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Koleksiyondaki öğeler
- MakaleYalnız künye2025Scopus SJRQ2Using Meta-Heuristic Optimization Algorithms to Determine Baseflow and Comparing Their Temporal Performance
Acar, Ramazan; Sapliogu, Kemal
Accurate and simple determination of baseflow is an extremely important issue. In this study, the baseflow levels of Göksu station number 1805 in the Seyhan basin were determined by calibrating the Lyne and Hollick, Chapman, Chapman and Maxwell methods available in the literature using meta-heuristic optimization methods. The meta-heuristic algorithms used for calibration in the study were run thirty times each. Thus the reliability, the reliability of the algorithms was tested with the standard deviations obtained. The study also measured the temporal performance of the algorithms. In addition, the baseflows of Göksu station were determined and their percentage rates were found separately according to all three methods and intuitive methods, and the results obtained were compared. By examining the results obtained in this section, the average baseflow rate of the basin was also determined. Furthermore, each phase of the study was repeated over a five-year period, with the objective of measuring its sustainability. Consequently, it has been demonstrated that the method can be employed over extended periods.
AtıfOpenAlex1
İncele - MakaleAçık Erişim2026Scopus SJRQ4Prevalence of Depression and Associated Factors in Pregnant Women Attending the Emergency Obstetrics Clinic; [Acil Kadın Doğum Polikliniğine Başvuran Gebelerde Depresyon Sıklığı ve Etkileyen Faktörler]
Ak, Öznur; Beydağ, Kerime Derya; Dal, Nursel Alp
The goal of this descriptive study was to assess the prevalence of depression among pregnant women attending the emergency obstetrics clinic and to evaluate the relationship between sociodemographic, obstetric, and social-life characteristics and depression levels. The study was conducted at the University of Health Sciences Sarıyer Hamidiye Etfal Training and Research Hospital between June 6, 2025, and August 1, 2025. The study population comprised 720 pregnant women, and the sample consisted of 251 volunteer pregnant women selected at random. Data were collected using a researcher-developed questionnaire and the Beck Depression Inventory (BDI). The mean BDI score was 10.65 ± 7.02. The vast majority of pregnant women exhibited mild depressive symptoms. While no significant differences were found between depression and educational level, attendance at childbirth preparation courses, or number of pregnancies, significant associations were identified between depression and economic status, type of marriage, whether the pregnancy was planned, the belief that social life would be restricted, a perceived negative impact on sexual life, deterioration in relationships with the spouse, and exposure to verbal violence (p < 0.05). Depression during pregnancy is a major public health problem and is shaped by multifactorial influences. It is recommended that psychosocial assessments be integrated into antenatal follow-up, that at-risk groups be identified early, and that appropriate counseling services be provided. Depression screening should be conducted during routine obstetric follow-ups, and pregnant women in high-risk groups should receive psychosocial support and social service support.
- MakaleYalnız künye2026Scopus SJRQ1Regionalized Bayesian site amplification models for Türkiye
İçen, Abdullah
Accurate representation of site amplification is essential for seismic hazard analysis, particularly in regions with complex geological structure. This study develops a Bayesian hierarchical site amplification model that captures nonlinear, period-dependent, and regionally variable behavior using an extensive strong-motion dataset from Türkiye. The model employs a piecewise VS30 functional form derived from nonparametric scaling and clustering, and incorporates nonlinear scaling with reference rock motion intensity (PSArock). Regional variability is introduced through random-slope adjustments, enabling spatial differences in amplification to be represented while maintaining statistical stability through partial pooling. Results indicate pronounced nonlinear and regional variability for soft soils and reduced regional differences for stiff sites (VS30 > 600 m/s). A key observation is a systematic amplification peak within the 400–550 m/s velocity range, where the model exhibits consistent underprediction across periods. Because this feature persisted even for stations located on flat basin interiors, a detailed investigation of stations in this VS30 interval was conducted and is presented in the study. Mapping and site-specific examination reveal that many of these stations are located near slope breaks, basin edges, or transitional geological settings, suggesting that 2D/3D wave-propagation or impedance-transition effects—rather than VS30 alone—may drive the increase in the amplification. Additionally, residual underprediction persists at high-VS30 rock sites, highlighting the need for improved empirical characterization of stiff-soil and rock conditions. Overall, the proposed Bayesian framework provides a flexible, data-driven, and uncertainty-aware approach for modeling site amplification, with direct applications in probabilistic seismic hazard analysis and performance-based earthquake engineering.
