Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Erten, Fusun(5)
- Sahin, Kazim(5)
- Orhan, Cemal(4)
- Sengur, Abdulkadir(4)
- Zengin, Gokhan(4)
- Akkurt, Nevzat(3)
- Er, Besir(3)
- Ertarğın, Merve(3)
- Gürgenç, Turan(3)
- Karabacak, Abdullah Hasan(3)
- Padigaru, Muralidhara(3)
- Türkoğlu, Muammer(3)
- Uyanık, Hakan(3)
- Zhilevski, Marin(3)
- Çelebi, Müslim(3)
- Çelik, Ertuğrul(3)
- Abderrahmane, Aissa(2)
- Akbulut, Taner(2)
- Akdağ, Mustafa(2)
- Akmaz, Düzgün(2)
- Aksakal, Bünyamin(2)
- Al-Khaleel, Mohammad(2)
- Aslan, Naim(2)
- Aygoren, Caner(2)
- Ayık, Merve(2)
- Beykumul, Aysegul(2)
- Can, Umit(2)
- Cinarli, Fahri Safa(2)
- Demir, Yakup(2)
- Gallo, Monica(2)
- Gašić, Uroš(2)
- Guedri, Kamel(2)
- Guler, Omer(2)
- Guler, Osman(2)
- Gunay, Mihriban(2)
- Ivanov, Marija(2)
- Jugreet, Bibi Sharmeen(2)
- Kilic, Ertugrul(2)
- Küçük, Deniz Bora(2)
- Laidoudi, Houssem(2)
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ2The Female Face of the Digital World: From Cooperatives to Global Financial Freedom
Veysikarani Geyik, Dilek; Akdağ, Nuran; Malkoç Kinikli, Filiz
The objective of this study is to examine, through a narrative literature review and a qualitative cross-case comparison, how women’s cooperatives and cooperative-linked financial mechanisms contribute to women’s financial inclusion and economic empowerment under different institutional and socioeconomic conditions. The study synthesizes evidence from successful initiatives, including the Self-Employed Women’s Association (SEWA) in India, digital savings cooperatives in Indonesia, and M-Pesa-enabled cooperative models in Kenya, women-oriented banking in Rwanda, Cooperative Home Care Associates in the United States, and women’s cooperatives in Türkiye. The findings indicate that cooperative systems enhance women’s access to financial services, strengthen financial and digital capabilities, support income generation, and foster leadership and economic participation. However, persistent barriers, including limited digital access, inadequate financial literacy, and restrictive socio-cultural norms, continue to constrain the effectiveness and scalability of these models. The study concludes that strengthening women’s cooperatives through targeted policy support, capacity-building initiatives, and digital financial inclusion strategies can significantly improve women’s economic empowerment while contributing to more inclusive financial systems and broader sustainable development objectives.
- MakaleAçık Erişim2026Scopus SJRQ1Freeze–Thaw Performance and Microstructural Stability of Alkali-Activated Slag Mortars Incorporating Mussel Shell Waste
Şahin Yön, Merve
This study investigates the use of mussel shells (MSs), a biogenic by-product of the food industry, as a partial replacement for ground granulated blast furnace slag (GBFS) in alkali-activated mortars. Given their high CaCO3 content, MSs represent a sustainable secondary raw material that reduces both waste disposal burden and reliance on natural resources, while offering a low-carbon alternative to conventional cement-based binders. Alkali-activated mussel shell/slag mortars (AAMSs) were produced with MS replacement ratios of 0%, 5%, 10%, 15%, and 20% by mass of GBFS. Sodium hydroxide (NaOH) and sodium silicate (Na2SiO3) were used as alkaline activators. Fresh specimens were cured at 60 °C for 48 h. The experimental program included workability, compressive and flexural strength, water absorption, porosity, density, capillarity, ultrasonic pulse velocity (UPV), and freeze–thaw (F-T) resistance tests. Increasing MS content slightly reduced flowability and mechanical strength, while increasing water absorption, porosity, and capillarity. The M0 series achieved the highest 28-day compressive strength (54.06 MPa), while M15 exhibited the highest flexural strength (5.23 MPa). Following F-T cycling, the 5% and 10% MS series demonstrated the best compressive strength (30 MPa). The 10% MS exhibits a relatively balanced overall performance, providing the best balance between mechanical performance, F-T resistance, and microstructural stability, as confirmed by scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDS) analyses showing elevated Ca/Si ratios and the formation of Ca-rich crystalline phases.
