Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Dertli, Enes(117)
- Kaya, Yunus(51)
- Şahin, Engin(48)
- İspirli, Hümeyra(47)
- Özen, Soner(46)
- Şenay, Volkan(45)
- Pat, Suat(44)
- Can, Zehra(39)
- Bayraktar, Bülent(37)
- Bayram, Sinan(37)
- Korkmaz, Şadan(36)
- Cuce, Erdem(35)
- Ecem Bayram, Nesrin(33)
- Kutlu, Naciye(33)
- Yilmaz, Mustafa Tahsin(32)
- Artar, Musa(31)
- Ertugrul, Mehmet(30)
- Öner, Erdal(30)
- Ayata, Ümit(29)
- Hasar, Ugur Cem(29)
- Esmer, Yusuf(28)
- Mercan, Emin(27)
- Cuce, Pinar Mert(26)
- Özdemir, Akın(25)
- Çolak, Murat(24)
- Bayram, Ozkan(23)
- Kamiloğlu, Aybike(23)
- Sert, Durmuş(22)
- Ayazoglu, Rabil(21)
- Abdioglu, Murat(20)
- Bayram, Nesrin Ecem(20)
- Kadakal, Mahir(20)
- Kara, Yakup(20)
- Kizil, Hamit Emre(20)
- Tozlu, Alperen(20)
- Birinci, Ahmet(19)
- Gerçek, Yusuf Can(19)
- Battal, Fetullah(18)
- Dirican, Ebubekir(18)
- Ergün, Mustafa(18)
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1Screening-based low energy availability risk, disordered eating risk, and their co-occurrence among female volleyball players: a cross-sectional study
André, Hülya; Sarışık, Derya Çetin; Özçelik, Arda
Low energy availability (LEA) and disordered eating (DE) are important health concerns in female athletes, yet their occurrence and co-occurrence in team-sport contexts, particularly volleyball, remain underexplored. This cross-sectional online survey study examined the proportions of athletes classified as being at risk of LEA and DE overall and across competition levels, and assessed the co-occurrence between the two risk classifications in female volleyball players in Türkiye, using the Low Energy Availability in Females Questionnaire (LEAF-Q) and Eating Attitudes Test-26 (EAT-26). It also examined correlations between EAT-26 total score and LEAF-Q total and domain scores. A total of 213 athletes, recruited via non-probability convenience sampling, completed the LEAF-Q and EAT-26 to assess LEA risk and DE risk, respectively. In this sample, 33.3% of athletes were classified as being at risk of LEA, with no difference across competition levels (p = 0.825). A total of 17.4% were classified as being at risk of DE, with variation across competition levels and the highest proportion among Turkish Women’s Volleyball Second League athletes (p = 0.034). LEA risk and DE risk co-occurred in 10.3% of the sample, and DE risk was associated with higher odds of LEA risk (unadjusted OR = 3.80, 95% CI: 1.82–7.92). EAT-26 total score was positively correlated with LEAF-Q total and gastrointestinal function scores (p < 0.001) and injury history score (p = 0.004), but not menstrual function score (p = 0.133). These results suggest that LEA risk was commonly identified in this sample, DE risk was comparatively less common, and their co-occurrence was only partial, highlighting the value of screening for both risks rather than assuming one from the other.
- MakaleAçık Erişim2026Scopus SJRQ3Low-Data Modeling of Shallow Foundations on Cohesive Slopes: A Comparison between Hybrid FEM–RSM and ML Models*
Kâmiloğlu, Hakan Alper; Gücüyener, Alper
In geotechnical engineering, accurately predicting the seismic bearing capacity of shallow foundations on cohesive slopes requires proper consideration of nonlinear effects and parameter interactions. However, most studies in the literature address these complex effects inadequately, either with numerical methods that demand a large number of analyses or with machine learning (ML) models that require large and heterogeneous data sets. In this study, it is aimed to propose an approach that allows for a robust analysis of the system and at the same time provides accurate forecasting for situations where working with small and homogeneous data sets is mandatory. Within the scope of the study, seismic bearing capacity analyses were performed using Plaxis 2D for 273 cases where eight independent variables took different values according to the Face Centered Composite Design (FCCD), and a database was created. Multiple Linear (MLR), Multiple Nonlinear (MNLR) Regression Models were established, and ML models, such as Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were trained. The models were compared with both in-sample and out-of-sample estimation performance, and parametric sensitivity analyses were evaluated by methods such as ANOVA and SHAP. The results show that the MNLR model with natural logarithmic transformation is the most successful method in terms of both accuracy (R² ≈ 0.98) and reflecting parameter interactions, while the SVR algorithm demonstrates the best generalization ability among ML models under low-data conditions. The results also confirm that the ML models effectively capture the parameter effects.