AtıfOpenAlex1
İncele - MakaleYalnız künye2026Scopus SJRQ2Structural evolution and dual γ–neutron shielding performance of nano-Gd2O3 reinforced lithium borotellurite glasses
Durmus, Hasan; Kilic, G.; Ilik, E.; Kavaz, E.; Güler, Ömer; Birdogan, Selcuk; ALMisned, Ghada; Tekin, H.O.
This study investigates the structural, physical, and radiation-shielding properties of nano-Gd2O3-reinforced lithium borotellurite glasses with the composition 50TeO2-30B2O3-(20-x)Li2O-xGd2O3 (x= 0-10 mol %). X-ray diffraction and transmission electron microscopy confirmed the fully amorphous nature of the glasses and the homogeneous distribution of nanoscale Gd clusters. Density increased from 4.03 to 4.77 g/cm3, accompanied by compositional shifts, decreasing boron and increasing oxygen and tellurium contents, indicating enhanced structural compactness and electron density. Gamma-ray attenuation measurements revealed a 74 % increase in the linear attenuation coefficient from 8.33 to 14.53 cm−1 at 81 keV and a nearly 27 % reduction in the half-value layer from 1.21 to 0.89 cm. Effective atomic number values remained highest for Gd(n)10 across the photon energy range investigated. Experimental neutron dose measurements showed absorption improvements from 37.66 % to 51.91 %, while the effective removal cross-section increased from 0.1066 to 0.1096 cm−1, outperforming water, B4C, and graphite. Compared with the Gd-doped zinc borotellurite glasses reported in the literature, the present lithium-based system exhibited higher densification and superior dual γ-neutron attenuation. These results demonstrate that controlled nano-Gd2O3 integration into the lithium borotellurite matrix yields a stable, lead-free amorphous material with outstanding radiation-shielding efficiency for advanced photonic and nuclear applications.
AtıfScopus3OpenAlex3İncele - MakaleAçık Erişim2026Scopus SJRQ1Optimization of ultrasound-assisted extraction and biological activities of Crataegus monogyna Jacq. flowering branches using experimental design and artificial neural networks
Uysal, Sengul; Cvetanović Kljakić, Aleksandra; Lončar, Biljana; Zengin, Gokhan; Cakilcioglu, Ugur
Crataegus monogyna is widely used in traditional medicine due to its rich content of bioactive compounds. The present study aimed to evaluate the influence of extraction parameters, including solvent concentration, extraction time, temperature, and solid-to-liquid ratio, on total phenolics, total flavonoids, antioxidant DPPH (2,2-diphenyl-1-picrylhydrazyl), ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid), FRAP (ferric reducing antioxidant power), and CUPRAC (cupric reducing antioxidant capacity)), and enzyme inhibitory properties (α-amylase and tyrosinase). Furthermore, an Artificial Neural Network (ANN) model was applied to optimize the extraction conditions. The results demonstrated that extraction conditions significantly affect both chemical composition and biological activity. The optimal conditions (70% ethanol, 30 min, 45 °C, (1 : 30)) yielded extracts with high total phenolic (109.15 mg GAE per g), and total flavonoid content (70.74 mg RE per g), strong antioxidant activity (DPPH: 473.57 mg TE per g, ABTS: 537.58 mg TE per g, CUPRAC: 407.11 mg TE per g, FRAP: 393.91 mg TE per g) and notable enzyme inhibitory potential (0.53 mmol ACAE per g against α-amylase and 75.00 mg KAE per g against tyrosinase). The ANN model showed excellent predictive performance, confirming its suitability for modeling and optimization of complex extraction systems. The present study provides comprehensive insights into optimized ultrasound-assisted extraction methods for the recovery of maximum bioactive compounds and biological activities (antioxidant and enzyme inhibition effect) from C. monogyna flowering branches using experimental design and ANN model. These findings provide a reliable basis for the development of functional and pharmaceutical products. This journal is
AtıfScopus1WoS1OpenAlex1
İncele - MakaleAçık Erişim2025Scopus SJRQ2Advancing Air Quality Monitoring: Deep Learning-Based CNN-RNN Hybrid Model for PM2.5 Forecasting
Utku, Anıl; Can, Umit; Alpsülün, Mustafa; Balıkçı, Hasan Celal; Amoozegar, Azadeh; Pilatin, Abdulmuttalip; Barut, Abdulkadir
Particulate matter, particularly PM2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health.
