Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Spatial Proximity and Accessibility Patterns of X-Minute Cities(Elsevier Ltd, 2026) Östh, John; Türk, Umut; Kourtit, Karima; Nijkamp, PeterThe 15-minute city concept has gained prominence in urban planning as a framework linking proximity, mobility, and quality of life at the intra-urban scale. An open question is whether this concept remains meaningful when applied to spatially connected urban regions or national urban systems. To address this issue, the new X-minute city concept allows proximity thresholds to vary with spatial context, mobility conditions, and service distribution. This paper develops an operational framework with the aim to examine and highlight spatial accessibility patterns from an X-minute city perspective, using the relatively urbanized area of the Netherlands as a national-scale case study. Accessibility is analyzed at the building level using OpenStreetMap data and a combination of object-based k-nearest-neighbor measures, distance-decay functions, inequality metrics, Average Nearest Neighbor analysis, and quantile regression. Concepts from central place theory are employed as an interpretive lens to understand service clustering and accessibility patterns, rather than as a formal model to be tested. The results show pronounced spatial disparities in accessibility between urban and rural areas, as well as substantial variation within cities. Major urban centers such as Amsterdam, Rotterdam, and Utrecht exhibit high accessibility levels due to dense service provision, while peripheral and rural areas face systematically lower accessibility. Next accessibility outcomes also differ sharply across population groups. Elderly residents and groups reliant on walking experience the highest levels of inequality, with accessibility distributions remaining highly uneven at short-distance thresholds. Cycling expands the range of effective activity spaces and reduces inequality, but does not offset structural disadvantages in areas with sparse service provision. The findings point to the limits of uniform proximity targets and reveal the need for context-sensitive accessibility planning that explicitly accounts for service distribution, mobility constraints, and population heterogeneity within national urban systems.Article Machine 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 Citation - Scopus: 54EdgeAISim: 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: 15An Effective Colorectal Polyp Classification for Histopathological Images Based on Supervised Contrastive Learning(Elsevier Ltd, 2024-04) Yengec-Tasdemir, Sena Busra; Aydin, Zafer; Akay, Ebru; Doǧan, Serkan; Yilmaz, BulentEarly detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 9Impact of Sustainable Energy, Fossil Fuels and Green Finance on Ecosystem: Evidence From China(Elsevier Ltd, 2024) Wang, Zuoteng; Zeng, Sheng; Khan, ZohanThe adoption of sustainable energy has increased as a substitute for petroleum derivatives due to growing concerns about environmental degradation caused by pollution and non-renewable energy sources. This study aims to investigate the impact of sustainable energy, green finance, and fossil fuels on the ecology of China. Instead of using traditional intermediaries like CO2 and EF, we employed the ecosystem habitat index to evaluate the conservation of terrestrial ecosystems. This index measures the extent of habitat destruction, deterioration, and fragmentation. The research demonstrated that implementing ecological power and green finance in China has enhanced the country's ability to safeguard and enhance its ecosystem in the short and long term. Furthermore, the findings suggest that using non-renewable energy sources in China has heightened the risk to biodiversity and the ecosystem. The analysis indicates that prioritizing green funding and renewable energy sources is crucial for policymakers, legislators, and investors to safeguard and enhance ecosystem diversity. © 2024 Elsevier B.V., All rights reserved.