- MakaleAçık Erişim2026Scopus SJRQ1Shape-Directed Hydrothermal Design of Zinc Oxide Nanoparticles for Antimicrobial and Anticancer Applications
Özkan, Elvan Hasanoğlu; Yetim, Nurdan Kurnaz; Kızıl, Hamit E.; Öğütçü, Hatice
The emergence of antibiotic-resistant bacteria represents one of the most pressing challenges in global healthcare. In this study, metal oxide–based nanomaterials are gaining prominence due to their antimicrobial and anticancer potential. In the present study, six new zinc oxide nanoparticles (ZnO-NPs) synthesized via the hydrothermal method using different surfactants were characterized, and their biological activities were evaluated. ZnO-NPs, whose structural properties were determined by a range of analytical methods including BET, FT-IR, SEM-EDX, XRD, and XPS, exhibited significant antibacterial and antifungal effects on a range of bacterial and fungal strains. The study revealed that variations in the morphology and surface area had a direct impact on antimicrobial efficacy. In antimicrobial assays, the inhibition zones ranged from 10.5 mm to 25.5 mm, with ZnO-6 exhibiting the highest efficacy against S. epidermidis (25.5 mm). In cytotoxicity assays, ZnO-6 demonstrated the strongest anticancer potential against H460 cells with the lowest IC50 value of 31.9 µg/mL. Furthermore, a strong correlation was revealed between the physicochemical properties of ZnO-NPs and their anticancer activity, as evidenced by the results of tests conducted on H460 lung cancer cells. Specifically, ZnO-6, which possesses a flower-like morphology and the highest surface area, exhibited the strongest anticancer effect with an IC50 value of 31.9 µg/mL. The parallel enhancement in both antimicrobial and anticancer activities observed in ZnO-6 suggests a common underlying mechanism, likely driven by the high surface area and specific flower-like morphology that facilitates increased interaction with cell membranes and ROS generation. The findings demonstrate that the controlled design of ZnO-NPs in terms of morphology and surface area offers significant potential in both antimicrobial and anticancer applications.
AtıfScopus3WoS3OpenAlex2
İncele - MakaleYalnız künye2026Scopus SJRQ2Social support mediates relationship between death anxiety and stress in chronic illness
Akeren, Zahide; Kalyoncuo, Servet
Background: Fear of death may become salient among individuals with chronic illness. This study examined the mediating role of perceived social support in the relationship between death anxiety and perceived stress in individuals with chronic illness. Methods: The study was conducted with 265 individuals diagnosed with chronic illness. The sample included participants with hypertension (20.7%), diabetes (19.6%), and cardiovascular diseases (16.2%), among conditions. Data were collected using the Templer Death Anxiety Scale, the Perceived Stress Scale, and the Multidimensional Scale of Perceived Social Support. Results: A weak negative correlation was found between death anxiety and perceived social support (r = -0.156, p = 0.011), while a positive correlation was observed between death anxiety and perceived stress (r = 0.362, p < 0.001). The proportion of the total effect mediated by perceived social support was 32.7%. Death anxiety was associated with perceived stress (β = 0.347, p < 0.001). The of perceived social support as a mediator, the coefficient decreased (β = 0.234, p < 0.001), indicating partial mediation. Conclusion: Perceived social support partially mediated the relationship between death anxiety and perceived stress. Higher levels of death anxiety were associated with higher perceived stress, and this association was attenuated in the presence of perceived social support.