AtıfScopus10WoS6OpenAlex11İncele - MakaleAçık Erişim2026Scopus SJRQ2Risk Assessment via Hazard Scenario in Cereal Production Under Rising CO
Susam, Selim Orhun; Peker, Ayse Esra; Hudaverdi, Burcu
Global food security has been seriously threatened by air pollution and climate change. The impact of CO on crop output is complicated because of its interactions with temperature, nutrient availability, and other pollutants, even though it is necessary for photosynthesis. This study develops a copula-based hazard modeling framework to assess how increasing CO concentrations affect cereal production risk across multiple crops. By integrating hazard scenario analysis with localized dependence measures, we estimate both failure probabilities and dynamic associations between CO emissions and cereal yields. A novel localized version of Kendall’s implemented via a Bernstein copula approximation, enables flexible modeling of heterogeneous and nonlinear dependence structures. As the relationship between CO emissions and crop-specific yields changes in both density and direction, it may indicate asymmetries or nonlinearities that are missed by measures of global dependence. Empirical results reveal distinct crop-specific responses. While CO levels enhance yields for rice, millet, and sugar beet, they reduce or destabilize production for rye and lentil, indicating asymmetric sensitivities. Failure probabilities increase with the time horizon, reflecting the accumulation of climate-related production risk. The findings emphasize the significance of integrating probabilistic hazard modeling into climate-resilient agricultural planning and food security policies, as crop-specific risk exposure has increased in recent time periods.
AtıfWoS1OpenAlex1İncele - MakaleYalnız künye2026Scopus SJRQ2Institutions, gendered informality, and intrahousehold distribution: A dual-economy model
Ateş-Saygılı, Çiğdem
We study how enforcement institutions shape formalization and the intrahousehold distribution of resources in a developing economy. The model combines an enforcement wedge inside informal technology, gendered formal-attachment margins, and a collective household with a state-dependent female Pareto weight. In the benchmark wage-responsive specification, enforcement raises formalization by compressing informal wages and reallocating work toward formal jobs. Yet aggregate formalization and women's intrahousehold gains need not move together. When women are more exposed to informality and men convert the induced formal-to-informal wage ratio into formal attachment more strongly in level terms, enforcement can lower women's relative earnings, relative formal status, the female Pareto weight, and the consumption ratio during adjustment. An exact effective-access transition and local calculations for the effective-access and reduced-form labor-disutility margins show that this adverse incidence can be offset within the model while preserving formalization gains. Descriptive quarterly evidence for Türkiye documents gendered dynamics in non-agricultural social-security non-registration around salient macroeconomic and institutional episodes. The empirical evidence motivates the labor-market exposure and access channels but does not observe household bargaining or individual consumption.
- MakaleAçık Erişim2026Scopus SJRQ1Comparative and systematically analysis of the effects of single and high-entropy rare earth oxide additives on the some physical properties and radiation shielding properties of copper composites
Kanca, Muhammed Sait; Yılmaz, Demet; Tasgın, Yahya; Kök, Mediha; Güler, Seval Hale
In this study, the effects of rare earth oxides (Gd₂O₃, La₂O₃, Eu₂O₃, Pr₂O₃, Dy₂O₃, and Y₂O₃) and high-entropy oxide (GdLaEuPrDyY)₂O₃ additives on the structural, mechanical, and radiation shielding properties of copper matrix composites were investigated. XRD and SEM-EDS analyses showed that the high-entropy oxide successfully formed in a single-phase bixbyite structure after mechanical alloying and was homogeneously distributed within the copper matrix. The rare earth oxide additive significantly reduced the crystal grain size of the Cu matrix and improved its mechanical properties. Specifically, the microhardness value reached 68.3 Hv in the composite with 20% high-entropy oxide additive, representing an increase of approximately 111% compared to pure copper (32.3 Hv). Gamma shielding results showed that the rare earth oxide additive significantly increased the photon attenuation capacity. Specifically, at 242 keV energy, the Dy₂O₃ additive increased the MAC value from 0.141 cm²/g to 0.246 cm²/g, providing a 74.5% improvement. The most important result is that the composite with 20% high-entropy oxide additive showed the highest shielding performance, achieving a MAC value of 0.278 cm²/g, which is significantly superior to pure copper. Furthermore, this composite exhibited the lowest HVL and MFP values, demonstrating the highest photon shielding efficiency. Neutron shielding analyses revealed that Gd₂O₃ and high-entropy oxide additives, in particular, increased thermal neutron attenuation performance to approximately 100%. These results demonstrate that high-entropy oxide additives provide a stronger impact compared to single oxide additives, offering superior performance in both gamma and neutron shielding. Consequently, copper composites doped with (GdLaEuPrDyY)₂O₃ stand out as promising alternative shielding materials for nuclear facilities, radiation protection systems, and advanced engineering applications due to their high shielding efficiency, improved mechanical properties, and non-toxic composition.