- MakaleYayıncıda açık erişim2026Scopus SJRQ1Phenolic Profiling of Some Bryophyte Species: In Vitro and In Silico Assessment of Their Antimicrobial Potentials
Çambay, Zafer; Uslu, Harun; Coteli, Ebru; Göktaş, Bünyamin; Özdemir Bayçinar, Kevser; Emre, Mustafa Yunus; Güngören, Muhammed; Alataş, Mevlüt
This study explores the phytochemical composition and antimicrobial potential of Palustriella commutata, Philonotis calcarea, Cinclidotus riparius, Rhynchostegium riparioides, Plagiomnium ellipticum, and Porella platyphylla, with a focus on bryophyte species phenolic constituents and biological activities. HPLC analysis identified seven phenolic acids (4-hydroxybenzoic acid, 2,5-dihydroxybenzoic acid, 3,4-dihydroxybenzoic acid, caffeic acid, vanillic acid, syringic acid, and p-coumaric acid) across all species in varying concentrations. In in vitro antimicrobial tests, the bryophyte extracts used showed significant activity against Pseudomonas aeruginosa (ATCC 9027), while exhibiting relatively lower inhibition against Staphylococcus aureus (ATCC 25923). The study showed that all species extracts exhibited comparable activity to erythromycin against P. aeruginosa, while Cinclidotus riparius and Plagiomnium ellipticum extracts showed no activity against S. aureus, and the remaining species exhibited lower activity compared to erythromycin. Molecular docking studies demonstrated strong binding affinities of these phenolics to the active site of the target macromolecule (PDB ID: 3FRQ), suggesting meaningful interactions with residues such as ARG122 and ASN123, similar to erythromycin binding patterns. The findings indicate that bryophyte-derived phenolic compounds possess significant antimicrobial potential and may serve as promising candidates for the development of novel bioactive agents. This study provides a broad framework for the insufficiently researched antimicrobial potential of bryophytes by establishing direct links between species-specific phenolic fingerprints, experimentally observed antibacterial activities, and predicted receptor-level interactions. Further complementary biological studies are needed to confirm and expand upon these results.
- MakaleAçık Erişim2026Scopus SJRQ2Effect of Zinc Content on the Mechanical, Corrosion, Tribological and Electrical Properties of Spark Plasma-Sintered Copper/Graphene Composites
Özkaya, Serdar; Adabaş, Yaren; Çelebi, Müslim; Karabacak, Abdullah Hasan; Çelik, Ertuğrul
Copper-based hybrid metal matrix composites reinforced with graphene and zinc were developed to achieve a balanced combination of mechanical strength, corrosion resistance, wear performance, and electrical conductivity. In this study, Cu matrix composites containing a constant graphene content of 1 wt.% and varying Zn contents (0, 5, 10, and 15 wt.%) were fabricated through mechanical alloying followed by Spark Plasma Sintering (SPS). The effects of zinc content on microstructure, densification, hardness, corrosion behavior, tribological performance, and electrical conductivity were systematically investigated. Microstructural analyses revealed that the combined use of graphene and Zn significantly influenced grain refinement, interfacial stability, and densification behavior. The composite containing 10 wt.% Zn exhibited the highest relative density (~90.5%) and maximum hardness (62 HB), indicating an optimal reinforcement level. Corrosion tests conducted in 3.5 wt.% NaCl solution demonstrated that the 10 wt.% Zn composite showed the most noble corrosion potential and the lowest corrosion current density, which was attributed to reduced porosity and improved microstructural homogeneity. Tribological results confirmed that graphene contributed to a self-lubricating effect, while Zn enhanced load-bearing capacity, leading to improved wear resistance under increasing normal loads. Electrical conductivity measurements showed a gradual decrease with increasing Zn content, mainly due to solid-solution-induced electron scattering in the Cu matrix; however, the fixed graphene addition and effective SPS consolidation helped preserve conductive pathways, allowing all composites to retain acceptable conductivity levels. The results indicate that the hybrid Cu–graphene–Zn composites exhibit a balanced combination of mechanical, corrosion, tribological, and electrical properties, with 10 wt.% Zn emerging as the optimal composition.