- MakaleYayıncıda açık erişim2026Scopus SJRQ3Effects of Artificial Weathering Resistance on the Hardness Value of Ash Wood Surfaces Coated with Polyurethane Modified Water-Based Varnish after Heat Treatment
Akter, Mete; Ulay, Göksel; Peker, Hüseyin; Çakıcıer, Nevzat; Atilgan, Abdi; Ayata, Ümit
Pendulum hardness test is important for determining the mechanical durability and long-term performance of the film layer formed on the surface of wood thermally treated at high temperatures and coated with water-based varnish (WBV). In this study, the effect of artificial weathering conditions on the pendulum hardness (König method) of ash (Fraxinus angustifolia Vahl.) wood samples varnished with polyurethane modified WBV was investigated. The samples were thermally treated under two conditions (190°C for 1.5 h and 212°C for 2 h) and compared with untreated control samples. The samples were exposed to artificial weathering conditions (for periods of 0, 168, 336 and 504 h). Pendulum hardness values were measured at the end of each weathering period. According to the results, increases in hardness values were observed in both heat-treated and untreated ash wood samples treated with varnish after weathering. In the varnished samples treated at 212°C for 2 h, the post-weathering hardness increases were lower compared to the samples treated at 190°C for 1.5 h and the control group. Additionally, analysis of variance for the pendulum hardness test, heat treatment, weathering time and their interaction were determined to be statistically significant. The significant increases in hardness values observed in the unweathered samples highlight the importance of the wood, varnish, and weathering process.
- MakaleAçık Erişim2026Scopus SJRQ2Diagnostic and Prognostic Significance of miR-155, miR-181, miR-221, miR-222, and miR-223 Expression in Myelodysplastic Syndromes and Acute Myeloid Leukemia
Ardıç, Cemile; Ay, Mustafa Ertan; Çevik, Kenan; Tombak, Anıl; İzci Ay, Özlem; Karakaş, Ümit; Doğru Özdemir, Gurbet; Bilgiç, Abdulkadir; Erdal, Mehmet Emin
Background: Myelodysplastic syndromes (MDSs) and acute myeloid leukemia (AML) are clonal hematological disorders that share molecular origins but present with distinct clinical features. MicroRNAs (miRNAs) are key post-transcriptional regulators, and their altered expression may reflect biological shifts contributing to disease progression. Methods: Expression levels of miR-155, miR-181, miR-221, miR-222, and miR-223 were analyzed by RT-qPCR in bone marrow samples from 37 MDS patients, 20 AML patients, and 7 controls. Group comparisons were performed using ANOVA (with Benjamini–Hochberg correction) and Tukey post hoc testing. Diagnostic performance and network behavior were evaluated using ROC analysis, Pearson correlation matrices, and principal component analysis (PCA). Results: miR-155, miR-181, and miR-223 were upregulated in AML, whereas miR-221 and miR-222 were downregulated. miR-222 showed the highest diagnostic accuracy (AUC ~0.87 for both AML vs. control and MDS vs. control). Its expression was significantly higher in high IPSS-R MDS cases (p = 0.046), with a similar upward tendency for miR-221 (p = 0.054). Progressive loss of coordinated miRNA expression was observed from controls to MDS and AML. PCA supported these findings by showing separation mainly driven by miR-222 and miR-155. Conclusions: Combined miRNA profiling highlights miR-222 and, to a lesser extent miR-155, as consistent indicators of myeloid disease transformation. While further validation in larger and genetically stratified cohorts is warranted, these findings support the potential contribution of miRNA signatures to diagnostic evaluation and risk stratification in MDS and AML, in line with precision hematology approaches.
