Scopus İndeksli Yayınlar Koleksiyonu

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Probabilistic Assessment of Vehicle-To Power of Electric Vehicle Parking Lots: a New Comprehensive Approach
(Institute of Electrical and Electronics Engineers Inc., 2025) Tourandaz Kenari, M.; Ozdemir, A.
The increasing market share and parking occupancy of electric vehicles have increased the charging stations in parking lots (PLs) and motivated the research to provide proper management strategies. Although there have been several efforts to assess, schedule, and model the load of fast electric vehicle charging stations (FEVCSs), they are inappropriate for charging stations in EVPLs. This paper proposes a novel comprehensive probabilistic approach to calculate the parking lot power and energy for battery and hybrid electric vehicles. At first, a decision is made for the operation mode of an EV arriving at the PL, using an algorithm considering ten affective random variables. Then, the aggregated parking lot power and energy are determined using Monte Carlo simulations. Finally, the Gaussian Mixture Model is used to estimate the parameters of the output probability density functions, where the maximum likelihood estimation is employed to find model components. The proposed approach is applied to a sample parking lot, and its performance is demonstrated by comparing it to a base case study and one of the pioneering techniques introduced in the literature. Finally, a thorough sensitivity analysis is applied to assess the robustness of the outputs under different scenarios. The results demonstrate the superiority of the proposed method compared to the available studies. © 1967-2012 IEEE.
Climate Change, Loss of Agricultural Output and the Macroeconomy: the Case of Tunisia
(Elsevier B.V., 2025) Yilmaz, S.D.; Ben-Nasr, S.; Mantes, A.; Ben-Khalifa, N.; Daghari, I.
This paper constructs an empirical, multi-sectoral, open-economy Stock-Flow Consistent (SFC) model to assess the long-term macroeconomic impact of a sustained climate-induced decline in Tunisia's agricultural production. Our framework captures the main interactions between climate-driven agricultural impacts, the real economy, and the financial system. We empirically calibrate our model using a large set of datasets including national accounts, input-output tables, balance of payments, banking sector balance sheets and agricultural production projections from crop models. We then simulate the model for the period 2018–2050. Our results show that the costs of inaction in the face of declining agricultural production are dire for Tunisia. The economy will face high unemployment and inflation, growing internal and external macroeconomic imbalances, and a looming balance of payments crisis, especially if global food inflation remains high in the coming decades. We then simulate two possible adaptation scenarios envisaged by policymakers and show that adaptation investments in water resources, increased water efficiency in production, and a public, investment-driven big push can put the economy back on a sustainable path in the long-run. © 2025 The Authors
Quantum Thermal Machine as a Rectifier
(Institute of Physics, 2025) Santiago-García, M.; Pusuluk, O.; Müstecaplıoğlu, O.E.; Çakmak, B.; Román-Ancheyta, R.
We study a chain of interacting individual quantum systems connected to heat baths at different temperatures on both ends. Starting with the two-system case, we thoroughly investigate the conditions for heat rectification (asymmetric heat transport), compute thermal conductance, and generalize the results to longer chains. We find that heat rectification in the weak coupling regime can be independent of the chain length and that negative differential thermal conductance occurs. We also examine the relationship between heat rectification with entanglement and the entropy production. In the strong coupling regime, the system exhibits an asymmetric Rabi-type splitting in the thermal conductance, leading to enhanced heat transport and improved rectification inaccessible in the weak coupling. This setup represents the simplest quantum thermal machine that consumes incoherent resources and delivers entanglement while acting as a rectifier and heat valve. © 2025 The Author(s). Published by IOP Publishing Ltd.
Artificial Intelligence-Enhanced Intrusion Detection Systems for Drone Security: a Real-Time Evaluation of Algorithmic Efficacy in Mitigating Wireless Vulnerabilities
(Springer, 2025) Senturk, K.; Gormus, A.F.; Gonen, S.; Bariskan, M.A.; Durmaz, A.K.
