WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/4465

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11 Beta-Hsd Type 1 Is Responsible for Low Plasma Hdl-Cholesterol and Abdominal Obesity in Metabolic Syndrome Patients
(Blackwell Publishing Ltd, 2006) Atalar, Fatmahan; Vural, Burçak; Ciftci, C.; Demirkan, A.; Susleyici Duman, Belgin; Çağatay, Penbe; Günay, Demet; Sagbas, E.; Akpinar, Belhhan; Ozbek, Ugur; Buyukdevrim, Ahmet Sevim
[Abstract Not Available]
Citation - WoS: 10
Citation - Scopus: 15
3d Indoor Positioning With Spatial Modulation for Visible Light Communications
(Elsevier, 2023) Sen, Umit; Yesilirmak, Yalin Evrim; Bayman, Irem Ozgur; Arsan, Taner; Panayırcı, Erdal; Stevens, Nobby
In this paper, a novel three-dimensional (3D) indoor visible light positioning (VLP) algorithm is proposed based on the spatial modulation (SM) and its error performance assessed as compared to the conventional received signal strength (RSS)-based 3D VLP systems. As contrasted to the traditional VLP system, the proposed SM-based 3D VLP system first estimates the optical channel gain between the transmitting light-emitting diodes (LEDs) and the two photo detectors (PDs) attached to the user by a pilot-based channel estimation technique. Then, unknown 3D positions of the receiver are determined by the trilateration algorithm with distances computed from the estimates of the channel gains. Consequently, the 3D VLP system achieves an interference -free transmission with increased spectral efficiency and without the need for a demultiplexing process at the receiving end. The algorithm's performance is evaluated regarding positioning error by applying the SM over four LEDs and the number of pilots selected as a function of the environmental signal-to-noise ratios (SNRs). The computer simulation results show that the positioning errors are obtained in an order of magnitude smaller than RSS-based techniques in an indoor industrial environment. This is mainly because the distances involved in determining the 3D positions can be determined more precisely by the pilot-aided channel estimation method without creating any data rate problem in transmission due to the higher spectral efficiency of the SM.
Citation - WoS: 7
Citation - Scopus: 7
3d Printer Selection for the Sustainable Manufacturing Industry Using an Integrated Decision-Making Model Based on Dombi Operators in the Fermatean Fuzzy Environment
(Mdpi, 2024) Gorcun, Omer Faruk; Zolfani, Sarfaraz Hashemkhani; Kucukonder, Hande; Antucheviciene, Jurgita; Pavlovskis, Miroslavas
Three-dimensional printers (3DPs), as critical parts of additive manufacturing (AM), are state-of-the-art technologies that can help practitioners with digital transformation in production processes. Three-dimensional printer performance mostly depends on good integration with artificial intelligence (AI) to outperform humans in overcoming complex tasks using 3DPs equipped with AI technology, particularly in producing an object with no smooth surface and a standard geometric shape. Hence, 3DPs also provide an opportunity to improve engineering applications in manufacturing processes. As a result, AM can create more sustainable production systems, protect the environment, and reduce external costs arising from industries' production activities. Nonetheless, practitioners do not have sufficient willingness since this kind of transformation in production processes is a crucial and irrevocable decision requiring vast knowledge and experience. Thus, presenting a methodological frame and a roadmap may help decision-makers take more responsibility for accelerating the digital transformation of production processes. The current study aims to fill the literature's critical theoretical and managerial gaps. Therefore, it suggests a powerful and efficient decision model for solving 3DP selection problems for industries. The suggested hybrid FF model combines the Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) and the Fermatean Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (FF-RAFSI) approaches. The novel FF framework is employed to solve a critical problem encountered in the automobile manufacturing industry with the help of two related case studies. In addition, the criteria are identified and categorized regarding their influence degrees using a group decision approach based on an extended form of the Delphi with the aid of the Fermatean fuzzy sets. According to the conclusions of the analysis, the criteria "Accuracy" and "Quality" are the most effective measures. Also, the suggested hybrid model and its outcomes were tested by executing robustness and validation checks. The results of the analyses prove that the suggested integrated framework is a robust and practical decision-making tool.
