WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/256

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Powder Metallurgical Synthesis, Thermochemical Calculations and Characterization Studies of HfB2 Powders
(Springer India, 2025) Akbari, Amir; Suzer-Cicek, Ilayda; Mertdinc-Ulkuseven, Siddika; Gokce, Hasan; Ovecoglu, M. Lutfi; Agaogullari, Duygu
This study reports on the thermochemical calculations, mechanochemical synthesis, purification process, and characterization studies of the HfB2 powders by using native sources. Firstly, HfO2, native B2O3, and Mg starting powders were prepared with a multi-axial vibratory ball mill (NanoMultimix) in stoichiometric and excess amounts. The milling process was optimized by varying the time (2, 4, 6, 8, 10, 12 h). Then, unwanted by-products (HfO2, MgO) were removed by leaching with 4 and 6 M HCl. Phase and Rietveld analysis, microstructure investigations with scanning electron microscopy/energy dispersion spectroscopy and transmission electron microscopy, and particle size measurement were conducted. The purest HfB2 was obtained in the powders milled for 8 h in stoichiometric ratios and leached with 6 M HCl. The resulting optimum powder has an average particle size of 135 nm. Oxidation kinetics (500, 600, 700, 800, and 900 degrees C) were also investigated. As the temperature increased, the amount of oxidation increased based on the TG result. As a result of the characterization studies, the synthesis of single-phase, high-purity HfB2 was achieved using domestic resources.
Gender Differences in Cyber Dating Violence Among Adolescents and Young Adults: A Systematic Review and Meta-Analysis
(Wiley, 2025) Erbicer, Eyup Sabir; Metin, Ahmet; Zencir, Tolga; Boranli, Ece Nur; Demirtas, Ezgi Toplu; Sen, Sedat
Despite the growing body of research on cyber dating violence, a comprehensive understanding of gender differences in cyber-violent behaviors across developmental stages remains limited. The main purpose of this meta-analytic review was to estimate the direction and magnitude of gender differences in cyber dating violence perpetration and victimization by synthesizing results from various studies. The second purpose of this study was to examine the effect of potential moderators (i.e., continent, age, grade level, time frame, method of survey administration, the metric of the outcome, study design, publication status, and publication year) on these differences. Various databases were used to identify relevant studies, including PubMed, Web of Science (WoS), Scopus, PsycINFO, ERIC, and ProQuest. Eighty-one individual studies with a total sample of 70,233 participants, ranging in age from 10 to 30 years (M = 18.94), were included based on the inclusion and exclusion criteria in the present study. Most studies were conducted in North America and Europe with the largest proportions from the United States and Spain. Results indicated that there were no statistically significant gender differences (women vs. men; girls vs. boys) in perpetration and victimization of cyber dating violence. Moderator analyses showed that grade level and sample age were statistically significant moderators of gender differences in cyber dating violence victimization. However, other moderators (continent, time frame, method of survey administration, the metric of the outcome, study design, publication status, and publication year) were not statistically significant. This study contributes to understanding gender differences in cyber-violent behaviors during adolescence and emerging adulthood and highlights the importance of some moderators when developing targeted prevention and intervention strategies.
Solving Baer Wave Equation Reduced to Three-Parameter Eigenvalue Problem by Dynamic Thread-Based Computing
(Springer, 2025) Ozer, Hayati Unsal; Tuncel, Mehmet; Duran, Ahmet; Duran, Fatih Said
Real-time computation of eigenvalues is valuable in science and engineering. This is possible via memory-efficient, scalable, robust, and high-performance algorithms when we take advantage of supercomputing. Baer wave equation arises from applying the separation of variables to the Helmholtz equation. When the Baer wave equation is discretized, a three-parameter eigenvalue problem is obtained. In this study, we consider the computationally challenging problem of finding eigenvalue tuples in a three-parameter eigenvalue problem reduced from the Baer wave equation. We solve this problem using a fused parameter optimization algorithm by implementing a dynamic thread-based computation in C and MATLAB. We achieved scaled speed-up for the dense coefficient matrices of the problem from the Baer wave equation to run up to 64 threads in our C implementation. To the best of our knowledge, this is the first study to solve the three-parameter eigenvalue problem using parallel thread-based computing.