Advancements in science and technology have provided extensive opportunities and conveniences for mankind. One prime example of these advancements is wireless communication technology. This technology provides users with mobility during communication, initiating a paradigm shift. The convenience of wireless communication technology has initiated the production of versatile devices. Among these technologies developed in recent years for observation and detection purposes in various fields, drones have taken a leading role. Drones, with their versatile applications and access to real-time data, are being used in various operations. With such utilization, humans are increasingly interacting with these systems, leading to natural human-drone interaction. However, in these human-drone interactions, as is the case with many wireless devices, security often becomes an afterthought, leaving many drones vulnerable to cyber attacks. The most effective way to protect against these attackers is to conduct vulnerability analyses of the systems we use against emerging threats and address the detected vulnerabilities. This paper investigates the vulnerabilities of wireless communication regarding remote connectivity usage of a commercial drone, the DJI Ryze Tello, with the aim of examining its weaknesses. In this context, a test environment was created to reveal problems and threats in drone technology through attacks executed on the test environment (DEAUTH ATTACK, Port Scan DOS, DDoS, and MitM). Following the identification of these vulnerabilities, an artificial intelligence-based study was carried out to detect these attacks. In the study, the percentages of attack detection using different algorithms were verified with graphs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Strategic Analysis of E-Trade Platforms in Automotive Spare Part Sector: a T-Spherical Fuzzy Perspective
(Elsevier B.V., 2025) Görçün, Ö.F.; Chatterjee, P.; Aytekin, A.; Korucuk, S.; Pamucar, D.
E-trade platforms are software applications that enable businesses to conduct online sales and manage their digital storefronts. These platforms provide a range of tools and features to facilitate the creation, operation, and management of an online business. This study comprehensively evaluates e-trade platforms within the automotive spare parts industry, examining various critical aspects to identify the optimal platform. The evaluation includes an in-depth analysis of the current state of the platforms, exploration of potential strategies and approaches for improvement, and identification and analysis of challenges and barriers. To address these issues, the study employs problem-solving within the framework of expert evaluations based on criteria defined by an extensive literature review. T-Spherical fuzzy (T-SF) subjective weighting approach and T-SF-weighted aggregated sum product assessment (WASPAS) method are used for this purpose. The analysis reveals that “security” is the most crucial criterion, with Amazon emerging as the most prominent e-trade platform. The findings indicate that prioritizing security, discounts, and delivery time will enable e-commerce platforms to gain a competitive edge. The study evaluates international e-commerce platforms, identifying weaknesses in critical business areas key competitive advantage factors, and offering forward-thinking recommendations. This research has significant implications for the rapid and effective development of logistical partnerships with e-trade platforms across various industries. Additionally, it serves as a foundational basis and template for future research in the e-commerce sector, particularly within the automotive spare parts industry. © 2025 Elsevier Inc.
The Impact of Economic Factors on Environmental Degradation: Price Instability, Monetary Growth and Renewable Energy Investments
(Emerald Publishing, 2025) Aydın, A.
Purpose: This study examines the complex relationship between price stability, monetary growth and renewable energy investments. The pursuit of environmentally sustainable economies is intertwined with the need to maintain price stability and poses a complex challenge for global policymakers. Design/methodology/approach: Through a comprehensive review, this study seeks answers to how price stability affects pollution, particularly carbon emissions, through various economic channels. Employing panel data analysis for 84 countries between 1999 and 2020, we find a multifaceted effect of price instability on carbon emissions. Findings: According to system-GMM estimation results, we find (1) price stability has no significant direct effect on carbon emissions. However, it emerges as a crucial environmental factor through consumption, investment and monetary policy channels. (2) Moreover, price stability reverses the positive effects of renewable energy investments on carbon emissions, and it slows down the carbon emissions-increasing effect of energy consumption. (3) Monetary expansion combined with price stability increases environmental pollution. These findings underscore the complexity of balancing economic stability and environmental sustainability and highlight the need for comprehensive policy approaches to address these global challenges effectively. Originality/value: There is a significant gap in the existing literature examining the impact of price stability on carbon emissions. Most of the studies observe the impact of carbon emissions on inflation. However, the complex interaction between economic and environmental factors reveals inflation as a factor affecting pollution, particularly the amount of carbon emissions. © 2025, Emerald Publishing Limited.
An Interval Rough Improved Ordinal Priority Approach-Based Decision Support System To Redesign Postal and Logistics Networks
(Elsevier Ltd, 2025) Pamucar, D.; Dobrodolac, M.; Simic, V.; Lazarevic, D.; Görçün, Ö.F.