6-Point Tripled Ashkin-Teller Global Phase Diagrams in Two and Three Dimensions
(Elsevier, 2025) Zeynioglu, Deniz Ipek; Berker, A. Nihat
The tripled Ashkin-Teller model including 6-point interactions is solved in d = 2 and 3 by renormalization-group theory that is exact on the hierarchical lattice and approximate on the recently first/second-order-transition improved Migdal-Kadanoff procedure. Five different ordered phases occur in the dimensionally distinct global phase diagrams. 16 different phase diagram cross-sections in the 2-point and 4-point interaction space are obtained, with first-and second-order phase transitions, multiple tricritical points and critical endpoints.
Citation - WoS: 4
Citation - Scopus: 6
Abortion Services at Hospitals in Istanbul
(Taylor & Francis Ltd, 2017) O'Neil, Mary Lou
Objective: Despite the existence of a liberal law on abortion in Turkey there is growing evidence that actually securing an abortion in Istanbul may prove difficult. This study aimed to determine whether or not state hospitals and private hospitals that accept state health insurance in Istanbul are providing abortion services and for what indications. Method: Between October and December 2015 a mystery patient telephone survey of 154 hospitals 43 public and 111 private in Istanbul was conducted. Results: 14% of the state hospitals in Istanbul perform abortions without restriction as to reason provided in the current law while 60% provide the service if there is a medical necessity. A quarter of state hospitals in Istanbul do not provide abortion services at all. 48.6% of private hospitals that accept the state health insurance also provide for abortion without restriction while 10% do not provide abortion services under any circumstances. Key conclusions: State and private hospitals in Istanbul are not providing abortion services to the full extent allowed under the law. The low numbers of state hospitals offering abortions without restriction indicates a de facto privatization of the service. This same trend is also visible in many private hospitals partnering with the state that do not provide abortion care. While many women may choose a private provider the lack of provision of abortion care at state hospitals and those private hospitals working with the state leaves women little option but to purchase these services from private providers at some times subtantial costs.
Citation - WoS: 31
Citation - Scopus: 42
Acculturation Attitudes and Social Adjustment in British South Asian Children: a Longitudinal Study
(Sage Publications Inc, 2013) Brown, Rupert; Baysu, Gülseli; Cameron, Lindsey; Nigbur, Dennis; Rutland, Adam; Watters, Charles; Hossain, Rosa; LeTouze, Dominique; Landau, Anick
A 1-year longitudinal study with three testing points was conducted with 215 British Asian children aged 5 to 11 years to test hypotheses from Berry's acculturation framework. Using age-appropriate measures of acculturation attitudes and psychosocial outcomes it was found that (a) children generally favored an integrationist attitude and this was more pronounced among older (8-10 years) than in younger (5-7 years) children and (b) temporal changes in social self-esteem and peer acceptance were associated with different acculturation attitudes held initially as shown by latent growth curve analyses. However a supplementary time-lagged regression analysis revealed that children's earlier integrationist attitudes may be associated with more emotional symptoms (based on teachers' ratings) 6 months later. The implications of these different outcomes of children's acculturation attitudes are discussed.
Citation - WoS: 1
Citation - Scopus: 2
Achieving Sustainability in Solar Energy Firms in Turkey Through Adopting Lean Principles
(Mdpi, 2022) Aldewachi, Bilal; Ayag, Zeki
Lean principles and sustainability are considered important terms in business. Solar firms are witnessing great competition to fulfill energy requirements, suffering from a huge amount of waste, negatively affecting the sustainability dimensions. Thus, the aim of the study is to build a framework for solar energy firms to achieve sustainability through adopting lean principles, which will help to fix many problems as waste and costs. The method included reviewing the literature to explore the founding of the relation between the two terms, and using a questionnaire that was directed to the responsible people in Turkish solar energy firms. The results of the survey were analyzed to: (1) Find out what the responsible people think about the two terms lean and sustainability; (2) Measure the probable relationship between lean principles and sustainability dimensions by applying a linear regression test; (3) Use the results of point number two to build the framework. The result showed there was a high level of relative importance about the two terms from the point of view of managers and experts in solar firms. In addition, the study found a relationship between adopting pull and flow principles of lean, and achieving economic and social dimensions of sustainability, this finding is represented in a framework.
