Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Book Part A Systematic Review of Optimization Studies Used in Renewable Energy Systems(Springer Science and Business Media Deutschland GmbH, 2026) Söylemez, İ.; Erdoğan, A.This study presents a literature review of recent studies on renewable energy systems. Due to the large number of studies, this study has been limited to some keywords. When only the word “renewable energy systems” is searched, there are more than 14,343 studies in the literature between 2017 and 2024. A systematic search was conducted for the studies in which “optimization” or “mathematical model” was mentioned as a solution methodology. A total of 755 studies were identified in the “Scopus database” and analyzed for these studies. A detailed examination was carried out for the type of studies (research article, review, conference paper, etc.), countries where the studies were carried out, authors who carried out the studies and their statistics with each other, and so on. With this study, an overview of the literature will be provided and it will be a guiding study for researchers on the direction of the studies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.Conference Object Citation - Scopus: 1Drug Repositioning via Entity Transformation in Biomedical Knowledge Systems(Springer Science and Business Media Deutschland GmbH, 2025) Erkantarci, B.; Bakal, G.The drug discovery process for known diseases is crucial in bioinformatics, given the extensive clinical trials, regulatory approvals, and high costs. Computational in silico methods are essential to mitigate these challenges, as they help identify promising drug candidates, thereby reducing the time and cost associated with drug discovery. An effective strategy in this domain is drug repositioning, where existing drugs, already approved for one disease, are repurposed for treating another. This approach is advantageous as it leverages the established safety profiles of existing drugs, avoiding toxic effects on human metabolism. In this effort, we employed a translational entity embedding-based neural network model to advance drug repositioning efforts. We utilize the Semantic Medline Database (SemMedDB) as the primary source of biomedical entity relationships for model training. The model is validated using repoDB, a gold standard dataset for drug repositioning. Technically, the model will learn to minimize the vector distance between related entities. This distance will serve as the basis for predicting potential drug-disease pairs in drug repositioning, offering a novel computational method to expedite the drug discovery process. © 2025 Elsevier B.V., All rights reserved.Book Part Citation - Scopus: 6Waste Lead-Acid Battery Recycling Technologies(Springer Science and Business Media Deutschland GmbH, 2023) Altiner, Mahmut; Top, S.; Kursunoglu, SaitThe growing of collected waste lead-acid battery quantity means the growing demand for secondary lead (Pb) material for car batteries, both needed for increased cars’ production and for replacing of waste batteries for the increased number of automobiles in service. Pb recycling is critical to keep pace with growing energy storage needs. In recent years, tightening emission regulations have forced many developed country smelters to close. This has driven battery manufacturers and distributors to increasingly rely upon unregulated smelting operations in developing nations, negatively impacting the environment and human health. Therefore, finding a cleaner and more cost-efficient Pb recovery and recycling method is critical to the Pb recycling community. © 2023 Elsevier B.V., All rights reserved.Book Part Citation - Scopus: 3ROSE: A Novel Approach for Protein Secondary Structure Prediction(Springer Science and Business Media Deutschland GmbH, 2021) Görmez, Yasin; Aydin, ZaferThree-dimensional structure of protein gives important information about protein’s function. Since it is time-consuming and costly to find the structure of protein by experimental methods, estimation of three-dimensional structures of proteins through computational methods has been an efficient alternative. One of the most important steps for the 3-D protein structure prediction is protein secondary structure prediction. Proteins which contain different number and sequences of amino acids may have similar structures. Thus, extracting appropriate input features has crucial importance for secondary structure prediction. In this study, a novel model, ROSE, is proposed for secondary structure prediction that obtains probability distributions as a feature vector by using two position specific scoring matrices obtained by PSIBLAST and HHblits. ROSE is a two-stage hybrid classifier that uses a one-dimensional bi-directional recurrent neural network at the first stage and a support vector machine at the second stage. It is also combined with DSPRED method, which employs dynamic Bayesian networks and a support vector machine. ROSE obtained comparable results to DSPRED in cross-validation experiments performed on a difficult benchmark and can be used as an alternative to protein secondary structure prediction. © 2021 Elsevier B.V., All rights reserved.Book Part Citation - Scopus: 2Potential Effects of Short-Range Order on Hydrogen Embrittlement of Stable Austenitic Steels—A Review(Springer Science and Business Media Deutschland GmbH, 2021) Koyama, Motomichi; Bal, Burak; Canadinc, Demircan; Habib, Kishan; Tsuchiyama, Toshihiro; Tsuzaki, Kaneaki; Akiyama, EijiHere, we present a review of the hydrogen embrittlement behavior of face-centered cubic (FCC) alloys with short-range order (SRO) of solute atoms. In this paper, three types of FCC alloys are introduced: Fe–Mn–C austenitic steels, high-nitrogen steels, and CoCrFeMnNi high-entropy alloys. The Fe–Mn–C austenitic steels show dynamic strain aging associated with Mn–C SRO, which causes deformation localization and acceleration of premature fracture even without hydrogen effects. The disadvantageous effect of dynamic strain aging on ductility, which is associated with the deformation localization, amplify plasticity-assisted hydrogen embrittlement. Cr–N and Co–Cr–Ni SRO effects in high-nitrogen austenitic steels and high-entropy alloys enhance the dislocation planarity, which causes stress concentration in the grain interior and near the grain boundaries. The stress concentration coupled with hydrogen effects causes quasi-cleavage and intergranular fractures. © 2021 Elsevier B.V., All rights reserved.Book Part Lead Blast Furnace Dust Recycling(Springer Science and Business Media Deutschland GmbH, 2023) Top, S.; Altiner, Mahmut; Kursunoglu, SaitThe recycling of lead (Pb), which has a limited reserve in the world, has great importance