WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 1Citation - Scopus: 1Predictive Modeling and SHAP-Based Interpretability of Manganese and Iron Dissolution in Multi-Acid Leaching Systems Using Hybrid Machine Learning(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Top, Soner; Altiner, Mahmut; Kursunoglu, Sait; Kuzu, EmrahHydrometallurgical leaching processes contain complex and nonlinear parameter interactions that are difficult to capture with conventional empirical models. In this study, a multiple hybrid machine learning approach was developed to predict manganese (Mn) and iron (Fe) dissolution efficiency in leaching systems and performed using sulfuric acid (H2SO4), hydrochloric acid (HCl), and nitric acid (HNO3). A large-format dataset consisting of 204 independent leaching experiments was generated in which acid type, acid concentration (0.5-5 M), temperature (25-90 degrees C), solid/liquid ratio (100-200 g/L), leaching time (1-4 h), and eight different reducing agent types were systematically varied. XGBoost, LightGBM, CatBoost, and Random Forest algorithms were individually trained and subsequently combined with a Soft Voting Ensemble architecture. Hyperparameters were optimized using the RandomizedSearchCV method with 3-fold cross-validation. The XGBoost model achieved the highest prediction accuracy for Mn dissolution (R-2 = 0.8993, RMSE = 8.06%), while CatBoost demonstrated the best performance in Fe dissolution (R-2 = 0.8415, RMSE = 4.43%). SHAP analysis suggested that the dosage and type of reducing agents are the most influential predictive features for Mn dissolution, while acid molarity and temperature were identified as the dominant predictors for Fe leaching. Friedman test confirmed that performance differences among both Mn and Fe models were statistically significant (Mn: chi(2) = 32.76, p < 0.001; Fe: chi(2) = 25.96, p < 0.001). The developed models contribute significantly to hydrometallurgical process optimization by predicting the nonlinear effects of leaching parameters on metal dissolution with high accuracy. This study presents a comprehensive and interpretable machine learning framework supported by an extensive experimental dataset, a substantial portion of which has not been previously utilized or comparatively analyzed within a unified multi-acid framework, enabling systematic modeling of selective Mn-Fe dissolution across multiple acid systems and reducing agents.Article Novel Statistical Approaches for Survival Analysis of RNA-Sequencing Data(Bentham Science Publ Ltd, 2026) Cephe, Ahu; Karabulut, Erdem; Zararsız, Gözde Ertürk; Sezgin, Ahmet; Koçhan, NeclaIntroduction/Objective Accurate patient survival predictions are vital for effective cancer treatments. Precision medicine uses gene expression data to improve prognosis by considering genetic variability. Predicting survival in cancer patients using high-dimensional gene expression data, such as RNA-sequencing (RNA-seq), attracted much attention in recent years. However, the literature contains limited algorithms for survival modeling that account for the high dimensionality, heterogeneity, and correlated genes of RNA-seq data. This study aims to develop novel approaches for predicting survival and identifying biomarkers using RNA-seq data.Introduction/Objective Accurate patient survival predictions are vital for effective cancer treatments. Precision medicine uses gene expression data to improve prognosis by considering genetic variability. Predicting survival in cancer patients using high-dimensional gene expression data, such as RNA-sequencing (RNA-seq), attracted much attention in recent years. However, the literature contains limited algorithms for survival modeling that account for the high dimensionality, heterogeneity, and correlated genes of RNA-seq data. This study aims to develop novel approaches for predicting survival and identifying biomarkers using RNA-seq data.Methods Survival data of RNA-seq is first transformed into binary classification data using a stacking algorithm. Then, block-based priority-Lasso and IPF-Lasso algorithms are applied to the dataset, which includes two distinct types of variables. Additionally, sample weights obtained from the voom transformation are incorporated. Our approaches, named voomStackLasso, are tested on 12 real datasets from the TCGA database. We used Harrell's concordance index and the integrated Brier score to evaluate model performance, and the number of selected features to assess model