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İncele - MakaleAçık Erişim2026Scopus SJRQ2Hybrid Supervised Classification and Deep Embedding–Based Profiling Framework for Electricity Consumption Analysis
Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup; Zhilevski, Marin; Mikhov, Mikho; Yordanov, Nikolay
This study proposes a hybrid deep learning framework that integrates supervised classification and unsupervised profiling for electricity consumption analysis. In the supervised phase, a one-dimensional Convolutional Neural Network combined with Long Short-Term Memory (1D CNN–LSTM) architecture is developed to classify daily load patterns. The performance of the proposed model is compared with traditional machine learning and deep learning approaches, including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), a standalone Long Short-Term Memory (LSTM) model, a Transformer-based model, and a standalone 1D CNN model. Experimental results on the Precon house dataset and the CU-BEMS dataset demonstrate that the proposed hybrid architecture outperforms the benchmark models, achieving classification accuracies of 87.59% and 86.40%, respectively. In the unsupervised phase, the trained CNN–LSTM encoder is utilized as a deep feature extractor. The resulting 32-dimensional latent embeddings are clustered using K-Means, Gaussian Mixture Model (GMM), Agglomerative, Spectral, and Ensemble methods. Clustering robustness is evaluated through bootstrap-based stability analysis using the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI). The results demonstrate stable and interpretable electricity consumption profiles, particularly in the residential dataset, where near-perfect clustering stability is observed for K-Means. The proposed framework provides both improved classification performance and robust consumption profiling based on deep embedding, offering a practical tool for energy management.
AtıfScopus1OpenAlex1İncele - MakaleAçık Erişim2026Scopus SJRQ1Path Dependence and Spatial Spillovers in Regional Digitalization: Evidence from Dynamic Spatial Panel Analysis in Europe
Kazar, Görkemli; Kazar, Altuğ
Digitalization is the driver of regional competitiveness and sustainable development, but its geographical impacts differ significantly across Europe. This study was conducted to determine if digital transformation results in regional sustainability or if it increases spatial inequalities, concentrating on European NUTS-1 regions for the period 2021–2025. A composite Regional Digitalization Index was developed by means of Principal Component Analysis (PCA) based on indicators measuring internet access, internet usage, and the availability of digital public services. Dynamic spatial panel econometric models were used for empirical investigation, including a Spatial Autoregressive (SAR) model and a Spatial Durbin model (SDM), which facilitated the exploration of both temporal dependence and spatial spillover. Three main conclusions can be derived from the results, as follows: The level of digitalization in a region is highly stable over time, whereby the development depends most on previous paths. Subsequently, human capital is highly significant for digital development, and its effects are not only local but also spill over to neighboring regions. Lastly, spatial interactions consist of two opposite forces—the positive diffusion from digitally advanced neighboring regions and the competitive effects related to the economic strength of neighboring regions—that further intensify the core–periphery divide.
AtıfScopus1WoS1OpenAlex1İncele - MakaleAçık Erişim2026Scopus SJRQ2Tribological Performance of SPS-Fabricated Cu-HEA/Gr Composites Under Current-Carrying Sliding Contact
Ozkaya, Serdar; Çelebi, Müslim; Yanar, Harun; Karabacak, Abdullah Hasan; Çelik, Ertuğrul; Güler, Onur; Coskun, Abdulkadir; Sekban, Dursun Murat
Cu-based hybrid composites reinforced with AlCrFeCuNi high-entropy alloy (HEA) particles and graphene were successfully fabricated by spark plasma sintering (SPS) to investigate the combined effects of microstructure, mechanical properties, and tribological performance under electrical current. Microstructural observations revealed a dense and homogeneous distribution of HEA particles within the Cu matrix, while the addition of 2.0 wt.% graphene promoted noticeable grain refinement due to its grain boundary pinning effect. Although graphene addition provided beneficial effects in terms of grain refinement and electrical conductivity, it also increased the porosity from 2.49% to 5.08%. Consequently, the beneficial contribution of graphene to mechanical strengthening was substantially offset by the adverse effect of increased porosity, resulting in only a marginal increase in hardness from 127 HB to 129 HB. The addition of 2.0 wt.% graphene led to a pronounced change in the tribological behavior of the composites. The Cu–HEA exhibited relatively stable friction behavior but experienced a higher wear rate under all test conditions. In comparison, the Cu–HEA–2.0 Gr composite demonstrated a lower coefficient of friction and wear rate at 0–10 A, which is consistent with the possible formation of a graphene-rich lubricating surface layer. A clear transition occurred at 30 A, where the coefficient of friction and wear rate began to increase, while at 50 A, further increases in both parameters were observed, suggesting progressive disruption of the proposed protective surface layer under the investigated current-carrying sliding conditions.