AtıfScopus1OpenAlex1İncele - MakaleAçık Erişim2026Scopus SJRQ1A two-stage volunteer assignment model for post-disaster search and rescue operations
Ozdemir, Umit; Mete, Suleyman; Gul, Muhammet
Effective volunteer management requires ensuring operational continuity and maximizing benefits from all team members. Prioritizing volunteers with adequate knowledge and experience in task assignments is crucial for maintaining workflow continuity. This study employs a two-stage model for allocating areas and tasks to volunteers in search and rescue operations. The first stage identifies the optimal number of volunteer response centers using the P-median problem. Considering population density as demand, 7 out of 14 accessible and protected disaster collection areas meeting basic needs are selected as optimal response centers based on distance. In the second stage, a mixed-integer programming model is proposed for volunteer assignment, which prioritizes assigning the most knowledgeable and experienced volunteers to critical positions. The model’s flexibility and applicability are demonstrated by incorporating voluntary rest periods, which are allocated based on the urgent needs of response centers in various post-disaster scenarios. Solved using GAMS/CPLEX, the model’s results show that expert volunteers in low-demand scenarios are prioritized, working 5 out of 9 shifts, while other volunteers receive insufficient assignments. In high-demand scenarios, some response centers face volunteer shortages, with a deficit of 62 volunteers across 9 shifts in non-emergency areas. The study evaluates the benefits and drawbacks of the volunteer assignment model and offers recommendations for future research.
AtıfScopus2WoS2OpenAlex3İncele - MakaleAçık Erişim2026Scopus SJRQ2Federated agricultural yield forecasting under an Edge–Fog–Cloud framework: a multi-country privacy-preserving approach
Utku, Anıl; Barut, Abdulkadir; Alofaysan, Hind; Henni, Mohamed Djafar; Amoozegar, Azadeh
Predicting agricultural yields is crucial for ensuring global food security and adaptation to climate change. However, the centralized collection of agricultural data across countries might pose governance, privacy, and data sovereignty issues. This research presents a privacy-preserving model for predicting agricultural yields using federated learning within an edge-fog-cloud integrated architecture. The experiments have been performed on a European cereal production dataset for the period 1990–2022, using a time-aware forecasting scheme. Centralized, edge-only, federated, and hybrid learning techniques have been systematically compared to examine the influence of distributed optimization on forecasting performance. It can be seen that central classical machine learning algorithms have yielded the best predictive performance. Federated deep learning models, meanwhile, showed relatively poor forecasting accuracy compared to their centralized counterparts under a non-IID distribution of multi-country agricultural data. Edge-only learning, meanwhile, has yielded comparatively low generalization capabilities due to the absence of collaborative learning.
- MakaleAçık Erişim2025Scopus SJRQ2A Comparative Study of Time–Frequency Representations for Bearing and Rotating Fault Diagnosis Using Vision Transformer
Orhan, Ahmet; Yordanov, Nikolay; Ertarğın, Merve; Zhilevski, Marin; Mikhov, Mikho
This paper presents a comparative analysis of bearing and rotating component fault classification based on different time–frequency representations using vision transformer (ViT). Four different time–frequency transformation techniques—short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert–Huang transform (HHT), and Wigner–Ville distribution (WVD)—were applied to convert the signals into 2D images. A pretrained ViT-Base architecture was fine-tuned on the resulting images for classification tasks. The model was evaluated on two separate scenarios: (i) eight-class rotating component fault classification and (ii) four-class bearing fault classification. Importantly, in each task, the samples were collected under varying conditions of the other component (i.e., different rotating conditions in bearing classification and vice versa). This design allowed for an independent assessment of the model’s ability to generalize across fault domains. The experimental results demonstrate that the ViT-based approach achieves high classification performance across various time–frequency representations, highlighting its potential for mechanical fault diagnosis in rotating machinery. Notably, the model achieved higher accuracy in bearing fault classification compared to rotating component faults, suggesting higher sensitivity to bearing-related anomalies.