in terms of sustainable and efficient use of resources. Currently, more than half of the lead, which is the softest of base heavy metals, is recovered by recycling. In addition to the insulation of the cables and its use as a radiation shield, lead is mostly used in the manufacture of lead-acid batteries (LABs). Generally, lead smelting flue dust, also known as lead smelting fly ashes, formed during the smelting stage in secondary Pb production is fed back into the smelter. However, the impurities contained in this dust and the other required specifications for feeding into the furnace prevent dust from being fed back into the furnaces. Therefore, it is essential to evaluate these by-products with an effective process and to obtain valuable content from them. In this chapter, firstly the characterization of lead smelting flue dust has been investigated. Afterwards, the processes that can be applied to obtain contents such as Pb, Sb, Zn, and As from these materials were compiled from the literature and a comprehensive review study was presented. © 2023 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Is City Love a Success Factor for Neighbourhood Resilience? Results From a Microcosmic Analysis of Rotterdam(Springer Science and Business Media Deutschland GmbH, 2022) Kourtit, Karima; Nijkamp, Peter; Türk, Umut; Wahlström, Marie H. HårsmanThis study examines and tests the concept of ‘city love’ in the context of social resilience for urban neighbourhoods. It introduces the notion of ‘city body’ and ‘city soul’ so as to create an operational framework for measuring the citizens’ appreciation and attachment for the local neighbourhood. Particular attention is given to the social bonds in urban community networks and language groups. A quantitative statistical analysis is carried out to test the relationships and determinants of city (or neighbourhood) love, based on extensive statistical, survey and social media data on the city of Rotterdam. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Hybrid Renewable Energy to Greener and Smarter Cities: A Case Study of Kayseri Province(Springer Science and Business Media Deutschland GmbH, 2024) Bekçi, Eyüp; Koca, KemalIn this study, a hybrid energy system was implemented to fulfill the electricity requirements of the trams operating in Kayseri province. The tram's annual electricity consumption data was acquired on a monthly basis from the local electricity company in Kayseri. Utilizing the obtained data, energy and cost simulations were conducted employing the Homer-Pro program. The primary objective of this investigation is to enhance sustainability while satisfying electricity demands with minimal carbon emissions. Consequently, the established hybrid energy system incorporates renewable energy sources, specifically wind, solar, and biomass energy, with the inclusion of batteries for energy storage. Furthermore, generators and converters are integrated for energy conversion purposes. The study encompasses a detailed cost analysis to identify the most economically efficient hybrid energy system, determined through optimization studies. Through this research, it is anticipated that the implementation of such a system will significantly diminish carbon emissions in Kayseri, contributing to a substantial increase in sustainability. © 2024 Elsevier B.V., All rights reserved.Conference Object Generating Linguistic Advice for the Carbon Limit Adjustment Mechanism(Springer Science and Business Media Deutschland GmbH, 2023-10-02) Fidan, Fatma Şener; Aydogan, Sena; Akay, DiyarLinguistic summarization, a subfield of data mining, generates summaries in natural language for comprehending big data. This approach simplifies the incorporation of information into decision-making processes since no specialized knowledge is needed to understand the generated language summaries. The present research employs linguistic summarization to examine the circumstances surrounding the Carbon Border Adjustment Mechanism, one of the most significant regulations confronting exporting nations to the European Union, and will be adopted to support sustainable growth. In this paper, associated with several attributes of the countries and product flow from exporting countries to European countries were defined as nodes and relations, respectively. Before the modeling phase, fuzzy c-means automatically identified fuzzy sets and membership degrees of attributes. During the modeling phase, summary forms were generated using polyadic quantifiers. A total of 1944 linguistic summaries were produced between exporting countries and European countries. Thirty-five summaries have a truth degree greater than or equal to the threshold value of 0.9, which is considered reasonable. The provision of natural language descriptions of the Carbon Border Adjustment Mechanism is intended to aid decision-makers and policymakers in their deliberations. © 2023 Elsevier B.V., All rights reserved.Book Part Citation - Scopus: 3Deep Learning Based Formation Control of Drones(Springer Science and Business Media Deutschland GmbH, 2021) Kabore, Kader Monhamady; Guler, SametRobot swarms can accomplish demanding missions fast, efficiently, and accurately. For a robust operation, robot swarms need to be equipped with reliable localization algorithms. Usually, the global positioning system (GPS) and motion capture cameras are employed to provide robot swarms with absolute position data with high precision. However, such infrastructures make the robots dependent on certain areas and hence reduce robustness. Thus, robots should have onboard localization capabilities to demonstrate a swarm behavior in challenging scenarios such as GPS-denied environments. Motivated by the need for a reliable onboard localization framework for robot swarms, we present a distance and vision-based localization algorithm integrated into a distributed formation control framework for three-drone systems. The proposed approach is established upon the bearing angles and the relative distances between the pairs of drones in a cyclic formation where each drone follows its coleader. We equip each drone with a monocular camera sensor and derive the bearing angle between a drone and its coleader with the recently developed deep learning algorithms. The onboard measurements are then relayed back to the formation control algorithm in which every drone computes its control action in its own frame based on its neighbors only, forming a completely distributed architecture. The proposed approach enables three-drone systems to perform in coordination indepen- dent of any external infrastructure. We validate the performance of our approach in a realistic simulation environment. © 2021 Elsevier B.V., All rights reserved.