sparsity.Results The results indicated that the voomStackLasso algorithms demonstrated comparable or superior performance compared to other existing survival algorithms. Furthermore, we have introduced an R package called MLSeqSurv, which allows for the utilization of both established survival algorithms from the literature and voomStackLasso algorithms for RNA-seq data.Conclusion This study introduces two new algorithms for the survival analysis of RNA-seq data. Additionally, this study has led to new research directions for applying both existing and newly developed classification algorithms to the survival analysis of RNA-seq data.Article Effects of Alkanolamines on Calcium Sulfoaluminate Belite Cement Hydration and Fresh Properties(Emerald Group Publishing Ltd, 2026) Uzal, Burak; Wilkinson, Angus P.; Qoku, Elsa; Nguyen, Tu-Nam; Kurtis, Kimberly E.Calcium sulfoaluminate belite cements have found widespread use in specialised applications but have not been applied broadly due to rapid setting times. Conventional set retarders such as citric and tartaric acids modify hydration kinetics, introducing ancillary effects that alkanolamines may avoid. This study examined the effects of three alkanolamines, triisopropanolamine (), triethanolamine () and diisopropanolamine (), on early hydration at 0.02% dosage; was also tested across a broader dosage range. Reaction kinetics and phase evolution were examined in pastes by calorimetry, thermogravimetric analysis () and in situ quantitative X-ray diffraction () for 72 h of hydration, along with setting time and amplitude-sweep rheometry. Calorimetry showed that and delayed and reduced the main cement hydration peak, while all alkanolamines delayed and enhanced the shoulder and secondary hydration peaks. In situ and suggested that alkanolamines decrease ettringite and alumina gel formation by 24 h and may lead to poorly crystallised ettringite that cannot be quantified by . Alkanolamine-containing samples demonstrated increased storage and loss moduli and extended linear viscoelastic ranges. A consistent grouping of similar behaviour emerged: control/TIPA versus TEA/DIPA, suggesting distinct mechanisms. Finally, among the alkanolamines, was the most effective, increasing the setting time by 18 min.Article A Machine-Learning-Based Multi-Hazard GIS-AHP Framework for Wind Turbine Siting under Earthquake–Landslide Coupling(IOP Publishing Ltd, 2026) Dinçer, Ali Ersin; Demir, Abdullah; Öztürk, Şevki; Kalpakcı, Volkan; Dilmen, ÖmerThis study presents a machine-learning-based multi-hazard geographical information system (GIS)-analytical hierarchy process (AHP) framework for wind turbine siting that explicitly accounts for the coupled effects of earthquake and landslide hazards. The primary innovation lies in the development of a conditional weighting algorithm that integrates machine-learning-derived hazard assessments with structural engineering logic. Landslide susceptibility is first modeled using a random forest classifier trained on a comprehensive inventory of historical landslide data and 12 geo-environmental conditioning factors, producing a high-resolution susceptibility map with excellent predictive performance (AUC = 0.86). Feature importance analysis indicates that slope, hydrological indices, and geological conditions are the dominant controls on landslide occurrence. This data-driven map is then integrated with earthquake hazard zones and additional environmental and technical constraints within a GIS-AHP framework to generate a comprehensive wind turbine suitability assessment. Results show that explicitly accounting for earthquake-landslide coupling leads to a nearly 20% reduction in high and very high suitability areas, accompanied by an expansion of low and moderate suitability zones, highlighting the limitations of single-hazard planning approaches. The main contribution of this study lies in advancing renewable energy planning through the explicit integration of interdependent natural hazards, demonstrating how earthquake-resistant foundation strategies can simultaneously mitigate landslide risks.Article Citation - WoS: 1Citation - Scopus: 1The Discouraged Worker Effect during the Covid-19 Pandemic in Türkiye(Cambridge Univ Press, 2026) Demirtaş, Burak Kağan; Güney, GülThe Covid-19 pandemic has negatively affected labour markets, among other aspects of life. This study examines the impact of the discouraged worker effect during the pandemic, focusing on the Turkish labour market from 2018 to 2021. Although