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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14411/19
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Conference Object Unveiling the Landscape of Requirements Engineering: Insights from Text Mining(Institute of Electrical and Electronics Engineers Inc., 2026) Uguz, Sezer; Nazlioglu, Selma; Tokdemir, GulThis study applies text mining to analyze the Requirements Engineering (RE) literature over a 21-year period aiming to understand its evolution, current state, and future trajectory. Our analysis of publication volume, citation data, and funding trends reveals a significant evolution in research focus. We identify a clear transition away from foundational topics like model specification and project management towards a greater emphasis on user-centric and quality-focused themes. Specifically, topics such as elicitation technique, evaluation/validation/testing, and user perspective/aspect have demonstrated a dramatic surge in academic interest, citations, and research funding in the last decade. These findings provide a data-driven roadmap of the RE domain, offering valuable guidance for stakeholders to align with current trends and for researchers to identify impactful future directions in software engineering. © 2026 IEEE.Conference Object The Impact of Prompting Strategies on the Quality of LLM-Generated Biomedical Explanations(Institute of Electrical and Electronics Engineers Inc., 2026) Bon, Mohammad; Kizilirmak, Merve; Alper, Barkin Mert; Nazlioglu, SelmaRecent advancements in Large Language Models (LLMs) have great potential for understanding and reasoning within the biomedical field. Yet, the ability to craft interpretable and dependable medical explanations is predominantly determined by the design and arrangement of prompts. This research evaluates three styles of prompting (structured, role-based, and a hybrid combining both) and their influence on the quality of explanations from the GPT-5 model on the DDXPlus medical dataset. Significant differences in clarity and confidence were observed across all prompt techniques in the selected cases. Role-based referrals achieved the highest clarity (5.0 out of 5.0) and confidence scores (81.2%), while hybrid referrals explained symptom-disease relationships in a detailed and structured manner. Furthermore, the inclusion of clinician assessment in the study enhances the importance of research into real-world clinical applications in terms of comparing ground-truth and model outcomes. Overall, the findings demonstrate that strategic referral design is crucial for optimizing LLM outcomes in clinical practice and enables a transition from accurate diagnoses to truly interpretable and clinically useful explanations. © 2026 IEEE.Article Thermodynamic and Environmental Assessment of Integrating a Heat-Pump Tumble Dryer with a PEM Electrolyzer(Taylor & Francis Inc, 2026) Basdemir, Aziz; Tosun, Burcu; Erdogan, Anil; Nalbant Atak, YagmurHydrogen is increasingly regarded as an energy carrier for electrification and long-duration storage. This study presents a combined modeling and experimentally validated thermodynamic analysis of a household-scale hydrogen production concept integrating a heat-pump tumble dryer with a proton exchange membrane electrolyzer (PEME). The work combines steady-state thermodynamic and exergy modeling, experimental validation using air-side measurements, and a scenario-based life cycle assessment (LCA). A thermodynamic model of the integrated system was developed in engineering equation solver (EES), and a parametric analysis examined ambient temperature, relative humidity, tumble-air temperature, and alternative working fluids. Key metrics include coefficient of performance (COP), exergy destruction, condensate production, and hydrogen yield. Under baseline conditions, the system achieves COP = 4.90, produces 5.546 kg condensate/cycle, and yields 0.5453 kg H-2/cycle. Increasing ambient temperature (15-40 degrees C) raises COP by up to 14% and lowers exergy destruction (0.1999 -> 0.1775 kW). Raising tumble-air temperature (65-75 degrees C) increases COP (3.033 -> 5.406), reduces exergy destruction by 29%, and increases hydrogen output threefold. Exergy losses are dominated by the expansion valve (45%) and the condenser (39%). Among refrigerants, R600a and R1270 provide the highest gains in condensate and hydrogen production. A use-phase Life-cycle assessment (LCA) shows per-cycle Global Warming Potential (GWP) is reduced for condensate reuse (0.4087 -> 0.4070 kg CO2-eq/cycle). Overall, results suggest that dryer-condensate-fed electrolysis is technically feasible and offers thermodynamic and climate-impact benefits at the household scale.Conference Object The Influence of Prompt Language on Code Generation: A Comparative Analysis of Turkish and English(Institute of Electrical and Electronics Engineers Inc., 2026) Ozcelik, Deniz; Kisla, Beyza; Nazlioglu, SelmaThis study investigates the effect of prompt language (Turkish vs. English) on code generated by the Gemini 3 Pro model. Contrary to