Postal and logistics companies are essential subjects in the economy, providing services of the corresponding assortment for a wide range of business and private users. Service providers strive to meet the needs of users and, at the same time, make as much profit as possible. The efficiency of each of the subsystems in companies from this area significantly impacts the sustainability of postal and logistics systems. Rural areas, which are characterized by a smaller number of users and services and a low level of system efficiency, can have an additional negative impact on sustainability. As a result, optimization tasks become complex but also necessary to solve. The paper proposes an interval rough improved Ordinal Priority Approach - Power Schweiyer-Sklar Combined Compromise Solution (I-OPA - PSS'CoCoSo) methodology for prioritizing different models of solving the problem of inefficient network units. Methodological novelties are: a) A new approach for defining the lower and upper limits of interval rough numbers is proposed, which is based on nonlinear Bonferroni functions; b) The classic OPA linear model is improved through the implementation of a new concept for defining relational relationships between criteria; c) The CoCoSo method is improved through the implementation of nonlinear PSS and implementation of a novel function for the integration of aggregate strategies. The application of the interval rough I-OPA - SSP'CoCoSo methodology is demonstrated through a case study on the example of a public postal operator operating in the territory of the Republic of Serbia. Since this is a system with a highly developed infrastructure and network throughout the entire country, this further implies the applicability of the methodology to smaller systems or sectors within larger companies that deal with parcel deliveries and other logistics activities. A new aggregation function is introduced to define the compromise index of the alternatives as well as eliminate the anomaly of the original function. The simulation of different scenarios is enabled depending on the degree of risk. The proposed methodology enables decision-making in conditions of incomplete and imprecise criteria values. In accordance with the aforementioned, this approach contributes to improving the accuracy of modeling expert opinions, and consequently, in making the final decision. © 2025 Elsevier Ltd
Placemaking Through Guerilla Gardens at the Toki Mass Housing Grounds in Bor (Niğde)
(Springer Science and Business Media Deutschland GmbH, 2025) Soyöz, U.
This paper investigates the guerilla gardens at the TOKI compound in Bor, Niğde, that add a vernacular touch to modern mass housing. TOKI, short for Turkish Republic Mass Housing Administration, was established to eradicate gecekondu, informal settlements that emerged in metropolitan cities in the 1950s due to rural-to-urban migration. Tasked with providing affordable housing for low-income families, TOKI implements standardized designs across Turkey's cities and towns, including the Bor (Niğde) complex. As opposed to the rigidity and uniformity of TOKI blocks, the architecture of guerilla gardens displays the spontaneity and ingenuity that characterized the original gecekondu. The research seeks answers to the questions of why guerilla gardens emerge at the Toki grounds, especially because TOKI was initially designed to eradicate vernacular gecekondu and what needs of the inhabitants these gardens accommodate. To answer these questions, ethnographic inquiry is conducted with the users of the guerilla gardens. Through the lens of the Bor TOKI residents' initiative, this paper offers insights into how vernacular input can inform future mass housing projects, possibly leading to more inclusive models that prioritize environmental sustainability while also respecting local lifestyles. © The Author(s) 2025.
Hydrogels From Protein–polymer Conjugates: a Pathway To Next-Generation Biomaterials
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Alayoubi, O.; Poyraz, Y.; Hassan, G.; Gül, S.B.; Çalhan, N.; Mert Şahin, N.M.; Pekcan, Ö.
Hybrid hydrogels from protein–polymer conjugates are biomaterials formed via the chemical bonding of a protein molecule with a polymer molecule. Protein–polymer conjugates offer a variety of biological properties by combining the mechanical strength of polymers and the bioactive functionality of proteins. These properties allow these conjugates to be used as biocompatible components in biomedical applications. Protein–polymer conjugation is a vital bioengineering strategy in many fields, such as drug delivery, tissue engineering, and cancer therapy. Protein–polymer conjugations aim to create materials with new and unique properties by combining the properties of different molecular components. There are various ways of creating protein–polymer conjugates. PEGylation is one of the most common conjugation techniques where a protein is conjugated with Polyethylene Glycol. However, some limitations of PEGylation (like polydispersity and low biodegradability) have prompted researchers to devise novel synthesis techniques like PEGylation, where synthetic polypeptides are used as the polymer component. This review will illustrate the properties of protein–polymer conjugates, their synthesis methods, and their various biomedical applications. © 2025 by the authors.