AtıfScopus1WoS1OpenAlex1İncele - Derleme MakaleAçık Erişim2026Scopus SJRQ2SURVEILLANCE OR SUPPORT? A SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE SUPPORTED PROCTORED ONLINE EXAMS
Ustun, Aysin Gaye; Kayali, Bunyami; Yavuz, Mehmet; Bozkurt, Aras
Proctored online exams have become widespread, especially after COVID-19, increasing exam security and efficiency. In recent years, artificial intelligence developments have increased these systems' importance by expanding their use. Accordingly, this study aims to provide an in-depth understanding of the conceptual structure of artificial intelligence-supported proctored online examination (AI-SPOE) research. Systematic literature review and text mining were used as methods. Thirty-two studies in Scopus and Web of Science databases were analyzed according to the PRISMA technique. While all the studies were examined in the context of content analysis, abstracts and keywords of the relevant studies were analyzed by text mining. The current study found it effective in increasing academic integrity and exam security, but problems such as privacy violations, exam anxiety, and algorithmic biases were encountered. Although methods such as biometric verification and behaviour analysis successfully detect cheating, technical difficulties, and false positives negatively affect the user experience. In the future, more inclusive designs, transparent algorithms, and alternative evaluation methods are suggested. In conclusion, the study emphasizes the potential of AI-SPOE systems and highlights the importance of ethical and technical improvements.
AtıfScopus1OpenAlex1İncele - MakaleYalnız künye2026Scopus SJRQ1Experimental investigation of magnetic forces in PMA and HTS configurations using a high-speed electrodynamic levitation test system
Mollahasanoglu, Hakkı; Abdioglu, Murat; Ozturk, Ufuk Kemal; Okumus, Halil Ibrahim
This study presents a comparative experimental investigation of magnetic force parameters in high-speed electrodynamic levitation systems using permanent magnet arrays and high-temperature superconductors. A novel modular test platform with a rotating aluminium rail, cryostat, and integrated three-axis force measurement unit was employed to evaluate levitation and drag forces under controlled conditions at linear velocities up to 280 km/h. Three different permanent magnet arrays configurations were designed and tested at working gaps of 10, 12, and 15 mm, while high-temperature superconductors bulks fabricated from YBCO by the top-seed-melt-growth method were tested at 10 and 12 mm gaps. The experimental results demonstrated that permanent magnet arrays configurations produced higher levitation forces, with a maximum of 100 N at working gap of 10 mm. High-temperature superconductors bulks, in contrast, generated lower levitation forces (maximum 17 N at 10 mm) due to limited trapped flux (∼0.25 T) but showed the advantage of substantially reduced drag forces (3.5 N at 10 mm). A lift-to-drag ratio analysis confirmed the trade-off between force generation and energy losses in permanent magnet arrays systems, while highlighting the efficiency potential of high-temperature superconductors-based levitation. The findings of this study provide valuable insights for the engineering development of high-temperature superconductors-based Maglev technologies, emphasizing their potential importance in future transportation systems.
AtıfScopus5WoS4OpenAlex2İncele - MakaleAçık Erişim2026Scopus SJRQ2The Effect of Data Types’ on the Performance of Machine Learning Algorithms for Cryptocurrency Prediction
Tanrikulu, Hulusi Mehmet; Pabuccu, Hakan
Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A slight improvement in forecasting performance can lead to increased profitability; Therefore, obtaining a realistic forecast is very important for investors. Bitcoin, frequently mentioned in recent due to its volatility and chaotic behavior, has become an investment tool, especially during and after the COVID-19 pandemic. In this study, selected ML techniques were investigated for predicting cryptocurrency movements by using technical indicator-based data sets and measuring the applicability of the techniques to cryptocurrencies that do not have sufficient historical data. In order to measure the effect of data size, Bitcoin’s last 1 year and 7 years of data were used. Following the related literature, Google trends and the number of tweets were used as input features, in addition to the most commonly used twelve technical indicators. Random Forest, K-Nearest Neighbors, Extreme Gradient Boosting (XGBoost-XGB), Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) network were optimized for best results. Accuracy, F1, and area under the ROC curve values were used to compare the model performance. For continuous data, ANN and SVM performed the best with the highest accuracy and outperformed the other ML models for complete and reduced sets. LSTM reached the best accuracy for trend data, but SVM, NB, and XGB models showed similar performance. The research shows that some indicators significantly affect prediction performance, and the data discretization process also improved the model’s accuracy. While the number of samples affects the results of many ML models, correctly optimized and fine-tuned models may also give excellent results even with less data.