AtıfScopus29İncele - MakaleAçık Erişim2026Scopus SJRQ4Effectiveness of organic tomato (Solanum lycopersicum L.) extracts added to yeast Cell Culture against H2O2 Toxicity; [Eficácia de extratos de tomate orgânicos adicionados à cultivos de células de levadura contra la toxicidad por H2O2]
Gonayli, Selahattin; Temizer, Ibrahim Akin; Aydin, Sevinq; Kirmizikaya, Gorkem; Okutan, Tuba; Yilmaz, Okke$
Growing tomatoes in season and organically are an important factor in terms of carrying higher levels of antioxidant molecules. This study aimed to demonstrate the protective effect of extracts obtained from naturally grown tomatoes, which are widely used as food, against oxidative damage caused by hydrogen peroxide on yeast cells (Saccharomyces cerevisiae L.). Lipophilic vitamins, phytosterol content, gluathione and oxidised gluathione content were analyzed by Shimadzu brand high performance liquid chromatography, while fatty acid analysis was performed by Shimadzu GC 2010 Plus Gas Chromatography. Protein content was analyzed spectrophotometrically. The study groups were designed as control, hydrogen peroxide, tomato and tomato + hydrogen peroxide. In all groups added with hydrogen peroxide and tomato extract compared to the hydrogen peroxide group; it was observed that vitamin D2, α-Tocopherol, β-Sitosterol and Stigmasterol contents increased significantly. Again, it was observed that ergosterol level increased significantly in the D + hydrogen peroxide group compared to the hydrogen peroxide group. When the fatty acid analysis results were examined, it was determined that in all groups added to the cultured yeast with hydrogen peroxide and tomato extracts (except the B + hydrogen peroxide group), there were increases in 16:0, 16:1, n-7, 17:0, 17:1 and 18:0 fatty acid levels compared to the hydrogen peroxide group. On the other hand, when its effect on protein and gluathione levels was examined, more significant increases were determined in all groups with tomato extract addition compared to the hydrogen peroxide group. At the end of the study, it was observed that tomato extracts had positive effects against hydrogen peroxide toxicity in yeast cells, although at different levels in terms of fatty acid, vitamin and phytosterol contents.
- MakaleYalnız künye2025Scopus SJRQ1Reduction of fluid forces on a circular cylinder in the laminar flow regime using a pair of airfoils
Fırat, Erhan; Oruç, Vedat; You, Rui; Biçer, Uğur; Devecioğlu, Atilla G.
Flow around the cylinder, with/without a pair of airfoils symmetrical to the cylinder's horizontal axis, was simulated for a diameter-based Reynolds number (Re) of 100. The objective of this numerical study is to simultaneously reduce the values of the time-averaged and fluctuating forces acting on a system consisting of a pair of airfoils and a cylinder below those of a single cylinder by using different combinations of the parameters chord (c), angle of rotation (β), angle of attack (α), and the shortest distance between the aerodynamic center of the airfoil and the surface of the cylinder (g). The values of these key parameters are determined primarily through two approaches. The first approach involves emulating the flow pattern surrounding the system to match the potential flow pattern. The second approach involves disrupting the interaction between the shear layers from the cylinder by stretching them in the mean flow direction. The second approach has been demonstrated to exhibit superior performance in terms of reducing both time-averaged and fluctuating forces concurrently. The second approach has been demonstrated to exhibit superior performance in terms of reducing both time-averaged and fluctuating forces concurrently. The optimal geometrical parameters that yield the desired end results are determined to be c = 0.5D, α = 0°, β = 60°, and g = 0.2D. Utilizing these parameters has been demonstrated to result in a substantial reduction in the time-averaged drag, fluctuating drag, and fluctuating lift coefficients of the system, with the respective percentage reductions reaching 14.4 %, 92.1 %, and 73 %, respectively, when compared to the values obtained for a single cylinder.
