Prioritizing Sustainable Energy Strategies Using Multi-Criteria Decision-Making Models in Type-2 Neutrosophic Environment

Emphasis on developing sustainable energy strategies is very important for the energy sector to meet its environmental, economic, and social goals. Energy policy mostly considers the need to improve energy efficiency, cut down on carbon emissions, as well as create a profitable market. One of the major problems faced by decision makers is the complication and multi dimensionality of the decision- making process with regard to assessments, which entails numerous decision criteria. Overlooking these complicating factors has the potential of creating huge bottlenecks such as prolonged energy transition timelines, cost overruns, and derailing of environmental targets. This research introduces an approach based on Type-2 Neutrosophic Fuzzy Sets to rank sustainable energy strategies, using Logarithmic Percentage Change based on Objective Weights (LOPCOW) and Ranking of Alternatives through Functional Mapping of Criterion Sub-Intervals into Single Intervals (RAFSI) methods. The proposed model seeks to improve the effectiveness and efficiency of multi-criteria decision-making model in the areas where decision making is characterized by uncertainty and incompleteness. Energy strategies are evaluated in a comprehensive manner along numerous dimensions, including ecological, economic, technological, as well as societal. This approach helps to identify strategies that effectively support the sustainable development objectives of the energy sector. The analysis indicates that prioritizing Research and Development (R&D) strategies is particularly beneficial in achieving these goals. © 2025 Scrivener Publishing LLC

The Synergy of Statistical and Fuzzy Logic Approaches in Mining Patterns from the Peer-to-Peer Lending Data

Statistical measures, such as correlation, compute numeric values. However, it is not always the best option for domain experts. A promising way is to augment these measures linguistically. Therefore, the main objective of this work is the synergy of statistical and fuzzy logic approaches in mining and interpreting valuable information from financial lending data. The correlation reveals whether attributes are related while exhibiting relatively low computational costs. Fuzzy functional dependencies recognize the direction of influence but are demanding in terms of computational cost. Finally, linguistic summaries explore and interpret dependencies between the subdomains of the considered attributes. These two approaches are less influenced by a smaller vagueness in the data. In addition, the support for decision making validated by diverse approaches and explained from different points of view is more reliable. These approaches are integrated and applied to peer-to-peer (P2P) anonymized lending data consisting of 266,483 loans. Among other things, a significant correlation between loan amount and loan duration (r = 0.25) is explained further, indicating that the direction of influence is slightly stronger from loan duration to loan amount than the opposite case. At the same time, the dependency is very strong from low duration to low amount, but relatively weak from high duration to high amount. Finally, further research and application directions are outlined.COST (European Cooperation in Science and Technology) [CA19130]; Ministry of Education, Research, Development and Youth of the Slovak Republic [1/0660/23]; European Union [CZ.10.03.01/00-/22_003/0000048, 101119635]; National Research, Development and Innovation Fund of Hungary [TKP2021-NVA-29]; PRIN 2022 [CUP: E53C24002270006]The authors thank Petra Vasanicova for providing data and valuable information. This article is based upon work from the COST Action CA19130, FinAI-Fintech and Artificial Intelligence in Finance-Towards a transparent financial industry, supported by COST (European Cooperation in Science and Technology); VEGA project No. 1/0660/23 by the Ministry of Education, Research, Development and Youth of the Slovak Republic entitled "Strengthening financial resilience of individuals and households by sound financial decisions"; support of the European Union under the REFRESH-Research Excellence For Region Sustainability and High-tech Industries project number: CZ.10.03.01/00-/22_003/0000048 via the Operational Programme Just Transition; the "Application Domain-Specific Highly Reliable IT Solutions" project, implemented with the support provided by the National Research, Development and Innovation Fund of Hungary, financed under the Thematic Excellence Programme TKP2021-NVA-29 (National Challenges Subprogramme); the Marie Sklodowska-Curie Actions under the European Union's Horizon Europe research and innovation program for the Industrial Doctoral Network on Digital Finance (acronym: DIGI-TAL, project no. 101119635); and the support from PRIN 2022-CUP: E53C24002270006.Science Citation Index Expande

Addressing Social Vulnerabilities Resulting From Low-Carbon Energy Transition Policies in EU-27 Countries: A Systematic Survey of the Literature

