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A Bibliometric Analysis on Bio-Inspired Responsive Facades
(Gazi Univ, 2025-12-01) Bilmez, Busra; Maden, Feray
The implementation of responsive facades offers a promising strategy for reducing operational energy use while enhancing indoor comfort. These facades dynamically adjust their configurations, mirroring adaptive behaviors observed in living organisms. The bio-inspired responsive facade approach integrates principles from biomimicry and responsive architecture to develop systems that react intelligently to environmental stimuli. This study aims to analyze existing literature to identify key developments and trends in bio-inspired responsive facades. The research is conducted in three main phases. First, the study establishes its conceptual framework. Second, a comprehensive bibliometric analysis is conducted using the Web of Science database, employing science mapping techniques via VOSviewer and the Bibliometrix R package. This analysis uncovers major trends, turning points, influential authors, leading journals, and significant conferences, offering a clear overview of the research landscape. In the third phase, 33 facade designs are selected from 141 identified publications for comparative analysis. Each design is examined based on material, control systems, movement mechanisms, and functional objectives. The review explores their natural inspirations, responsive stimuli, and material strategies to derive insights for future innovation. Results reveal that 45% of designs focus on improving thermal comfort in hot climates, often utilizing active systems or smart materials. Folding and rotating mechanisms are the most common modes of movement. However, only five designs progress beyond the conceptual phase, highlighting the need for practical implementation. By mapping the evaluation of this interdisciplinary field, the study establishes a systematic foundation for advancing bio-inspired responsive facade research.
A Knowledge-Driven Computer Vision Framework for Automated Atomic Force Microscopy Surface Characterization
(Elsevier Sci Ltd, 2026-02) 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 Knowledge-Driven Computer Vision Framework for Automated Atomic Force Microscopy Surface Characterization
(Elsevier Sci Ltd, 2026-02) 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 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 Novel GNSS Antenna and Array Design with 3D-Printed Stepped Backed Cavity
(Springer Heidelberg, 2025-10-13) Secmen, Mustafa; Yigit, Olcay
In recent years, the use of 3D printing technology for manufacturing RF components and antennas has grown significantly due to its advantages of cost-effectiveness, lightweight, and ease of fabrication. This paper presents a high-gain spiral antenna designed with FR4 and PLA materials, optimized for GNSS band applications. The antenna employs Archimedean spirals and a reflector with a 66 mm air gap, approximately lambda/4 at the lower frequency of the GNSS band, to ensure good circular polarization and axial ratio. The design features a novel conic shape to balance electrical length differences across the frequency band, improving performance. Our results show that the antenna achieves a gain 3 dB higher than similar designs in the literature. Additionally, the use of PLA material reduces coupling effects, allowing the antenna elements to be placed 35 mm closer together in an array configuration. The designed antenna gain is minimum 5.9 dBic and maximum 6.2 dBic, and the axial ratio is minimum 1.8 dB and maximum 2.2 dB in the frequency range. These findings underscore the potential of 3D printing in developing high-performance, compact antennas for advanced communication systems. Furthermore, an antenna array was constructed using the individual antenna elements. The array exhibited the targeted performance, achieving a half-power beamwidth of 36 degrees and a realized gain of 11 dBic.
A UHF RFID-Based System for Real-Time Production Monitoring and Tag Quality Control in the Textile Industry
(SAGE Publications Inc, 2026-01-08) 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.
A UHF RFID-Based System for Real-Time Production Monitoring and Tag Quality Control in the Textile Industry
(SAGE Publications Inc, 2026-01-08) 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.
Africa’s Green Hydrogen Trajectory: A Multidimensional Review of Technology, Economics, Infrastructure, and Social Justice
(Pergamon-Elsevier Science Ltd, 2026-04) Hepbasli, Arif; Tiktas, Asli
Although Africa possesses some of the world's most abundant renewable resources, it faces formidable infrastructural, financial and socio-environmental barriers to green-hydrogen deployment. To provide a holistic evidence base for policymakers, this study combines a systematic narrative review of 50 peer-reviewed and grey-literature sources with bibliometric mapping and a multidimensional evaluation matrix. Publications have increased dramatically since 2020. After removing query anchor terms from keyword rankings, the bibliometric mapping highlighted second-order thematic foci-such as desalination/water, ammonia/derivatives, infrastructure/logistics, certification/GoO, and policy/finance themes-that bridge technical and socio-economic research. A continental comparative matrix assessed technological readiness, economic viability (LCOH: levelized cost of hydrogen), infrastructure readiness, regulatory preparedness and socio-environmental justice across seven key countries. In this review study, socio-environmental justice was defined and assessed as a set of operational, evidence-traceable safeguards and distributional risk exposures associated with hydrogen deployment, covering (i) water sourcing and allocation governance (competition with domestic users, desalination/brine externalities, and conflict-mitigation measures), (ii) land-tenure and displacement protections for renewable plants and corridor rights-of-way, (iii) procedural justice via documented consultation/participation provisions (including FPIC where applicable) and grievance-redress mechanisms, (iv) benefit-sharing and local value-capture instruments (local content, training, community service co-benefits), and (v) environmental safeguards, monitoring, and enforcement capacity; these elements were scored through predefined sub-indicators, with inverse scoring applied for risk-type proxies so that higher environmental justice values consistently represented stronger