AtıfScopus1OpenAlex2İncele - MakaleYalnız künye2026Scopus SJRQ2Self-regulation in blood glucose management among middle-aged and older adults with type 2 diabetes in a rural and semi-rural setting: A qualitative study
Akeren, İhsan; Akeren, Zahide
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease with increasing prevalence, particularly among middle-aged and older adults. Effective blood glucose management is essential for preventing complications and improving quality of life. Although self-regulation has been widely examined in diabetes management, little is known about how middle-aged and older adults living in rural and semi-rural settings apply self-regulation strategies in daily life. This qualitative study explored the blood glucose management experiences of individuals aged 50 years and older with T2DM using Zimmerman’s cyclical self-regulated learning model. Face-to-face interviews were conducted with 16 hospitalized participants meeting the rural or semi-rural residence criterion. Data were analyzed using an inductive-deductive hybrid thematic approach and organized with MAXQDA 20. Three main themes and 13 subthemes were identified: preparation and awareness, barriers and management behaviors, and self-evaluation and internal awareness. During the forethought phase, participants described knowledge, motivation, awareness, and planning but also reported misconceptions about diet and blood glucose management. In the performance phase, behavioral, emotional, cognitive, and social barriers, comorbidities, and poor adherence to dietary recommendations were common. During self-reflection, participants recognized physical symptoms and emotional influences but rarely reported systematically evaluating outcomes or modifying subsequent management behaviors. The findings indicate that inadequate knowledge, emotional challenges, comorbidities, limited social support, and ineffective coping strategies may disrupt the cyclical self-regulation process. Educational interventions for middle-aged and older adults in rural and semi-rural settings should strengthen knowledge, support self-management behaviors, and promote evaluation of outcomes and adaptation of future actions.
- MakaleAçık Erişim2026Scopus SJRQ1A gated multi-scale attention framework for automated short answer grading in Turkish
Kaya, Mustafa; Kavun, Mahir
This study proposes a new deep learning architecture for automated short-answer grading (ASAG) in Turkish. The model integrates transformer-based encoders (BERTurk, RoBERTa-Turkish (RoBERTa), ELECTRA-Turkish (ELECTRA)) with a Gated Multi-Scale Head Attention (GMHA) layer. Additionally, a BiLSTM network and parallel CNN layers are employed to capture both global dependencies and local linguistic patterns.The model was evaluated on a newly created Turkish dataset containing 2200 student responses from 220 learners, each graded by four experts. Among the pretrained Turkish encoders, the BERT-based model with GMHA achieved the best performance (QWK = 0.839, Pearson = 0.856, RMSE = 0.918). The model was also compared with traditional machine learning approaches and large language models (LLMs) using the Leave-One-Question-Out (LOQO) validation protocol. Under this setting, it achieved QWK = 0.337, Pearson = 0.468, and RMSE = 1.213, outperforming all baselines.Ablation studies confirmed the effectiveness of the GMHA and parallel CNN components. These results highlight both the theoretical contribution of multi-scale gated attention and the practical potential of developing reliable automated grading systems for morphologically rich, low-resource languages like Turkish.
AtıfOpenAlex1İncele - MakaleAçık Erişim2026Scopus SJRQ4Determination of Biological Activities of Encapsulated Bee Venom
Keskİn, Şaban; Bayram, Sinan; Keskİn, Merve
Bee venom is an important bee product used directly in apitherapy applications. Once harvested, bee venom crystallizes rapidly upon contact with air. Furthermore, bee venom that is not produced and stored under optimal conditions deteriorates rapidly. For this reason, there is a need for new methods that can extend the shelf life of bee venom. Encapsulation techniques are widely used to extend the shelf life of bioactive components by immobilizing them within a biocompatible biopolymer. In this study, the potential of encapsulating bee venom using ionic gelation with chitosan, a biocompatible polymer, was investigated. It was found that chitosan had a 91% encapsulation efficiency for bee venom. It has been determined that bee venom inhibits the acetylcholinesterase enzyme by 84%±2.1, whilst its total protein content is 64.2%±1.8. The antimicrobial activity of the encapsulated bee venom was evaluated against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Bacillus subtilis. The inhibition zones ranged from 11 to 12 mm. The results showed that the encapsulated bee venom retained its biological activity without significant loss.