How Does Type of Moral Responsibility Affect the Extent of the Moral Circle? The Influence of Relational Models
(Sage Publications Inc, 2025) Sunar, Diane; Cesur, Sevim; Tepe, Beyza; Piyale, Zeynep Ecem; Hill, Charles T.
The "moral circle" defines entities toward which a person feels moral responsibility. Relational Models Theory (RMT) proposes four basic relational models (communal sharing, authority ranking, equality matching, and market pricing), each with distinct moral motivations. This study applies RMT to define different types of moral responsibility: caring, guiding, obeying/deferring, ensuring equality, or equity. We proposed that the type of moral responsibility may alter a judge's rating of degree of responsibility, affecting the entity's placement within the moral circle. Linear mixed model analyses of responsibility ratings toward various human and other targets across six closeness levels confirmed that relational models significantly affected felt responsibility ratings. Specifically, asking about Equality Matching responsibility (assuring equal rights and treatment) led to higher moral responsibility ratings than other definitions (Communal Sharing, Authority Ranking, Market Pricing), even for negatively judged targets like rapists. The two cultures tested (US and T & uuml;rkiye) differed in average responsibility ratings for various targets, but culture did not interact with Relational Models. Differences in moral inclusiveness are interpreted through relational model characteristics, such as boundedness and rule orientation. In addition to individual, situational, cultural differences, relational models between judge and target also affect extent of the moral circle.
Methane Emissions Forecasting Using Hybrid Quantum-Classical Deep Learning Models: Case Study of North Africa
(Springer, 2025) Belkadi, Widad Hassina; Drias, Yassine; Drias, Habiba; Ferkous, Sarah; Khemissi, Maroua
This study explores climate change by predicting methane emissions in North Africa using classical and quantum deep learning methods. Using data from Sentinel-5P, we developed hybrid quantum-classical models, such as quantum long short-term memory (QLSTM) and quantum-gated recurrent unit networks (QGRUs), along with a novel hybrid architecture combining quantum convolutional neural networks (QCNNs) with LSTM and GRU, namely QCNN-LSTM and QCNN-GRU. The results show that these quantum models, especially the proposed hybrid architectures, outperform classical models by approximately seven percent in root-mean-squared error with fewer training epochs. These findings highlight the potential of quantum methodologies for enhancing environmental monitoring accuracy. Future research will aim to refine model performance, incorporate explainable AI techniques, and expand to forecasting other greenhouse gases, contributing to climate change mitigation efforts.
Big-5 Personality Traits as Predictors of Allostatic Load in Latino Americans: A Longitudinal Study
(Oxford Univ Press Inc, 2025) Sevi, Baris; Supiyev, Adil; Gutierrez, Angela; Graham, Eileen K.; Mroczek, Daniel K.; Muniz-Terrera, Graciela
Objectives Allostatic load (AL) refers to the measure of cumulative wear and tear resulting from chronic stress and life events. AL presents adverse consequences for a diverse range of health conditions, and Latino populations show a high risk for elevated AL. This study aimed to test the Big-5 personality traits as possible predictors of AL in Latinos.Methods Using data from the Health and Retirement Study, we examined the Big-5 and AL connection through three time points in 8 years (Time 1 = 2006/2008; Time 2 = 2010/2012; Time 3 = 2014/2016). Only self-identified Latinos were included in the analysis sample (N = 319). Big-5 and demographics were obtained at baseline, and AL scores were computed for each time point.Results First, separate longitudinal linear mixed-effect models examined the effects of each Big-5 personality trait on AL change over time, then a fully adjusted longitudinal linear mixed-effect model was tested entering the Big-5 personality traits simultaneously. All models controlled for sociodemographic factors. Conscientiousness emerged as the only consistent significant predictor, for the separate and the simultaneous models. In baseline associations, higher conscientiousness was associated with lower AL. For predicting change in AL over time, none of the personality traits had significant associations in any of the models.Discussion The findings bolster prior evidence that conscientious can be a protective factor against elevated AL. Conscientiousness is a possible protective factor and improving related traits can be a path to achieve better health in Latino Americans.
