Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - Scopus: 1Machine Learning and Scenario-Based Forecasting of Türkiye’s Renewable Energy Transition toward Net-Zero 2053(Elsevier Ltd, 2026-05) Sutcu, Muhammed; Yildiz, Baris; Sahin, Nurettin; Almomany, Abedalmuhdi; Gulbahar, Ibrahim TumayThe issue of global warming has been identified as one of the most critical challenges of the 21st century, with the consumption of fossil fuels being identified as a major contributor to greenhouse gas emissions. In response to these challenges, countries worldwide are expediting their transition towards renewable energy sources to meet international climate commitments, such as the Paris Agreement, and to achieve long-term sustainability goals. Türkiye has established a target to achieve net-zero emissions by 2053. This objective is consistent with both the nation's domestic energy strategy and its international commitments. Nevertheless, the transition from fossil fuels to renewable energy sources is impeded by geographical, economic, and technological constraints. The present study aims to assess the capacity and efficiency of renewable energy in Türkiye with environmental protocols and future electricity demand projections. Electricity generation, transmission data, and national energy plans are used to identify future electricity generation and capacity trends. In the context of this study, a range of machine learning models is executed across diverse scenarios, yielding a series of outcomes. Consequently, the repercussions of regulatory measures and financial investments were examined, and prospective inferences were derived. The findings underscore the pivotal role of scenario-based modeling in formulating sustainable energy policies and directing investment decisions within the context of climate change mitigation.Article GraphUnet-SS: A Novel Deep Learning Model for Protein Secondary Structure Prediction Based on U-Net Architecture(Elsevier Ltd, 2026-04) Aydin, Zafer; Görmez, Yasin; Sabzekar, MostafaArticle Citation - WoS: 1Citation - Scopus: 1A Comparative Study of Existing and Current On-Site Documentation of Anatolian Seljuk Kümbets(Elsevier Ltd, 2025-12) Güzelci, O.Z.; Türel, A.During the Anatolian Seljuk period (1077–1307), monumental tombs known as kümbets emerged as a distinct architectural typology in present-day Türkiye. 2D drawings of these structures, produced since the early 20th century, contain inconsistencies that necessitate verification and accurate documentation. This study digitally documents Anatolian Seljuk kümbets in 3D to generate updated 2D sections reflecting their current condition and compares these with previously published drawings. The methodology includes collecting available 2D sections, digitally documenting kümbets through field studies, generating new 2D sections from 3D models, and systematically comparing these datasets. Two image-based metrics are employed in the comparison: the Exact Pixel Match Ratio (EPMR), which evaluates pixel-level alignment, and the Structural Similarity Index Measure (SSIM), a standard indicator for visual similarity. The results provide a comparative framework for assessing previous drawings and present a verified, up-to-date dataset of kümbet sections for future research. © 2025 Elsevier B.V., All rights reserved.Article Effect of Different Pitch Ratios on the Flow Around Tandem Circular Cylinders with Spoilers(Elsevier Ltd, 2025-12) İlkentapar, M.; Akşit, S.; Öner, A.A.; Genç, M.S.This study experimentally investigates the unsteady aerodynamic characteristics of two tandem circular cylinders subjected to various pitch ratios and spoiler configurations in a controlled wind tunnel environment. The primary objective is to understand how the placement and presence of spoiler's influence flow separation, wake interference, surface pressure distributions, and overall aerodynamic performance. The experiments were conducted for three pitch ratios (2D, 4D, and 7D) and four spoiler configurations: NN (no spoilers on either cylinder), NS (spoiler on the downstream cylinder only), SN (spoiler on the upstream cylinder only), and SS (spoilers on both cylinders). Measurements included surface pressure, velocity distribution via hot-wire anemometry, and aerodynamic forces, while qualitative flow patterns were assessed using smoke-wire visualization. The results indicate that the usage of spoilers substantially alters the wake structure and pressure profiles, especially in closely spaced configurations. In the NN configuration, increasing the pitch ratio led to a progressive decoupling of the flow between the cylinders, transitioning from a merged wake to more isolated vortex shedding. In the SN and NS configurations, the asymmetrical placement of spoilers induced unsteady wake interactions and altered reattachment dynamics on the downstream body. The SS configuration exhibited the most disturbed flow regime at low pitch ratios, which gradually stabilized as the spacing increased. Violin plots derived from velocity measurements provided statistical insight into flow symmetry and turbulence intensity, while smoke visualizations captured coherent structures and transition zones across the configurations. The combined analysis demonstrates that both pitch ratio and spoiler configuration are critical parameters in controlling aerodynamic interference and unsteady wake behavior in tandem arrangements. These findings offer valuable implications for flow management and control strategies in offshore structures, cylindrical risers, and heat exchanger tube banks, where vortex-induced vibrations and flow separation play crucial roles. © 2025 Elsevier B.V., All rights reserved.Article Citation - Scopus: 22Transition Towards the Sustainable Development: Unraveling the Effects of Mineral Markets, Belt & Road Initiative, and the Paris Agreement on Green Economic Growth(Elsevier Ltd, 2024-04) Xia, Xiqiang; Chishti, Muhammad