Citation - WoS: 32
Citation - Scopus: 35
Activating Reflective Thinking With Decision Justification and Debiasing Training
(Society for Judgment and Decision making, 2020) İsler, Ozan; Yılmaz, Onurcan; Doğruyol, Burak
Manipulations for activating reflective thinking, although regularly used in the literature, have not previously been systematically compared. There are growing concerns about the effectiveness of these methods as well as increasing demand for them. Here, we study five promising reflection manipulations using an objective performance measure — the Cognitive Reflection Test 2 (CRT-2). In our large-scale preregistered online experiment (N = 1,748), we compared a passive and an active control condition with time delay, memory recall, decision justification, debiasing training, and combination of debiasing training and decision justification. We found no evidence that online versions of the two regularly used reflection conditions — time delay and memory recall — improve cognitive performance. Instead, our study isolated two less familiar methods that can effectively and rapidly activate reflective thinking: (1) a brief debiasing training, designed to avoid common cognitive biases and increase reflection, and (2) simply asking participants to justify their decisions.
Acylation Stimulating Protein and Complement C3 Mrna Expression in Metabolic Syndrome
(Blackwell Publishing Ltd, 2006) Duman, Belgin Süsleyici; Çiftçi, C.; Atalar, Fatmahan; Demirkan, A.; Vural, B.; Çağatay, Penbe; Günay, Demet; Sağbaş, E.; Akpınar, Belhhan; Özbek, U.; Büyükdevrim, Ahmet Sevim
[Abstract Not Available]
Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
(MDPI, 2025) Jamil, Mahnoor; Trpcheska, Hristina Mihajloska; Popovska-Mitrovikj, Aleksandra; Dimitrova, Vesna; Creutzburg, Reiner
Image-based spam poses a significant challenge for traditional text-based filters, as malicious content is often embedded within images to bypass keyword detection techniques. This study investigates and compares the performance of six machine learning models-ResNet50, XGBoost, Logistic Regression, LightGBM, Support Vector Machine (SVM), and VGG16-using a curated dataset containing 678 legitimate (ham) and 520 spam images. The novelty of this research lies in its comprehensive side-by-side evaluation of diverse models on the same dataset, using standardized dataset preprocessing, balanced data splits, and validation techniques. Model performance was assessed using evaluation metrics such as accuracy, receiver operating characteristic (ROC) curve, precision, recall, and area under the curve (AUC). The results indicate that ResNet50 achieved the highest classification performance, followed closely by XGBoost and Logistic Regression. This work provides practical insights into the strengths and limitations of traditional, ensemble-based, and deep learning models for image-based spam detection. The findings can support the development of more effective and generalizable spam filtering solutions in multimedia-rich communication platforms.
Citation - WoS: 4
Citation - Scopus: 4
Adverse selection in cryptocurrency markets
(Wiley, 2023) Tinic, Murat; Sensoy, Ahmet; Akyildirim, Erdinc; Corbet, Shaen
In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse-selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse-selection costs for major cryptocurrencies. Also, our results suggest that adverse-selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse-selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse-selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.
Citation - WoS: 1
Citation - Scopus: 2
Aesthetic Approach for Critical Sociology of Contemporary Communication Technology
(Sage Publications inc, 2024) Arda, Balca
Critical theory has already marked that technology often threatens civil liberties, personal autonomy, and rights. Heidegger, later Marcuse, emphasized how technology is not value-free in its own revealing power of the surrounding environment, external and inner nature. Throughout this paper, I explore how the aesthetic approach engages with critical theory and contributes to the sociology of media and communication. For this, I will theoretically survey the terms of sociality under the forces of immediate communication, ubiquitous surveillance, and the compression of time and space that Baudrillard and Virilio once problematized through the lens of critical technology theory to adapt it to media and communication studies. I contend that techno-aesthetics that converge with Ranciere's dissensus can provide practical suggestions on an updated vocation of critical sociology. This article discusses the potential of aesthetic and social criticism of media for democratizing technology that Feenberg inserted. It is urgent to acknowledge the changing spatio-temporal aesthetic regimes that affect the societal imagination and limits of sociality and action to determine the next steps for achieving a commons-based society.