Moral Framing Effects on Environmental Attitudes: A Conceptual Replication and Extension of Feinberg and Willer (2013)
(Academic Press Ltd- Elsevier Science Ltd, 2025) Cavdar, Dilara; Tepe, Beyza; Saribay, S. Adil; Yilmaz, Onurcan
This study investigates the relationship between moral framing, political orientation, and pro-environmental attitudes, replicating and extending Feinberg and Willer (2013) in a non-Western context. Using a Turkish-speaking sample (N = 699), we examined the effectiveness of care and sanctity-framed messages and the moderating role of actively open-minded thinking (AOT). Our findings partially replicated the original study. Sanctity framing increased pro-environmental attitudes among conservatives, while care framing had no significant effect. Political conservatism was negatively associated with pro-environmental attitudes, confirming prior findings. Exploratory analyses revealed that AOT moderated the effects of sanctity framing on environmental attitudes, with individuals low or moderate in AOT being more responsive. Both care and sanctity frames increased environmental donation, addressing the intention-behavior gap. However, cultural nuances, such as the collectivist orientation of the sample, may have influenced the care frame's ineffectiveness. The study highlights the importance of cultural context in moral framing research and underscores the need for context-specific climate communication strategies.
Quantum FP-Growth Algorithm Using GPU Simulation-Application to Digital Soil Mapping
(Elsevier, 2026) Belkadi, Widad Hassina; Drias, Yassine; Drias, Habiba
This study introduces a novel quantum version of the FP-growth algorithm for frequent itemset mining, leveraging the combined strengths of classical FP-growth and quantum machine learning. Key contributions include the theoretical and practical framework for Quantum FP-growth, along with a comprehensive analysis of its time and space complexity. We implemented Quantum FP-growth using IBM Qiskit and conducted a comparative evaluation of various quantum amplitude estimation (QAE) methods, including Canonical QAE, Faster QAE, Maximum Likelihood QAE, and Iterative QAE for support estimation. Our findings reveal that Iterative QAE surpasses the other methods in both accuracy and speed. Additionally, we explored the advantages of GPU simulation with IBM Qiskit and NVIDIA cuQuantum. Notably, this research marks the first application of a quantum frequent itemset mining algorithm to a real-world dataset in Digital Soil Mapping (DSM), pioneering the use of quantum technologies in soil science. This study underscores the potential of quantum computing to revolutionize data mining and promote sustainable soil management practices.
Burdens of Masculinity Among Heterosexual, Gay, and Bisexual Men in Turkey: More Masculine, More Conflicted, Less Satisfied
(Springer, 2025) Toplu-Demirtas, Ezgi; Oztemur, Gizem; Keskin, Berat; Fincham, Frank D.
Although bivariate associations among masculinity ideology, gender role conflict, and life satisfaction have been documented in Western countries, they have received limited attention in Turkey. Moreover, the majority of peer-reviewed research on masculinity has focused on heterosexual men's experiences. The current study, therefore, explored the relationship between masculinity ideology and life satisfaction in Turkish men with gender role conflict as a mediator and sexual orientation (heterosexual men vs. gay or bisexual men) as a moderator variable. Data were collected online from 195 men (128 heterosexual, 53 gay, and 14 bisexual) between the ages of 18 and 42 (M = 25.39, SD = 3.53) using the Life Satisfaction Scale, Masculinity Ideology Scale, and Gender Role Conflict Scale. The moderated-mediation analysis revealed that masculinity ideology and life satisfaction were significantly associated via the mediator of gender role conflict. Both heterosexual and gay or bisexual men who adhered more to masculine ideology experienced greater gender role conflict and thus felt less satisfaction with life. After discussing the results and their limitations, recommendations for further research and practice are offered. We conclude that addressing gender role conflict in clinical work may be a profitable approach to increasing men's life satisfaction.