- MakaleAçık Erişim2026Scopus SJRQ2Eigenfunction Expansion and Parseval Identity of β-Dirac Operators on the Whole Line
Koşar, Nida Palamut; Koşar, Cem; Akcay, Ozge
In the present work, we investigate a (Formula presented.) -Dirac problem on the whole-line. Our primary objectives are threefold: to demonstrate the existence of a spectral function, to deduce the associated Parseval identity, and to formulate an eigenfunction expansion theorem that characterizes the behavior of the problem under consideration.
- DüzeltmeYayıncıda açık erişim2026Scopus SJRQ1Correction: Comprehensive Biological and Chemical Evaluation of Two Seseli Species (S. gummiferum and S. transcaucasicum) (Antioxidants, (2021), 10, 10, (1510), 10.3390/antiox10101510)
Zengin, Gokhan; Stojković, Dejan; Mahomoodally, Mohamad Fawzi; Jugreet, Bibi Sharmeen; Paksoy, Mehmet Yavuz; Ivanov, Marija; Gašić, Uroš; Gallo, Monica; Montesano, Domenico
In the original publication [1], there was a mistake in Figure 1 as published. The figure, as published, did not include the appropriate statistical information. We have revised the figure accordingly, and the corrected version of “Figure 1” is presented below. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.
- MakaleAçık Erişim2025Scopus SJRQ2Hybrid CNN-Transformer Model for Accurate Impacted Tooth Detection in Panoramic Radiographs
Küçük, Deniz Bora; Imak, Andaç; Özçelik, Salih Taha Alperen; Çelebi, Adalet; Türkoğlu, Muammer; Sengur, Abdulkadir; Koundal, Deepika
Background/Objectives: The integration of digital imaging technologies in dentistry has revolutionized diagnostic and treatment practices, with panoramic radiographs playing a crucial role in detecting impacted teeth. Manual interpretation of these images is time consuming and error prone, highlighting the need for automated, accurate solutions. This study proposes an artificial intelligence (AI)-based model for detecting impacted teeth in panoramic radiographs, aiming to enhance accuracy and reliability. Methods: The proposed model combines YOLO (You Only Look Once) and RT-DETR (Real-Time Detection Transformer) models to leverage their strengths in real-time object detection and learning long-range dependencies, respectively. The integration is further optimized with the Weighted Boxes Fusion (WBF) algorithm, where WBF parameters are tuned using Bayesian optimization. A dataset of 407 labeled panoramic radiographs was used to evaluate the model’s performance. Results: The model achieved a mean average precision (mAP) of 98.3% and an F1 score of 96%, significantly outperforming individual models and other combinations. The results were expressed through key performance metrics, such as mAP and F1 scores, which highlight the model’s balance between precision and recall. Visual and numerical analyses demonstrated superior performance, with enhanced sensitivity and minimized false positive rates. Conclusions: This study presents a scalable and reliable AI-based solution for detecting impacted teeth in panoramic radiographs, offering substantial improvements in diagnostic accuracy and efficiency. The proposed model has potential for widespread application in clinical dentistry, reducing manual workload and error rates. Future research will focus on expanding the dataset and further refining the model’s generalizability.