Deviation From the Balanced Time Perspective and Depression and Anxiety Symptoms: the Mediating Roles of Cognitive-Behavioral Emotion Regulation in a Cross-Cultural Model
(Frontiers Media SA, 2025) Abdollahpour Ranjbar, H.; Altan-Atalay, A.; Habibi Asgarabad, M.; Turan, B.; Eskin, M.
Background: Time perspective (TP) influences how individuals perceive and classify their past, present, and future, impacting their cognition, behavior, and psychological outcomes. Deviation from the balanced time perspective (DBTP) is associated with mental health problems (e.g., depression and anxiety). Emotion regulation (ER) encompasses cognitive and behavioral processes to regulate emotions, with maladaptive strategies like rumination and withdrawal linked to depression and anxiety. Despite extensive research on TP and ER, their joint impact, particularly in the context of depression and anxiety, and cultural differences remain underexplored. Method: Participants (N = 513 Iranian, N = 470 Turkish) completed self-report questionnaires on time perspective, cognitive and behavioral ER, anxiety, and depression symptoms. A moderated mediation model was assessed, incorporating the exogenous variable of DBTP, with ER strategies as mediators, and endogenous variables of depressive and anxiety symptoms. The model accounted for cultural variations in the paths as a moderator. Results: Significant associations were found between DBTP, ER strategies, depression, and anxiety symptoms. Mediation analyses revealed that both cognitive and behavioral ER strategies (except for adaptive behavioral ER strategies) significantly mediated the associations between DBTP and depression and anxiety. Additionally, multigroup analyses suggested that these mediating effects were consistent across Iranian and Turkish samples, with exceptions in adaptive cognitive ER strategies. Conclusion: The study highlights the crucial role of TPs and ER strategies in predicting anxiety and depression symptoms, with notable cultural nuances. Specifically, maladaptive strategies exacerbate symptoms, while adaptive strategies mitigate them primarily in Iranian contexts. Cultural subtleties are discussed in detail. Copyright © 2025 Abdollahpour Ranjbar, Altan-Atalay, Habibi Asgarabad, Turan and Eskin.
Anomaly Detection and Performance Analysis With Exponential Smoothing Model Powered by Genetic Algorithms and Meta Optimization
(Institute of Electrical and Electronics Engineers Inc., 2025) Guler, A.K.; Fuat Alsan, H.; Arsan, T.
This study employs a genetic algorithm to optimize the parameters of the Third Order Exponential Smoothing model for predicting on the real-time traffic datasets of the Numenta Anomaly Benchmark (NAB). The genetic algorithm process was executed with different population sizes and gene sets. In addition, a parameter sensitivity analysis was conducted, through which the ideal number of genes and population size providing the best results within the specified range were determined. Moreover, a novel approach incorporating meta-optimization techniques is proposed to enhance the efficiency of the genetic algorithm optimization process, aiming to achieve improved accuracy in anomaly detection. The proposed methodology has been tested on various traffic data scenarios across different datasets to detect deviations critical to traffic management systems. Performance comparisons using the NAB scoring system demonstrate that the method developed in this study outperforms the majority of existing NAB algorithms, as well as the contemporary approaches of Isolation Forest, Multi-Layer Perceptron Regressor (MLPRegressor), and hybrid K-Nearest Neighbors - Gaussian Mixture Models (KNN + GMM), and is competitive with leading algorithms. The proposed approach, which achieved scores of 54.41 for 'Standard', 53.95 for 'reward_low_FP_rate', and 69.61 for 'reward_low_FN_rate', indicates improvements of 3.67%, 4.45%, and 2.63%, respectively, compared to the average scores of the NAB algorithms. The findings indicate that the proposed approach not only detects anomalies with high precision but also dynamically adapts to changing data characteristics without requiring manual recalibration. This study proposes a robust traffic anomaly detection method that ensures reliable monitoring and potentially facilitates effective traffic management and planning.The results of the study can be extended to other areas requiring real-time data monitoring and anomaly detection, offering a scalable solution adaptable to different contexts and requirements. © 2013 IEEE.