Heterogeneous Impact of Innovation on Economic Development: Evidence from EU Regions
(Elsevier Sci Ltd, 2026) Pinar, Mehmet; Karahasan, Burhan Can
This paper investigates the heterogeneous impact of innovation on economic development across European Union (EU) regions, with a focus on regional competitiveness driven by innovation-based capabilities. While innovation is a key driver of economic growth, its effects are not uniformly distributed. Using the Multiscale Geographically Weighted Regression models, the study examines how different dimensions of innovation (technological readiness, business sophistication, and overall innovation capacity) affect regional GDP per capita. The results show that regions with higher innovation-based competitiveness generally achieve higher income levels. However, the impact of innovation is spatially uneven. While core EU regions (particularly, in Northern and Western Europe) benefit more strongly from innovation, peripheral regions (in Southern and Eastern Europe) often experience weaker and in some cases even negative, effects. These results highlight the importance of accounting for spatial variation when designing innovation and cohesion policies. The paper calls for tailored, place-based strategies to address regional disparities in innovation-driven development and suggests that current EU policies should be adjusted to better support lagging regions.
Ruling Through Exception: Lawfare, Securitised Warfare and the Intermestic Logic of Authoritarianism
(Routledge Journals, Taylor & Francis Ltd, 2025) Çağlar, Barış
This article develops an original interdisciplinary framework for analysing authoritarian regimes. It coins, for the first time, the concept of securitised warfare, theorising its conceptual foundations, and it also originates and develops an original theoretical framework synthesising securitisation, authoritarianism and structuration. Securitised warfare - defined here as the outward intermestic manifestation of lawfare - is shown to be mutually constitutive with lawfare, the strategic misuse of the legal system for political gain, with both reinforcing the consolidation of authoritarian rule. Focusing on Turkey (2015-2025), the article illustrates how the regime employed legal repression as a political instrument, particularly in the cases of Selahattin Demirta & scedil; and Ekrem & Idot;mamo & gbreve;lu. Simultaneously, the suppression of Kurdish groups in Syria exemplifies securitised dynamics shaped in conjunction with domestic politics. Using Lijphart's hypothesis-generation method and within-case process tracing, the study demonstrates how lawfare and securitised warfare function both as Schmittian exceptions and as routinised Giddensian institutional practices. The framework conceptualises the historical transition from national security state to neoliberal security state, culminating in the consolidation of an autocratic regime whose logic exceeds conventional regime security. This transformation is theorised through securitised warfare - explaining how domestic and foreign policy are increasingly governed by a unified logic of authoritarian control.
Anamorphic Projection as a Novel Game Mechanic for Investigating Impossible Spaces in 3D Puzzle Games
(IEEE Computer Society, 2025) Aydındoğan, Irem; Alaçam, Sema
This study introduces a novel game mechanic for 3D puzzle games based on anamorphic projection to explore impossible spaces. By using perspective-driven spatial interactions, the mechanic creates environments that challenge conventional Euclidean logic. Players advance by aligning their viewpoint with distorted projections, making perception a central element of gameplay. A usability test with 33 participants assessed the mechanic's effectiveness through a structured questionnaire focusing on six dimensions: Ease of Control, Goals and Rules, Challenge, Mastery, Curiosity, and Immersion. Results indicate high engagement and cognitive stimulation, especially in mastery and goal clarity. These findings highlight the potential of anamorphic projection to support perceptually rich and mentally engaging puzzle experiences in future game design. © 2025 IEEE.