the common assumption that English yields superior results, our experiments reveal a significant performance difference: Turkish prompts achieved a functional correctness rate of 78.62%, decisively outperforming the 50% rate from English prompts on our unit test suite. However, this functional superiority did not equate to structural similarity. Analysis using Abstract Syntax Trees (AST) showed a low structural similarity of only 34.55% between the code pairs, indicating that the model's choice of algorithmic strategy was fundamentally altered by the input language. Furthermore, code generated from Turkish prompts was 28.22% more verbose, suggesting the model utilized more granular steps or a different syntactic approach. These findings demonstrate that the prompt language influences not only the model's comprehension of a task but also the fundamental logic it employs to generate a solution. This suggests the model does not simply translate the problem internally but accesses distinct problem-solving pathways based on the linguistic context of the prompt. © 2026 IEEE.Article The Use of Artificial Intelligence in Early Intervention(Ayse Sahin Ozturk, 2026) Pretis, Manfred; Todorova, Katerina; Er, MelekThe use of Artificial Intelligence (AI) is steadily increasing. This paper explores the extent to which AI is currently utilized in Early Childhood Intervention (ECI) services. Data were collected through an online survey conducted in German-speaking countries (Germany, Austria, Switzerland) and Turkey (n = 123). Results indicate that up to 50% of professionals in ECI services already use AI, predominantly ChatGPT. AI use is associated with younger age and professional background. Home visitors report less frequent use compared to professionals working in kindergarten settings. Non-users primarily cite a lack of information and general skepticism toward AI tools. Among users, AI is applied in methodological research, translation processes, and, to some extent, in planning interventions. Time efficiency is perceived as the main advantage of AI use; however, concerns remain regarding the validity of information and data protection. Overall, there is a clear demand for more training and well-defined guidelines concerning the handling of personal data when using AI in professional practice. © International Journal of Technology in Education and Science (IJTES).Article The Effect of Tax Justice on Income Redistribution: Empirical Evidence from OECD Countries(Akademiai Kiado Zrt, 2026) Ozkok Cubukcu, Dilek; Unalan, GokhanIncome taxation is theoretically considered one of the most effective and widely used tools of public finance for correcting income inequality. However, its actual redistributive capacity varies significantly across countries and depends on the design of tax instruments. This study isolates the effects of taxation from transfers to examine whether justice-based tax instruments-such as progressive tax rates, minimum living allowance (MLA), and tax credits-enhance redistribution. To empirically assess these relationships, a dynamic panel dataset covering 38 countries (37 OECD members and Bulgaria) between 2005 and 2020 is constructed. The System GMM method is applied to estimate the model. On average, taxation accounts for 26% of total redistribution, and its effect is significantly enhanced by equity-oriented tax policies. A one-point increase in tax progressivity leads to a 0.731-point rise in redistribution (P < 0.01), and tax credits have a similarly significant positive effect (+0.266). In contrast, personal allowances and zero-rate brackets show no statistically meaningful impact. A 10-point increase in the MLA index leads to a 0.4-point reduction in redistribution, suggesting that applying horizontal equity (equal treatment across household types) may undermine efforts to improve vertical equity (reducing income inequality). These findings highlight the importance of designing targeted and fairness-driven tax instruments to strengthen the redistributive role of taxation, beyond dependence on transfer mechanisms.Conference Object The Impact of Input Structure on Creating Quality Requirements: A Gemini Case(Institute of Electrical and Electronics Engineers Inc., 2026) Ceylan, Halil Mert; Aslan, Main Idal; Ciftaslan, Seyit; Nazlioglu, SelmaLarge Language Models (LLMs) are increasingly used in software engineering, but it is still unclear how consistently they can produce requirement sets that engineers can rely on. This study examines Gemini 3 Pro's requirement generation performance under a fixed persona-and-template prompt that enforces a strict output format and an ISO/IEC/IEEE 29148:2018-oriented requirements-writing style. To understand the role of input structure, we compare outputs generated from the same features written in two forms: unstructured prose and a structured, list-based format. We evaluate the results using an evaluation framework developed in this study, focusing on template-rule compliance (FR/NFR labeling, singular statements, traceable numbering, no-invention flagging, and a maximum of 