WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Structural Optimization for Robotic Concrete Construction: A Systematic Review(MDPI, 2026) Gürer, Ethem; Pektaş, Ömer Korkut; Alaçam, Sema; Güzelci, Orkan Zeynel; Çevik, Ayşe Nesligül; Türel, Ahmet; Özdemir, SalihConcrete construction is associated with high environmental impact and geometric limitations imposed by conventional formwork, which has led to growing interest in combining structural optimization with robotic fabrication. In this study, structural optimization refers to computational methods such as topology optimization, shape optimization, and form finding that aim to improve material efficiency and load-bearing performance by modifying the geometry of structural elements. This systematic review investigates how these optimization approaches are translated into fabrication-aware design workflows for robotic concrete construction. Following a PRISMA-based methodology, 90 peer-reviewed studies published between 2015 and 2025 were analyzed. The review focuses on fabrication routes including (i) 3D concrete printing, (ii) 3D-printed formwork, (iii) shotcrete-based additive manufacturing, and (iv) controlled casting systems, and examines how each route constrains geometry representation, design decisions, toolpath generation, and robotic execution. The review analyzes design-to-fabrication workflows that link optimized structural geometry to production logic and process control. Key findings indicate that incorporating fabrication constraints at early design stages can support buildability and potential material efficiency, while reinforcement integration and quality control remain critical challenges for structural reliability. The review also highlights the increasing role of in situ sensing and feedback-driven automation in improving process stability. Overall, the study clarifies current practices, limitations, and emerging directions for integrating structural optimization with robotic concrete fabrication.Article Modeling Commuter Mobility in Stockholm: A Spatial Panel Approach Using Mobile Phone Data(Springer Heidelberg, 2026) Fischer, Manfred M.; Osth, John; Toger, Marina; Turk, UmutThis paper examines the sociodemographic and socioeconomic determinants of regional commuter mobility in the Greater Stockholm Area using a heteroscedastic spatial Durbin panel data model estimated via Bayesian Markov Chain Monte Carlo methods. Drawing on mobile phone-derived origin-destination flows from the MIND database, the analysis exploits unusually fine spatial and temporal granularity across a balanced panel of 675 regions over the period 2018-2023. A k-nearest neighbor spatial weight matrix (k = 18), selected via Bayesian model comparison, captures the topological structure of interregional connectivity. By modeling spatial lags in both the dependent and independent variables, the framework enables explicit recovery of direct (own-region) and indirect (spillover) effects from scalar summary measures of the matrix of partial derivatives - providing robust posterior inference on how sociodemographic and socioeconomic conditions propagate through space. This approach addresses a key limitation of conventional non-spatial methods, which risk producing biased estimates by ignoring spatial interdependence. Empirical results confirm that spatial spillovers predominate over direct effects, with educational attainment and car ownership emerging as the principal determinants of commuter mobility, while age composition plays a comparatively modest role. These findings underscore that evaluating direct effects in isolation systematically underestimates the broader societal returns to mobility-enhancing regional policies.Article Electrospun PCL/PEG Nanofibers Incorporating Plantago Lanceolata Extract and Clove Oil for Dual-Function Wound Dressings(IOP Publishing Ltd, 2026) Teke, Selin Nur; Yuruk, Adile; Isoglu, Ismail AlperIn this study, we developed electrospun polycaprolactone/polyethylene glycol (PCL/PEG) nanofibers loaded with Plantago lanceolata (P. lanceolata) extract and clove oil to evaluate their combined potential for wound healing and antibacterial activity. PCL/PEG nanofibers were electrospun and post-loaded with P. lanceolata extract at 5%, 10%, and 15% (w/v), together with 1% (v/v) clove oil. Scanning electron microscope analysis showed a uniform, bead-free nanofibrous structure, with