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A Knowledge-Driven Computer Vision Framework for Automated Atomic Force Microscopy Surface Characterization
(Elsevier Sci Ltd, 2026) Deveci, D. Gemici; Celebi, C.; Barandir, T. Karakoyun; Unverdi, O.
This study presents an innovative analytical framework developed to automate Atomic Force Microscopy (AFM)-based surface characterization. The proposed methodology integrates computer vision (CV) algorithms and machine learning (ML) techniques to overcome the limitations of conventional observer-dependent approaches and visual inspection methods. In the first stage of the two-step data processing pipeline, raw AFM signals were converted into structured datasets, correspondences between images acquired under different loading conditions were identified, and drift effects in both direction and magnitude were predicted using a LightGBM-based machine learning (ML) model to guide subsequent analytical processes. This process establishes a unified coordinate reference across varying force levels, enabling pixel-level comparability of surface maps. In the second stage, the aligned datasets are systematically analyzed through block-based local maxima detection, edge-based contour extraction, morphological filtering, and skeletonization algorithms. In this way, ridge-like surface features are reliably identified and quantitatively evaluated along their axes under varying force conditions. The framework ensures data integrity while enabling high-resolution and reproducible analyzes. Beyond its automation capability, it is distinguished by its integrated, modular architecture, where each component operates sequentially along a unified processing pipeline. The methodology was validated using epitaxial monolayer graphene grown on the C-face of SiC, successfully demonstrating its ability to resolve both geometric and force-dependent mechanical responses. In this regard, the proposed system extends beyond conventional cross-sectional analysis by providing a drift-aware, knowledge-guided compensation mechanism and directionally resolved evaluation, offering a robust, automation-ready infrastructure for nanoscale surface characterization.
A Multi-Objective Stochastic Optimization Model for Pharmaceutical Supply Chain Management Based on Time and Cost
(Amer Inst Mathematical Sciences-AIMS, 2026) Ghoroqi, Mahyar; Abbasi, Sina; Shahab, Erfan; Ghadi, Hossein Gholami; Khorrami, Sepideh
This paper presents a multi-objective stochastic optimization model for pharmaceutical supply chain management (PSCM), focusing on minimizing both time and cost. By considering these objectives and uncertainties simultaneously, the model aims to provide robust and efficient supply chain (SC) strategies for pharmaceutical companies. In this study, abi-objective mixed-integer linear programming (BOMILP) model is developed for a pharmaceutical supply chain network design (PSCND) problem. The model supports various strategic decisions, such as opening pharmaceutical production centers and main or local distribution centers, as well as determining optimal material flows over a mediumterm planning horizon and making tactical decisions. It aims to minimize total costs and flow constraints as the first and second objective functions, respectively. To verify and analyze the proposed model, it is tested on a real case study.
A UHF RFID-Based System for Real-Time Production Monitoring and Tag Quality Control in the Textile Industry
(SAGE Publications Inc, 2026) Altuglu, Ogeday; Gunduzalp, Mustafa
In the textile industry, labeling errors during the production process present significant challenges for both quality control and product traceability. This study proposes a real-time RFID-based production tracking and label quality control system to address these issues. In the developed system, RFID tags attached to products are read by embedded devices equipped with RFID readers integrated into the production lines. The captured product code data is transmitted to a local server via MQTT. The server verifies the tags by querying a central database and provides instant feedback to the corresponding device. The system runs entirely over a local network and does not require an external internet connection, ensuring uninterrupted functionality even in infrastructure-limited environments. This architecture enables all devices on the production line to communicate synchronously with the server, maintaining system-wide consistency and integrity. In this way, the use of a single tag per product is ensured, and faulty tags are filtered out. Furthermore, the system evaluates tag readability to detect quality issues such as stitching errors, physical deformation and alerts the operator through feedback. As a result, defective products are identified and removed before production is completed, helping to reduce time loss, and improve overall production reliability.