few studies exist on this topic, they rely on labour force participation rates, whereas our dataset includes direct questions and data specifically related to the discouraged worker effect, allowing for a microeconomic analysis. Probit regression results show that the discouraged worker effect was stronger during the pandemic, with job seekers being 1.6% more likely to become discouraged than before. Higher education levels generally reduce this likelihood, both before and during the pandemic. While age negatively correlates with discouragement, this effect diminishes with increasing age. Single women were more adversely affected than single men and married women than married men. Higher unemployment rates increase discouragement, as expected, while an increase in the unemployment rate has a greater effect on individuals during the pandemic period. Findings suggest that the pandemic had a disproportionate impact on certain individuals, particularly with respect to education level and gender, while Türkiye's societal structure may help explain the observed gender-based differences.Article Citation - WoS: 3Citation - Scopus: 4Rare Earth Elements in the Global Economy: Usage, Recovery, and the Quest for Supply Security – A Review(Springer Heidelberg, 2026) Top, Soner; Ayten, Asim Mustafa; Altiner, Mahmut; Demir, Idris; Kursunoglu, SaitOften described as the vitamins of modern industry, rare earth elements (REEs) are indispensable for the deployment of low-carbon and clean energy technologies. However, ensuring a secure and sustainable REE supply remains a major challenge due to the strong interdependence between application-driven demand, extraction and processing technologies, and the geopolitical concentration of resources. This review adopts an integrated analytical framework in which these three dimensions are treated as interconnected components shaping the resilience of global REE supply chains. First, the major application sectors of REEs are examined to clarify how emerging energy and advanced manufacturing technologies drive demand for specific elements and amplify their strategic importance. Second, extraction and processing technologies are reviewed in relation to both primary and secondary resources, highlighting how technological maturity, process selection, and material characteristics constrain or enable supply expansion. Finally, geopolitical and strategic aspects of the REE supply chain are analyzed to demonstrate how resource concentration, policy instruments, and international dependencies directly influence technological deployment and industrial competitiveness. By explicitly linking application-driven demand, technological pathways for extraction and processing, and geopolitical supply structures within a unified framework, this review provides a coherent understanding of the systemic challenges facing the REE sector and identifies key leverage points for improving the robustness and sustainability of REE supply chains in the context of the global clean energy transition.Article Optimizing Nanoclay-Enhanced Membranes for Oil Rejection Using Response Surface Methodology(Wiley, 2026) Gul, Ayse; Baris, Mesut; Boyraz, Pınar; Senol-Arslan, Dilek; Alibaz, Name NurThe efficient separation of waste oil from contaminated water is critical due to its challenges in environmental and industrial applications. This study investigated the production and optimization of polysulphone (PSF) membranes using two different types of clay (nanomer clay/CN and commercial nanoclay/NC). Response Surface Methodology (RSM) was applied to optimize the basic production parameters and nanoclay concentrations systematically to maximize oil rejection and permeability flow. The experimental results showed that NC and CN significantly increased the hydrophilicity, permeability, and fouling resistance of the membrane compared to pure PSF membranes. The contact angle significantly decreased from 64.34 degrees (pristine PSF) to 36.23 degrees (2% NC), indicating highly improved hydrophilicity. Consequently, the pure water flux increased from 177.2 L/m2 h to a maximum of 248.6 L/m2 h (1% NC). Furthermore, the modified membranes exhibited outstanding anti-fouling properties; the flux recovery ratio (FRR) improved from 88.09% to 96.20% (1% CN), while the decline ratio (DR) drastically dropped from 60.89% to 32.14%. The optimized condition for maximum removal efficiency using a modified quadratic model revealed that 2572 mg/L oil can be treated with a PSF