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The Synergy of Statistical and Fuzzy Logic Approaches in Mining Patterns from the Peer-to-Peer Lending Data
No full textStatistical 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
No full textLow-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
Climate Change, Loss of Agricultural Output and the Macroeconomy: the Case of Tunisia
No full textThis 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.Science Citation Index Expanded - Social Science Citation Inde
Causal Mechanisms of Ontological (In)security in Turkish Politics and Foreign Policy: Anxiety Transmittance From Sèvres To Lausanne
No full textIn 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 S & egrave;vres 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.Social Science Citation Inde
Risk Assessment for Critical Infrastructure: a Novel Approach Using Osint Framework
No full textKritik altyapılar, modern toplumlarımızın önemli bir bölümünü oluşturarak ekonomilerimizin ve toplumlarımızın istikrarını desteklemektedir. Bunlar arasında elektrik şebekesi, bu sektörün kritikliği ve başarısızlığının potansiyel etkisi nedeniyle özel bir yer tutuyor. Ancak siber saldırılar da dahil olmak üzere modern tehditlerin doğası gereği, bu sektörün tehdit tanımlama ve ortadan kaldırma konusunda yenilikçi yaklaşımlara ihtiyacı var. Araştırmamız, kritik altyapıları korumak için Açık Kaynak İstihbarat teknolojilerinin kullanımına odaklanıyor. Bu çalışma aynı zamanda son on yılda kritik altyapılara (CI) yönelik çok sayıda önemli siber saldırıyı tartışarak Kritik Altyapıların ne kadar savunmasız olduğunu ve kötü niyetli saldırılara maruz kaldığını da tartışmaktadır. Bu araştırma, OSINT araçlarının bu sektördeki durumu analiz edebileceğini ve potansiyel riskleri azaltmak için nasıl kullanılabileceğini savunarak mevcut çalışma için geliştirilen bir çerçeveye dayanmaktadır. İstanbul'a elektrik sağlayan bir şirketi hedef alarak kötü niyetli faaliyetlerin karmaşık bir şekilde saldırması için bir giriş noktası olan IP'ler, e-posta adresleri, açık portlar, hizmetler vb. gibi önemli bilgileri tek bir platform altında topladık. Bildiğimiz kadarıyla bu, OSINT araçları kullanılarak geliştirilen ve enerji sektörlerine yönelik potansiyel riskleri belirlemek amacıyla OSINT araçlarının entegrasyonunu sağlayan ilk çerçevedir.Critical infrastructures account for a significant portion of our modern societies, underpinning the stability of our economies and societies. Among these, the electricity grid takes a special place due to the criticality of this industry and the potential impact of its failure. However, due to the nature of modern threats, including cyber-attacks, this sector needs innovative approaches to threat identification and elimination. Our research focuses on the utilization of Open Source Intelligence technologies to protect critical infrastructures. This study also discusses how Critical Infrastructures are vulnerable and exposed to malicious attacks by discussing several significant cyber-attacks on critical infrastructure (CI) in the last decade. This research is based on a framework developed for the current study, arguing that OSINT tools can analyze the landscape in this industry and how it can be used to mitigate potential risks. Targeting a company that supplies electricity to Istanbul, we have extracted key information like IPs, email addresses, open ports, services, etc, under a single platform, which is an entry point for malicious activity to attack in a sophisticated way. To the best of our knowledge, this is the first framework developed utilizing OSINT tools and creating an integration of OSINT tools to identify potential risks for energy sectors
Hydrogels From Protein-Polymer Conjugates: a Pathway To Next-Generation Biomaterials
No full textPoyraz, Yagmur/0000-0002-5318-7978Hybrid 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.Personal Research Funds of Onder PekcanPersonal Research Funds of Onder Pekcan funded this research at Kadir Has University.Science Citation Index Expande
Resource Allocation for Discrete Rate Multi-Cell Energy Constrained Communication Networks
No full textRadio frequency energy harvesting is a promising technique to extend the lifetime of wireless powered communication networks (WPCNs) due to its controllability. In this paper, we consider a novel discrete rate based multi-cell WPCN, where multiple hybrid access points (HAPs) transmit energy to the users and users harvest this energy for the information transmission by using a transmission rate selected from a finite set of discrete rate levels. We formulate an optimization problem to minimize the schedule length through optimal rate allocation and scheduling of the users while considering the traffic demand, energy causality and interference constraints. The problem is mixed integer non-linear programming problem. Initially, we investigate the problem for non-simultaneous and simultaneous transmission considering both predetermined and variable transmission rates. We propose optimal and heuristic algorithms for all these categories by using optimality analysis, Perron-Frobenius conditions and iterative improvement of the total schedule length. Then, for the general problem, we propose heuristic algorithm based on the maximization of the number of concurrently transmitting users within each time slot by considering the maximum allowed interference level of the users. Via extensive simulations, we demonstrate significant improvement in schedule length through rate selection and proper scheduling of concurrently transmitting users. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Ford Otomotiv Sanayi; 2247-A National Leaders Research Grant and Ford Otosan; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (117E241, 2247-A, 121c314); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAKScience Citation Index Expande