Causal Mechanisms of Ontological (in)security in Turkish Politics and Foreign Policy: Anxiety Transmittance From Sèvres To Lausanne
(Routledge, 2025) Ermihan, E.; Karamık, İ.
In the ‘age of anxiety,’ research on trauma, memory, and syndromes in politics and foreign policy is growing. This article examines Turkey's unique case, focusing on anxieties over the Treaties of Sèvres and Lausanne. It explores how both generate anxiety, using an interdisciplinary approach to ontological (in)security, trauma, and memory with a Discourse-Historical Approach. It identifies two processes: anxiety informing policy choices and policies rationalized through collective anxiety. Tracing Turkey's history shows how anxiety is amplified and mitigated. The findings highlight anxiety’s role in policymaking, revealing how elites use trauma and memory to shape politics and foreign policy. © 2025 Informa UK Limited, trading as Taylor & Francis Group.
The Role of the Third Plague Pandemic in Colonial India as the Impetus for the Improvement Trusts and Urban Transformation in Bombay, Mysore, and Calcutta
(Routledge, 2025) Vardı Topal, H.; Atasoy, Z.B.
This paper explores the intersection of public health crises, urban planning, and colonial governance in British India during the late 19th and early 20th centuries, focusing on the plague epidemic from 1896 to 1911. It examines improvement trusts as precursors to formal town planning, using archival reports, maps, and photographs. Case studies of Bombay, Mysore, and Calcutta reveal how these trusts undertook urban renewal projects such as slum clearance, infrastructure upgrades, and suburban expansion. However, these efforts often displaced marginalized communities, exacerbating housing shortages without providing adequate or affordable alternatives. Improvement trust interventions frequently demolished more housing than rebuilt, forcing displaced populations into peripheral areas with poor living conditions. These measures, framed as public health initiatives, prioritized colonial economic interests over social equity, perpetuated unsanitary environments, and deepened socio-economic inequalities. The 1896 plague marked a turning point, enabling sweeping changes to the urban fabric under the pretext of disease prevention. This study highlights how colonial administrations instrumentalized health crises to consolidate control, leaving a legacy of recurring housing crises and ingrained spatial inequalities. It highlights the intertwined roles of public health and urban interventions in promoting colonial agendas while disregarding the needs of vulnerable populations. © 2025 Informa UK Limited, trading as Taylor & Francis Group.
Evaluation of Crawler Cranes for Large-Scale Construction and Infrastructure Projects: an Intuitionistic Fuzzy Consensus-Based Approach
(Elsevier B.V., 2025) Görçün, Ö.F.; Saha, A.; Ecer, F.
Choosing the proper and best crawler crane is a complicated decision-making issue due to several conflicting criteria and vagueness in the construction and project logistics industries. This decision-making problem has become compounded due to insufficient studies on crawler crane selection in the relevant literature. The current study introduces an intuitionistic fuzzy consensus-based complex proportional assessment model (IF-c-COPRAS) developed to address the existing research gaps and identify the best and most suitable crawler crane. The acquired conclusions revealed that the most potent criterion influencing the crawler crane selection is "job potential," with a weighted score of 0.7665, followed by "periodic control and inspection" and "crane model year." Once the following findings of the paper regarding crawler crane variants are evaluated, the crawler crane manufactured by Liebherr Co. is the most feasible alternative, with a relative significance score of 0.8324. These outcomes provide sensible implications and insights for practitioners and decision-makers in the construction and project logistics (overweight/oversized cargo lifting and transport firms) industries, providing an applicable guideline for improving the quality of construction operations. Additionally, crane manufacturers can consider these managerial and policy implications and insights to improve the abilities and quality of the crawler cranes they produce. © 2025 Elsevier Inc.
From ‘safe Haven’ To ‘Zone of Precarity’: Locating Istanbul Through the Perceptions and Everyday Urban Practices of Skilled Migrants
(Springer Science and Business Media Deutschland GmbH, 2025) Tuncer, E.