AtıfScopus4OpenAlex3İncele - MakaleAçık Erişim2026Scopus SJRQ2Cadmium-induced changes in haemato-immunological, oxidative stress and antioxidant parameters of Cyprinus carpio: Protective role of curcumin
Pala, Ayşegül; Eroğlu, Mücahit; Mişe Yonar, Serpil; İspir, Ünal; Yonar, Muhammet Enis
This study examined the adverse effects of sublethal concentrations of cadmium (Cd) and the potential of curcumin (CUR) to mitigate Cd-induced toxicity. We administered 1.5 and 3 mg/L of Cd and 100 mg/kg of CUR to fish (Cyprinus carpio) separately and in combination over 14 days. At the end of the experiments, various haematological and immunological parameters, oxidative stress levels and antioxidant parameters of fish were measured, and the differences between the groups were evaluated. Simultaneous CUR treatment with Cd reversed the changes in haematological parameters in fish, increased immune responses, reduced oxidative stress and normalized the impaired antioxidant enzyme activities. In summary, our study showed that dietary supplementation with CUR may be an effective strategy to reduce the toxic effects of Cd in carp.
- MakaleAçık Erişim2026Scopus SJRQ2Electrochemical analysis of tetracycline in drug formulations with glutardialdehyde-Fe2O3 modified glassy carbon electrode
Koçak, Berna; İpek, Yeliz
In this study, a glutardialdehyde-Fe2O3 modified glassy carbon electrode (GA2–Fe2O3/GCE) was used as a sensor for the electrochemical analysis of tetracycline (TC) in drug formulations. The electrochemical behavior of TC on GA2-Fe2O3/GCE was investigated using cyclic voltammetry (CV), while the quantitative determination was performed via differential pulse voltammetry (DPV). After optimizing the experimental conditions, the linear working range, limit of (LOD) detection, and limit of quantification (LOQ) of the GA2-Fe2O3/GCE electrode developed for the electrochemical analysis of TC were found to be 1.0 × 10−6–2.0 × 10−5 M and 2.0 × 10−5–9.9 × 10−6 M; 4.78 × 10−7 M; 1.59 × 10−6 M, respectively. In the sensor application study conducted on a Tetra® Gensenta tablet to demonstrate the applicability of the developed method, the linear working range, LOD, and LOQ values of the sensor were found to be 1.0 × 10−6–14.8 × 10−5 M; 5.72 × 10−7 M and 1.74 × 10−6 M, respectively. The applicability of the method was demonstrated through tablet recovery studies, and the tablet recovery ratio was determined to be 100.94%.
- MakaleYayıncıda açık erişim2026Scopus SJRQ1Thematic Structure of Pedagogical Agent Studies: LDA Analysis for the Period 2020–2025
Turgut, Yigit Emrah; Aktı Aslan, Seda; Kopuz, Tuba; Aslan, Alper; Allison, Jordan; Özyurt, Özcan
This study examines 2,360 pedagogical agent (PA) articles published between 2020 and 2025 to identify thematic structures, trends, and research gaps through bibliometric analysis and LDA-based topic modeling. The optimal model produced 10 themes (c_v = 0.4767). The dominant themes—Learner Interaction with Virtual Assistants in Education, Student-Centred Learning and Individual Guidance, and NLP Foundations for Pedagogical Agents—account for 51.3% of publications. Trend analyses revealed consistent growth in both Artificial Intelligence and Pedagogical Design in Education and NLP Foundations for Pedagogical Agents, while Learner Interaction with Virtual Assistants in Education reached saturation. Interaction network results showed these themes at the conceptual core of the field, whereas AI-Driven Medical Chatbots, Knowledge-Based Learning Models, and Student Experiences in Intelligent Learning Environments emerged as future research areas. Overall, findings highlight a strong thematic shift toward artificial intelligence, natural language processing, and student-centered learning.
