WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Ten Questions Concerning Circularity in the Built Environment(Pergamon-Elsevier Science Ltd, 2026) Kayacetin, N. Cihan; Aslanoglu, Rengin; Piccardo, Chiara; Afacan, Yasemin; Masera, Gabriele; Li, Qiuxian; Van Hoof, JoostThe rapid urbanisation of our societies calls for an urban renewal movement, including developing new areas to accommodate housing facilities and services and regenerating existing urban areas. Yet, urban renewal projects pose trade-offs impacting both environmental and socio-economic aspects. The renovation and new construction of buildings can escalate the use of energy and material resources as well as increasing greenhouse gas emissions. The European Union plays a leading role in promoting the transition towards sustainable and inclusive cities, whereas other regions such as North America, Australia and Asia follow suit via Circular Economy Action Plans or Frameworks, highlighting the need to enhance resource efficiency in buildings through the use of durable and circular materials. Current research on resource efficiency in buildings follows the Circular Economy concept, which aims to reduce the use of raw materials and the waste of existing materials while retaining their value for as long as possible. However, the role of the circular economy in sustainable transition and the adoption of its principles in urban contexts remain unclear while its practical implementation still faces significant challenges, including the lack of analytical instruments and assessment methods as well as co-creative approaches. This 'Ten Questions contribution' provides an overview of the pressing issues concerning circularity in the built environment, the state-of-the-art and best practices, challenges and benefits, policies and regulations, as well as numerous strategies applied on the building and neighbourhood level, assessment methodologies and future trends.Book Part Mediated Narratives of Syrian Refugees: Mapping Victim-Threat Correlations in Turkish Newspapers(Routledge, 2025) Cox, Ayca TuncTurkey has become the first and main transition hub for Syrian refugees. Furthermore, Turkey is spatially as well as culturally simultaneously referred to as European and Asian or Middle Eastern depending the point of view. Therefore, the representation of refugees in the Turkish press proves significant for the knowledge produced about refugees. Accordingly, this chapter strives to investigate the coverage of Syrian refugees in newspapers, which constitutes only one aspect of the overall reception of the issue in Turkey, and therefore does not claim to be exhaustive. Yet, because daily newspapers are still among the most important media sectors in Turkey, they constitute a special case of knowledge production worth investigating.Article Univariate Deep Learning Models for Short-Term Electricity Load Forecasting from Renewables(Ankara University, Faculty of Science, 2025) Kabran, Fatma Basoglu; Unlu, Kamil DemirberkRenewable energy offers a cost-effective, carbon-free solution for energy needs, while protecting the environment. Accurate forecasting of electricity generation from renewable sources is crucial for the efficiency of modern power grids. This study employs a univariate deep learning approach to predict daily renewable energy generation, evaluating Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) as candidate models. Five performance metrics-mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error and the coefficient of determination-are employed to assess the forecasting power of the algorithms. The empirical results show that CNN outperforms other models, achieving an R2 of almost 94%. This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.Article Ensemble Machine Learning Algorithms for Thermal Comfort Prediction in HVAC Systems of Smart Buildings(Golden Light Publishing, 2025) Erdem, Merve Kuru; Gokalp, Osman; Calis, GulbenPredicting the thermal comfort of building occupants is of paramount importance in the operation of smart buildings, providing a data-driven approach to control Heating, Ventilation, and Air Conditioning (HVAC) systems for managing occupant thermal comfort and energy use, which aligns with modern sustainability and efficiency goals. Recently, ensemble machine learning (ML)-based thermal comfort prediction models have been proposed to provide more accurate estimation of thermal comfort; however, these efforts often lack a systematic and comprehensive evaluation across a wide range of ML models within a single study. To address this gap, this study presents a systematic comparative analysis of four ensemble ML frameworks (bagging, boosting, stacking, and voting) with six basic ML algorithms (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Multilayer Perceptron, and Multinomial Na & iuml;ve Bayes) and six advanced ensemble ML algorithms (Random Forest, Rotation Forest, Extra Trees, Gradient Boosting Classifier, Histogram Gradient Boosting Classifier, and Extreme Gradient Boosting). The analysis is conducted using the widely recognized ASHRAE Global Thermal Comfort Database II, providing both 3-point and 7-point Thermal Sensation Vote (TSV) predictions. Accuracy, precision, recall and F1 metrics are used for evaluation and 10-fold cross validation is applied for further comparison. The results demonstrate the Histogram Gradient Boosting (HGB) algorithm achieved the highest F1 score (0.638) for 7-point TSV prediction whereas the Random