Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/1248

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Securereg: Combining Nlp and Mlp for Enhanced Detection of Malicious Domain Name Registrations
(Institute of Electrical and Electronics Engineers Inc., 2024) Ecevit, Mert İlhan; Dağ, Hasan; Dag,H.; Creutzburg,R.; 0
The escalating landscape of cyber threats, charac-terized by the registration of thousands of new domains daily for lar ge-scale Inter net attacks such as spam, phishing, and drive-by downloads, underscor es the imperati ve for innovative detection methodologies. This paper introduces a cutting-edge approach for identifying suspicious domains at the onset of the registration process. The accompanying data pipeline generates crucial featur es by comparing new domains to register ed do-mains, emphasizing the crucial similarity score. The proposed system analyzes semantic and numerical attrib utes by leveraging a novel combination of Natural Language Processing (NLP) techniques, including a pretrained CANINE model and Multilayer Perceptr on (MLP) models, providing a robust solution for early threat detection. This integrated Pretrained NLP (CANINE) + MLP model showcases the outstanding perf ormance, surpassing both individual pretrained NLP models and standalone MLP models. With an PI score of 84.86% and an accuracy of 84.95%on the SecureReg dataset, it effecti vely detects malicious domain registrations. The finding demonstrate the effecti veness of the integrated appr oach and contrib ute to the ongoing efforts to develop proactive strategies to mitigate the risks associated with illicit online activities through the ear ly identificatio of suspicious domain registrations. © 2024 IEEE.
Evaluating the Financial Credibility of Third-Party Logistic Providers Through a Novel Frank Operators-Driven Group Decision-Making Model With Dual Hesitant Linguistic Q-Rung Orthopair Fuzzy Information
(Elsevier Ltd, 2025) Görçün, Ömer Faruk; Görçün,Ö.F.; Ecer,F.; Senapati,T.; Küçükönder,H.
In the relevant literature, there is no study dealing with the financial credibility of third-party logistic providers with the help of decision-making frames. Further, there are no criteria to evaluate the third-party logistics providers' creditworthiness in practice, and decision-makers in the banks consider their judgments and experiences to assess the demand of the logistics firms. This study proposes a multi-criteria group decision-making framework through a dual hesitant linguistic q-rung orthopair fuzzy (DHLq-ROF) set to manage uncertainties more effectively and make a theoretical contribution to the academic literature. For ranking, the score function and accuracy function are defined. Additionally, some novel operational laws based on Frank t-norms and t-conorms are defined for DHLq-ROF numbers. A wide range of generalized aggregation operators, such as DHLq-ROF Frank weighted averaging, DHLq-ROF Frank weighted geometric, DHLq-ROF Frank generalized weighted averaging, and DHLq-ROF Frank generalized weighted geometric operators, are also investigated. Beyond that, several prominent characteristics of the proposed operators are studied. It is applied to a financial credibility problem for a multinational organization to demonstrate the introduced model's applicability. Considering the results obtained regarding the importance of the criteria, the most crucial criterion is market indebtedness, followed by fleet vehicle structure and current rate criteria, respectively. The results indicate that UPS, Kuhne & Nagel and DHL Deutsche Post are the best third-party logistic providers. The sensitivity analysis shows that the framework possesses favourable flexibility and effectiveness. Thanks to the framework's ability to produce practical solutions to challenging decision-making problems, it can be reliably preferred in engineering and other fields. © 2024 Elsevier Ltd
Splitout: Out-Of Training-Hijacking Detection in Split Learning Via Outlier Detection
(Springer Science and Business Media Deutschland GmbH, 2025) Erdoğan,E.; Tekşen,U.; Çeliktenyıldız,M.S.; Küpçü,A.; Çiçek,A.E.
Split learning enables efficient and privacy-aware training of a deep neural network by splitting a neural network so that the clients (data holders) compute the first layers and only share the intermediate output with the central compute-heavy server. This paradigm introduces a new attack medium in which the server has full control over what the client models learn, which has already been exploited to infer the private data of clients and to implement backdoors in the client models. Although previous work has shown that clients can successfully detect such training-hijacking attacks, the proposed methods rely on heuristics, require tuning of many hyperparameters, and do not fully utilize the clients’ capabilities. In this work, we show that given modest assumptions regarding the clients’ compute capabilities, an out-of-the-box outlier detection method can be used to detect existing training-hijacking attacks with almost-zero false positive rates. We conclude through experiments on different tasks that the simplicity of our approach we name SplitOut makes it a more viable and reliable alternative compared to the earlier detection methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Reducing Consumer–brand Incongruity Through Corporate Social Responsibility and Brand Trust: Exploring Negative Word-Of (nwom)
(John Wiley and Sons Inc, 2024) Tosun, Petek; Cagliyor,S.I.; Gürce,M.Y.
