Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
45 results
Search Results
Now showing 1 - 10 of 45
Conference Object Differential and Linear Analyses of Dizy Through MILP Modeling(Springer Science and Business Media Deutschland GmbH, 2026) İlter, M.B.; Koçak, O.; Kara, O.; Sulak, F.In this work, we present the first independent security analysis of DIZY, a recently proposed ultra-lightweight stream cipher with two variants: DIZY-80 and DIZY-128. Our analysis focuses on DIZY’s resistance to linear and differential cryptanalysis. We employ a formal technique known as Mixed Integer Linear Programming (MILP), which enables us to model the internal structure of DIZY and search for characteristics that describe how XOR differences or linear masks propagate through the cipher. Specifically, we construct such characteristics to evaluate how many S-boxes become “active” during keystream generation, as this number directly affects the cipher’s resistance to these attacks. Contrary to the designers’ claim that any linear or differential characteristic over 8 rounds must involve at least 20 active S-boxes in DIZY-80 and 22 in DIZY-128, we identify characteristics with only 18 differentially or linearly active S-boxes and 20 linearly active S-boxes, respectively. We mount two distinguishing attacks on each cipher. Our 3-round linear distinguishing attack requires 223 bits of keystream, while the 4-round version requires 235 bits for DIZY-128 and DIZY-80, respectively. Our 2-round differential resynchronization attacks succeed using only the first four bytes of keystream data from approximately 230 and 226 different initializations with chosen initialization vectors (IVs) for DIZY-128 and DIZY-80, respectively. While these attacks do not compromise the full 15-round version of the cipher, they provide valuable insights into the design of DIZY and contribute to a deeper understanding of the security requirements of its diffusion layer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.Book Part Greenmetric Journey of Izmir Institute of Technology: Agile Strategies Towards a Green Campus(Springer Science and Business Media Deutschland GmbH, 2026) Keskin, E.; Ökten, H.E.; Akpinar, İ.; Baran, Y.Recently, there has been growing attention towards sustainable approaches on university campuses through disseminating international evaluation systems, the UI GreenMetric World University Rankings (GM) attracting specific attention in particular. Türkiye is one of the countries where the number of participating universities in GM rises annually at a significant pace. Most of the large-scale university campuses in Türkiye were already built by the 1990s, which led these campuses to adapt themselves to higher standards for sustainability. In this context, Izmir Institute of Technology (IZTECH), a 33-year-old university, has applied for the GM with its Gülbahçe Campus since 2020. This paper aims to reveal IZTECH’s institutional agile sustainability strategy, energetic and collective processes, and good practices in the last five years while examining the outcomes through the GM’s evaluation of six assessment criteria. In this regard, the sustainability practices of IZTECH have been monitored since 2019 and compared to how the developments have improved the GM scores for the past 3 years. This study, focusing on the IZTECH campus through historical, social, educational, and technological perspectives, unveils the barriers between developing and implementing sustainability practices and examines the cohesion between GM scores and annual reports of campus activities for further projections towards a greener campus. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.Article Improved Colorectal Gland Segmentation in Histopathology Images with Adaptive Resizer-Enhanced U-Net Models(Springer Science and Business Media Deutschland GmbH, 2026) Fidan, E.; Gumus, A.Utilizing low-resolution images for computer vision tasks such as classification and segmentation can sometimes hinder the model’s ability to accurately learn essential features. While using high-resolution images and designing compatible models might seem like viable solutions, they are not always feasible due to energy efficiency and graphical computation constraints. Downsizing images for model training and application is an effective approach for improving computational efficiency and optimizing model performance.The bilinear resizing method, commonly employed for this purpose, inherently causes information loss due to its numerical approach, which relies solely on the four nearest pixel values to compute each target pixel. This limitation becomes more pronounced with high-resolution images, where the down sampling process intensifies the loss of critical information. However, recent advancements have introduced adaptive resizer modules, which dynamically adjust image dimensions to better preserve essential features before processing by deep learning models. In this study, an adaptive resizer-based segmentation framework is proposed for the gland segmentation task, which is crucial for accurate disease diagnosis, particularly in cancer analysis. Three distinct encoder-decoder architecture segmentation models are assessed for image segmentation using the Colorectal Adenocarcinoma Gland (CRAG) gland segmentation database. Each architecture was tested separately, employing six different backbone encoders that were pretrained on the ImageNet dataset. The comparative analysis showed that the adaptive resizer improved segmentation