Forest (RF) algorithm provided the highest F1 score (0.549) for 7-point TSV prediction. In practice, these findings suggest that integrating RF and HGB models into Building Management Systems or IoT-based HVAC platforms can support real-time adaptive control, helping practitioners to reduce energy use while maintaining occupant comfort.Article Optimizing Housing Floor Layout for Cool Terraces: A Comparative Analysis Using Constrained Problem Formulation(Yildiz Technical University, Faculty of Architecture, 2025) Durmusoglu, Betul; Ekici, BerkAs urban densification increases, thermal stress in cities becomes a problem. The integration of climate-sensitive strategies into housing design has become a necessity. As a strategy, design of terraces, as thermally configured outdoor spaces can reduce solar radiation gain. Parametric modeling, one of the computational approaches, provides significant contributions to optimizing the integration of environmental analysis into the terrace design. Although some related studies have focused on optimizing urban mass organizations for thermal comfort and solar performance, none of them have addressed spatial organization of terraces in residential buildings. This study presents a computational housing model to investigate terrace allocation with respect to solar gain, including circulation and residential units. The interstitial spaces are considered "cool terraces", and the objective is to minimize the solar radiation on terraces by optimizing the location and size of the residential units using a genetic algorithm via the Galapagos plug-in, radial basis function optimization (RBFOpt), and covariance matrix adaptation with evolution strategy (CMA-ES) using Opossum plug-in. To provide feasible spatial organization, constraints are determined using the near feasibility threshold with the Optimus plug-in. Results showed that only CMA-ES discovered feasible spatial organization while improving the solar performance of cool terraces. When compared to the benchmark design scenarios, the optimized alternative performed 11-26% improvement in solar radiation minimization. The study discusses the challenges in identifying well-performing cool terrace solutions, the complexity of the problem, and the applicability of optimization algorithms.Article Relationships Between Light Exposure and Aspects of Cognitive Function in Everyday Life(Springer Nature, 2025) Didikoglu, Altug; Woelders, Tom; Bickerstaff, Lucien; Mohammadian, Navid; Johnson, Sheena; van Tongeren, Martie; Lucas, Robert J.Light exposure can modulate cognitive function, yet its effects outside of controlled laboratory settings remain insufficiently explored. To examine the relationship between real-world light exposure and cognitive performance, we assessed personal light exposure and measured subjective sleepiness, vigilance, working memory, and visual search performance over 7 days of daily life, in a convenience sample of UK adults (n = 58) without significant circadian challenge (shiftwork or jet-lag). A subset of participants (n = 41) attended an in-lab session comprising a battery of pupillometric and psychophysical tests aimed to quantify melanopsin-driven visual responses. We find significant associations between recent light exposure and subjective sleepiness. Recent light exposure was also associated with reaction times for both psychomotor vigilance and working memory tasks. In addition, higher daytime light exposure and an exposure pattern with reduced fragmentation were linked to improved cognitive performance across visual search, psychomotor vigilance, and working memory tasks. Higher daytime light exposure and earlier estimated bedtimes were associated with stronger relationships between recent light exposure and subjective sleepiness. These results provide real world support for the notion that intra- and inter-individual differences in light exposure meaningfully influence aspects of cognition, with beneficial effects of short-term bright light and of habitual light exposure patterns characterized by brighter daytimes, earlier rest phase, and greater intra- and inter-daily stability.Article Interaction of Hazelnut-Derived Polyphenols With Biodegradable Film Matrix: Structural, Barrier, and Functional Properties(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Hızır-Kadı, I.; Demircan, E.; Özçelik, B.The study presents a sustainable approach to valorizing hazelnut processing by-products, specifically skins and shells, through their conversion into bioactive polyphenol-rich extracts using pressurized hot water extraction (PHWE), an environmentally friendly green technology. PHWE yielded extracts with total phenolic contents of 25.4 mg GAE/g dw (shell) and 83.7 mg GAE/g dw (skin), which were incorporated into biodegradable poly(vinyl alcohol)/carboxymethyl cellulose (PVA/CMC) films at concentrations of 1–3% (w/v). The resulting composites were comprehensively characterized in terms of structural, mechanical, thermal, and barrier properties. FTIR, DSC, and XRD analyses demonstrated strong hydrogen bonding, increased thermal stability, and reduced crystallinity due to polyphenol–polymer interactions. Phenolic incorporation enhanced UV-blocking capability, increased antioxidant activity by up to five-fold, and reduced oxygen permeability from 0.048 to 0.015 (cm3·mm·m−2·day−1·atm−1) (69% reduction, p < 0.05), compared to neat PVA while maintaining desirable transparency (>70%). Optimal formulations (HSkE-II) exhibited a 