membrane containing 2.0% CN to remove 98.271% of the oil. The model also suggests superiority of CN over NC with desirability factors of 0.978 and 0.900, respectively, while both demonstrated high efficiency. This theoretically modeled experimental comparative study highlights the importance of PSF membrane technology for efficient and sustainable oil-water separation and demonstrates the promising potential of nanoclay modifications.Article Identification of Potential Dual HDAC6 and HSP90 Inhibitors for the Treatment of Cancer Using Molecular Docking, Molecular Dynamics and MM/PBSA Studies: A Comprehensive In Silico Study(Bentham Science Publ Ltd, 2026) Yucel, Muhsin Samet; Akcok, IsmailBackground Histone deacetylase 6 (HDAC6) and heat shock protein 90 (Hsp90) are crucial therapeutic targets in cancer research with their interconnected roles in regulating protein homeostasis and cellular processes. The interaction of these proteins within the cytosolic complex plays a critical role in regulating cancer cell survival and progression. Notably, current studies highlight that the simultaneous inhibition of HDAC6 and Hsp90 can produce synergistic effects and offer a promising therapeutic potential for combating malignant cancers.Objective The objective of this study was to explore potential compounds that can inhibit both HDAC6 and Hsp90 proteins.Methods In this study, a number of in-silico computational techniques were employed. A total of 791 molecules, sharing at least 30% similarity with previously identified four HDAC inhibitors, were obtained from the ZINC15 database and subjected to docking on HDAC6 and Hsp90 proteins. The top eight ligands demonstrating the best binding scores against both targets, with panobinostat and ganetespib serving as reference compounds for HDAC6 and Hsp90, respectively, were selected for further analysis. Subsequently, ADME prediction and molecular dynamics simulations were conducted on the selected ligands.Results A detailed molecular docking, molecular dynamics simulations and ADME studies have revealed that ZINC27653366 exhibited the highest inhibitory potential against both Hsp90 and HDAC6 target proteins, making it the most promising inhibitor.Conclusion In conclusion, although additional in vitro and in vivo studies are required for the validation, in silico evaluation of ZINC27653366 may position it as a promising candidate for the treatment of different types of cancers.Editorial Editors’ Introduction: Spring 2026(Cambridge Univ Press, 2026) Kolluoğlu, Biray; Dinçer, Evren M.; Yükseker, DenizArticle Minimization of Thermal Stresses in Instrumented Cutting Tools with Embedded Thin Film Thermocouples(Korean Society of Mechanical Engineers, 2026-04) Kesriklioglu, Sinan; Sivesoglu, AbdurrahmanThis study investigates the optimization of multilayer coatings on cutting tools to minimize thermal stress and temperature differences between the tool-chip interface and embedded thermocouples. The novelty of this study lies in directly linking coating architecture to temperature measurement accuracy, revealing that coatings not only affect heat dissipation and stress development but may also distort the apparent temperature recorded by embedded sensors. The types and thickness ranges of thin film layers in instrumented cutting tools were determined, and multi-physics finite element simulations were then used to evaluate coating configurations under thermal loading, assessing both stress distribution and temperature variance in the multilayer coating system. The Taguchi method, coupled with desirability analysis, identified optimal coating parameters that simultaneously minimize thermal stresses and temperature disparities, which are critical for accurate temperature measurements and extending the lifespan of cutting inserts. This framework enables a controllable trade-off between mechanical reliability and thermal measurement fidelity. The results reveal significant interactions among coating configurations (settings) and between thermal and mechanical properties of the materials used, demonstrating that careful selection of layer materials and thicknesses optimizes stress and temperature responses yielding thermal stress of 1628 MPa (second lowest and only 0.4 % higher than the minimum) and temperature difference of 12.1 degrees C (third lowest and 55 % lower than average). These findings underscore the potential of precise coating design to enhance tool performance and longevity in high temperature machining applications.