WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article AI-Driven Drug Repositioning: A Diffusion Model Approach on Knowledge Graphs(Elsevier, 2026) Erkantarci, Betul; Şen, Tarık Üveys; Bakal, GokhanDrug repositioning - discovering new therapeutic applications for existing drugs - offers a promising pathway to accelerate cancer treatment development. This study proposes a diffusion model-driven framework that leverages biomedical knowledge graphs and graph-based learning to enhance drug repositioning predictions. The framework integrates data from the Semantic MEDLINE Database (SemMedDB), the Unified Medical Language System (UMLS), and the Repurposing Drugs Database (RepoDB) to construct a comprehensive therapeutic knowledge graph. Drug embeddings are generated using a one-layer Relational Graph Convolutional Network (R-GCN) incorporating semantic type-guided structural perturbations. These embeddings are refined through a flow-matching algorithm to denoise and reconstruct biologically meaningful representations. To evaluate the model's effectiveness, we apply a consensus strategy using Cosine Similarity, Euclidean Distance, and Manhattan Distance as proximity metrics. The model successfully identified, on average, 74 candidate drugs for repositioning in the context of leukemia. Qualitative analysis using t-distributed stochastic neighbor embedding (t-SNE) revealed enhanced clustering of pharmacologically relevant drugs in the denoised embedding space. Trastuzumab, in particular, emerged as a strong repositioning candidate for leukemia, supported by 156 co-mentions in PubMed. These findings demonstrate that the proposed framework improves embedding robustness and semantic fidelity, offering a powerful artificial intelligence (AI)-driven approach for precision oncology. Integrating structural noise modeling with diffusion-based denoising advances the discovery of novel drug-disease associations and holds potential for translational research and clinical hypothesis generation in drug repurposing.Article Densification-Induced Chemical Reorganization and Mechanical Enhancement in Amorphous Si2BC3N(Elsevier, 2026-02) Durandurdu, MuratThe atomistic mechanisms that govern the mechanical performance of amorphous silicon-boron carbonitride (SiBCN) ceramics remain insufficiently understood, particularly regarding the role of density. Here, we employ ab initio molecular dynamics simulations to elucidate the structural evolution and mechanical response of low-density (LDA, 2.20 g/cm3) and high-density (HDA, 2.53 g/cm3) amorphous Si2BC3N prepared via melt-quench. The HDA phase exhibits markedly higher atomic packing and network connectivity, accompanied by a nontrivial chemical reorganization. Densification significantly enhances heteronuclear bonding-especially Si-C coordination-while suppressing C-C and Si-Si homopolar bonds. These changes yield substantial mechanical strengthening: the HDA phase exhibits a 48% increase in bulk modulus (130 GPa vs. 88 GPa), along with elevated Young's (266 GPa) and shear (112 GPa) moduli. Our findings reveal a clear density-structure-property relationship in amorphous SiBCN, demonstrating that densification suppresses weak self-bonded motifs and promotes a robust, interconnected atomic network. This insight provides a pathway for designing high-performance amorphous SiBCN ceramics for extreme-environment applications.Article Supervised Learning-Driven Dead Band Control of Occupant Thermostats for Energy-Efficient Residential HVAC(Elsevier, 2026-03) Savasci, Alper; Ceylan, Oguzhan; Paudyal, SumitHeating, ventilation, and air conditioning (HVAC) systems play a crucial role in demand-side management (DSM) by shaping residential electricity consumption and enabling flexible, grid-responsive operation. Thermostats in HVAC systems regulate indoor temperature as part of a closed-loop control framework, typically incorporating a fixed temperature dead band-a range around the setpoint where no action is taken-to reduce energy use and prevent frequent cycling of the HVAC system. Although essential for efficiency and equipment longevity, fixed dead bands limit adaptability, as dynamically adjusting them under varying environmental conditions remains challenging for occupants. To address this limitation, we propose a machine learning (ML)-based dead band tuning framework that optimally adjusts thermostat settings in real time. The method integrates conventional optimization with data-driven modeling: a mixed-integer linear programming (MILP) model is first used to gen erate optimal dead band values under measured outdoor temperature records (diverse seasonal weather scenarios) which are then employed to train the ML-based predictor to learn a real-time discrete dead band decision policy that approximates the MILP-optimal hysteresis-aware decisions. Among the evaluated models, Random Forest demonstrates superior predictive performance, achieving a mean squared error (MSE) of 0.0399 and a coefficient of determination (R2) of 95.75 %.Article Tuning Properties of Amorphous Boron Via Hydrogenation: An Ab Initio Study(Elsevier, 2026-01) Durandurdu, MuratAb initio simulations are employed to investigate the structural, mechanical, and electronic properties of hydrogenated amorphous boron (a-B:H) across a range of hydrogen concentrations (approximate to 6-21 at.