Drawing upon consumer–brand disidentification theory and balance theory, this study examines symbolic and ideological incongruity in consumer–brand relationships through an original conceptual model shaped by negative past experiences, brand trust, perceived corporate social responsibility (CSR), and negative word-of-mouth (NWOM). A preliminary study was conducted to explore the dimensions of consumers' negative past experiences by topic detection. Latent Dirichlet allocation (LDA) topic modeling was undertaken to analyze online consumer reviews (n = 6095) about a coffee chain brand. The dimensions detected in this preliminary study were included in the research model and further analyzed in the main study. The main study, a cross-sectional consumer survey (n = 522), tested the original research model by way of partial least squares structural equation modeling (PLS-SEM) on SmartPLS. The findings showed that negative past experiences consisted of product-related, service-related, and technology-related problems and negatively influenced brand trust. It was found that brand trust and perceived CSR negatively affected symbolic and ideological incongruity, while symbolic and ideological incongruity positively influenced NWOM. The findings provide empirical evidence for balance theory by showing that the three critical domains of consumer–brand relationships (ideological, symbolic, and experiential) provide a complex cognitive model that covers personal-symbolic and moral-societal aspects of consumer–brand disidentification and consequent NWOM intentions. In line with consumer–brand disidentification theory, the results contribute to the literature by demonstrating the direct negative impacts of brand trust and perceived CSR on symbolic and ideological incongruity, as well as the direct positive impacts of symbolic and ideological incongruity on NWOM. © 2024 The Author(s). International Journal of Consumer Studies published by John Wiley & Sons Ltd.
From Mind To Mind: Understanding the Role of Mothers in Children's Theory of Mind
(Elsevier Ltd, 2024) Koç,N.; Tahiroğlu,D.; Uzundağ,B.A.
Theory of mind (ToM) enables children to comprehend mental states of themselves and others. In this first study investigating the mediating role of mothers' mental state talk between mothers' sociocognitive skills (i.e., mothers' ToM and parental reflective functioning) and children's ToM, 89 children (M(SD)age = 57.0 months (5.49)) and their mothers from Türkiye participated. Results revealed that mothers with higher prementalization scores used fewer affective and desire words. Mothers exhibiting greater interest and curiosity in mental states used more cognitive words, while those with more proficient ToM skills tended to use more mental state terms indicating certainty (e.g., ‘perhaps’). Furthermore, mothers' use of certainty words mediated the relationship between mothers' ToM and children's ToM. These cross-sectional findings underscore the significant role of mothers' socio-cognitive abilities in mother-child interactions regarding mental states and the development of children's ToM skills, and call for a longitudinal investigation into these relationships. © 2024 Elsevier Inc.
Art and Collective Healing Sarkis Zabunyan and the Politics of Denial
(Duke University Press, 2024) Aslan,N.T.
Sarkis Zabunyan, one of the prominent figures in Turkish contemporary art, was selected to represent the Turkish pavilion in the Venice Biennale in 2015. Since 2015 was the centennial of the Armenian Genocide, a genocide that has not been recognized by the Turkish Republic for more than a hundred years, and Sarkis being an Istanbul Armenian born and raised in Turkey, the selection caused quite a stir and sparked a public discussion on art and collective healing when it was announced. As a result, the catalog of Sarkis’s work Respiro was subjected to censorship. Through this censorship case, this article scrutinizes various reconciliation discourses developed in Turkey in the early 2000s regarding the Armenian Genocide and how contemporary art could possibly engage/disengage with those discourses. © 2024 Duke University Press.
MOBRO: multi-objective battle royale optimizer
(Springer, 2024) Dehkharghani, Rahim; Dehkharghani,R.; Akan,T.; Bhuiyan,M.A.N.
Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimization algorithms in the literature, according to the no-free-lunch theorem, no optimization algorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimization algorithms: the multi-objective Gray Wolf optimization algorithm (MOGWO), the multi-objective particle swarm optimization algorithm (MOPSO), the multi-objective artificial vulture’s optimization algorithm (MOAVAO), the optimization algorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimization algorithms in most of the benchmark suites and operates competitively with them in the others. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
Effective Drug Design Screening in Bacterial Glycolytic Enzymes Via Targeting Alternative Allosteric Sites
(Academic Press Inc., 2024) Turkmenoglu,I.; Kurtulus,G.; Sesal,C.; Kurkcuoglu,O.; Ayyildiz,M.; Celiker,S.; Akten,E.D.
Three glycolytic enzymes phosphofructokinase (PFK), glyceraldehyde-3-phosphate dehydrogenase (GADPH) and pyruvate kinase (PK) that belong to Staphylococcus aureus were used as targets for screening a dataset composed of 7229 compounds of which 1416 were FDA-approved. Instead of catalytic sites, evolutionarily less conserved allosteric sites were targeted to identify compounds that would selectively bind the bacteria's glycolytic enzymes instead of the human host. Seven different allosteric sites provided by three enzymes were used in independent screening experiments via docking. For each of the seven sites, a total of 723 compounds were selected as the top 10 % which displayed the highest binding affinities. All compounds were then united to yield the top 54 drug candidates shared by all seven sites. Next, 17 out of 54 were selected and subjected to in vitro experiments for testing their inhibition capability for antibacterial growth and enzymatic activity. Accordingly, four compounds displaying antibacterial growth inhibition above 40 % were determined as Candesartan cilexetil, Montelukast (sodium), Dronedarone (hydrochloride) and Thonzonium (bromide). In a second round of experiment, Candesartan cilexetil and Thonzonium displayed exceptionally high killing efficiencies on two bacterial strains of S.aureus (methicillin-sensitive and methicillin-resistant) with concentrations as low as 4 μg/mL and 0.5 μg/mL. Yet, their enzymatic assays were not in accordance with their killing effectiveness. Different inhibitory effects was observed for each compound in each enzymatic assay. A more effective target strategy would be to screen for drug compounds that woud inhibit a combination of glycolytic enzymes observed in the glycolytic pathway. © 2024
Perceptions at War: Exploring Public Attitude Formation in the Armenian-Azerbaijani Conflict
(Brill Academic Publishers, 2024) Gadimova-Akbulut,N.; Petrosyan,A.
The post-2020 escalations in the Armenian-Azerbaijani conflict, culminating in the Azerbaijani military offensive of September 2023 and the subsequent mass exodus of the Armenians from Nagorno-Karabakh, once again underscored the persistent deadlock in the peace process and the failure to achieve compromise. This article aims to analyze the genesis of maximalist attitudes within Armenian and Azerbaijani societies in the years preceding the 2020 war, as well as the factors contributing to the endurance of these attitudes in the years that followed. Drawing on the analysis of maximalist attitudes formation during the interwar period (1994–2020), the research delves into the obstacles encountered by peace activists and peacebuilders in their efforts to counter dominant nationalism, alongside other challenges and structural impediments in the peacebuilding process. Finally, the study investigates the determinants that shaped the post-war attitudes in Armenia and Azerbaijan preceding the pivotal events of September 2023. © Nazrin Gadimova-Akbulut and Anush Petrosyan, 2024.
Energy-Efficient Secure Communication for Ios Aided Cfmmimo Network
(Institute of Electrical and Electronics Engineers Inc., 2024) Panayırcı, Erdal; Hu,Y.; Dong,Z.; Panayirci,E.; Jiang,H.; Wu,Q.
In this article, we investigate the security energy efficiency (SEE) of intelligent omni-surface (IOS) aided cellfree massive MIMO (CFMMIMO) networks. Firstly, we provide a SEE maximization design for the IOS-assisted CFMMIMO network. To address the formulated non-convex, multivariate problem, in particular, we first decouple the problem into two sub-problems, and design corresponding low-complexity algorithms for each sub-problem, including the joint optimization algorithm of the access point (AP) transmission beamforming and artificial noise covariance matrix based on semi-smooth Newton method (SSNM) as well as the joint optimization algorithm of IOS reflection-transmission phase shift matrix based on Riemannian product manifolds-conjugate gradient method (RPM-CG). The two subproblems are then iterated iteratively using the Block Coordinate Descent (BCD) algorithm to obtain the maximum SEE of the IOS-assisted CFMMIMO network. Simulation results show that the proposed algorithm outperforms the five baseline schemes in terms of SEE. © 2024 IEEE.