AID: Assistant-Based Approach for K-Leader Network Formation in Multi-Robot Systems
(Institute of Electrical and Electronics Engineers Inc., 2025) Abuzahra, Hamza; Ozkasap, Oznur; Ozsoyeller, Deniz
AIndividualism and Algorithmic Collectivism: Rethinking Individual-Collective Dynamics in the Age of AI
(Springer, 2026) Ozerim, Mehmet Gokay
This study explores how artificial intelligence reshapes the long-standing relationship between individualism and collectivism. It argues that AI does not simply shift the balance between these perspectives, but transforms how they coexist and evolve in contemporary societies. Drawing on Deleuze's idea of Societies of Control and Taylor's theory of self-determining individualism, the paper introduces two related concepts: AIndividualism and Algorithmic Collectivism. The first describes new forms of hyper-personalized autonomy produced through algorithmic personalization and continuous data feedback. The second refers to emerging modes of collective identity and coordinated action that develop within digital infrastructures guided by algorithms. By examining these dynamics together, the study shows that technology can both expand individual agency and cultivate collective forms of organization. AI enhances self-determination while also generating algorithmic collectivities that influence belonging, meaning, and social coordination. These intertwined processes reveal a central paradox of digital modernity: technology empowers individuals even as it aligns them through shared algorithmic systems. The discussion moves beyond binary thinking about autonomy and community, suggesting that both now unfold within the same technological frameworks. Understanding this dual process is crucial for developing ethical and policy responses that reflect AI's impact on human agency and social life. The paper concludes that the challenge of the AI age is not to choose between the individual and the collective, but to sustain both in a balanced and responsible way within an increasingly algorithmic world.
The Alien Series: An Ideological Analysis within the Framework of Postcolonial Theory
(IGI Global, 2024) Gülpinar, Dilara Balci
An ALNS-Based Decision Support System for Scheduling and Routing in Home Healthcare with Lunch Break Constraints
(Growing Science, 2025) Ozsakalli, Gokberk; Qadri, Syed Shah Sultan Mohiuddin; Ozturkoglu, Omer
This study addresses the daily scheduling and routing problem for home healthcare workers while incorporating lunch break requirements. The Home Healthcare Scheduling and Routing Problem is analysed alongside its common constraints, including patient and caregiver time windows, caregiver qualifications, and mandated breaks. To address this, four different variants of an effective Adaptive Large Neighbourhood Search (ALNS) algorithm were developed to provide high-quality solutions. The algorithms demonstrate significant efficiency, solving 30-patient instances optimally within an average of 12 seconds. For scenarios involving 100 patients, they maintained robust performance with a slight increase in computational time of about 54 seconds. Results indicate operational efficiency improvements of up to 36% through optimized travel routes and patient visitation schedules. To translate these findings into practice, a decision support system, the Home Healthcare Decision Support System (HHDSS), was designed to assist administrators by automating the complex task of scheduling and routing of caregivers. Tested using realistic patient data generated from Turkey, the system effectively allocates healthcare resources and improves responsiveness. Overall, the proposed framework shows strong potential as a valuable practical tool for improving the responsiveness and efficiency of home healthcare logistics. (c) 2026 by the authors; licensee Growing Science, Canada
Analysis of M/M/s Make-to-Stock Queues with Production Start-up Costs for Both Lost Sales and Backordering Cases
(Taylor & Francis Ltd, 2025) Ozkan, Sinem; Bulut, Onder; Dincer, Mehmet Cemali
This study considers a production-inventory system with production start-up costs and parallel production lines. Production times are independent and identically distributed exponential random variables and demands are generated according to a stationary Poisson process. Production and inventory are controlled by the extended-two-critical-number policy. The system is modelled as an M/M/s make-to-stock queue and analysed for both lost sales and backordering cases. A renewal approach is developed to calculate the expected average system cost. Furthermore, an approximation is proposed to calculate the control parameters of the extended-two-critical-number policy. An extensive numerical study is conducted to illustrate the effects of changes in system parameters and the effectiveness of the proposed approximation. The analysis shows that the proposed approximation performs well across various system conditions.
An Analysis of Roland Dyens’s Niterói: Unified Sonority and Pedagogical Approach for Two Guitars
(Nilgun SAZAK, 2025) Gelenler, Anıl; Ünlenen, Emre
Ant Colony Optimization for Solving Tsp with Sub-Route Elimination Constraints on Turkiye Map
(Turkic World Mathematical Soc, 2025) Erdemci, V.; Nuriyeva, F.
The Traveling Salesman Problem is the famous optimization problem in the NP-hard class. Many problems with applications in computer science and engineering can be modeled using the Traveling Salesman Problem. In this study, one of the artificial intelligence techniques, ant colony method, is used to solve the traveling salesman problem. In the study applied on the map of Turkiye, it is aimed to plan the best route.