Citation - WoS: 1
Citation - Scopus: 1
AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
(MDPI, 2025) Yildirim, Senda; Yucekaya, Ahmet Deniz; Hekimoglu, Mustafa; Ucal, Meltem; Aydin, Mehmet Nafiz; Kalafat, Irem
Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning-Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)-were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project.
Citation - WoS: 78
Citation - Scopus: 108
Air Quality Prediction Using Cnn Plus Lstm-Based Hybrid Deep Learning Architecture
(Springer Heidelberg, 2022) Gilik, Aysenur; Ogrenci, Arif Selcuk; Ozmen, Atilla
Air pollution prediction based on variables in environmental monitoring data gains further importance with increasing concerns about climate change and the sustainability of cities. Modeling of the complex relationships between these variables by sophisticated methods in machine learning is a promising field. The objectives of this work are to develop a supervised model for the prediction of air pollution by using real sensor data and to transfer the model between cities. The combination of a convolutional neural network and a long short-term memory deep neural network model was proposed to predict the concentration of air pollutants in multiple locations of a city by using spatial-temporal relationships. Two approaches have been adopted: the univariate model contains the information of one pollutant whereas the multivariate model contains the information of all pollutants and meteorology data for prediction. The study was carried out for different pollutants which are in the publicly available data of the cities of Barcelona, Kocaeli, and Istanbul. The hyperparameters of the model (filter, frame, and batch sizes; number of convolutional/LSTM layers and hidden units; learning rate; and parameters for sample selection, pooling, and validation) were tuned to determine the architecture that achieved the lowest test error. The proposed model improved the prediction performance (measured by the root mean square error) by 11-53% for particulate matter, 20-31% for ozone, 9-47% for nitrogenoxides, and 18-46% for sulfurdioxide with respect to the 1-hidden layer long short-term memory networks utilized in the literature. The multivariate model without using meteorological data revealed the best results. Regarding transfer learning, the network weights were transferred from the source city to the target city. The model has more accurate prediction performance with the transfer of the network from Kocaeli to Istanbul as those neighbor cities have similar air pollution and meteorological characteristics.
Alienation and Hedonic Values in Mass Tourism
(Routledge Journals, Taylor & Francis LTD, 2025) Golcheshmeh, Sueheyla; Kozak, Metin
This study aims to reveal (1) the alienation of tourists, (2) whether locals experience alienation or not, and (3) the kind of relationship between alienation, hedonic consumption and hedonic wellbeing. The study employed a qualitative research method on two samples of locals and domestic tourists. Study findings demonstrate that locals' deprivation of hedonic consumption and alienation may negatively affect their hedonic wellbeing. Tourists may experience alienation because they hardly meet their hedonic consumption needs. The study contributes to the gap in the current tourism literature dealing with alienation, which tourists and residents can experience. The study also develops an understanding of the approaches to the subjects of tourist motivations, attitudes of locals, and impacts of tourism.
Citation - WoS: 17
Citation - Scopus: 20
All the Dark Triad and Some of the Big Five Traits Are Visible in the Face
(Pergamon-Elsevıer Scıence Ltd, 2021) Alper, Sinan; Bayrak, Fatih; Yılmaz, Onurcan
Some of the recent studies suggested that people can make accurate inferences about the level of the Big Five and the Dark Triad personality traits in strangers by only looking at their faces. However, later findings provided only partial support and the evidence is mixed regarding which traits can be accurately inferred from faces. In the current research, to provide further evidence on whether the Big Five and the Dark Triad traits are visible in the face, we report three studies, two of which were preregistered, conducted on both WEIRD (the US American) and non-WEIRD (Turkish) samples (N = 880). The participants in both the US American and Turkish samples were successful in predicting all Dark Triad personality traits by looking at a stranger's face. However, there were mixed results regarding the Big Five traits. An aggregate analysis of the combined dataset demonstrated that extraversion (only female), agreeableness, and conscientiousness were accurately inferred by the participants in addition to the Dark Triad traits. Overall, the results suggest that inferring personality from faces without any concrete source of information might be an evolutionarily adaptive trait.