Low-carbon transition research has experienced exponential growth in recent years, driven by the urgent need to mitigate climate change and achieve sustainability goals. The disruption of traditional industries, increased energy costs, and changes in land use are inevitable consequences of the low-carbon turn, often adversely impacting the least equipped to handle it. Vulnerable groups often face the greatest risks from climate change and the side effects of the policies designed to combat it. This study conducts a systematic literature review following the PRISMA 2020 guidelines, covering publications from the Web of Science and Scopus databases. Data were extracted into spreadsheets for descriptive analytics, and trends in publication years, countries, and policy tools were visualized with Python-generated heatmaps and summary tables. The findings reveal that despite best efforts to unburden vulnerable groups, many unaddressed concerns remain in the European 27 countries, where one might least expect them. The analysis highlights how one-size-fits-all policies ignore regional and social differences, disproportionately burdening vulnerable groups while favoring wealthier segments through subsidies and incentives. The mixed effectiveness of countermeasures-such as social tariffs, subsidies, and the Just Transition Mechanism-highlights ongoing challenges, including misrecognition, elite capture, and institutional constraints, while also underscoring notable successes like participatory community energy projects and locally tailored retrofitting initiatives. This research underscores the necessity of moving beyond uniform solutions, advocating for locally sensitive, equitable approaches that address affected communities' diverse needs and aspirations while ensuring social and environmental justice in the transition to a lowcarbon economy.Dynamic General Equilibrium Analysis [121K522]This research was supported by the TUBITAK project titled European Green Deal: Threats and Opportunities for Turkey, International Comprehensive and Dynamic General Equilibrium Analysis, with project number 121K522

The Impact of COVID-19 Pandemic on Tourism Employees: Was It the Last Straw?

Tourism, as one of the most vulnerable industries, has survived numerous global crises with substantial negative impact on economies, communities, businesses, and individuals. Despite the circumvention of the industry after those experiences of mild and severe crises, COVID-19 pandemic has been the most serious case with deep global impact in every corner of the world leading to the explosion of academic research on a plethora of pandemic aspects. However, research offering insights on tourism and hospitality employees' experiences, is scarce in the relevant literature in spite of the chronic problems of employee retention, qualified and long-term labor force. Therefore, the aim of this study addresses at examining the experiences of hotel employees in T & uuml;rkiye during and after COVID-19, which caused sudden and deep changes in the lives following the severe decline in tourism employment and economic problems it ushered in. The data was collected through in-depth interviews with 21 individuals who formerly worked in city or resort hotels at various positions and departments. Two sensemaking perspectives were integrated to find out the consequences of the pandemic leading to the causes and factors to end working in the industry. Study findings offer important insights into pandemic-related dynamics and could support the development of effective tourism policy and practices leading to improve crisis management efforts in the tourism and hospitality industry.Social Science Citation Inde

Feedback-Based Quantum Strategies for Constrained Combinatorial Optimization Problems

Feedback-based quantum algorithms have recently emerged as potential methods for approximating the ground states of Hamiltonians. One such algorithm, the feedback-based algorithm for quantum optimization (FALQON), is specifically designed to solve quadratic unconstrained binary optimization problems. Its extension, the feedback-based algorithm for quantum optimization with constraints (FALQON-C), was introduced to handle constrained optimization problems with equality and inequality constraints. In this work, we extend the feedback-based quantum algorithms framework to address a broader class of constraints known as invalid configuration (IC) constraints, which explicitly prohibit specific configurations of decision variables. We first present a transformation technique that converts the constrained optimization problem with invalid configuration constraints into an equivalent unconstrained problem by incorporating a penalizing term into the cost function. Then, leaning upon control theory, we propose an alternative method tailored for feedback-based quantum algorithms that directly tackles IC constraints without requiring slack variables. Our approach introduces a new operator that encodes the optimal feasible solution of the constrained optimization problem as its ground state. Then, a controlled quantum system based on the Lyapunov control technique is designed to ensure convergence to the ground state of this operator. Two approaches are introduced in the design of this operator to address IC constraints: the folded spectrum approach and the deflation approach. These methods eliminate the need for slack variables, significantly reducing the quantum circuit depth and the number of qubits required. We show the effectiveness of our proposed algorithms through numerical simulations.Independent Research Fund Denmark (DFF) [0136-00204B]This work was supported by Independent Research Fund Denmark (DFF) , project number 0136-00204B.Science Citation Index Expande

An Integrated Decision-Making Framework to Evaluate the Route Alternatives in Overweight/Oversize Transportation