Financial Inputs Prediction with Machine Learning and Covariance Matrix Applications
(Institute of Electrical and Electronics Engineers Inc., 2025) Benli, Harun; Gunes, Peri; Ulkgun, Mert; Cakar, Tuna
Reliable estimation of the time-varying covariance matrix of asset returns is indispensable for portfolio construction, risk budgeting, and automated advisory services. Conventional estimators-rolling-window sample covariances, EWMA filters, and GARCH families-remain anchored to historical prices and therefore adapt slowly when market conditions pivot. To overcome this latency, we propose an end-to-end, machine-learning-driven framework that forecasts future covariances directly from high-frequency equity data, largely decoupling risk estimation from past observations. Our pipeline ingests heterogeneous stock feeds through a real-time API, applies error-minimising imputation (forward/backward fill, spline, VAR, wavelet, and co-kriging), and standardises returns via empirically selected scaling schemes. The processed features are then fed to a model zoo comprising linear and penalised regressions, tree ensembles (Random Forest, XGBoost, LightGBM, CatBoost), and kernel-based Support Vector Regression. Weekly walk-forward evaluation on a universe of Turkish insurance equities shows that LightGBM and SVR cut out-of-sample covariance prediction error by up to 35 % versus classical benchmarks. We embed the predicted matrices into five allocation engines-Markowitz mean-variance, Black-Litterman, minimum-variance, Risk Parity, and CVaR optimisation-demonstrating that Risk Parity delivers the most consistent variance reduction across 15 stock pairs, while Black-Litterman excels for idiosyncratic combinations such as ANSGR-AKGRT. A granular analysis reveals that day-to-day sign changes in returns create structural breaks that generic regressors miss; augmenting the architecture with a volatility-state classifier and regime-specific learners markedly sharpens turning-point detection. Beyond statistical gains, the framework is production-ready: it is fully implemented in Python, runs on cloud notebooks, and plugs into robo-advisory dashboards. The study thus bridges academic advances in covariance prediction with operational portfolio management, paving the way for broader cross-sector deployment and future research on deep sequential models, transaction-cost awareness, and multi-asset scalability. © 2025 Elsevier B.V., All rights reserved.
Suggestions in Digital Discourse: The Case of MOOC Reviews
(Elsevier, 2025) Ciftci, Hatime
This study examines the speech act of suggestions in digital discourse through linguistic and functional approaches and explores how suggestions are performed along with cooccurring discourse-pragmatic particles, supporting moves, and aspects in their propositional content. More specifically, this paper presents findings regarding the speech act of suggestions in MOOC reviews as a recent and emerging genre of digital discourse. Embracing a discourse analytic perspective, this study indicates how suggestions are situated within the context they are used, and their multi-functionality is evidently relevant to the linguistic choices and supporting moves by MOOC learners, going beyond the utterance level meaning. Additionally, suggestion head acts involve certain aspects of online courses or their experience where learners often express their expectations or opinions for improvement. Overall, this study contributes to speech act research in digital discourse and provides insights into the use of suggestions in the discourse of MOOC reviews. (c) 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
The Shadows of Internalized Bisexual Myths: Jealousy and Psychological Intimate Partner Violence Perpetration Among Bisexual Plus Individuals in Turkiye
(2025) Zurnaci, Burcu; Demirtas, Ezgi Toplu
Longitudinal Relations Between Early Prosocial Behaviors Toward Parents and Later Prosocial and Aggressive Behaviors in Turkish Early Adolescents
(2025) Gulseven, Zehra; Kumru, Asiye; Carlo, Gustavo; Maiya, Sahitya; Sayil, Melike; Selcuk, Bilge
AI-Driven Digital Soil Mapping: Leveraging Generative AI, Ensemble Learning and Geospatial Technologies for Data-Scarce Regions
(Springer Science and Business Media Deutschland GmbH, 2025) Drias, Yassine; Drias, Habiba; Belkadi, Widad Hassina; Cakar, Tuna; Abdelhamid, Zakaria; Bensemmane, Riad Yacine
This study presents a methodology for generating highresolution digital soil maps by integrating artificial intelligence (AI) with geospatial technologies. The research begins with comprehensive data collection and the extraction of relevant soil properties with the help of generative AI. To improve predictive accuracy, ensemble learning algorithms were employed due to their ability to capture complex relationships within soil characteristics. Additionally, a structured preprocessing pipeline was developed to refine and standardize the collected soil data, ensuring its suitability for modeling. The model's performance was evaluated using spatial cross-validation techniques to identify the most effective predictive approach. To validate the proposed methodology, experiments were conducted in northern Algeria, a region characterized by diverse landscapes ranging from arid zones to fertile plains. The results demonstrate the potential of AI-driven approaches to enhance soil mapping, particularly in regions where high-quality and up-to-date soil data are limited. This study underscores the efficacy of AI and geospatial technologies in advancing precision agriculture and land management.
