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 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 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 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.Article Experimental Comparative Analysis of Hole-Making Strategies and Cutting Parameters on Flexural Properties and Induced Delamination in S2 Glass and Basalt Fiber-Reinforced Polymers(John Wiley and Sons Inc, 2026) Eltahir, Sara Saeed Abdulrahman; Yilmaz, Cagatay; Kesriklioglu, SinanOpen holes are often required in the applications of fiber-reinforced polymers (FRP). However, machining them leads to fiber and matrix damage, reducing the overall quality of composites. The novelty of this study lies in investigating the influence of multiple hole machining strategies under varying feed rates and cutting speeds on the flexural strength, chord modulus and hole quality for S2-Glass (S2-GFRP) and basalt fiber-reinforced polymers (BFRP), followed by three-point bending tests and statistical analysis to determine the optimum machining parameters in terms of flexural properties, delamination, and time efficiency. Findings indicate that conventional milling yields an increase in the flexural strength and chord modulus by 22.2 MPa and 1.9 GPa, respectively, for S2 GFRP. While climb milling performs best for BFRP, enhancing flexural performance by 7.6 MPa and 0.4 GPa. Direct drilling at high feed rate and cutting speed shows the poorest performance. Hole machining at low feed rate using climb milling minimizes delamination at the entrance by 14.8% for S2 GFRP and by 2.7% for BFRP using conventional milling. At the exit, helical milling at a low feed rate suppresses delamination damage by 58.5% and 10.7% for S2 GFRP and BFRP. The most time-efficient method is direct drilling at a feed rate of 0.075 mm/rev and cutting speed of 75 m/min or climb milling with same feed rate, but 25 m/min cutting speed. Within the tested ranges, the optimized drilling setup significantly improved structural performance of S2-GFRP and GFRP, confirming the effectiveness of the proposed experimental-statistical framework.Article Cellulose-Based Hydrogel Matrix Enhances Antimicrobial and Biofilm-Inhibitory Responses of Palatal Mesenchymal Stem Cells(Springer Heidelberg, 2026) Fidan, Özkan; Bicer, Mesude; Öztürk, Esengül; Sener, FatmaMesenchymal stem cells (MSCs) have emerged as promising alternatives to fight drug-resistant bacterial infections. This study investigates the antibacterial activity of palatal adipose tissue-derived MSCs (PMSCs), particularly when cultured within a 3D nanofibrillar cellulose hydrogel, against four clinically relevant pathogens: Pseudomonas aeruginosa K6, Staphylococcus aureus ATCC 25,923, Bacillus cereus K9 and Escherichia coli O157:H7. This study showed that both PMSCs alone and PMSCs in 3D cellulose-based hydrogel effectively inhibited the growth of bacterial burden. Notably, PMSCs cultured in the 3D system demonstrated an excellent effect, reducing bacterial burden by up to 14 log in E. coli and 12 log in P. aeruginosa K6 at a 120 & micro;L inoculum after 2 h of incubation. RT-PCR and immunocytochemical analyses found out a remarkable upregulation of the Cathelicidin (LL-37) in PMSCs 3D cultures compared to PMSCs. Furthermore, 3D cellulose-based hydrogel exhibited a significant biofilm-inhibitory effect, reaching a 57.65% reduction. The results demonstrated the importance of 3D cellulose-based hydrogel for treating antibiotic-resistant infections. PMSC therapy based on 3D hydrogel may therefore be offered as more effective antimicrobial agent to overcome drug-resistant bacterial infections.Article An Attention-Based Autoencoder Model with Gated Recurrent Unit for Stock Price Movement Prediction(Springer Nature, 2026) Kolukisa, Burak; Bakir-Gungor, Burcu; Akkas, HuseyinPredicting stock movements is crucial for investors looking to maximize their profits in rapidly changing financial markets. However, noise in stock price data makes it harder to detect the trends and ultimately decreases the performance of predictive models. To address these challenges that are caused by noise, this study proposes two novel models, i.e., an Attention-based Autoencoder (ABA) and an Attention-based Variational Autoencoder with Gated Recurrent Units (AGRUA), which uniquely integrate denoising, attention, and GRU layers. Firstly, a data set is created by adding 9 different technical indicators to the historical data of Borsa Istanbul (BIST 30) stocks. Secondly, proposed methods and other well-known deep learning models were used to remove noise from the data sets. Finally, each denoised dataset was fed separately to the Extreme Gradient Boosting model and subjected to a buy-sell process. The results were measured using trading indicators such as amount of the profits and Sharpe Ratio, Sortino Ratio and Maximum Drawdown. The proposed models produced substantial financial gains, with AGRUA achieving the highest total profit and ABA achieving the lowest average Maximum Drawdown, thereby demonstrating superior risk-adjusted performance. Lastly, Friedman and Nemenyi tests confirmed that AGRUA and ABA surpass most of the benchmarks in profit and risk-adjusted returns. The performance of the proposed method demonstrates its value in capturing nonlinear market patterns and improving decision accuracy, emphasizing the need for noise reduction before forecasting.