A Topology Detector Based Power Flow Approach for Radial and Weakly Meshed Distribution Networks
(Institute of Electrical and Electronics Engineers Inc., 2024) Yetkin, Emrullah Fatih; Ceylan, Oğuzhan; Pisica,I.; Ozdemir,A.
Power distribution networks may need to be switched from one radial configuration to another radial structure, providing better technical and economic benefits. Or, they may also need to switch from a radial configuration to a meshed one and vice-versa due to operational purposes. Thus the detection of the structure of the grid is important as this detection will improve the operational efficiency, provide technical benefits, and optimize economic performance. Accurate detection of the grid structure is needed for effective load flow analysis, which becomes increasingly computationally expensive as the network size increases. To perform a proper load flow analysis, one has to build the distribution load flow (DLF) matrix from scratch cost of which is unavoidable with the growing size of the network. This will considerably increase the computation time when the system size increases, compromising applicability in online implementations. In this study we introduce a novel graph-based model designed to rapidly detect transitions between radial and weakly meshed systems. By leveraging the characteristic properties of Sparse Matrix-Vector product (SpMV) operations, we accelerate power flow calculations without necessitating the complete reconstruction of the DLF matrix. With this approach we aim to reduce the computational costs and to improve the feasibility of near-online implementations. © 2024 IEEE.
Customer Purchase Intent Prediction Using Feature Aggregation on E-Commerce Clickstream Data
(Institute of Electrical and Electronics Engineers Inc., 2024) Tokuc,A.A.; Dag,T.
This paper presents a machine learning model for predicting customer purchase intent using e-commerce clickstream data. The model is built using the LightGBM framework, chosen for its efficiency in handling large-scale datasets and complex feature interactions. Key challenges addressed include the high dimensionality of clickstream data, the inherent class imbalance between purchase and non-purchase sessions, and the temporal variability of user behavior. The feature engineering process involved creating and selecting features that capture relevant user behaviors, such as session duration, event counts, and interaction diversity. The model was evaluated using ROC-AUC, F1-score, precision, and recall metrics, demonstrating strong performance in identifying sessions likely to result in a purchase. This study contributes to the field of e-commerce analytics by providing a robust framework for conversion prediction, enabling more effective customer engagement strategies. Our findings underscore the potential of machine learning to enhance e-commerce conversion rates, thereby optimizing customer engagement. © 2024 IEEE.
Optimal Dead Band Control of Occupant Thermostats for Grid-Interactive Homes
(Institute of Electrical and Electronics Engineers Inc., 2024) Ceylan, Oğuzhan; Ceylan,O.; Paudyal,S.
Efficient and grid-aware management of home-scale heating, ventilation, and air conditioning (HVAC) systems is one of the key enablers of demand-side management (DSM) and associated grid services in the residential sector. HVACs regulate the indoor temperature around a set point through a thermostat operating within a closed-loop control scheme. Conventional thermostats typically have a built-in temperature dead band or differential where the thermostat is idle, and HVAC stays at the most recent state (On/Off). The temperature dead band is an important control parameter that can help save energy as well as preventing frequent On/Off switching cycles leading to excessive wear and tear on the equipment. However, strategic and dynamic adjustment of the dead band can be a challenging task for an occupant. This paper proposes a mixed-integer linear program (MILP)-based tuning scheme to optimally determine the dead band. The novelty in this formulation is the inclusion of thermostat hysteresis curve modeled by piecewise techniques for tuning the dead band accurately. The proposed formulation is solved as a receding horizon manner for normal as well as under a demand response (DR) event and has been found it can achieve up to 10% reduction in energy consumption without degrading the regulation performance significantly. © 2024 IEEE.
Courier Payout Cash-Flow Prediction in Crowdsourced E-Commerce Logistics: a Hybrid Machine Learning Approach
(Springer Science and Business Media Deutschland GmbH, 2024) Çay,A.; Küp,E.T.; Bayram,B.; Çıltık,A.