This article seeks to position Istanbul through the practices of everyday life of middle-class, skilled migrants from both the Global North and South and their perceptions of urban safety and precarity. It examines individuals’ processes of migration to Turkey, revealing their initial impressions of Istanbul as a safe city of opportunities, and then analyses their everyday urban lives, highlighting hidden forms of precarity and discrimination. Through in-depth interviews with 45 women and 34 men—more than half of whom are North American and European—and participant observation in people’s living environments and at various social events, I argue that Istanbul, while perceived as a ‘safe haven’ at first, becomes a ‘zone of precarity’ where most of the participants have experienced intersectional forms of precarity, latent patterns of discrimination, and insecurities that belie the common perception that skilled migrants are privileged. To substantiate this argument, this ethno-spatial study presents an analysis of qualitative data as well as an online subjective mapping of Istanbul, where perceptions of urban safety and spatial precarity are displayed through socio-spatial experiences encountered in neighbourhoods, workplaces, and public spaces. © The Author(s) 2025.
Life Cycle Assessment of Black Tea Production and Consumption in Türkiye: Insights From Waste Management Scenarios
(Elsevier B.V., 2025) Üçtuğ, F.G.; Ediger, V.Ş.; Küçüker, M.A.; Berk, İ.; İnan, A.; Tuğcu, M.
This study conducts a life cycle assessment (LCA) of tea production and consumption in Türkiye, the world leader in per capita tea consumption. Aiming to identify environmental hotspots and propose sustainable solutions, a cradle-to-grave LCA was performed using CCaLC2 software, CML methodology, and the Ecoinvent 3.0 database. It covers cultivation, processing, transportation, and consumption stages, focusing on key environmental indicators like carbon footprint and acidification potential. The results reveal that consumption dominates the environmental footprint (91%) due to energy-intensive brewing methods. Cultivation and transportation contribute minimally (4% each). This highlights the need for promoting energy-efficient brewing practices and consumer adoption of renewable energy sources. The study also explores the environmental implications of different waste management strategies. Composting emerged as the most beneficial approach for reducing the carbon footprint and photochemical oxidants creation, while incineration might be preferable for other impact categories. This study underscores the importance of addressing energy consumption during tea brewing and encouraging renewable energy use among consumers. Additionally, it promotes composting as a crucial waste management strategy for a more sustainable tea value chain in Türkiye. These findings offer valuable insights for policymakers, industry players, and tea drinkers to make informed decisions that minimize environmental impact. © 2025 Elsevier B.V.
Differentiating Functional Connectivity Patterns in Adhd and Autism Among the Young People: a Machine Learning Solution
(SAGE Publications Inc., 2025) Sütçübaşı, B.; Ballı, T.; Roeyers, H.; Wiersema, J.R.; Çamkerten, S.; Öztürk, O.C.; Sonuga-Barke, E.
Objective: ADHD and autism are complex and frequently co-occurring neurodevelopmental conditions with shared etiological and pathophysiological elements. In this paper, we attempt to differentiate these conditions among the young people in terms of intrinsic patterns of brain connectivity revealed during resting state using machine learning approaches. We had two key objectives: (a) to determine the extent to which ADHD and autism could be effectively distinguished via machine learning from one another on this basis and (b) to identify the brain networks differentially implicated in the two conditions. Method: Data from two publicly available resting-state functional magnetic resonance imaging (fMRI) resources—Autism Brain Imaging Data Exchange (ABIDE) and the ADHD-200 Consortium—were analyzed. A total of 330 participants (65 females and 265 males; mean age = 11.6 years), comprising equal subgroups of 110 participants each for ADHD, autism, and healthy controls (HC), were selected from the data sets ensuring data quality and the exclusion of comorbidities. We identified region-to-region connectivity values, which were subsequently employed as inputs to the linear discriminant analysis algorithm. Results: Machine learning models provided strong differentiation between connectivity patterns in participants with ADHD and autism—with the highest accuracy of 85%. Predominantly frontoparietal network alterations in connectivity discriminate ADHD individuals from autism and neurotypical group. Networks contributing to discrimination of autistic individuals from neurotypical group were more heterogeneous. These included language, salience, and frontoparietal networks. Conclusion: These results contribute to our understanding of the distinct neural signatures underlying ADHD and autism in terms of intrinsic patterns of brain connectivity. The high level of discriminability between ADHD and autism, highlights the potential role of brain based metrics in supporting differential diagnostics. © The Author(s) 2025.
