Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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 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 A Systematic Review of Symbolic Aggregate Approximation (SAX)(Ankara University Faculty of Science, 2026) Nalici, Mehmet Eren; Söylemez, İsmet; Ünlü, RamazanTime series data can be analyzed through various techniques to tackle classification or regression tasks. Symbolic Aggregate Approximation (SAX) is one such technique used for time series data reduction that converts the data into a symbolic representation, enabling more efficient storage, retrieval, and analysis by reducing the dimensionality while preserving the essential patterns within the time series. In this paper, we provide a systematic literature review of SAX by examining relevant literature from 2007 to 2025. The review includes 321 articles sourced from the Web of Science (WOS) database. However, the 85 most cited and recently published studies are summarized. Utilizing collaboration network analysis, the study identifies the nations, affiliations, and authors involved in SAX research, as well as their co-authors and commonalities. Additionally, an analysis is conducted to explore the potential relationship between the articles and the United Nations' Sustainable Development Goals. These findings provide insights into the current landscape of SAX research and offer potential avenues for future exploration. By pinpointing research gaps, scholars can use this review to anticipate forthcoming research trajectories.Article Citation - Scopus: 1Preparation of Nano Magnesium Hydroxide as a Potential Flame Retardant: Effects of Alcohol Type and Drying Method(Institute of Physics, 2025-12-01) Erdem, İlker; Yıldız, UğurArticle Feasibility Analysis of Granitic Rocks for Use in the Dimension Stone Industry(Univ Zagreb, FAC Mining, Geology & Petroleum Engineering, 2026) Koken, EkinThis study presents two objective evaluation tools for assessing the feasibility of granitic rocks in dimension stone applications. The developed methods integrate fundamental physical and mechanical properties, including dry density (rho d), effective porosity (ne), P-wave velocity (Vp), uniaxial compressive strength (UCS), and Böhme abrasion value (BAV). Feasibility analyses based on the conditional formatting (CF) and ranking method RM reveal that the adopted input parameters are essential for determining the suitability of granitic rocks as dimension stones. The strong relationship between the CF and RM results highlights their consistency and broad applicability. Both methods exhibit good agreement with the recommendations of the American standard for granitic rocks. Consequently, the suggested methods provide practical guidance for selecting suitable rock exposures in field studies, also offering a time- and energy-efficient decision-making framework for the dimension stone industry. To comprehensively evaluate the strengths and limitations of the proposed approaches, it is recommended that these tools be applied to a wider range of dimension stone types and geological settings.Article Optimization of Precision Machine Part Manufacturing by Integration of Grey-Taguchi Method with Principal Component Analysis(Yildiz Technical University, 2026) Kapan Ulusoy, Selda; Şenyiğit, Ercan; Erol, Kübra; Ulusoy, Selda KapanDetermining and optimizing the process parameters impacting the outputs at each production stage is necessary to reduce production costs. The Taguchi Method (TM) and the Grey Relational Analysis (GRA) are commonly utilized two techniques for process parameter optimization. In precision machine part manufacturing, Computer Numerical Control (CNC) production is the most critical process. In this study, the objective is to optimize CNC manufacturing parameters using TM, GRA and Principal Component Analysis (PCA) in metal sector. Process parameters like operator experience level (in years), CNC machine brand, CNC machine age, and CNC machine size were determined and optimized based on their degree of impact on the outputs. The experiments were carried out using a four-factor, four-level Taguchi orthogonal array (L16), and Analysis of Variance (ANOVA) was conducted aiming to determine the effects of these process parameters on production time, dimension conformity, and surface roughness performance factors. Selection of these input parameters and performance factors in the study is to provide a solution to a problem in the company from which the data are obtained with scientific methods and to contribute to the literature. Utilizing TM, the optimal values of process parameters are determined as ten years for operator experience, as Mazak for CNC machine brand, as two years for machine age, and as 500x550x550 for machine size. Utilizing the combination of GRA and PCA optimal parameter values are determined as ten years for operator experience, as Yuntes for CNC machine brand, as two years for machine age, and as 700x450x500 for