumayThe issue of global warming has been identified as one of the most critical challenges of the 21st century, with the consumption of fossil fuels being identified as a major contributor to greenhouse gas emissions. In response to these challenges, countries worldwide are expediting their transition towards renewable energy sources to meet international climate commitments, such as the Paris Agreement, and to achieve long-term sustainability goals. Türkiye has established a target to achieve net-zero emissions by 2053. This objective is consistent with both the nation's domestic energy strategy and its international commitments. Nevertheless, the transition from fossil fuels to renewable energy sources is impeded by geographical, economic, and technological constraints. The present study aims to assess the capacity and efficiency of renewable energy in Türkiye with environmental protocols and future electricity demand projections. Electricity generation, transmission data, and national energy plans are used to identify future electricity generation and capacity trends. In the context of this study, a range of machine learning models is executed across diverse scenarios, yielding a series of outcomes. Consequently, the repercussions of regulatory measures and financial investments were examined, and prospective inferences were derived. The findings underscore the pivotal role of scenario-based modeling in formulating sustainable energy policies and directing investment decisions within the context of climate change mitigation.