safeguards and lower socio-environmental risk. The evaluation matrix was operationalized using a transparent, rule-based scoring rubric in which multiple sub-indicators were normalized to a consistent 0-1 scale (via threshold-based and min-max transformations, with inverse scoring for risk-type indicators), aggregated into five pillar scores, and combined into a composite readiness score; robustness was examined through alternative entropy-based weighting and Monte Carlo uncertainty propagation (with percentile intervals reported). Egypt topped the ranking owing to plans for more than 15 GW of electrolyzer capacity and 47 desalination plants (6.4 million m(3)/day, 20% earmarked for hydrogen), achieving source-reported plant-gate PV-only LCOH values in the range of 3.13-4.21 EUR/kg H-2 (source-reported; providing FX-harmonized USD/kg H-2, 2050 scenario), under the financial and technical assumptions (e.g., WACC 7.49-11.26% and 26-year project life). Morocco and South Africa followed closely due to liberalized electricity frameworks and established port infrastructure, whereas Namibia and Mauritania, despite world-class solar and wind resources, scored lower because of remote project locations and high capital costs. Kenya's nascent program anchored on geothermal power resulted in the highest LCOH (>4.2 USD/kg H-2) and low policy scores. Analysis of policy documents revealed only Namibia and Mauritania have enacted dedicated hydrogen legislation; most other countries lack certification schemes and Guarantees of Origin.Physical observations-extreme solar irradiance (>2200 kWh/m(2) yr ), steady coastal winds, acute water scarcity and desert conditions-explain why desalination, transmission networks and port upgrades are decisive for lowering costs. High financing costs (with WACC varying strongly by project structure: a high/risk-adjusted case of 11-15% is used in some studies, concessional or de-risked structures can fall in the 2-6% range, and a central benchmark of similar to 8% is also commonly reported) and reliance on export markets further constrain competitiveness. The study concludes that unlocking Africa's hydrogen potential requires synchronizing infrastructure investment with transparent regulatory frameworks and socio-environmental safeguards, thus transforming abundant natural resources into inclusive industrial development.
Citation - WoS: 1
AIndividualism and Algorithmic Collectivism: Rethinking Individual-Collective Dynamics in the Age of AI
(Springer, 2026-02-15) 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.
AIndividualism and Algorithmic Collectivism: Rethinking Individual–Collective Dynamics in the Age of AI
(Springer, 2026-02-15) Özerim, Mehmet Gökay
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.
An ALNS-Based Decision Support System for Scheduling and Routing in Home Healthcare with Lunch Break Constraints
(Growing Science, 2026) 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
An ALNS-Based Decision Support System for Scheduling and Routing in Home Healthcare with Lunch Break Constraints
(Growing Science, 2026) 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, 2025-11-20) 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.
Analysis of M/M/s Make-to-Stock Queues with Production Start-up Costs for Both Lost Sales and Backordering Cases
(Taylor & Francis Ltd, 2025-11-20) 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.
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.
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 for Biodiversity Conservation in the Galapagos: Integrating Animal-Aided Design and Climate-Responsive Strategies
(Springer, 2026-04-02) Morales-Beltran, Mauricio; Alkan, Zeynep Naz
The Galapagos Islands, a UNESCO World Heritage Site renowned for their unique biodiversity, face escalating ecological threats from unsustainable tourism-driven development. Protected habitats and conservation policies have preserved much of the archipelago's ecological function, but growing tourism and urban settlement-concentrated on a narrow similar to 3% of inhabitable land-have strained ecosystems and disrupted wildlife cycles. These dynamics reveal the limits of a "natural laboratory" framing and demand a shift from a binary human versus nature model to a sustainable human-with-nature approach. Reconciling local livelihoods and biodiversity therefore requires integrated, site-specific strategies that treat the built environment as an active component of conservation. This study proposes an architectural response exploring how human-centered spaces can coexist with habitats for other species while avoiding further degradation of ecological integrity, ultimately enhancing resilience. Using a research center modeled after the Charles Darwin Foundation on Santa Cruz Island as a design case, the proposal integrates animal-aided design (AAD) and climate-responsive principles. Central to the investigation is enabling humans to inhabit spaces alongside other species while respecting ecological cycles, safeguarding the Galapagos' natural heritage, and supporting sustainable development. Programmatically, roughly half the total area is allocated to species-specific zones, reflecting a deliberate balance between conservation and research/education. Site selection balances proximity to settlements, minimizing transport-related disturbance, with isolation to foster wildlife habitation. Five keystone species-land and marine iguanas, giant tortoises, sea lions, and Darwin's finches- guide ecosystem-sensitive strategies for shared habitats. Design innovations include elevated platforms preserving animal movement, open-flow layouts responsive to equatorial climate, multi-layered structures minimizing land footprint, and natural recyclable materials supporting passive climate control. Building forms adapt to topography, avoiding visual dominance and fostering ecological integration. Through this sequenced design process, the outcome of this novel AAD + climate-responsive design integration demonstrates architecture's potential to harmonize human and non-human occupancy, preserving ecosystems while supporting scientific and educational functions.
Architecture in Translation: Germany, Turkey, the Modern House
(Intellect Ltd, 2013) Baydar, Gulsum
Assessing Seasonal Drought Persistence Using a Bayesian Logistic Regression Approach
(Pergamon-Elsevier Science Ltd, 2026-02) 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.

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