39% increase in elongation at break and improved flexibility without compromising film integrity. Application tests using fresh-cut apples, watermelon, and chicken revealed significant reductions in microbial growth (up to ~1.2 log CFU/g), lipid oxidation, and weight loss during storage, confirming the films’ potential for active food packaging. This work highlights an efficient valorization strategy for nut industry by-products and demonstrates their functional integration into sustainable biodegradable packaging systems. © 2025 by the authors.Article A Study of the Environmental Challenges En Marche Towards Net-Zero: Case Study of Turkish Steel Industry(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Özdamar, A.B.; Kaya, M.; Bektas, A.; Bhattacharyya, S.; Şahindoğan, M.; Birat, Jean-Pierre; Dutta, A.The Turkish steel industry aims to reduce its sectoral carbon dioxide (CO<inf>2</inf>) emissions by 55% by 2030, in line with Türkiye’s Paris Agreement commitments and the European Green Deal (EGD), and consistent with the ambition of the European Union’s economy-wide ‘Fit for 55’ emissions-reduction target. Türkiye faces significant challenges in achieving net-zero greenhouse gas (GHG) emissions, particularly as a developing country confronting the impacts of climate change and in the market situation, such as the effects of the ongoing Russia-Ukraine conflict, limited access to affordable raw materials, and rising operational costs. This study serves as a guideline for the Turkish steel sector’s roadmap towards modernization and eventual compliance with net-zero targets. The consideration and integration of new technologies planned for the Turkish steel industry, in both electric arc furnace (EAF) and blast furnace-basic oxygen furnace (BF-BOF) facilities, have been outlined in conjunction with green hydrogen and with Carbon Capture and Storage (CCS) technologies. Four different scenarios were analysed to understand the reduction in CO<inf>2</inf> emissions: (1) In a Business-As-Usual (BAU) scenario without any reduction, (2) 39.9% CO<inf>2</inf> emission reduction with the Moderate scenario, (3) 59.6% reduction with the Advanced scenario, and (4) 82.9% reduction in CO<inf>2</inf> emissions from the Turkish steel sector with the Net-Zero scenario. To quantify the uncertainty in these long-term projections, a Monte Carlo simulation was conducted, generating probabilistic confidence intervals that reinforce the robustness and credibility of the net-zero pathway. The official roadmap for the sector is not available as of today; however, an in-depth discussion with a policy innovation leading to it is the objective of this study. © 2026 by the authors.Article Dissecting the Metabolic Landscape of Breast Cancer Subtypes via Elastic Net Modeling and Examining Its Immune Correlates(Walter de Gruyter GmbH, 2026) Kus, M.E.; Ekiz, H.A.Objectives: Breast cancer is a heterogeneous disease, and the estrogen receptor (ER) status is a key factor in disease classification and treatment planning. While metabolomic profiling has revealed subtype-specific differences, cross-study comparisons have been limited, posing challenges for data extrapolation. This study aims to investigate metabolites that differentiate ER-positive and ER-negative tumors via integrative analyses of multi-omics data. Methods: We jointly analyzed two untargeted metabolomics datasets via elastic net modeling using consistent analysis pipelines tuned for low sample sizes, namely multiple bootstrapping and stability selection. Significant metabolite predictors from two studies were cross-examined to reveal distinctions and commonalities. We also performed differential gene expression analysis using RNA sequencing data from matching samples to link metabolic patterns with transcriptomic signatures and intratumoral immune cell signatures. Results: This study identified unique metabolite signatures in distinct datasets and a limited overlap of discriminating metabolites that can be broadly generalizable for subtyping. Nevertheless, several glycolysis and fatty acid metabolism intermediates exhibited variation depending on the tumor ER status. Consistently, genes related to fatty acid metabolism and glycolysis were enriched in ER-positive and ER-negative tumors respectively. Furthermore, we used multiple immune cell deconvolution algorithms to correlate various immune cell types with the metabolite levels within the tumor microenvironment. Conclusions: Together, these findings highlight the metabolic and immunological diversity of breast cancer and establish a reproducible machine-learning framework for integrating multi-omics data to interrogate tumor complexity. © 2025 the author(s), published by De Gruyter, Berlin/Boston.Article Completeness Relation in Renormalized Quantum Systems(Frontiers Media SA, 2025) Erman, F.; Turgut, O.T.In this work, we show that the completeness relation for the eigenvectors, which is an essential assumption of quantum mechanics, remains true if the Hamiltonian, having a discrete spectrum, is modified by a delta potential (to be made precise by a renormalization scheme) supported at a point in two- and three-dimensional compact manifolds or Euclidean spaces. The formulation can be easily extended to an (Formula presented.) center case and the case where delta interaction is supported on curves in the plane or space. We finally give an interesting application for the sudden perturbation of the support of the delta potential. © © 2025 Erman and Turgut.