Altarpieces and Their Viewers in the Churches of Rome From Caravaggio To Guido Reni
(Cambridge Univ Press, 2010) Walberg, Helen Deborah
[Abstract Not Available]
Citation - WoS: 2
Citation - Scopus: 2
Altered Dynamics of S. Aureus Phosphofructokinase Via Bond Restraints at Two Distinct Allosteric Binding Sites
(Academic Press Ltd- Elsevier Science Ltd, 2022) Celebi, Metehan; Akten, Ebru Demet
The effect of perturbation at the allosteric site was investigated through several replicas of molecular dynamics (MD) simulations conducted on bacterial phosphofructokinase (SaPFK). In our previous work, an alternative binding site was estimated to be allosteric in addition to the experimentally reported one. To highlight the effect of both allosteric sites on receptor's dynamics, MD runs were carried out on apo forms with and without perturbation. Perturbation was achieved via incorporating multiple bond restraints for residue pairs located at the allosteric site. Restraints applied to the predicted site caused one dimer to stiffen, whereas an increase in mobility was detected in the same dimer when the experimentally resolved site was restrained. Fluctuations in C-alpha-C-alpha distances which is used to disclose residues with high potential of communication indicated a marked increase in signal transmission within each dimer as the receptor switched to a restrained state. Cross-correlation of positional fluctuations indicated an overall decrease in the magnitude of both positive and negative correlations when restraints were employed on the predicted allosteric site whereas an exact opposite effect was observed for the reported site. Finally, mutual correspondence between positional fluctuations noticeably increased with restraints on predicted allosteric site, whereas an opposite effect was observed for restraints applied on experimentally reported one. In view of these findings, it is clear that the perturbation of either one of two allosteric sites effected the dynamics of the receptor with a distinct and contrasting character. (c) 2022 Elsevier Ltd. All rights reserved.
Analytic Solution of the Feldtkeller Equation
(Elsevier GMBH Urban & Fischer Verlag, 2009) Şengül, Metin Y.
In every reflectance-based application like broadband matching circuit modeling etc. a nonlinear equation following from energy conservation the Feldtkeller equation must be solved in order to obtain real networks. In the literature however there is no analytic solution available but only numerical solutions. Consequently the resulting error depends on the accuracy of the numerical tools. In this paper an analytic solution is proposed which is based on the modified ABCD-parameters of a lossless reciprocal two-port network. An algorithm is presented and examples are included to illustrate the implementation of the analytical method. (C) 2008 Elsevier GmbH. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 10
Anomalyadapters: Parameter-Efficient Multi-Anomaly Task Detection
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Unal, Ugur; Dag, Hasan
The emergence of technological innovations brings sophisticated threats. Cyberattacks are increasing day by day aligned with these innovations and entails rapid solutions for defense mechanisms. These attacks may hinder enterprise operations or more importantly, interrupt critical infrastructure systems, that are essential to safety, security, and well-being of a society. Anomaly detection, as a protection step, is significant for ensuring a system security. Logs, which are accepted sources universally, are utilized in system health monitoring and intrusion detection systems. Recent developments in Natural Language Processing (NLP) studies show that contextual information decreases false-positives yield in detecting anomalous behaviors. Transformers and their adaptations to various language understanding tasks exemplify the enhanced ability to extract this information. Deep network based anomaly detection solutions use generally feature-based transfer learning methods. This type of learning presents a new set of weights for each log type. It is unfeasible and a redundant way considering various log sources. Also, a vague representation of model decisions prevents learning from threat data and improving model capability. In this paper, we propose AnomalyAdapters (AAs) which is an extensible multi-anomaly task detection model. It uses pretrained transformers' variant to encode a log sequences and utilizes adapters to learn a log structure and anomaly types. Adapter-based approach collects contextual information, eliminates information loss in learning, and learns anomaly detection tasks from different log sources without overuse of parameters. Lastly, our work elucidates the decision making process of the proposed model on different log datasets to emphasize extraction of threat data via explainability experiments.

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