machine size. A sensitivity analysis was performed using 21 different weight sets for performance factors (production time, dimension conformity, and surface roughness). Compared to the initial CNC production process parameters, 45%, 95%, and 504% improvements were obtained in production time, dimension conformity, and surface roughness process parameters. Companies, especially operating in the metal sector, can benefit from managerial practices by considering the ranking of parameters affecting CNC production according to the results obtained from this study.Article Process Optimization of Buckwheat Starch Myristic Acid Complex Film(John Wiley and Sons Inc, 2026-02) Koca, E.; Oskaybaş-Emlek, B.; Kahraman, K.; Özbey, A.; Aydemir, L.Y.; Oskaybas Emlek, BetulIn this study, it was aimed to develop an edible film from an amylose-lipid complex with better mechanical properties and water vapor barrier. For this purpose, the buckwheat starch (BS) is modified with myristic acid (MA) and the edible film production process was optimized by using central composite design with 4 center points where film forming solution's glycerol concentration, pH, and the temperature of as dependent variable and tensile strength (TS), elongation at break (EAB) value and Young's modulus (YM) as response. The models were significant for TS and YM, and the glycerol concentration and temperature had a significant effect on the TS of the films. The edible film produced in validated optimized conditions had better EAB (149%) and TS (1.064 MPa), and lower water solubility (44.7%) and water vapor permeability (0.39 g × mm/m2 × h × kPa) than control film (p < 0.05). There was no significant change in color values, but an increase in opacity (2.14). With the formation of the BS-MA complex, increased surface roughness and more hydrophilic (contact angle = 92.4°) films were obtained. These findings demonstrate that the BS-MA complex film has significant potential for practical applications as an edible film. © 2026 Wiley-VCH GmbH.Article Citation - Scopus: 1Noninvasive Condition Monitoring for Eccentricity Fault Detection in Large Hydro Generators(TÜBİTAK Scientific & Technological Research Council Turkey, 2026-01-16) Lemeski, Atena Tazikeh; Tekgun, Didem; Keysan, Ozan; Leblebicioglu, Kemal; Gol, Murat; Leblebicioglu, Mehmet KemalEccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in T & uuml;rkiye. The effects of DE faults on the SPSG's magnetic equivalent circuit parameters are then examined under various fault severities. A comprehensive hydropower plant model-including the synchronous generator, governor, and excitation system-is developed in MATLAB/Simulink, with all input parameters obtained from real plant data and equivalent circuit variations extracted from FEA. After completing the modeling stage, including fault scenarios, MATLAB and Simulink are employed together to estimate key magnetic equivalent circuit parameters using a modified particle swarm optimization (MPSO) algorithm, achieving highly accurate parameter estimation. Since the hydropower system allows measurement of the three-phase output currents, parameter estimation is performed based on current variations under different fault conditions. The simulation results verify the method's ability to detect faults with high accuracy; thus, this integrated and noninvasive approach provides a robust framework for ensuring the operational reliability and longevity of large hydro generators.Article Spatial Dimension of the Local Phenomenon in Kayseri(Gazi University, Faculty of Engineering Architecture, 2025-12-31) Ozmen, Nihan Mus; Asiliskender, BurakKayseri is in the centre of Anatolia, at the intersection of trade and military routes, and possesses a rich cultural heritage. Throughout its history, the city has hosted various civilizations, developing around a central castle and continuing to expand, particularly after the 19th century. Kayseri has long served as a meeting point for diverse cultures. Within this diversity, families known as locals, whose origins date back to the oldest neighbourhoods within the city walls, have held significant mercantile power. These local families regard themselves as the actual owners of Kayseri and have influenced the city's developmental trajectory. Over time, they have moved outward from the centre to newly developed neighbourhoods, first to the north and then to the east. This study examines the urban development of Kayseri in the 20th century and the spatial mobility of these local families. It employs qualitative methods such as ethnographic observation, oral history interviews, and GIS-based thematic mapping to analyse these movements in a multi-layered way. The study also aims to understand Kayseri's socio-cultural dynamics and historical texture by investigating the role of local families in the city's physical and functional transformations. In this context, it addresses the physical and functional changes in neighbourhoods vacated by these relocations.