Zubair; Dogan, EyupThe Agenda 2030 strongly emphasizes implementing effective and equitable measures to address the urgent challenge of global warming, primarily driven by unsustainable fossil-fuel combustion, and one of its core focuses is Sustainable Development Goal (SDG) – 8, among others. In light of this, the recent article aims to explore the dynamic nexus between minerals (MNR), the Belt and Road Initiative (BRI), the Paris Agreement (PA), green technologies (GT), and green growth, with a specific focus on developing a policy framework for advancing SDG – 8. The study utilizes daily data and advanced econometric tools such as QVAR, Cross-quantileogram, and wavelet-quantile correlation to examine the diverse effects of these factors on green growth across various time horizons. The short-run analysis reveals that MNR, BRI, and GT discourage green growth under most market conditions, except for a few quantiles that exhibit positive or insignificant relationships. In the medium run, impacts are mixed, with both positive and negative effects observed. However, in the long run, MNR, BRI, and GT consistently demonstrate favorable effects on green growth. For PA, short and medium-run effects are mixed, but medium-run results indicate a predominantly positive impact on green growth. In the long run, PA significantly benefits green growth across the majority of market conditions. Overall, the diversified results suggest that minerals, BRI, the Paris Agreement, and green technologies play a crucial role in stimulating green growth to achieve SDG - 8 in the long term. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 404The Moderating Role of Renewable and Non-Renewable Energy in Environment-Income Nexus for Asean Countries: Evidence From Method of Moments Quantile Regression(Elsevier Ltd, 2021-02) Anwar, Ahsan; Siddique, Muhammad; Dogan, Eyup; Sharif, Arshian AslamA vast body of studies estimates the impact of energy consumption on the environment. A typical empirical study either use aggregate energy consumption or apply conventional econometric techniques in modelling the nexus of energy, income and environment. To correct these gaps, the objective of the study is to use renewable and non-renewable energy consumption in analyzing energy-income-environment nexus, and to apply the novel Method of Moments Quantile Regression for ASEAN countries. The outcomes indicate that non-renewable energy consumption stimulate carbon emissions across all quantiles (10th to 90th), the value of the 10th quantile is 0.257 which rises to 0.501 till 90th quantile. Whereas, the renewable energy consumption leads to a decrease in CO<inf>2</inf> emissions across all the quantiles (10th to 90th) but this association is statistically insignificant at higher quantiles from 60th to 90th. The empirical outcomes also verify the presence of the environmental Kuznets curve relationship, which is statistically significant from the middle (30th) to higher (90th) quantiles. Moreover, the finding of panel estimation approaches (FMOLS, DOLS, FE-OLS) also verify the existence of the EKC hypothesis in ASEAN countries. Their finding also describes that 1% increase in non-renewable energy consumption increase CO<inf>2</inf> emission by 0.29%, 0.26% and 0.30% whereas 1% increase in the usage of renewable energy reduces CO<inf>2</inf> emission by 0.17%, 0.15% and 0.17% in case of FMOLS, DOLS and FE-OLS respectively. The empirical results conclude that the government should encourage and subsidize the sources of green energy to tackle environmental degradation. More policy implications are further discussed in the study. © 2020 Elsevier B.V., All rights reserved.Article Citation - Scopus: 2Prediction of Colorectal Cancer Based on Taxonomic Levels of Microorganisms and Discovery of Taxonomic Biomarkers Using the Grouping-Scoring (G-S-M) Approach(Elsevier Ltd, 2025-03) Bakir-Güngör, Burcu; Temiz, Mustafa; Canakcimaksutoglu, Beyza; Yousef, MalikColorectal cancer (CRC) is one of the most prevalent forms of cancer globally. The human gut microbiome plays an important role in the development of CRC and serves as a biomarker for early detection and treatment. This research effort focuses on the identification of potential taxonomic biomarkers of CRC using a grouping-based feature selection method. Additionally, this study investigates the effect of incorporating biological domain knowledge into the feature selection process while identifying CRC-associated microorganisms. Conventional feature selection techniques often fail to leverage existing biological knowledge during metagenomic data analysis. To address this gap, we propose taxonomy-based Grouping Scoring Modeling (G-S-M) method that integrates biological domain knowledge into feature grouping and selection. In this study, using metagenomic data related to CRC, classification is performed at three taxonomic levels (genus, family and order). The MetaPhlAn tool is employed to determine the relative abundance values of species in each sample. Comparative performance analyses involve six feature selection methods and four classification algorithms. When experimented on two CRC associated metagenomics datasets, the highest performance metric, yielding an AUC of 0.90, is observed at the genus taxonomic level. At this level, 7 out of top 10 groups (Parvimonas, Peptostreptococcus, Fusobacterium, Gemella, Streptococcus, Porphyromonas and Solobacterium) were commonly identified for both datasets. Moreover, the identified microorganisms at genus, family, and order levels are thoroughly discussed via refering to CRC-related metagenomic literature. This study not only contributes to our understanding of CRC development, but also highlights the applicability of taxonomy-based G-S-M method in tackling various diseases. © 2025 Elsevier B.V., All rights reserved.Article Citation - Scopus: 49EdgeAISim: A Toolkit for Simulation and Modelling of AI Models in Edge