In the rapidly growing sector of crowdsourced e-commerce logistics, where delivery volumes are highly variable, the effective management of courier payouts becomes essential to maintain operational efficiency. This paper introduces a comprehensive hybrid approach, blending clustering methods with multiple advanced regression models, to accurately predict daily courier payout cash-flows. By utilizing real-world data from e-commerce operations, our methodology estimates the daily financial outflows for courier payments, a critical component for adapting to the dynamic and unpredictable nature of crowdsourced logistics. Our approach includes a thorough comparative analysis of several stateof-the-art regression models-namely, XGBoost Regressor, LightGBM Regressor, and Facebook’s PROPHET-in conjunction with clustering techniques that categorize similar cross-docks based on distinct characteristics. This integrated, hybrid strategy aims to provide precise daily financial predictions for each cross-dock, which is crucial for robust financial planning and effective resource allocation. The practical implications of this research are significant, offering logistics companies a powerful tool to navigate the complexities of e-commerce environments. By ensuring more accurate cash-flow predictions, companies can optimize their operations, reduce financial uncertainties, and improve overall service quality in the highly competitive and fast-paced world of e-commerce logistics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
From Mass Marketing To Personalized Digital Marketing in Tourism: a 2050 Horizon Paper
(Emerald Group Publishing Ltd, 2024) Kozak, Metin; Correia, Antonia
Purpose - The academic background of tourism marketing dates back to the 1960s. There had been a slight increase in its capacity until the early 1990s. However, since then, it has boomed, reaching thousands of scientific journal articles and tens of scientific books published only in English each year. Therefore, this study aims to present how tourism marketing has progressed academically within the past 60 years over four waves and how this progress may move forward as the next wave. Design/methodology/approach - A bibliometric analysis grounds this study, which characterizes the past and present of tourism marketing research and anticipates the future. Content analysis, such as word clouds and social network analysis, was adopted to identify topic clusters and their connections. A total of 9,239 articles published between 1969 and 2024 were extracted from Scopus. Software packages such as VOSviewer were used to determine connections within topics. Findings - The authors have assessed the findings broadly. Four waves were from the late 1960s to the first quarter of 2000. In spite of the limited size of papers at the early stage, the last wave saw a boom and a diversified number and category of subjects studied. In each wave, new subjects were added to broaden the picture. Research limitations/implications - The discussion of findings is based only on those scientific papers published in English since 1969 but excludes the whole list of textbooks. Subsequent research should also consider all papers and textbooks released in different languages to have the broadest picture assessment worldwide. Practical implications - The study conveys various suggestions for industry practitioners and policymakers to focus on carefully assessing trends in marketing tourism services and how these may be shaped shortly. This may help practitioners and policymakers redesign their services and marketing strategies in light of future developments. Originality/value - This study continues a recent study published by Kozak (2023) that has been elaborated, particularly from the industry perspective. The current study examines the academic profile of all subjects investigated in the case of tourism marketing, but mainly in an academic sense. Accordingly, this paper outlines the facts and trends researchers may follow throughout the research frame published in the past six decades.
Botnets Unveiled: a Comprehensive Survey on Evolving Threats and Defense Strategies
(Wiley, 2024) Asadi, Mehdi; Jamali, Mohammad Ali Jabraeil; Heidari, Arash; Navimipour, Nima Jafari
Botnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control of command and control (C&C) servers. These malevolent entities enable a range of malicious activities, from denial of service (DoS) attacks to spam distribution and phishing. Each bot operates as a malicious binary code on vulnerable hosts, granting remote control to attackers who can harness the combined processing power of these compromised hosts for synchronized, highly destructive attacks while maintaining anonymity. This survey explores botnets and their evolution, covering aspects such as their life cycles, C&C models, botnet communication protocols, detection methods, the unique environments botnets operate in, and strategies to evade detection tools. It analyzes research challenges and future directions related to botnets, with a particular focus on evasion and detection techniques, including methods like encryption and the use of covert channels for detection and the reinforcement of botnets. By reviewing existing research, the survey provides a comprehensive overview of botnets, from their origins to their evolving tactics, and evaluates how botnets evade detection and how to counteract their activities. Its primary goal is to inform the research community about the changing landscape of botnets and the challenges in combating these threats, offering guidance on addressing security concerns effectively through the highlighting of evasion and detection methods. The survey concludes by presenting future research directions, including using encryption and covert channels for detection and strategies to strengthen botnets. This aims to guide researchers in developing more robust security measures to combat botnets effectively. Exploring botnets: evolution, tactics, countermeasures. This survey dives into botnets, covering life cycles, communication, and evasion tactics. It highlights challenges and future strategies for combating cyber threats. image
Composite Hydrogel of Polyacrylamide/Starch as a Novel Amoxicillin Delivery System
(Mdpi, 2024) Poyraz, Yagmur; Baltaci, Nisa; Hassan, Gana; Alayoubi, Oubadah; Uysal, Bengu Ozugur; Pekcan, Onder
This study investigates the development and characterization of a novel composite hydrogel composed of polyacrylamide (PAAm), starch, and gelatin for use as an amoxicillin delivery system. The optical properties, swelling behavior, and drug release profile of the composite hydrogel's were studied to evaluate its efficacy and potential applications. UV-visible spectroscopy was employed to determine the optical properties, revealing significant transparency in the visible range, which is essential for biomedical applications. The incorporation of starch and gelatin into the polyacrylamide matrix significantly enhanced the hydrogel's swelling capacity and biocompatibility. Studies on drug delivery demonstrated a sustained release profile of amoxicillin in simulated gastrointestinal fluids, which is essential for maintaining therapeutic levels for a prolonged amount of time. The results indicate that the composite hydrogel of PAAm/starch/gelatin has good swelling behavior, appealing optical characteristics, and a promising controlled drug release mechanism. These results point to this hydrogel's considerable potential as a drug delivery method, providing a viable path toward enhancing the medicinal effectiveness of amoxicillin and maybe other medications.