Architecture in Translation: Germany, Turkey, the Modern House
(Intellect Ltd, 2013) Baydar, Gulsum
Citation - Scopus: 2
Artificial Intelligence in Screenplay: ChatGPT Application
(IGI Global, 2024) Sönmez, Sevcan
Assessing Seasonal Drought Persistence Using a Bayesian Logistic Regression Approach
(Pergamon-Elsevier Science Ltd, 2026) Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Ahmed, Abdelkader T.; Ali, Zulfiqar; Raza, Muhammad Ahmad; Danandeh Mehr, Ali; Niaz, Rizwan
This study investigates the patterns and intraseasonal predictability of meteorological drought (MD) through exploring the frequency and persistence of drought events. To this end, 52 years of precipitation measurements at six meteorology stations located in Ankara Province of Türkiye were used. Standardized Precipitation Index (SPI) at 3-month accumulation period, i.e., SPI-3, was calculated to represent local MD conditions. To evaluate the likelihood and odds of MD events a single variable Bayesian Logistic Regression approach was employed. Our findings showed that both frequency and intraseasonal persistence of MD events range from 40 % to 90 % in the region. Certain areas, such as Beypazari, Nallihan, and Kizilcahamam were found particularly vulnerable to drought and are more likely to experience drought persistence between successive seasons. Furthermore, the results revealed a negative correlation between spring drought occurrences and winter SPI-3 records, indicating a heightened exposure to drought persistence from winter to spring, while demonstrating reduced vulnerability during the transition from summer to fall. Providing a robust probabilistic framework for assessing drought persistence, this study contributes to improving drought risk management in the region.
Assessing the Effects of Seed Priming on the Tolerance of Sugar Beet Varieties to Salt and Drought Stress Using Plant Phenotyping Methods
(2025) Killi, Dilek; Dolu, Hüsna; Güngör, Zehra Selin
Sugar beet (Beta vulgaris L.) is a vital global crop, with Türkiye being the fifth-largest producer, contributing about 10% of the world’s supply. The Konya Basin accounts for 35% of Türkiye’s production. This region faces significant drought and salt challenges. In a study, six commonly grown sugar beet varieties in Konya (‘Lider’, ‘Libellule’, ‘Lizard’, ‘Mohican’, ‘Bison’, and ‘Rodeo’) were subjected to NaCl and PEG priming under salt and drought stress. ‘Lizard’ had the highest germination rate of 75.5% for NaCl-primed seeds, while ‘Bison’ reached 80.5% for PEG-primed seeds. The varieties varied in their germination rates, morphological traits, and photosynthetic parameters. NaCl-priming slightly increased leaf area under salt stress, PEG-priming improved stomatal conductance in ‘Bison’ under drought conditions. Overall, priming had limited effectiveness in alleviating stress impacts on sugar beet varieties, indicating a need for further research on the efficacy of priming methods and their integration with other mitigation strategies.
Assessment of Drought in Izmir District Using Standardized Precipitation Index
(Springer Nature, 2025) Tayfur, Gokmen; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Mersin, Denizhan; Gulmez, Ayse
Automated Detection and Quantification of Honey Adulteration Using Thermal Imaging and Convolutional Neural Networks
(Pergamon-Elsevier Science Ltd, 2026) Unluturk, Mehmet S.; Unluturk, Sevcan; Berk, Berkay
Honey is a valuable natural food rich in bioactive substances beneficial to health. Despite strict regulations prohibiting adulteration, honey remains one of the most frequently adulterated foods, often with low-cost commercial syrups. Conventional detection methods require expensive instruments, expert operators, and lengthy analysis times, limiting their practical use. This study introduces a rapid and automated method for detecting and quantifying honey adulteration using thermal image analysis combined with a tailored Convolutional Neural Network (CNN) architecture. Thirty-six pure honey samples (blossom and honeydew) from different regions of Türkiye were adulterated with inverted sugar, maltose, and glucose syrups at varying levels (3 %-60 % weight/weight (w/w)). Samples were heated to 60 degrees C and thermal images were captured during cooling using a custom image-capturing unit. The CNN model employed a multi-layer structure, starting with a shallow network for binary classification (pure vs adulterated honey) achieving 100 % accuracy, followed by specialized deeper CNN regressors to quantify adulterant levels with mean squared errors of 0.0003, 0.001, and 0.0002 for glucose, maltose, and inverted sugar, respectively. This layered CNN approach leverages thermal patterns linked to adulteration, enabling sensitive, rapid, and non-destructive quality control. Furthermore, the method is integrated into a user-friendly hardware-software system called Compact Adulteration Testing Cabinet on Honey (CATCH), requiring no specialized expertise, demonstrating strong potential for automated honey authenticity verification in practical settings.