%). The results indicate that pentagonal-like boron clusters constitute the primary structural motifs. The bonding environment consists of both B-H terminal bonds and B-H-B bridging bonds, with the fraction of bridging bonds ranging from 10 % to 16 %. Increasing the hydrogen content leads to a reduction in density and bulk modulus, accompanied by a systematic widening of the electronic band gap. These results demonstrate that hydrogen incorporation profoundly modifies the atomic structure, softens the network, and enhances the semiconducting character of a-B:H, highlighting the tunability of properties in boron-based amorphous materials.Article Development and Characterization of Starch-Fatty Acid Complexes Produced with Buckwheat Starch and Capric/Stearic Acid Using Different Reaction Conditions(Elsevier, 2025-12) Oskaybas-Emlek, Betul; Ozbey, Ayse; Aydemir, Levent Yurdaer; Kahraman, KevserThe aim of present study was to investigate the impact of reaction parameters on the complex formation between buckwheat starch and capric acid (B-Capric) or stearic acid (B-Stearic). The most effective parameters on complex formation indicator (Complex index (CI) value) were found as reaction temperature (60-90 degrees C) and pH (5-8). Additionally, the effect of these parameters on physicochemical, pasting, and in-vitro digestibility properties of complex samples were evaluated. XRD and FTIR was also used in characterize the complex samples. In general, increasing pH increased the CI values of B-Stearic samples while decreasing those of B-Capric samples. Syneresis of buckwheat starch increased after complexation while paste clarity and swelling power diminished. The pasting properties of native starch significantly changed after complex formation. The FTIR results showed that starch structure changed with complex formation. XRD revealed that buckwheat starch, having an A-type pattern, converted to V-type pattern after complexation. Complex formation of buckwheat starch with capric and stearic acid significantly increased the RS content of buckwheat starch (19.01 %) by up to 36.25 % and 30.60 %, respectively. These results highlight the possibility of using buckwheat starch-capric acid/stearic acid complexes in food formulation to enhance the RS content.Correction Citation - Scopus: 1Understanding the Effects of Artificial Intelligence on Energy Transition: the Moderating Role of Paris Agreement(Elsevier, 2025-02) Chishti, Muhammad Zubair; Xia, Xiqiang; Dogan, EyupArticle Citation - WoS: 7Citation - Scopus: 7Unveiling the Multifaceted Properties of a 3D Covalent-Organic Framework: Pressure-Induced Phase Transition, Negative Linear Compressibility and Auxeticity(Elsevier, 2023-08) Erkartal, MustafaHigh-pressure behavior and mechanical properties of a three-dimensional covalent-organic framework (NPN-1) were investigated by using different types of first principles molecular simulations. An irreversible pressureinduced first-order isosymmetric phase transition was predicted at 0.14 GPa. The subunit of NPN-1 retains its rigidity under pressure thanks to the strong covalent bonds. However, compression leads to significant tilting of the nitrophenyl groups. The mechanical properties of frameworks are highly anisotropic. Remarkably, both phases exhibit not only negative linear compressibility along the c-axis but also negative Poisson's ratio in certain directions. Detailed structural analysis revealed that the origin of the phase transition and anomalous mechanical properties of both phases are the wine-rack motif and strut-hinge mechanism. To the best of our knowledge, this study is the first report of such behavior in COFs, opening up new avenues for the exploration of COFs as materials for many promising applications.Article Citation - WoS: 54Citation - Scopus: 58Towards Green Recovery: Can Banks Achieve Financial Sustainability Through Income Diversification in ASEAN Countries(Elsevier, 2022-12) Najam, Hina; Abbas, Jawad; Alvarez-Otero, Susana; Dogan, Eyup; Sial, Muhammad SafdarEstablishing sustainable and balanced development for green financing is critical for improving financial sustainability and banks' capability. Banks struggle to achieve economic sustainability in the current highly competitive business environment. This research examines the impact of income diversification on financial sustainability proxy by return on assets (ROA) by applying the quantile regression technique to the data from banks of ASEAN countries over the period 2008-2019. In addition, liquidity risk, bank size, interest and non-interest incomes, and market capitalization are studied as control variables. The empirical findings indicate that income diversification positively impacts return on assets at all countries' lower, middle, and upper quantiles, even though sizes can differ across countries and quantiles. Moreover, market capitalization, non-interest income, and banks' size favorably impact banks' performance. In contrast, liquidity risk and interest incomes are negatively linked to the performance of banks for all countries at each quantile. These results have significant strategic implications for managers, regulators, and policymakers who share a common interest in boosting financial sustainability and performance and significantly shaping green recovery. (c) 2022 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.Article Citation - WoS: 358Citation - Scopus: 392The Use of Ecological Footprint in Estimating the Environmental Kuznets Curve Hypothesis for BRICST by Considering Cross-Section Dependence and Heterogeneity(Elsevier, 2020-06) Dogan, Eyup; Ulucak, Recep; Kocak, Emrah; Isik, CemA vast body of literature estimates the impact of economic growth on environmental degradation in the framework of EKC model. Typical empirical studies proxy environmental degradation with CO2 emissions; however, this indicator does not consider the complex nature of environmental degradation. To fulfill this omission, ecological footprint that tracks the use of multiple categories of productive surface areas is used as proxy for the environment. Moreover, studies that do not consider issues of heterogeneity and cross-sectional dependence may not produce reliable outcomes. Hence, the present study re-investigates the validity of the EKC hypothesis for BRICST (Brazil, Russia, India, China, South Africa, Turkey) by using ecological footprint and considering the mentioned issues in the estimation process. Based on the annual data covering the period of 1980-2014, excluding Russia due to data unavailability, empirical results show that the EKC hypothesis is not valid, and energy intensity and energy structure are important determinants of environmental degradation. In line with the empirical outputs, possible policy suggestions are discussed in the present study. (C) 2020 Elsevier B.V. All rights reserved.Conference Object Citation - WoS: 3The Revealing of Periods in Lempel-Ziv Complexity of EEG Signal(Elsevier, 2018-10) Mekler, A.; Borisenok, S. V.