performance, increasing the Intersection over Union (IoU) metric by an average of 5.6%. This enhancement raised the lowest IoU from 62% to 70% and the highest to 78%. The code is available on GitHub at https://github.com/miralab-ai/adaptive-resizer-segmentation. © The Author(s) 2026.Book Part Groundwater Arsenic in an Urban Area: Izmir’s Comprehensive Response and Remediation Blueprint(Springer Science and Business Media Deutschland GmbH, 2025) Kırçiçek, N.T.; Güngör, E.B.; Baba, A.The contamination of groundwater with arsenic poses a critical challenge to the environment and public health, affecting millions of people worldwide. In the rapidly urbanising regions of Türkiye, understanding the origin, mobility and effective treatment of arsenic contamination is crucial to ensure water safety. This study analyses the spatial distribution of arsenic contamination of groundwater, specifically in the province of İzmir, while attempting to delineate the potential sources of risk. The arsenic concentrations in groundwater samples from different districts were analysed, and the variations at district level were visualised using a point-based density map. The resulting values were then critically compared with the World Health Organization (WHO) limits and Turkish national regulations (10 μg/L) to draw attention to the pronounced spatial differences in concentrations. Following the arsenic crisis in 2008, the İzmir Municipality has taken a decisive course and implemented targeted arsenic remediation strategies that represent significant progress in solving and addressing this pervasive problem. In 2023 alone, more than 139 million m3 of groundwater were treated, accounting for almost 30% of the city’s drinking and industrial water supply. This considerable magnitude represents a remarkable level of implementation, especially against the backdrop of numerous global cities struggling with similar contamination problems. The results of this study should serve as a basis for sustainable groundwater management strategies, not only for İzmir, but also for other regions with hydrogeological and urban dynamics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Article On the Strengths and Weaknesses of Virtual Reality in Distance Estimation in AEC Domain: A Meta-Analysis of Literature 2014–2024(Springer Science and Business Media Deutschland GmbH, 2026) Kurpınar, G.; Doǧan, F.; Çevik, A.; Kasali, A.Virtual reality (VR) became the most used extended reality system in architecture, engineering, and construction domains. It offers advantages through its immersive and interactive interface. There is, however, a need to investigate both its strengths and weaknesses especially in relation to the claim that it is a close surrogate for real-world performances. This study reports the findings of a meta-analysis on distance estimation (DE) in VR. Distance estimation, essential for spatial perception, remains to be a problem in VR even with advanced head-mounted displays. The study questions whether VR can match the real-world performance in DE to highlight its shortcomings as well as its potentials. The meta-analysis includes 77 pieces of data from 29 studies and investigates whether DE accuracy has improved and how DE interacts with task type, task environment, and target range. The results indicate VR is still underperforming in DE, head-mounted display’s weight is the significant factor, and task type and task environment significantly interact with DE. We conclude VR needs to be specifically tailored regarding the needs of practitioners in architecture and engineering industry and that it is not yet a substitute for real-world performances. © The Author(s) 2025.Article Future Circular Collider Feasibility Study Report: Volume 3 Civil Engineering, Implementation and Sustainability(Springer Science and Business Media Deutschland GmbH, 2025) Benedikt, M.; Zimmermann, F.; Auchmann, B.; Bartmann, W.; Burnet, J.P.; Carli, C.; Zykova, M.Volume 3 of the FCC Feasibility Report presents studies related to civil engineering, the development of a project implementation scenario, and environmental and sustainability aspects. The report details the iterative improvements made to the civil engineering concepts since 2018, taking into account subsurface conditions, accelerator and experiment requirements, and territorial considerations. It outlines a technically feasible and economically viable civil engineering configuration that serves as the baseline for detailed subsurface investigations, construction design, cost estimation, and project implementation planning. Additionally, the report highlights ongoing subsurface investigations in key areas to support the development of an improved 3D subsurface model of the region. The report describes the development of the project scenario based on the ‘avoid-reduce-compensate’ iterative optimisation approach. The reference scenario balances optimal physics performance with territorial compatibility, implementation risks, and costs. Environmental field investigations covering almost 600 hectares of terrain—including numerous urban, economic, social, and technical aspects—confirmed the project’s technical feasibility and contributed to the preparation of essential input documents for the formal project authorisation phase. The summary also highlights the initiation of public dialogue as part of the authorisation process. The results of a comprehensive socio-economic impact assessment, which included significant environmental effects, are presented. Even