A New Quantum-Enhanced Approach To Ai-Driven Medical Imaging System
No full textMedical Imaging Systems (MIS) play a crucial role in modern medicine by providing accurate diagnostic and treatment capabilities. These systems use various physical processes to create images inside the human body for healthcare professionals to identify and address medical conditions. There is a growing interest in integrating artificial intelligence (AI) in medicine from various sources recently. Presently, with improved algorithms and more significant availability of training data, AI can help or even replace some of the tasks that were being performed by medical professionals. Typically, most MIS performance enhancements are achieved by leveraging transistor-based technologies. However, such implementations showcase certain disadvantages: for instance, slow processing speeds, high power consumption, large physical footprints, and restricted switching frequencies, especially in the GHz range. This could limit the effective performance and efficiency of MIS. Quantum computing, in turn, today appears as an alternative, at least for fully digital circuits in MIS; QCA provides advantages related to higher intrinsic switching speeds (up to terahertz) compared with transistor-based technologies, along with an improved throughput owing to its inherent compatibility with pipelining. QCA also has minimum power consumption and a smaller area of circuitry, which makes it amply suitable for establishing frameworks in circuit design for AI applications. The performance requirement in AI is real-time with minimum energy consumption and minimum cost. The ALU, in this regard, forms the basis for processing and computation units within processor systems. The method presented in this work benefits from the merits of QCA for an ALU design featuring low complexity, high performance, minimum power consumption, maximum speed, and reduced area. This approach has been able to successfully integrate the design of adders and multiplexers with that of basic gates to reduce latency and energy consumption with the aim of improving AI in MIS. The development and simulation of the proposed designs are carefully carried out using QCADesigner 2.0.03 software. A comparison of the different structures proposed shows significant improvements in complexity vs. cell count vs. power consumption compared to earlier designs, hence promising quantum computing for the MIS capability development. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Science Citation Index Expande
La Enseñanza Culturalmente Receptiva En La Educación Superior: Los Efectos De La Personalidad Y De Los Perfiles De Significado Personal De Los Académicos En La Autoeficacia En La Enseñanza Culturalmente Receptiva.
No full textIn this study, we aimed to examine the factors affecting the culturally responsive teaching competencies of academics and tested whether the personality traits and personal meaning profiles of the academics had significant effects on culturally responsive teaching self-efficacy. Data were obtained from 1,883 academics by using the Culturally Responsive Teaching Self-Efficacy Scale, Ten-Item Personality Inventory and Personal Meaning Profile Scale - Short Form. Correlation, regression and structural equation modelling analyses were run to analyse the data. We found that culturally responsive teaching self-efficacy is low. In terms of personal meaning profiles, 'openness to experience', 'agreeableness' personality traits and 'self-transcendence' and 'fair treatment' had a significant effect on culturally responsive teaching self-efficacy while 'religion' and 'self-acceptance' were found to have no effect. Based on the findings, we made various suggestions for academics to participate in training on culture-sensitive teaching pedagogy to be more sensitive to different cultures. El objetivo de este estudio es explorar los factores que influyen en las competencias pedag & oacute;gicas culturalmente receptivas del profesorado, y comprobar si los rasgos de personalidad y los perfiles de significado personal ejercen un efecto significativo sobre la autoeficacia de la ense & ntilde;anza culturalmente receptiva. Se obtuvieron datos de 1,883 acad & eacute;micos mediante la Escala de Autoeficacia de Ense & ntilde;anza Culturalmente Receptiva, el Inventario de Personalidad de 10 & iacute;tems y la Escala breve de Perfil de Significado Personal. Para analizar los datos, se llevaron a cabo an & aacute;lisis de correlaciones, de regresi & oacute;n y de modelo de ecuaciones estructurales. Los resultados revelan que la autoeficacia de la ense & ntilde;anza culturalmente receptiva es baja. Por lo que respecta a los perfiles de significado personal, los rasgos de personalidad 'apertura a la experiencia' y 'amabilidad', y 'autotrascendencia' y 'trato justo', ten & iacute;an un efecto significativo en la autoeficacia de la ense & ntilde;anza culturalmente receptiva, mientras que 'religi & oacute;n' y 'autoaceptaci & oacute;n' no ten & iacute;an ning & uacute;n efecto. A la luz de los resultados obtenidos, formulamos varias sugerencias orientadas a la participaci & oacute;n del profesorado en cursos de formaci & oacute;n sobre una pedagog & iacute;a culturalmente receptiva para mejorar su sensibilidad hacia las distintas culturas.Social Science Citation Inde