WoS
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14411/18
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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.Article Rational Torsion on Hyperelliptic Jacobian Varieties(Wiley-VCH Verlag GmbH, 2026) Suluyer, Hamide; Sadek, MohammadIt was conjectured by Flynn that there exists a constant such that, for any integer , any , there exists a hyperelliptic curve of genus over with a rational -torsion point on its Jacobian. Lepr & eacute;vost proved this conjecture with . In this work, we prove that given an integer in the interval , , satisfying certain partition conditions, there exist parametric families of hyperelliptic Jacobian varieties with a rational torsion point of order . In particular, we establish the existence of such varieties for when is odd and for when is even. A few explicit applications of this result produce the first known infinite examples of torsion 13 when , torsion 15 when , and torsion 17,18,21 when . In fact, we show that infinitely many of the latter abelian varieties are absolutely simple.Article Rethinking the Second Life of Post-Disaster and Post-Conflict Temporary Housing(Ubiquity Press Ltd, 2026) Ay, Bekir Özer; Dino, İpek Gürsel; Akdede, NilProviding temporary housing (TH) units after natural hazards and social conflicts is often an urgent necessity. Beyond their initial configuration, the second life of these units is crucial, given their temporary nature. Despite growing interest in second-life strategies, many TH units and associated infrastructure remain unused or inefficiently managed after their initial deployment. Second-life strategies are presented for post-disaster and post-conflict (PDPC) TH units and settlements in Türkiye, using two case studies-the 2011 Van earthquakes and Syrians under temporary protection-and expert insights from the Disaster and Emergency Management Presidency of Türkiye (Afet ve Acil Durum Yönetimi Başkanlığı-AFAD). Athree-step methodology was employed, including a literature review, semi-structured expert interviews and hybrid deductive-inductive thematic analysis. Findings reveal that second-life outcomes are largely shaped by policy gaps, operational conditions, tenure constraints, institutional decisions and user practices rather than by design-based circular approaches. The proposed framework provides practical guidance for policymakers and practitioners in Türkiye and other crisis-prone contexts to improve resource efficiency and integrate second-life planning into preparedness and recovery processes.Article Investigating Emotional Conveyance in AI-Generated Interior Design: A User Perception Experiment(Gazi Univ, FAC Engineering Architecture, 2026) Hatunoğlu, Doğan Can; Ünal, Bülent; Güneş, ElifPurpose: This study aims to investigate the effectiveness of AI-generated interior environments in conveying specific emotional impressions, focusing on user recognition of core emotions through visual perception. Theory and Methods: Grounded in environmental psychology and emotion theory, the study used MidJourney (v5) to generate 28 hotel lobby images based on six basic emotions and a neutral control. Participants matched each image with a perceived emotion in an online survey. Results: The results indicated high recognition rates for happiness, surprise, and fear, while emotions such as disgust and anger were often misidentified. Spatial cues like light, openness, material, and color were frequently cited as influencing emotional responses. Conclusion: AI-based generative tools can successfully convey certain emotional atmospheres in interior design. However, their ability to communicate more complex or negatively valenced emotions remains limited, suggesting future improvements in AI-human emotional alignment are needed.Article From Facial Expressions to Thermal Sensation: POMS-Validated AI-Based Mood Estimation Driving Psychology-Adaptive HVAC Control(Elsevier Science SA, 2026) Saleh Saleh, Yousif Abed; Sümer, Mustafa Erdi; Saleh, Yousif Abed Saleh; Lotfi, Bahram; Turhan, Cihan; Özbey, Mehmet FurkanThe psychological state of building occupants plays a critical role in how thermal environments are perceived and experienced, yet conventional Heating, Ventilation, and Air Conditioning (HVAC) control strategies largely ignore this factor. Current systems typically focus on environmental and personal parameters, overlooking the influence of mood state on thermal comfort. This omission can lead to suboptimal comfort levels, decreased occupant satisfaction, and inefficient energy use. Integrating psychological feedback into the HVAC control has the potential to transform indoor climate management into a truly occupant-centric process. To this aim, this study presents a novel framework that employs artificial intelligence (AI) and image processing together to estimate occupants' mood states in real time. Facial expressions are analysed using a deep learning-based computer vision model, and the resulting mood predictions are validated with the Profile of Mood States (POMS) questionnaire to ensure accuracy and reliability. Validated mood data directly informs the HVAC setpoint adjustments, enabling psychology-adaptive control that responds dynamically to occupants' current mood states. Moreover, the system operates in real time, combining low-latency image analysis with adaptive control algorithms to continuously align thermal conditions with validated mood estimations. Additionally, implementing mood-driven HVAC control shows potential for enhancing perceived comfort while improving energy efficiency. By bridging the gap between psychological state assessment and environmental control, this research contributes to the advancement of intelligent building systems, paving the way for more responsive, energy-conscious, and human-centered indoor environments.Article Advances in Artificial Intelligence and Thermal Analysis for Brain Tumor Detection: A Review of Models, Methods, and Modalities(Frontiers Media SA, 2026) Al Assaf, Anwar; Damseh, Rafat; Sutcu, Muhammed; Yıldız, Barış; Soomro, Uzair; Almomany, Abedalmuhdi; Abd-Alrazaq, AlaaBrain tumors pose a major challenge in neuro-oncology due to their high mortality rates and complex diagnosis. This review summarizes recent advances in using artificial intelligence (AI), particularly deep learning, in conjunction with thermal imaging and simulated thermal mapping for brain tumor detection. AI methods such as convolutional neural networks (CNNs), hybrid architectures, and bioheat transfer models, including the Pennes equation, are evaluated to determine how temperature variations, tumor biology, and image preprocessing influence malignancy classification. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), provide detailed structural information but are often costly, invasive, and limited in their ability to capture physiological data. Recent studies indicate that integrating AI with thermal imaging, either through direct infrared thermography or simulated thermal maps derived from MRI, enables non-invasive, physiology-aware diagnosis. The review examines current approaches to thermal data preprocessing, simulation, deep learning-based tumor segmentation, and malignancy prediction, as well as key evaluation metrics, model interpretability tools, and recent performance outcomes. Despite ongoing progress, challenges remain, including limited availability of multimodal datasets, variability in thermal signatures, and the need for clinical validation. Future research directions include large-scale data collection, advanced thermal modeling, multimodal fusion frameworks, and the development of explainable AI tools that meet clinical standards. In resource-limited settings, AI-powered thermal imaging may serve as a valuable supplement to traditional diagnostics, offering safer, more precise, and more accessible brain tumor detection. This technology has the potential to improve patient outcomes and transform neuro-oncology practices by integrating anatomical and functional insights. This review critically evaluates current evidence and identifies the challenges that must be addressed to facilitate the translation of promising research into clinical practice.Article Citation - WoS: 1The Evaluation of System Reliability Under Dependent Shock Magnitudes(Springer, 2026) Eryilmaz, SerkanThis paper studies shock models by assuming a certain kind of dependence among shock magnitudes. In particular, discrete time extreme and run shock models are investigated when the shock magnitudes follow discrete autoregressive process of order 1. Exact expressions are obtained for the reliability functions and mean time to failure values under both models. The method for deriving the reliability characteristics is based on the use of probability generating functions. Numerical results are presented when the shocks arrive according to a Binomial process.Article Simulation-Based Optimization of HVAC Systems in Aging Educational Facilities: Addressing IAQ Challenges Through Retrofitting(MDPI, 2026) Saleh, Yousif Abed Saleh; Turhan, Cihan; Turhan, BurcuIndoor air quality (IAQ) in educational buildings plays a critical role in the health, cognitive performance, and well-being of occupants. Aging university facilities often rely on outdated ventilation systems that are not designed to meet current demands or respond to dynamic occupancy levels. This study investigates the performance and feasibility of various advanced ventilation strategies in comparison to an existing balanced mechanical ventilation (BMV) system in a university classroom accommodating 100 students. Using a Dynamic Building Energy Simulation Program, simulations were conducted to evaluate IAQ (using CO2 levels), energy consumption, and thermal comfort under three retrofitting scenarios: BMV, demand-controlled ventilation (DCV), and hybrid ventilation combining natural and mechanical airflow. The simulations indicate that DCV cuts annual HVAC energy use by 33% relative to the baseline, while the hybrid strategy achieves the greatest reduction of 42% and maintains CO2 levels and thermal comfort within recommended limits. Although hybrid systems provide seasonal advantages, their complexity may limit applicability. In addition to technical analysis, this study also explores the financial and tax-related challenges associated with retrofitting ventilation systems in university buildings. Investment payback periods, operational costs, and potential tax incentives are discussed to evaluate economic viability. Overall, the endorse hybrid ventilation as the most cost-effective strategy where mixed-mode control is feasible, and DCV as a practical alternative for buildings unable to employ natural ventilation.Article Malnutrition, Diet Quality, and Psychological Well-Being in Older Adults: A Hospital Outpatient Study(BMC, 2026) Cihan, Berna Betul; Naharci, Mehmet Ilkin; Konyaligil, Dilara Bersan; Topkaya, Merve Sena; Tel Adiguzel, Kubra; Koc, NevraBackground The ageing process can lead to malnutrition due to a variety of physiological and psychological issues. Investigating nutrition, lifestyle and psychological status is important for improving the health of the geriatric. The aim of this study was therefore to evaluate the relationship between malnutrition, dietary quality and psychological well-being in older adults. Methods One hundred three older adults treated at the Gülhane Training and Research Hospital Geriatrics polyclinic participated in this cross-sectional study. The researchers recorded sociodemographic characteristics, health information, anthropometric measurements, the Short Nutritional Assessment Questionnaire for 65 + (SNAQ(65+)) screening test, the Mediterranean Diet Adherence Scale (MEDAS) and the Psychological Well-Being Scale for the Older People (YPIOA) using a questionnaire during face-to-face interviews. Generalised Anxiety Disorder-7 (GAD-7) test was administered to older adults who applied to the geriatrics outpatient clinic at the time of application; individuals who did not meet the criteria were excluded from the study. Results The results of the study showed that the psychological well-being scale scores of individuals who strictly adhered to the Mediterranean diet (mean = 68.1) were significantly higher than those of individuals who did not adhere to the Mediterranean diet (mean = 59.5)(p < 0.05). Linear regression was used to evaluate older adults according to their MEDAS and YPIOA values. The test results revealed a significant positive correlation between the MEDAS score and the YPIOA (p < 0.05). However, no significant difference was found between the groups when the relationship between the MEDAS and SNAQ(65+) scores of older adults was evaluated (p > 0.05). Conclusion According to the results of the study, geriatric people who scored higher on the MEDAS also scored higher on the YPIOA. However, no significant relationship was found between SNAQ(65+) and MEDAS scores. These findings suggest that the quality of an individual's diet may affect not only their physical health, but also their psychological well-being. Therefore, treatment plans for the nutritional needs of older adults should take into consideration not only energy and nutrient intake, but also the individual's lifestyle, habits and psychological status.Article Citation - Scopus: 1Machine Learning and Scenario-Based Forecasting of Türkiye’s Renewable Energy Transition toward Net-Zero 2053(Elsevier, 2026) 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.