Automated Two-Story Housing Floor Plan Generation Using Generative Adversarial Networks
(Springer International Publishing AG, 2025) Yildiz, Berfin; Cagda, Gillen; Zincir, Ibrahim
Automating the generation of two-story housing floor plans has emerged as a significant area of focus in architectural design research, driven by the need to enhance efficiency, creativity, and functionality in the design process. This study introduces a GAN-based framework for the automated generation of two-story housing layouts, incorporating architectural constraints such as functional zoning, multi-level connectivity, open-plan configurations, and visual relationships. By leveraging advanced deep learning techniques, the proposed framework achieves a balance between design creativity and practical functionality, addressing the unique challenges posed by multi-level spatial arrangements. The results demonstrate the model's ability to generate diverse and coherent floor plans that effectively meet the complexities of two-story layouts. This research underscores the transformative potential of deep learning models in architectural design, while acknowledging existing limitations in managing multi-level spatial relationships and user interaction. With continued advancements, AI has the potential to play a pivotal role in supporting architects-optimizing workflows, enabling creative exploration, and fostering user-centered, innovative designs. Ultimately, this work sets the stage for further progress in automated multi-story housing design, paving the way for a more collaborative and technology-driven architectural future.
Automatic Short-Answer Grading in Sustainability Education: AI-Human Agreement
(Wiley, 2026) Emirtekin, Emrah; Ozarslan, Yasin
Background Sustainability education emphasises critical thinking and interdisciplinary understanding, making the assessment of students' learning outcomes complex. While Large Language Models (LLMs) have shown promise in educational assessment, their reliability in domains requiring contextual reasoning-such as sustainability-remains unclear. Objectives This study aims to evaluate the agreement between human raters and several LLMs (GPT-4o, Gemini 2.0 Flash, DeepSeek V3, LLaMA 3.3) in assessing short-answer responses from a university-level Sustainability course. It also investigates how this agreement varies across cognitive skill levels. Methods A total of 232 short-answer responses were evaluated using a rubric aligned with Bloom's Revised Taxonomy. Consensus scores from human raters were compared to LLM-generated scores using multiple statistical measures, including Quadratic Weighted Kappa (QWK), Intraclass Correlation Coefficient (ICC), Pearson correlation, and distributional overlap. Results Moderate agreement was found between LLMs and human raters in total scores (QWK: 0.585-0.640; r: 0.660-0.668; eta: 0.681-0.803). Inter-rater reliability among humans was good to excellent (ICC: 0.667-0.800). Criterion-level agreement declined as cognitive complexity increased, with notably low agreement on evaluating higher-order skills. Conclusions Overall, LLM-human agreement was moderate on total scores but declined at higher cognitive levels, indicating that LLMs are suitable for basic comprehension checks while human oversight remains necessary for complex reasoning.
AVRUPA ÇIKARLARI VE AB’NİN MISIR’A YÖNELİK İKLİM DEĞİŞİKLİĞİ POLİTİKASI
(2020) Gunay, Defne
Uluslararası İklim Değişikliği Paneline göre iklim değişikliği sonucunda Akdeniz bölgesindeki nehirler etkilenecek, çölleşme artacak, yükselen deniz seviyesi kıyı yerleşimlerini etkileyecek ve tarım mahsullerinde verim düşecektir. Bu nedenlerle iklim değişikliği Akdeniz bölgesi için önemli bir konudur. Avrupa Birliği, iklim değişikliği politikasında öncülük ederken 2050 yılı itibariyle karbondan arındırılmış ekonomi hedefini de tutturmayı hedeflemektedir. Avrupa Birliği aynı zamanda iklim diplomasisi aracılığıyla dünyada da iklim politikalarının benimsenmesini teşvik etmektedir. Avrupa Birliği’nin bu etkinlikleri, ekonomik çıkarlarıyla açıklanabilmektedir. Bu çalışmada AB’nin, Mısır’a yönelik iklim değişikliği politikası sayesinde ekonomik çıkar elde edip etmediği incelenmektedir. Belge analizi yapan çalışma, ABMısır iklim değişikliği ilişkileri sayesinde Avrupa şirketlerinin Kyoto Antlaşması tarafından oluşturulan piyasa temelli çözümlerden yararlandığını, Mısır’a yenilenebilir enerji teknolojileri ihraç ettiğini ve AB tarafından Mısır’da teşvik edilen AB düzenlemeleri sayesinde avantajlı bir konum elde ettiklerini ortaya koymaktadır.
Barriers Challenging Social Gastronomy Entrepreneurs
(Edward Elgar Publishing Ltd., 2025) Caliskan, Aylin; Pirnar, Ige; Celebi, Duygu

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