- MakaleAçık Erişim2026Scopus SJRQ3Analysis of Visible Light Communication Performance in LOS Propagation Model with PSO-Cosh-Based Filter
Fethiye Merve, Kavak Imak; Fatmanur, Atar; Duygu, Kaya; Turgay, Kaya
Due to the increasing data transfer in wireless communication technologies, Visible Light Communication (VLC), one of the branches of wireless communication technology, is also developing within this framework. VLC holds an advantage over other communication branches in terms of energy efficiency, high bandwidth, and reliability. In this context, research on VLC-based Light Fidelity (Li-Fi) technology has gained momentum in recent years. In systems built with this Li-Fi technology, data is transmitted through an LED bulb, which is used for illumination. In this study, to analyze the performance of systems where data transmission is provided by VLC, the capacity to improve the shape of the transmitted signal is investigated using systems designed with different filtering techniques under LOS channel conditions. The main objective of this study is to demonstrate the improving effect of the proposed PSO-Cosh-based model on the shape of the transmitted signal compared with traditional filtering methods. In this way, in addition to IIR digital filtering techniques, the analysis of the filter coefficients obtained by optimizing the Cosh window function with Particle Swarm Optimization (PSO) demonstrates the originality of the study.
- MakaleAçık Erişim2025Scopus SJRQ2Multifunctional properties and microstructural correlations of NiCoMnTi-Based high entropy alloys with focus on magnetic, thermodynamic, and radiation shielding characteristics
Özkul, İskender; Karaduman, Oktay; Şimşek, Tuncay; Şimşek, Telem; Canbay, Canan Aksu; Güler, Ömer; ALMisned, Ghada; Baykal, Duygu Sen; Tekin, Huseyin Ozan
High-entropy alloys (HEAs) are a novel class of materials known for their complex compositions and exceptional multifunctional properties. In this study, NiCoMnTi-based HEAs were modified with Fe, Cr, and Cu as fifth elements to systematically investigate their structural, magnetic, thermodynamic, and nuclear radiation shielding characteristics. The alloys were synthesized via arc melting and characterized using XRD, SEM-EDS, VSM, and theoretical modeling methods. The findings reveal that each additional element uniquely alters the alloy’s properties. The Fe-containing alloy exhibited a well-defined lamellar microstructure, the highest saturation magnetization (10.79 emu/g), and a Curie temperature near 450 K, along with strong gamma-ray shielding performance across both low and high energy ranges. The Cr-containing alloy demonstrated soft magnetic behavior, featuring the lowest coercivity (29.64 Oe) and the highest fast neutron removal cross-section. In contrast, the Cu-containing alloy enhanced low-energy photon attenuation due to its higher atomic number but exhibited limited magnetic homogeneity. Despite these differences, all samples displayed stable thermodynamic behavior and comparable buildup factor trends. Overall, the addition of Fe led to a synergistic improvement in both magnetic and radiation shielding capabilities of the NiCoMnTi-based HEA system.
AtıfScopus3OpenAlex3İncele - MakaleYalnız künye2025Scopus SJRQ3Cotton Fabric and Polypropylene Fabrics Treated with Synthesized Zinc Diborate
İpek, Yeliz; Ertekin, Özlem
Cotton fabrics and polypropylene fabrics are mostly used fabrics in many areas. Zn(B4O7) powder was synthesized with a sequential wet precipitation and sintering method. Focused ion beam scanning electron microscopy (FIB-SEM) figures and energy dispersive X-ray spectroscopy (EDS) mapping results showed that zinc diborate was highly and homogeneously adsorbed on the surface of the treated fabrics. When differential thermal analysis (DTA) results were examined, heat release seemed to decrease by 0.71 kJ g−1, 0.61 kJ g−1, and 0.46 kJ g−1for cotton fabric treated with zinc diborate (C-117-ZB), polypropylene fabric (20 g m−2) treated with zinc diborate (PP-20-ZB), and polypropylene fabric (65 g m−2) treated with zinc diborate (PP-65-ZB), respectively. According to vertical burning test, burning rates of cotton fabric (C‒117), C‒117‒ZB, polypropylene fabric (PP‒65), PP‒65‒ZB, polypropylene fabric (PP‒20), and PP‒20‒ZB were calculated as 3.25 cm s−1, 1.93 cm s−1, 0.75 cm s−1, 0.36 cm s−1, 0.88 cm s−1, and 0.71 cm s−1, respectively. Heat releases and burning rate decreases were in good agreement for all treated fabrics. Also breaking strengths of both fabric types were improved. The washing test exhibited that the amount of zinc diborate powder on the cotton fabric sample was released by 25.49% while PP-65-ZB and PP-20-ZB fabrics released about 90% by weight of adsorbed powder. Although improvement was observed in the fire resistance of PP fabrics, antibacterial activity against Escherichia coli and Staphylococcus aureus could not be achieved. However, antibacterial activity was provided only for the cotton fabric.