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İncele - MakaleAçık Erişim2025Scopus SJRQ2Effect of B4C Amount on Microstructural and Mechanical Properties of Cu/h-BN/B4C Metal Matrix Composites Fabricated via Spark Plasma Sintering
Çelebi, Müslim; Karabacak, Abdullah Hasan; Özkaya, Serdar; Çelik, Ertuğrul; Sekban, Dursun Murat; Çanakçı, Aykut; Yanar, Harun
Copper (Cu) is widely used in electrical, electronic, and tribological systems owing to its excellent electrical and thermal conductivity. However, its relatively low hardness and poor wear resistance limit its use in demanding engineering applications. In this study, Cu-based hybrid metal matrix composites (MMCs) reinforced with hexagonal boron nitride (h-BN) and boron carbide (B4C) were fabricated via spark plasma sintering (SPS) to improve their mechanical and tribological performance. The h-BN content was fixed at 1 wt.% to ensure solid lubrication, while the B4C content was varied (0.25, 0.5, 0.75, and 1 wt.%) to examine its influence on the microstructural, mechanical, electrical, and wear properties of the composites. Microstructural analyses confirmed a homogeneous distribution of h-BN and B4C particles in the Cu matrix at low and moderate reinforcement levels, whereas excessive B4C resulted in partial agglomeration and reduced densification. All composites achieved relative densities above 95%, demonstrating the high densification efficiency of the SPS process. Hardness increased markedly with B4C addition due to dispersion strengthening and grain refinement, while electrical conductivity decreased slightly because of the insulating nature of the reinforcements. Tribological tests showed that the composite containing 0.75 wt.% B4C exhibited the best performance, with the lowest wear rate and stable friction behavior. Overall, the results indicate that co-reinforcing Cu with h-BN and B4C through SPS is a promising strategy for developing multifunctional materials suitable for electrical contact and sliding applications.
AtıfScopus11WoS12OpenAlex12İncele - MakaleAçık Erişim2025Scopus SJRQ2Generalized Statistical Convergence in Sequence Spaces: A Modulus Function Perspective
Atıcı Turan, Gülcan
This study introduces a flexible framework for analyzing how sequences of numbers approach a limit, even when traditional convergence criteria fail. By incorporating modulus function mathematical tools that quantify growth rates, this research extends the concept of statistical convergence to handle sequences with irregular or sparse behavior. Key results establish connections between this generalized convergence theory and related properties, like boundedness, providing a unified approach to understanding sequence dynamics. The findings enhance our ability to model and analyze complex data patterns in mathematics and beyond.
AtıfScopus1WoS2OpenAlex3İncele - MakaleYayıncıda açık erişim2025Scopus SJRQ2COVID-19 Alters Respiratory Function Associations in High-Level Athletes
Kabak, Banu; Erdoğan, Murat; Tortu, Erkan; Deliceoğlu, Gökhan; Bulgay, Celal; Kızar, Oktay; Baydaş, Giyasettin; Szabo, Attila
Background and Objectives: Coronavirus disease 2019 (COVID-19) has affected multiple physiological systems, including respiratory function, which is critical for athletic performance. Although alterations in pulmonary dynamics have been observed in high-level athletes recovering from COVID-19, the effects on respiratory function remain unclear. In this context, the present study aimed to examine the impact of COVID-19 on the interrelationships among respiratory function parameters in high-level athletes. Materials and Methods: Sixty-eight high-level athletes participated in the present study, including 34 with a history of COVID-19 and 34 controls without prior infection. Respiratory function and respiratory muscle strength were assessed using a digital spirometer (Pony FX, Cosmed, Italy). Key variables included Forced Vital Capacity (FVC), Peak Expiratory Flow (PEF), Maximum Voluntary Ventilation (MVV), Forced Expiratory Volume in one second (FEV1), and Maximum Inspiratory/Expiratory Pressure (MIP/MEP). Results: High-level athletes with prior COVID-19 infection exhibited significant differences in the correlations (p < 0.05) between FVC and PEF, FVC and MVV, FEV1 and FEV1/FVC, and MIP and MVV compared to controls. Conclusions: These findings suggest that COVID-19 can disrupt the interrelationships among respiratory function parameters in high-level athletes, highlighting the need for further longitudinal investigations.