10 requirements) and baseline alignment against an expert-defined reference in terms of requirement counts and semantic coverage. Overall, Gemini reaches an average template compliance of 80%. When compared with the expert baseline, it captures the intended content well, achieving 89.17% total semantic coverage (89% for FRs and 94.33% for NFRs), but it is less consistent at matching the baseline counts, especially for FRs and the total number of requirements (both 25%). By comparison, NFR count alignment is 83.33%, suggesting that non-functional requirement quantities are easier for the model to reproduce than functional ones. Taken together, these results indicate that strict template is effective for keeping the output structure consistent, while a structured input format mainly supports better coverage on the functional side. © 2026 IEEE.Book Part Sustainable Aviation Fuel to Decarbonize the Aviation Industry: A Case Study on the United States(CRC Press, 2025) Özdemir, Mehmet FurkanClimate change is a major global challenge that both current and future generations must address. This issue demands collaboration from governments, companies, and communities. The years leading to 2030 are critical for making significant progress towards a sustainable future. Therefore, our ability to grow food in the future will depend on the steps we take now. The 2016 Paris Agreement, part of the United Nations Framework Convention on Climate Change (UNFCCC), brought all countries together to acknowledge climate change and commit to tackling it. Various governments have set out plans to achieve the goal of limiting global temperature increases, aiming for no more than 2 degrees Celsius, and ideally, only 1.5 degrees Celsius. Likewise, corporations are putting forth ambitious emissions reduction and net zero carbon strategies, but the pace of action must be significantly accelerated on a global scale (World Economic Forum 2024). © 2026 Hande Girard and Durdu Hakan Utku.Article Sex-Based Morphometric and Morphological Insights into the Head of Femur and Fovea for Ligament of Head of Femur(Universidad de la Frontera, 2026) Tekmen, Esra; Sever, Sinem Nur; Cirak, Mustafa Tolga; Turhan, Begumhan; Golpinar, Murat; Canbeyli, Ibrahim DenizThis study investigated the sex-based morphometric and morphological parameters of the fovea for ligament of head of femur (Fovea capitis ossis femoris) (FCF) and head of femur (Caput ossis femoris). Measurements from 72 dry femurs were obtained using calipers and ImageJ software (Version 1.53q) on digital images. Results showed that, except for the head of femur area (FHA) and the vertical diameter of the head of femur (FHD-V) (p>0.05), most parameters were similar across sexes. Males exhibited significantly higher values of FHD-V and FHA (p<0.05). In contrast, females had higher mean values for the area of the FCF (A-FCF), foramina number, and both the longitudinal (LL-FCF) and transverse (TL-FCF) lengths of the FCF, but these differences were not statistically significant (p>0.05). Overall, the findings indicate that while most parameters did not differ significantly between sexes, males had larger vertical diameters and areas of the head of femur, whereas females tended to show higher but non-significant values in FCF-related parameters. These results provide valuable insights into the anatomical variations of the FCF and head of femur, highlighting subtle sex differences with potential clinical and anatomical relevance. © 2026 Universidad de la Frontera. All rights reserved.Article Robust Inference for an Interval-Monitored Step-Stress Experiment under Proportional Hazards(Taylor & Francis Ltd, 2026) Balakrishnan, Narayanaswamy; Jaenada, María; Pardo, LeandroAccelerated life tests (ALTs) are widely used in reliability analysis to infer on product lifetimes under normal operating conditions from data collected at higher stress levels. In step-stress ALTs, the stress is increased at predetermined times and kept constant between changes, accelerating failures and reducing test duration and cost. While many ALT studies assume a specific lifetime distribution, some applications require a more flexible formulation satisfying the proportional hazards (PH) assumption, under which stress acts multiplicatively on the hazard rate. In this paper, we study step-stress ALTs under a PH framework with linear and quadratic baseline hazard functions. We focus on settings where continuous monitoring is impractical and failures are observed only at scheduled inspections, yielding interval-censored count data. To achieve robustness and efficiency in this context, we introduce a family of minimum density power divergence estimators (MDPDEs) for model parameters, device reliability, and lifetime measures such as the mean lifetime and distributional quantiles. We derive the corresponding asymptotic distributions and construct approximate confidence intervals. Monte Carlo simulations assess the estimators' efficiency and robustness, and real-data examples illustrate the practical value of the proposed model and inferential methods.