Computing Environments(Elsevier Ltd, 2024-02) Nandhakumar, Aadharsh Roshan; Baranwal, Ayush; Choudhary, Priyanshukumar; Golec, Muhammed; Gill, Sukhpal SinghTo meet next-generation Internet of Things (IoT) application demands, edge computing moves processing power and storage closer to the network edge to minimize latency and bandwidth utilization. Edge computing is becoming increasingly popular as a result of these benefits, but it comes with challenges such as managing resources efficiently. Researchers are utilising Artificial Intelligence (AI) models to solve the challenge of resource management in edge computing systems. However, existing simulation tools are only concerned with typical resource management policies, not the adoption and implementation of AI models for resource management, especially. Consequently, researchers continue to face significant challenges, making it hard and time-consuming to use AI models when designing novel resource management policies for edge computing with existing simulation tools. To overcome these issues, we propose a lightweight Python-based toolkit called EdgeAISim for the simulation and modelling of AI models for designing resource management policies in edge computing environments. In EdgeAISim, we extended the basic components of the EdgeSimPy framework and developed new AI-based simulation models for task scheduling, energy management, service migration, network flow scheduling, and mobility support for edge computing environments. In EdgeAISim, we have utilized advanced AI models such as Multi-Armed Bandit with Upper Confidence Bound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, and Actor-Critic Network to optimize power usage while efficiently managing task migration within the edge computing environment. The performance of these proposed models of EdgeAISim is compared with the baseline, which uses a worst-fit algorithm-based resource management policy in different settings. Experimental results indicate that EdgeAISim exhibits a substantial reduction in power consumption, highlighting the compelling success of power optimization strategies in EdgeAISim. The development of EdgeAISim represents a promising step towards sustainable edge computing, providing eco-friendly and energy-efficient solutions that facilitate efficient task management in edge environments for different large-scale scenarios. © 2023 Elsevier B.V., All rights reserved.Article Citation - Scopus: 2Deformation Behavior of Nanostructured Aluminum: Experiment and Computational Study(Elsevier Ltd, 2023-04) Deka, Surja; Mozafari, Farzin; Mallick, Ashis K.Nanocrystalline metals have been processed from powder predecessors in recent times in significant ways, and nowadays, materials are starting to be manufactured which are not only strong but also ductile. Nanocrystalline aluminum (Average grain size 51 nm) was synthesized through high-energy ball milling at the room temperature of microcrystalline powder. The particle size and crystallite sizes were obtained by Williamson Hall and found to be in good correlation with transmission spectroscopy (TEM) data. There was a significant increase in the mechanical properties of nanostructured aluminum in comparison to coarse-grained aluminum. Moreover, a phenomenological model of large-deformation, isotropic, rate-dependent plasticity is developed, which takes into account pressure dependency, plastic dilatation, and non-normal flow. The model has been incorporated into a finite element program. Compression and tension experiments were performed on nanocrystalline aluminum, and the constitutive parameters within the model were estimated from these experiments. The present study shows that the constitutive model successfully simulates the mechanical response of nanocrystalline aluminum with reasonable accuracy using our numerical finite-element capability. © 2023 Elsevier B.V., All rights reserved.Article Citation - Scopus: 4CCPred: Global and Population-Specific Colorectal Cancer Prediction and Metagenomic Biomarker Identification at Different Molecular Levels Using Machine Learning Techniques(Elsevier Ltd, 2024-11) Bakir-Güngör, Burcu; Temiz, Mustafa; Inal, Yasin; Cicekyurt, Emre; Yousef, MalikColorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the complex interplay between disease development and metagenomic data is essential for CRC diagnosis and treatment. Current computational models employ machine learning to identify metagenomic biomarkers associated with CRC, yet there is a need to improve their accuracy through a holistic biological knowledge perspective. This study aims to evaluate CRC-associated metagenomic data at species, enzymes, and pathway levels via conducting global and population-specific analyses. These analyses utilize relative abundance values from human gut microbiome sequencing data and robust classification models are built for disease prediction and biomarker identification. For global CRC prediction and biomarker identification, the features that are identified by SelectKBest (SKB), Information Gain (IG), and Extreme Gradient Boosting (XGBoost) methods are combined. Population-based analysis includes within-population, leave-one-dataset-out (LODO) and cross-population approaches. Four classification algorithms are employed for CRC classification. Random Forest achieved an AUC of 0.83 for species data, 0.78 for enzyme data and 0.76 for pathway data globally. On the global scale, potential taxonomic biomarkers include ruthenibacterium lactatiformanas; enzyme biomarkers include RNA 2′ 3′ cyclic 3′ phosphodiesterase; and pathway biomarkers include pyruvate fermentation to acetone pathway. This study underscores the potential of machine learning models trained on metagenomic data for improved disease prediction and biomarker discovery. The proposed model and associated files are available at https://github.com/TemizMus/CCPRED. © 2024 Elsevier B.V., All rights reserved.