A Practical PCB-Based Framework for Spiking Neural Networks with a Half-Adder Example
(IEEE, 2025) Cikikci, Sevde Vuslat; Orek, Eren; Aysoy, Ayhan; Ozgen, Ali Kagan; Yavuz, Arda; Ayhan, Tuba
This paper addresses the half-adder problem using Spiking Neural Networks (SNNs). In a previous study, the XOR operation was successfully realized on a breadboard and in this study it is integrated into the half-adder structure. The system uses input signals at frequencies of 50 Hz and 100 Hz and the neurons are generated by the Leaky Integrate and Fire (LIF) model. Unlike other neuron models, the LIF model is less complex. In addition, it was preferred because of its biological meaningfulness compared to the Integrate and Fire model. The network, consisting of 18 neurons in total, shows that basic arithmetic operations can be performed with SNN. Overall, this study demonstrates that basic logic operations can be implemented in neural networks, thus providing new perspectives for digital calculation. The successful solution of the Half Adder problem using SNNs not only proves the calculation capabilities of SNNs, but also opens new perspectives for the development of more complex logical circuits using these biologically inspired neural circuits.
Fast and Accurate Multi-Neural Network Ensemble Model
(IEEE, 2025) Nakci, Veli; Altun, Mustafa
In image classification, having a high accuracy is a significant metric for a model. Therefore, some certain methods such as ensemble technique etc. are commonly used for this objective. However, while trying to achieve high accuracy, other important metrics such as training time must also be considered. Transfer learning method is widely applied in image classification to reduce training time and enhance model efficiency. Even though transfer learning with models such as AlexNet, VGG16, and DenseNet121 is applied on some image datasets, it requires a great amount of training time to achieve high accuracy. In this study, we propose a model that utilizes weighted voting ensemble technique with an auxiliary network. We evaluate our model and pre-trained models - Alexnet, VGG1, and DenseNet121 - on CIFAR-10 dataset. The results show that the proposed model outperforms pre-trained models in terms of achieving high accuracies and requiring less training time. To achieve 80% accuracy, our model requires 15,38%, 10%, and 87.78% of the training time used by Alexnet, VGG16 and DenseNet121, respectively. While the proposed model achieves 85% and 90% accuracy, AlexNet and VGG16 cannot. In addition, it achieves 90% accuracy in 38.23 min, whereas DenseNet121 - more efficient than the other two pre-trained models - only reaches 87% accuracy in over three hours.
Development of the Pediatric Physical Activity Tracking Platform (Pedi@ctivity) and Smartwatch-Based Big Data Analysis: A Digital Health Application in Society 5.0
(Elsevier, 2025) Arman, N.; Cakar, T.; Gullu, S.; Ayaz, N. Aktay; Yekdaneh, A.; Albayrak, A.

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