Overweight and oversized transport (O&OT) has become one of the most critical elements of project logistics, driven by advancements in transportation and lifting technologies that now allow high-volume loads to be moved across long distances. This type of transportation operation, also called abnormal transportation, is greatly affected by technical factors such as the weight and geometry of the load, road surface, axle load limitations, slope, and ground strength, as well as external variables such as weather conditions, traffic density, and legal regulations. In planning and operational processes, Decision-Makers (DMs) and practitioners who plan and execute operations without adequately considering these factors and variables can lead to delays in operations, serious risks, and loss of productivity. This research proposes a flexible decision support model that integrates Step-wise Weight Assessment Ratio Analysis (SWARA) and Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), and a ranking technique; i.e., Mixed Aggregation by Comprehensive Normalization Technique (MACONT) techniques to address the decision problems related to route selection, one of the most critical problems in transporting heavy and bulky loads, and to produce reasonable solutions. The proposed model significantly reduces information losses by processing subjective and objective information and integrating subjective (SWARA) and objective (LOPCOW) methods. Unlike traditional ranking approaches, the MACONT method combines three different normalization techniques to determine the ranking performance of alternatives. In this way, it provides more reliable and accurate results by reducing the deviations of the results provided by the single normalization technique. In addition, it shows each alternative's good and bad performance compared to the others and is more convincing about the results obtained. According to the results obtained by applying the proposed model, fuel consumption (0.096) is determined as the most effective and critical factor in selecting the route on which heavy and bulky loads will be transported. In this context, choosing routes that allow lower fuel consumption can contribute to reducing carbon emissions and external costs arising from transportation. The extensive robustness and validation check to test the proposed model prove that the proposed model is a reliable, robust, and practical decision-making tool for making reasonable and rational decisions in O&OT. © 2025 Elsevier B.V., All rights reserved

Bilimsel İletişimde Dönüşüm: Açık Bilim, Yüksek Etkili Yayınlar ve Yapay Zeka

Bu sunum, 28 Ağustos 2025 tarihinde Kadir Has Üniversitesi’nde gerçekleştirilen ‘Bilimsel İletişimde Dönüşüm: Açık Bilim, Yüksek Etkili Yayınlar ve Yapay Zekâ’ başlıklı etkinlik kapsamında yapılmıştır

Effects of Imagining Someone Else Experience a Negative Autobiographical Memory on Phenomenological Experience

We investigated whether the phenomenological experience of a negative autobiographical memory changes when the self is presumably distanced from it. In session 1, participants described and phenomenologically rated an important negative event. One week later, in session 2, they imagined and described the event as if either a similar or a dissimilar friend experienced it. Afterward, they once more rated the original event that they described in session 1. Results showed increased observer perspective and decreased vividness, accessibility, and reliving of the original event after imagining that a friend experienced it. Importantly, when the negative event was imagined as experienced by a friend, preoccupation with overwhelming emotions related to the event, the event's emotional intensity, and its centrality to identity and life story also decreased. When the imagined friend was dissimilar, the emotional valence of the memory became more positive, and the emotional distance to the memory increased.Social Science Citation Inde