under the most conservative and stringent conditions, a positive benefit-cost ratio for the FCC-ee is obtained. Finally, the report provides a summary of the studies conducted to document the current state of the environment. © 2025 Elsevier B.V., All rights reserved.Conference Object Measuring the Size of Change Requests in Microservice-Based Software Projects(Springer Science and Business Media Deutschland GmbH, 2026) Yenel, M.; Ünlu, H.; Demirors, O.Accurately estimating the effort required for implementing change requests remains a critical challenge in software engineering, especially in microservice-based software architectures (MSSA). Traditional functional size measurement methods often fail to capture the distinct characteristics of MSSAs. To address this limitation, we propose a change size measurement method based on MicroM, a size measurement approach specifically developed for MSSAs. The proposed method counts added, deleted, and modified events across functional, architectural, and algorithmic levels, and includes the number of affected initial requirements. We conducted an exploratory case study with 18 change requests and built four regression-based effort estimation models. The results show that combining event counts with the number of affected requirements improves estimation accuracy. Our method provides a more precise and context-aware way to estimate change-related effort in MSSA projects. © 2025 Elsevier B.V., All rights reserved.Article Citation - Scopus: 1Evaluating the Seismic Performance of Advanced Tuned Mass Dampers Considering Soil–Structure Interaction Effect(Springer Science and Business Media Deutschland GmbH, 2025) Shahraki, M.A.; Roozbahan, M.This study examines the seismic effectiveness of four different tuned mass damper (TMD) configurations: classical TMD, Tuned Mass Damper Inerter (TMDI), Elastoplastic Tuned Mass Damper Inerter (PTMDI), and Dual-Stiffness Tuned Mass Damper (DSTMD), focusing on their ability to reduce structural responses. A model of a 10-story steel shear frame is used, accounting for soil–structure interaction (SSI) effect to represent realistic conditions. The damper parameters are optimized using the Mouth Brooding Fish (MBF) algorithm with a hybrid objective function combining normalized peak displacement and kinetic energy reduction. The optimization process is tested against fourteen near- and far-field earthquake scenarios, with an additional ten records used to validate performance. The findings reveal that, under fixed-base conditions, TMD and TMDI achieve the largest displacement reductions (37.6% and 37.5%, respectively), while PTMDI provides the greatest kinetic energy mitigation (56.4%). DSTMD shows moderate reductions in both responses (≈ 23% displacement, 29.3% energy). When soil–structure interaction is considered, the efficiency of all systems decreases. TMDI maintains the best displacement reduction (12.9%), whereas PTMDI offers the highest energy reduction (25.5%). Additional assessments of roof acceleration and base shear highlight important trade-offs, stressing the importance of a multidimensional evaluation. In summary, this research underscores the significance of energy-based metrics and the influence of the SSI effect in evaluating dampers. Instead of advocating for or against any specific system, the analysis offers a comparative perspective on their performance under various conditions, helping to inform decisions in performance-based seismic design. © 2025 Elsevier B.V., All rights reserved.Article Predicting the Area Moment of Inertia of Beam and Column Using Machine Learning and Hypernetexplorer(Springer Science and Business Media Deutschland GmbH, 2025) Aydın, Y.; Nigdeli, S.M.; Roozbahan, M.; Bekdaş, G.; Işıkdağ, Ü.Beams and columns are the most important elements of steel frame structures. Damage to the beam or column can lead the structure to serious hazards and cause collapse. In the structural engineering literature, it has been observed that there is not much work for area moment of inertia estimation of beam and column. The aim of this study was to predict the area moment of inertia of beam and column using HyperNetExplorer developed by the authors. This method aims to bring innovation by optimizing artificial neural networks (ANNs). In this study, a prediction study is performed using 306 collected data on beam and column area moment of inertia. Classical ML models (linear regression (LR), decision tree regression (DTR), K neighbors regression (KNN), polynomial regression (PR), random forest regression (RFR), gradient boosting regression (GBR), histogram gradient boosting regression (HGBR)) and NAS and HyperNetExplorer were applied to predict beam and column area moment of inertia. The prediction performances were compared using different performance metrics (coefficient of determination (R2) and mean squared error (MSE)) and HyperNetExplorer developed by the authors showed the highest performance (R2 = 0.98, MSE = 246.88). Furthermore, SHapley additive explanations (SHAP) were used to explain the effects of features in the prediction models and it was observed that the most effective features for model predictions were loading on beam and length. The results show that the proposed NAS base approach and the developed tool, HyperNetExplorer, provides better performance when compared with classical ML methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.Editorial Preface(Springer Science and Business Media Deutschland GmbH, 2025) Lazou, A.; Gökelma, M.