fiber diameters ranging from 768 +/- 140 nm to 892 +/- 206 nm and pore sizes from 3.93 +/- 0.97 & micro;m to 6.20 +/- 1.16 & micro;m. The nanofibers exhibited swelling ratios between 103.23 +/- 16.42% and 133.93 +/- 40.45% within 1 h and showed gradual degradation ranging from 25.92 +/- 2.84% to 57.76 +/- 0.92% over 21 d, with cumulative extract release approaching a plateau by day 28 under the experimental conditions. The incorporation of plant extract and essential oil initially increased the water contact angle from 15.97 +/- 1.07 degrees to 48.61 +/- 7.85 degrees, indicating that the nanofiber surface remained hydrophilic; at higher extract contents, the nanofibers transitioned to a superhydrophilic state. Antibacterial activity was primarily governed by clove oil, yielding up to 85.16 +/- 0.36% efficacy against E. coli (E. coli) and 79.90 +/- 0.29% against S. aureus (S. aureus). While P. lanceolata extract alone showed limited antibacterial activity, its presence within clove oil-loaded nanofibers consistently enhanced antibacterial performance at the composite level. In vitro scratch assays demonstrated pronounced wound closure at later time points (72-96 h), particularly for nanofibers containing higher extract concentrations, confirming the dominant contribution of P. lanceolata to the healing response. Overall, the electrospun PCL/PEG nanofibers represent a dual-function wound dressing, with clove oil contributing antibacterial protection and P. lanceolata extract supporting wound healing.Article Comprehensive Evaluation of Microstructure–Property Relationships in Al-Added Sn-Zn Eutectic Solder Alloys from Thermal, Electrical, and Mechanical Perspectives(Springer, 2026) Bayram, Ümit; Şahin, Mevlüt(Sn-8.8Zn)-XAl (X = 0, 0.5, 1.0, 2.5, 5.0 wt.%) solder alloys were produced using a vacuum muffle furnace. The microstructural images, chemical compositions, and phase structures of the alloys were characterized by field emission scanning electron microscopy (FESEM), field emission scanning electron microscopy-energy dispersive x-ray spectroscopy (FESEM-EDX), and x-ray diffraction (XRD) analyses, respectively. According to FESEM images, a fully eutectic microstructure was observed in the Sn-8.8Zn alloy. With increasing Al content, dendritic structures formed and became denser. The mechanical properties of the alloys (ultimate tensile strength sigma UT, tensile yield strength sigma TY, compressive yield strength sigma CY, and Vickers hardness HV) were measured as a function of composition. The highest strength and hardness values were obtained for the (Sn-8.8Zn)-2.5Al alloy, whereas the (Sn-8.8Zn)-5.0Al alloy exhibited the highest ductility. The melting enthalpies (Delta H) and the specific heat differences between the solid and liquid phases (Delta CP) of the alloys were measured by differential scanning calorimetry (DSC) analysis. It was determined that an increase in the Al content of the eutectic alloy resulted in higher measured thermophysical properties. Finally, electrical resistivity (rho) values at T = 300 K, measured using the standard four-point probe method (FPPM), revealed an increase in resistivity up to 1.0 wt.% Al content, followed by a decrease beyond this value. In contrast, thermal conductivity values calculated using the Wiedemann-Franz law exhibited an opposite trend, decreasing up to 1.0 wt.% Al, and increasing thereafter. Based on microstructure-property relationships, the results were compared with previous studies, highlighting that (Sn-8.8Zn)-XAl alloys represent promising alternatives to lead-free solders.Article Time Distributed Classification of Alzheimer’s Disease on MRI Scans(John Wiley and Sons Ltd, 2026) Dundar, Mehmet Sait; Yilmaz, BulentThe diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study combines volumetric feature analysis with computational modeling techniques, focusing on spatial and temporal analysis, to categorize individuals as cognitively normal (CN), mild cognitive impairment (MCI), or AD using magnetic resonance imaging (MRI) data. In the initial phase, volumetric changes, comprising cortical thickness, white matter, grey matter, cerebrospinal fluid, and total intracranial volume, were derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset utilizing the CAT12 toolbox in statistical parametric mapping (SPM). Linear regression was utilized on these variables over time to create