Physics-Informed Power Grid Reconstruction: Complex Systems Perspective

Enerji, modern yas¸amın tum y ¨ onlerini beslerken, elektrik s¸ebekesi bu sistemin temel ¨ altyapısını olus¸turur. Ancak bu s¸ebeke, buy¨ uk¨ olc¸ekli, do ¨ grusal olmayan ve mek ˘ ana ˆ gom¨ ul¨ u yapısıyla insan eliyle yapılmıs¸ en karmas¸ık sistemlerden biridir. K ¨ uc¸¨ uk bir bozul- ¨ ma bile ardıs¸ık arızaları tetikleyerek genis¸ c¸aplı elektrik kesintilerine ve toplumsal etkilere yol ac¸abilir. Gunes¸ ve r ¨ uzg ¨ ar gibi yenilenebilir kaynaklara gec¸is¸in hızlanmasıyla birlikte, ˆ s¸ebeke degis¸kenlik ve merkezsiz ˘ uretim gibi yeni zorluklarla kars¸ı kars¸ıyadır. Bu nedenle, ¨ s¸ebekenin kararlılıgını ve dayanıklılı ˘ gını sa ˘ glamak hem bilimsel bir zorunluluk hem de ˘ pratik bir ihtiyac¸ haline gelmis¸tir. ˆ Bu tez, karmas¸ık sistemler bakıs¸ ac¸ısıyla tasarlanmıs¸ ac¸ık kaynaklı bir yazılım hattı sunar. Bu hat, ac¸ık eris¸imli cografi verileri kullanarak y ˘ uksek gerilimli iletim a ¨ gı modelleri ˘ olus¸turmayı mumk ¨ un kılar. Y ¨ ontem genel olarak uygulanabilir olsa da, detaylı bir ¨ ornek ¨ c¸alıs¸ma olarak Turkiye elektrik iletim s¸ebekesi ele alınmıs¸tır. Ac¸ık eris¸imli, fiziksel olarak ¨ detaylı s¸ebeke modellerinin azlıgını gidermek amacıyla, OpenStreetMap verileri is¸lenerek ˘ MATLAB MATPOWER ile uyumlu modeller uretilmis¸tir. Ortaya c¸ıkan veri seti; hat ¨ empedansları, termal sınırlar ve yuk da ¨ gılımları gibi temel elektriksel parametreleri ic¸erir. ˘ Bu parametreler muhendislik tahminleriyle elde edilip g ¨ uc¸ akıs¸ı ac¸ısından do ¨ grulanmıs¸tır. ˘ ˙Iki ornek c¸alıs¸ma, yazılım aracının ve olus¸turulan veri setinin yararlılı ¨ gını g ˘ ostermektedir. ¨ ˙Ilk c¸alıs¸ma, senkronizasyon kararlılıgını, kararsızlı ˘ gın erken uyarı sinyallerini tespit et- ˘ mek ic¸in stokastik perturbasyon analizi kullanarak incelemektedir. ¨ ˙Ikinci c¸alıs¸ma ise termal as¸ırı yuklenmelere ba ¨ glı ardıs¸ık arızaları aras¸tırmakta, kırılgan iletim hatlarını belir- ˘ lemekte ve dayanıklılıgı artırmak ic¸in hedefe y ˘ onelik g ¨ uc¸lendirme ¨ onerileri sunmaktadır. ¨ Bu ornekler, elektrik s¸ebekesi dinamiklerini modellemede fiziksel ve yapısal gerc¸ekc¸ili ¨ gin ˘ onemini vurgulamaktadır. ¨ Bu c¸alıs¸maların otesinde, veri seti ve yazılım aracı, g ¨ uc¸ sistemi modellemesi ic¸in ¨ olc¸ekle- ¨ nebilir ve tekrarlanabilir bir c¸erc¸eve sunmaktadır. Yenilenebilir entegrasyonu, genis¸leme planlaması, dayanıklılık analizi ve gerc¸ek zamanlı izleme gibi uygulamaları desteklemektedir. Hem arac¸ların hem de verilerin kamuya ac¸ık hale getirilmesiyle, bu tez modern enerji altyapısının kararlılıgı ve s ˘ urd ¨ ur¨ ulebilirli ¨ gi˘ uzerine veri odaklı, disiplinlerarası ¨ aras¸tırmalara katkı saglamaktadır. ˘ Anahtar Sozcükler: Elektrik S¸ ebekesi Altyapısı, OpenStreetMap Veri Entegrasyonu, Karmas¸ık Sistemler Analizi, Senkronizasyon, KararlılıkEnergy powers all aspects of modern life, with the electrical power grid serving as its foundational infrastructure. Yet, the grid is also one of the most complex man-made systems: large-scale, nonlinear, and spatially embedded. Even minor disturbances can trigger cascading failures, leading to widespread blackouts and serious societal impacts. As the global energy transition accelerates with increasing reliance on renewables like solar and wind, the grid faces new challenges from variability and decentralised generation. Ensuring its stability and resilience has become both a scientific imperative and a practical necessity. This Thesis introduces an open-source software pipeline for constructing high-voltage transmission network models using open-access geospatial data, framed through a complex systems lens. While the approach is broadly applicable, the Turkish power grid serves as a detailed case study. In response to the scarcity of openly available, physically detailed grid models, this work transforms raw OpenStreetMap data into MATLAB MATPOWER-compatible power system models. The resulting dataset includes key electrical parameters—line impedances, thermal limits, and load distributions—generated through engineering estimation and validated for power flow feasibility. Two case studies demonstrate the software tool and generated dataset's utility. The first examines synchronisation stability using stochastic perturbation analysis to detect early warning signals of instability. The second investigates cascading failures due to thermal overloads, identifying vulnerable transmission lines and proposing targeted reinforcements to enhance resilience. These examples highlight the importance of physical and structural realism in modelling power grid dynamics. Beyond these studies, the dataset and software establish a scalable, reproducible framework for power system modelling. They support applications in renewable integration, expansion planning, resilience analysis, and real-time monitoring. By making both tools and data publicly available, this Thesis contributes to data-driven, interdisciplinary research on the stability and sustainability of modern power infrastructure. Keywords: Power Grid Infrastructure, OpenStreetMap Data Integration, Complex Systems Analysis, Synchronisation, Stabilit

The Impact on Risk Management and Price Discovery due to Governmental Intervention: A Case of Two Commodity Futures

The study explored the impact of government intervention on price discovery and hedging efficacy concerning the agricultural commodities futures market, specifically on Chickpea (Chana) and wheat futures traded on India's NCDEX. This study examines variations in price discovery events before and after the intervention, utilizing the Garbade-Silber (GS) model and Granger causality tests. The Minimum Variance Hedge Ratio (MVHR) assesses the efficacy of hedging. The results demonstrate significant changes: post-intervention, the futures price of chickpeas exhibits enhanced efficacy in price discovery, but the future-led relationship within the wheat market diminishes. Moreover, chickpea futures exhibit stable hedging efficacy, but wheat futures experience a decline in effectiveness, likely attributable to heightened volatility. The findings indicate the varying effects of regulation on commodity markets, suggesting that targeted interventions could enhance market stability and efficiency. It is beneficial for policy analysts, market participants, and researchers in agricultural futures markets. © 2025 Elsevier B.V., All rights reserved