AtıfScopus1OpenAlex1İncele - MakaleAçık Erişim2025Scopus SJRQ1The Mediating Role of Physical Literacy in the Relationship Between e-Health Literacy and a Sustainable Healthy Lifestyle Among Adolescents
Akarsu, Mehmet; Güllü, Mehmet; Günata, Gül Polat; Kızılkaya, Aysel; Aydın, Savaş; Özcan, Ecesu; Norman, Göktuğ; Kurhan, Cihad Onur
Background: It is well-established that, for adolescents to adopt sustainable healthy lifestyle behaviors, not only access to information but also the skills required to translate that information into action are critical. In this field, research that examines the relationship between e-health literacy and sustainable healthy lifestyle behaviors within the context of physical literacy is notably scarce. In this context, the aim of this study is to examine the effect of e-health literacy on a sustainable healthy lifestyle and to evaluate the mediating role of physical literacy in this relationship. Methods: A total of 835 adolescents from high schools across Türkiye voluntarily participated in this study. During the data collection process, the e-Health Literacy Scale, the Perceived Physical Literacy Scale, and the Healthy and Sustainable Lifestyle Scale were utilized. Data were analyzed using the JASP (version 0.18.3.0) software. Correlation analysis and structural equation modeling were conducted, and the bootstrap method (n = 5000) was employed for mediation analysis. Results: The effect of e-health literacy on a sustainable healthy lifestyle was found to be positive and statistically significant (β = 0.452, p < 0.001). Similarly, e-health literacy significantly predicted physical literacy (β = 0.755, p < 0.001), and physical literacy significantly predicted a sustainable healthy lifestyle (β = 0.310, p < 0.001). The mediating effect was also statistically significant (β = 0.234, p < 0.001). The model explained 32% of the variance in healthy lifestyle behaviors. Conclusions: The findings indicate that evaluating e-health literacy and physical literacy together provides a holistic approach to fostering sustainable healthy lifestyle habits among adolescents. It is recommended that intervention programs be structured to encompass both areas of competence.
AtıfScopus4OpenAlex6İncele - MakaleAçık Erişim2025Scopus SJRQ2A New Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Türkiye
Çeken, Ulubey; Sertçelik, Fadime; İçen, Abdullah
The recent expansion of the strong-motion observation network, along with the increase in data obtained during major earthquakes and efforts to create consistent metadata for source, path, and site effects for both old and new records, has significantly improved the quality of data and the level of modeling in Türkiye. The mainshock and aftershock records of the 6 February 2023 Kahramanmaraş earthquake doublet (MW 7.8 and 7.7), which are among the most destructive earthquakes in world history, constitute an up-to-date and important data source for this study. In this study, we present new ground-motion prediction models (GMPMs) for shallow crustal earthquakes using strong-motion data recorded in Türkiye. Our GMPMs are calibrated using 20,173 strong-motion records from 1565 shallow crustal earthquakes with depths of less than 35 km that occurred in Türkiye and its vicinity between 1976 and 2023. Our model is valid for magnitudes ranging from 4.0 to 7.8 (MW), and for the time-averaged 30 m shear wave velocity (VS30) values of 975 stations, which ranged from 131 to 1862 m/s. In the analyses performed, using the recently developed site amplification model, we calculated the model coefficients using the mixed-effects regression algorithms used by the GMPM developers. Additionally, a heteroscedastic model was created for aleatory variability as a function of MW. The closest distance to the surface projection of the fault plane (RJB) is between 0 and 350 km. Using the metadata prepared according to these criteria, we derived up-to-date ground-motion prediction models for horizontal-component peak ground velocity (PGV), peak ground acceleration (PGA), and 5% damped pseudo-spectral acceleration (PSA) response spectra, at 36 periods ranging from 0.01 to 10 s. The variability in the predictions was decomposed into within-event, between-event, and site-to-site deviations to determine the total standard deviations (σ). Compared to previous models, the proposed GMPMs were developed using a much richer database with recent major earthquakes, and the consistent estimates and lower residuals in the comparisons support the reliability of the models.