Critical Digital Data Enabling Traceability for Smart Honey Value Chains
(Taylor and Francis Ltd., 2025) Ziemba, E.W.; Maruszewska, E.W.; Karmańska, A.; Aydın, M.N.; Şahin, A.
Data analysis and sharing are becoming increasingly important in creating value within food supply chains, including honey value chains. While some data is readily shared between supply chain actors, unlocking further benefits requires additional investments in digital data capturing, particularly for value-based claims such as sustainability, equity, and traceability from hives to customers. This study aims to identify critical digital data necessary for smart honey value chains to ensure traceability and transparency while fostering trust among beekeepers, intermediaries, and consumers. Semi-structured interviews with 30 beekeeping experts were conducted to explore their perspectives. The analysis identified four critical categories of data—beekeeper data, apiary data, honey data, and apiary practices data—encompassing 21 specific data points essential for ensuring transparency, traceability, and trust. These findings provide novel insights into the digital data requirements necessary to support the honey industry’s evolving needs for transparent and traceable value chains. © 2025 International Association for Computer Information Systems.
Reflection Predicts and Leads To Decreased Conspiracy Belief
(Elsevier B.V., 2025) Bayrak, F.; Sümer, V.; Dogruyol, B.; Saribay, S.A.; Alper, S.; Isler, O.; Yilmaz, O.
Recent research indicates a generally negative relationship between reflection and conspiracy beliefs. However, most of the existing research relies on correlational data on WEIRD (Western, Educated, Industrialized, Rich, Democratic) populations. The few existing experimental studies are limited by weak manipulation techniques that fail to reliably activate cognitive reflection. Hence, questions remain regarding (1) the consistency of the negative relationship between conspiracy beliefs and cognitive reflection, (2) the extent of cross-cultural variation and potential moderating factors, and (3) the presence of a causal link between cognitive reflection and conspiracy beliefs. In two preregistered studies, we investigated the association between cognitive reflection and conspiracy beliefs. First, we studied the correlation between two variables across 48 cultures and investigated whether factors such as WEIRDness and narcissism (personal and collective) moderate this relationship. In the second study, we tested the causal effect of reflection using a reliable and effective manipulation technique—debiasing training—on both generic and specific conspiracy beliefs. The first study confirmed the negative association between reflection and belief in conspiracy theories across cultures, with the association being notably stronger in non-WEIRD societies. Both personal and collective narcissism played significant moderating roles. The second study demonstrated that debiasing training significantly decreases both generic and COVID-19 conspiracy beliefs in a non-WEIRD context, with more pronounced effects for general conspiracy beliefs. Our research supports that reflection is a consistent cross-cultural predictor of conspiracy beliefs and that activating reflection can reduce such beliefs through rigorous experimental interventions. © 2025 Elsevier B.V.
Advanced Restoration Management Strategies in Smart Grids: the Role of Distributed Energy Resources and Load Priorities
(Elsevier Ltd, 2025) Ahmadi, B.; Ceylan, O.; Ozdemir, A.
Fast restoration following long outages is a challenge in the smart city management process. It is necessary to accurately characterize the real operating conditions of the system for optimal restoration. This study focuses on two key factors of a practical distribution system restoration. The first factor is cold load pickup (CLPU), which commonly occurs after an outage and is caused by thermostatically controlled loads. A time-dependent CLPU is modeled to accurately describe the restored load behaviors. The second factor is the effect of the distributed generators (DG), energy storage systems (ESSs), and load priority factors on the system's restoration process. To address this challenge, a robust optimization model is proposed that fully considers the effect of DG, and ESS units and uncertainty of CLPU. The proposed models are tested on the IEEE 33-node and 69-node test systems using the Advanced Grey Wolf Algorithm (AGWO). The simulation scenarios are designed to uncover optimal scheduling strategies for the restoration process corresponding to each Pareto solution of a previous study. The results are discussed for several distinct initial conditions. Moreover, a comparative evaluation is done, contrasting the outcomes achieved through the AGWO algorithm with those stemming from alternative heuristic methods. © 2025 The Authors

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