slopes that reflect volumetric change rates, which then served as inputs for machine learning classifiers. The slopes of cortical thickness exhibited the greatest classification accuracy, reaching 82.5% with a random forest model for differentiating AD from CN individuals. During the second phase, a deep learning methodology was utilized, relying solely on the MRI scans and excluding the outcomes from the first phase. A pre-trained 3D ResNet-101 convolutional neural network (CNN) model extracted spatial characteristics from MRI volumes, whereas long short-term memory (LSTM) networks recorded temporal dynamics across subsequent annual scans. This hybrid CNN-LSTM design markedly improved classification performance, attaining 96.7% accuracy for AD against CN and enhancing the distinction of MCI cases. Nonetheless, discrepancies in MCI categorization were chiefly ascribed to the restricted access to annual MRI data and the model's pre-training on CN and AD cohorts. These findings highlight the potential of integrating volumetric statistical analysis with deep learning for automated AD categorization. This work enhances neuroimaging diagnostic methods by utilizing both spatial and temporal MRI data, enabling early diagnosis and better evaluation of disease development.Article Three-Dimensional Culture Enhances the Antimicrobial Activity of Mesenchymal Stem Cells against Shiga Toxin-Producing Escherichia Coli O157:H7 in Vitro(Oxford Univ Press, 2026) Fidan, Özkan; Türkyılmaz, Süheyla; Bicer, Mesude; Öztürk, Esengül; Sener, FatmaAims This study examines the in vitro antibacterial activity of palatal adipose tissue-derived mesenchymal stem cells (PAT-MSCs) and the expression of antimicrobial peptide LL-37, with a particular focus on the effect of three-dimensional (3D) nanofibrillar cellulose-based hydrogel against Shiga toxin-producing Escherichia coli (STEC) harboring stx1 and/or stx2 genes isolated from mastitis milk in Turkey.Methods and results The antibacterial activity of conventionally cultured PAT-MSCs and 3D-cultured PAT-MSCs (PAT-MSCs-3D) was evaluated against STEC isolates and Escherichia coli ATCC 35150 using quantitative colony-forming unit (CFU) assay. The expression levels of antimicrobial peptide (AMP)-encoding genes were evaluated by quantitative real-time PCR, and AMP production was further validated by immunocytochemical staining. The results indicated that PAT-MSCs-3D exhibited significantly enhanced antibacterial efficacy, resulting in marked bacterial inhibition of all tested STEC strains, with bacterial reductions reaching up to 6-7 log under specific experimental conditions. Molecular and immunocytochemical analyses demonstrated increased expression of the antimicrobial peptide LL-37 in PAT-MSCs-3D compared to 2D cultures.Conclusions Our results show that culturing PAT-MSCs in 3D conditions leads to a significant enhancement in their antimicrobial properties, which could be linked to the upregulation of LL-37.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 Radar Resolution Enhancement Based on Burg-Aided MIMO-DBS and Burg-Aided MIMO-SAR †(MDPI, 2026) Bekar, Muge; Bekar, Ali; Baker, Christopher John; Gashinova, Marina; Pirkani, AnumAutonomous systems require sensors that provide high-resolution imagery in adverse lighting and weather conditions for advanced situational awareness. In this regard, radars are a mandatory component of autonomous systems. Although Multiple-Input Multiple-Output (MIMO) radars provide high angular resolution beyond that of their actual physical dimension, much higher cross-range resolutions are required, especially in traffic congested areas, to differentiate and recognize closely positioned targets. The motion of the MIMO radar platform can be exploited to obtain higher cross-range resolution in the off-boresight direction, using Synthetic Aperture Radar (SAR) and Doppler Beam Sharpening (DBS) techniques, but improvements in the boresight direction, the most crucial direction for path planning, require the use of super-resolution techniques. This paper proposes a technique that combines the Burg algorithm with MIMO-SAR and MIMO-DBS radar data to enhance the cross-range resolution in the boresight direction and to achieve further enhanced cross-range resolution in off-boresight directions. The proposed technique is applied to both frequency domain and time domain data in back-projection (BP) and