AtıfScopus4WoS3OpenAlex4İncele - MakaleAçık Erişim2025Scopus SJRQ1Spring-Based Soil–Structure Interaction Modeling of Pile–Abutment Joints in Short-Span Integral Abutment Bridges with LR and RSM
Polat, Erkan; Göçmen Polat, Elifcan
Integral abutment bridges (IABs) are increasingly adopted in transportation infrastructure due to their durability, reduced maintenance needs, and cost-effectiveness compared to conventional bridges. However, their reliable performance under live loads is strongly influenced by the nonlinear soil–structure interaction (SSI) at the pile–abutment joint, which remains challenging to quantify using conventional analysis methods. This study develops simplified spring-based models to capture the SSI behavior of pile–abutment joints in short-span IABs. Predictive equations for joint rotation, deflection, moment, and shear are formulated using Linear Regression (LR) and Response Surface Methodology (RSM). Unlike prior studies relying solely on FEM or traditional p–y curves, the novelty of this work lies in deriving regression-based spring constants calibrated against FEM analyses, which can be directly implemented in standard structural software. This approach significantly reduces computational demands while maintaining predictive accuracy, enabling efficient assessment of pile contributions and global bridge response. Validation against finite element method (FEM) results confirms the reliability of the simplified models, with RSM outperforming LR in representing nonlinear parameter interactions.
AtıfScopus2WoS2OpenAlex3İncele - MakaleAçık Erişim2025Scopus SJRQ2Multiscale Time Series Modeling in Energy Demand Prediction: A CWT-Aided Hybrid Model
Sezer, Elif; Yıldırım, Güngör; Özdemir, Mahmut Temel
In the contemporary energy landscape, the increasing demand for electricity and the inherent uncertainties associated with the integration of renewable resources have rendered the accurate and reliable forecasting of short- and long-term demand imperative. Energy demand forecasting, fundamentally a time series problem, can be inherently complex, nonlinear, and multi-scale. Therefore, interest in artificial intelligence–based methods that provide high performance for short- and long-term forecasting, rather than traditional methods, has increased in order to solve these problems. In this study, a hybrid artificial intelligence model based on LSTM, GRU, and Random Forest, utilizing a distinct mechanism to address these types of problems, is proposed. The Multi-Scale Sliding Window (MSSW) approach was utilized for the model’s input data to capture the dynamics of the time series at different scales. The optimization of windows was conducted using the Continuous Wavelet Transform (CWT) method to determine the optimal window sizes within the MSSW structure in a data-driven manner. Experimental studies on Panama’s real energy demand data from 2015 to 2020 show that the CWT-aided MSSW-hybrid model forecasts better with lower error rates (0.007 MAE, 0.009 RMSE, 1.051% MAPE) than single models and manually determined window sizes. The results of the study demonstrate the importance of hybrid structures and window optimization in energy demand forecasting.
AtıfScopus1OpenAlex2İncele - MakaleAçık Erişim2025Scopus SJRQ1Green Synthesis of Multi-Walled Carbon Nanotube-Reinforced Hydroxyapatite Doped with Silver and Silver-Core Selenium-Shell Nanoparticles: Synthesis, Characterization, and Biological Activity
Unal, İlkay
Hydroxyapatite (HAp) is widely used in biomedical applications due to its biocompatibility, osteoconductivity, and bioactivity. However, its low mechanical strength, tendency toward rapid corrosion, and lack of bactericidal properties present significant limitations in applications. This study aimed to improve the properties of HAp by reinforcing it with multi-walled carbon nanotubes (MWCNTs) and doping it with silver nanoparticles (AgNPs) and silver-core selenium-shell nanoparticles (Ag@SeNPs). Ocimum basilicum extract was used as both a reducing and stabilizing agent in the synthesis of nanoparticles using an environmentally friendly and non-toxic method as an alternative to traditional methods. The synthesized HAp, HAp/MWCNT, Ag-HAp/MWCNT, and Ag@Se-HAp/MWCNT nanocomposites were characterized by TEM, SEM, XRD, Raman spectroscopy, and BET analysis. BET analysis showed a reduction in surface area from 109.4 m2/g for pure HAp to 71.4 m2/g, 47.5 m2/g, and 35.3 m2/g for HAp/MWCNTs, Ag- HAp/MWCNTs, and Ag@Se-HAp/MWCNTs, respectively. Antimicrobial activities against P. aeruginosa, E. coli, S. aureus, E. faecalis, and C. albicans were evaluated. HAp and HAp/MWCNT did not show any antimicrobial activity, while Ag-HAp/MWCNTs showed inhibition zones of 14 mm for Escherichia coli and 18 mm for Pseudomonas aeruginosa at 5 mg/mL. Ag@Se-MWCNTs/HAp exhibited superior efficacy with inhibition zones of 18 mm, 12 mm, and 20 mm for S. aureus, E. faecalis, and Candida albicans, respectively. The incorporation of Ag@SeNPs enhanced HAp’s antibacterial and antifungal properties through a synergistic mechanism.