Editorial Boards of Finance Journals: the Gender Gap and Social Networks
(Springer, 2024) Bedowska-Sojka, Barbara; Tarantola, Claudia; Mare, Codruta; Paccagnini, Alessia; Ozturkkal, Belma; Pisoni, Galena; Skaftadotti, Hanna Kristin
We investigate gender disparities and network linkages among editors of Finance journals at the end of 2022. The role of journal editors in shaping academic disciplines is crucial, yet gender imbalances and the geographic concentration of editors remain poorly understood. Ethical considerations arise when examining the representation of women on editorial boards, as these imbalances can impact academic equity and the diversity of perspectives. We examine the gender composition of editorial boards and uncover the network structures among editors, seeking to shed light on the concentration of editorial power and its implications for diversity and inclusion. Our findings reveal that women account for an average of 20% of all editors, with notable variations across countries. Additionally, editorial affiliations are heavily concentrated in the United States and the United Kingdom. Through typological metrics, we identify highly connected editors with significant board memberships. While gender ratios remain consistent in substructures involving highly central editors or those serving on multiple boards, men consistently outnumber women.
Electric Vehicle Selection for Industrial Users Using an Interval-Valued Intuitionistic Fuzzy Copras-Based Model
(Springer, 2024) Görçün, Ömer Faruk; Simic, Vladimir; Kundu, Pradip; Ozbek, Asir; Kucukonder, Hande
According to reports from international bodies such as the World Health Organization and the United Nations, transportation is one of the leading contributors to environmental pollution and climate change. Electric vehicles present a practical solution to reducing emissions, particularly for industrial users. However, industrial users' selection of electric vehicles involves different dynamics than individual users, making it a more complex process for companies. This paper aims to evaluate the selection criteria for electric vehicle fleets among industrial users using a novel multi-criteria decision-making framework based on interval-valued intuitionistic fuzzy sets. The model assesses various factors influencing industrial users' decisions and ranks the available electric vehicle options accordingly. The results indicate that driving range, purchase price, and charging time are the most influential factors in the decision-making process. Furthermore, the findings confirm that the Tesla Model S P100D is the most suitable option for industrial users, given its superior performance in these critical criteria.
The Unlimited Joy, 'once You Start You Can't Stop': Masculinity in Domestic Technology Commercials in Turkey
(Taylor & Francis Ltd, 2024) Karaosmanoglu, Defne; Ata, Leyla Bektas; Emgin, Bahar
Recently, studies have begun examining men's interaction with domestic space to explore changing forms of masculinity and domesticity, arguing that housework has become a leisure activity for men, with domestic technologies serving as tools (toys) for them to engage with. In this article, we explore how men in Turkish television commercials of domestic technologies are portrayed and how these portrayals construct and reconstruct discourses of domesticity and masculinity. We aim to understand men's relationship with masculinity, home and domestic work in these commercials. Alongside leisure and fun, we explore the construction of discourses of masculinity and domesticity through specific themes such as the naughty scientist, the self-seeking purchaser, and the flirtatious chef. We argue that seeing more men on screen does not democratise domesticity since the equal share of workload at home is still far from being realised even in these portrayals. We also argue that domesticity is aestheticized with the participation of men and technology. Finally, women are used as instruments by men in reconstructing their masculinity through heterosexuality.

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