DBS image formation processing. A comprehensive comparison is made, with evaluation of corresponding performance and operational complexity. The performance of the technique is validated through simulation, lab-based and real-world experiments at a frequency of 77 GHz.Article Citation - WoS: 1Citation - Scopus: 1Predictive Modeling and SHAP-Based Interpretability of Manganese and Iron Dissolution in Multi-Acid Leaching Systems Using Hybrid Machine Learning(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Top, Soner; Altiner, Mahmut; Kursunoglu, Sait; Kuzu, EmrahHydrometallurgical leaching processes contain complex and nonlinear parameter interactions that are difficult to capture with conventional empirical models. In this study, a multiple hybrid machine learning approach was developed to predict manganese (Mn) and iron (Fe) dissolution efficiency in leaching systems and performed using sulfuric acid (H2SO4), hydrochloric acid (HCl), and nitric acid (HNO3). A large-format dataset consisting of 204 independent leaching experiments was generated in which acid type, acid concentration (0.5-5 M), temperature (25-90 degrees C), solid/liquid ratio (100-200 g/L), leaching time (1-4 h), and eight different reducing agent types were systematically varied. XGBoost, LightGBM, CatBoost, and Random Forest algorithms were individually trained and subsequently combined with a Soft Voting Ensemble architecture. Hyperparameters were optimized using the RandomizedSearchCV method with 3-fold cross-validation. The XGBoost model achieved the highest prediction accuracy for Mn dissolution (R-2 = 0.8993, RMSE = 8.06%), while CatBoost demonstrated the best performance in Fe dissolution (R-2 = 0.8415, RMSE = 4.43%). SHAP analysis suggested that the dosage and type of reducing agents are the most influential predictive features for Mn dissolution, while acid molarity and temperature were identified as the dominant predictors for Fe leaching. Friedman test confirmed that performance differences among both Mn and Fe models were statistically significant (Mn: chi(2) = 32.76, p < 0.001; Fe: chi(2) = 25.96, p < 0.001). The developed models contribute significantly to hydrometallurgical process optimization by predicting the nonlinear effects of leaching parameters on metal dissolution with high accuracy. This study presents a comprehensive and interpretable machine learning framework supported by an extensive experimental dataset, a substantial portion of which has not been previously utilized or comparatively analyzed within a unified multi-acid framework, enabling systematic modeling of selective Mn-Fe dissolution across multiple acid systems and reducing agents.Article Leveraging Clinical Data for Early Heart Disease Prediction: A Machine Learning Approach With Interpretability Analysis(SAGE Publications Ltd, 2026) Qumsiyeh, Emma; Al-Wirdian, Qassam; Ersoz, Nur SebnemBackground: Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for early and accurate diagnosis to support effective prevention and treatment strategies. Methods: This study presents a machine-learning-based approach for predicting heart disease using clinical and demographic data from a publicly available dataset. Four widely used classification algorithms-Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), and Decision Trees-were evaluated to identify the most effective predictive model. The dataset underwent comprehensive preprocessing, including handling missing values, categorical encoding, and feature normalization, to enhance data quality and model robustness. Model performance was assessed using accuracy, precision, recall, and AUC-ROC metrics. Results: Findings show that hyperparameter-optimized models, particularly Random Forest and KNN, demonstrated strong predictive performance. Explainability techniques, specifically SHapley Additive exPlanations (SHAP), were incorporated to improve interpretability, transparency, and clinical trust. SHAP values were used to analyze feature importance and provide explanations for individual predictions. Conclusion: The results underscore the potential of interpretable machine-learning models as valuable tools for early diagnosis, risk stratification, and clinical decision support. Future research should employ larger datasets and investigate real-time predictive applications further to enhance the generalizability and clinical utility of these models.