AtıfScopus12WoS10OpenAlex12İncele - MakaleAçık Erişim2025Scopus SJRQ2Effect of Tungsten Content on the Microstructure, Mechanical and Tribological Properties of AlCoCrFeNi High-Entropy Alloys
Bahceci, Ersin; Gul, Ali Oktay; Basgoz Orhan, Oykum; Kumruoglu, Levent Cenk; Guler, Omer
High-entropy alloys (HEAs) have recently attracted considerable attention due to their unique combination of high strength, hardness, and corrosion and wear resistance, making them promising candidates for advanced structural and functional applications. Among these, AlCoCrFeNi-based HEAs are well known for their high hardness and good wear resistance; however, their limited tribological stability under operational conditions restricts their broader application. To address this limitation, tungsten (W) was incorporated into the AlCoCrFeNi system to enhance its mechanical and tribological performance. In this study, the microstructural, mechanical, and tribological properties of AlCoCrFeNiWx (x = 0, 0.1, 0.25, 0.5 and 1 mol) HEAs were systematically investigated. The alloys were fabricated using the vacuum arc melting method and characterized by XRD, SEM-EDS, elemental mapping, microhardness, and wear tests. The addition of W caused a shift in the 2θ ≈ 44° (110) peak toward lower angles. While the W-free alloy exhibited Body-Centered Cubic (BCC) + B2 phases, W addition led to the formation of a new W-rich phase, and at higher W contents, a pure W phase appeared. The hardness increased from 507.11 HV1 to 651.81 HV1 with increasing W content. Furthermore, wear resistance improved and the coefficient of friction decreased with higher W addition. When comparing the W-free alloy to the alloy with the highest W content, the wear rate decreased by approximately 1.85 times under a 2 N load and 1.89 times under a 5 N load. These results demonstrate that W addition significantly enhances the wear resistance of AlCoCrFeNi-based HEAs by nearly twofold.
AtıfScopus1WoS1OpenAlex1İncele - MakaleAçık Erişim2025Scopus SJRQ1Assessing Motor Performance and Ankle Mobility in Pre-Adolescent Male Fencers
Aydin, Yalcin; Tokgoz, Gurkan; Yilmaz, Nurkan; Coskun, Ilgin Ali; Beykumul, Aysegul; Colak, Enes; Aygoren, Caner; Koc, Samet; Cinarli, Fahri Safa
Ankle mobility plays a critical role in dynamic stability and propulsion during fencing-specific actions. However, its relationship to performance metrics in young athletes remains unclear. This study aimed to investigate the relationship between ankle range of motion (ROM), acceleration, and jump height in pre-adolescent male fencers, as well as to examine bilateral differences in ankle mobility between the front and rear foot. Fifteen fencers (age 10.86 ± 0.91 years) were assessed for ankle ROM (dorsiflexion, plantar flexion, inversion, eversion) using a goniometer. Performance tests included a 7 m sprint lunge (acceleration) and countermovement jump. Significantly large correlations were observed between front and rear foot ankle plantar flexion range of motion and both acceleration (r = 0.625–0.628, p < 0.05) as well as vertical jump height (r = 0.579–0.647, p < 0.05). Rear foot ankle plantar flexion range of motion significantly predicted acceleration (r2 = 0.335, p < 0.05) and jump height (r2 = 0.418, p < 0.05). In contrast, no meaningful associations were found between dorsiflexion, inversion, or eversion range of motion and performance metrics. Additionally, bilateral comparisons revealed significantly greater dorsiflexion in the front foot ankle and greater eversion in the rear foot ankle (p < 0.05). Plantar flexion ROM is a key contributor to acceleration and jump capacity in youth fencers. Incorporating ankle mobility training may support physical development in this population.
AtıfScopus2WoS2OpenAlex2İncele