- MakaleYalnız künye2026Scopus SJRQ2Effects of giant fennel (Ferula communis L.) on some performance traits in honey bees during supplementary feeding period
Çıvracı, Sadık; Yalçın, Sakine; Onbaşılar, Esin Ebru
This study was carried out to determine the effects of seeds and roots of giant fennel (Ferula communis L.) on some performance characteristics of Apis mellifera caucasia L. colonies. Equalized colonies were distributed with 4 colonies for each group. In the study, 5 groups were arranged, and only syrup was given to the control group. On the other hand, 0.1% and 0.2% levels of root and seed of giant fennel were added to the syrup in the experimental groups. Measurements were made at 21-day intervals in the experiment. At the end of the experiment, supplemental feeding by adding fennel seeds and roots to the syrup provided a statistically significant increase in the number of bee frames, hive weight, nectar flow weight gain, and honey yield. Although the use of seed and root at the levels of 0.1% and 0.2% did not make a difference in these parameters, the use of the 0.2% level of seed had a more positive effect than the level of 0.1%. Usage of giant fennel seed and root increased total antioxidant status, total oxidant status, and oxidative stress index in honey according to the control group. The total antioxidant status in honey was increased statistically with the usage of 0.1% fennel seed. As a result of this study, it was concluded that the brood production and honey yield in honey bees and the antioxidant status in honey can be increased with the use of fennel seed and root, especially at the level of 0.2%, in supplementary feeding.
- MakaleAçık Erişim2026Scopus SJRQ1Trust in AI: scale development and validation for online distance learners
Üstün, Ayşin Gaye; Yavuz, Mehmet; Kayalı, Bünyami; Uçar, Hasan; Erdoğdu, Erdem; Bozkurt, Aras
Trust in artificial intelligence (AI) has emerged as a critical factor influencing students’ interactions with AI-supported learning environments. However, validated instruments for measuring student trust in AI within educational contexts remain limited. This study developed and validated a multidimensional scale to assess student trust in AI systems in online distance education. Scale development followed a sequential multi-stage design, including item generation based on literature review, expert evaluation for content validity, and psychometric validation through exploratory and confirmatory factor analyses. A total of 837 distance learning students participated in the study (EFA = 412; CFA = 307; validation sample = 118). Exploratory factor analysis supported a five-factor structure explaining 63.20% of the total variance. Confirmatory factor analysis with an independent sample indicated good model fit (/df = 2.17; CFI =.95; TLI =.94; RMSEA =.06). The scale demonstrated high internal consistency (=.94; =.94). Multi-group confirmatory factor analysis supported metric invariance across gender, indicating comparable measurement across groups. No significant gender differences were observed, and trust in AI was not significantly associated with grade point average or system engagement frequency. These results suggest that trust represents a distinct perception that may influence how students engage with AI-supported learning environments. The resulting 21-item scale provides a reliable instrument for investigating student trust in AI and offers practical implications for the design and implementation of AI-supported learning in higher education.
AtıfScopus3WoS2OpenAlex3İncele - MakaleYalnız künye2026Scopus SJRQ2Attracting Volunteers Through Social Media Marketing in Environmental Organizations
Çetinkaya, Ali; Bilgin, Yusuf; Elmalı, Kübra
Nowadays, environmental organizations have strategically leveraged social media platforms to raise environmental awareness, promote pro-environmental behavior, manage environmental campaigns, and encourage environmental volunteerism. This study aims to investigate the impact of social media marketing activities conducted by environmental organizations on followers’ pro-environmental behaviors and their intentions to engage in environmental volunteering. The research sample consists of social media users who follow the ten most prominent environmental organizations in Turkey–organizations known for their commitment to protecting nature and the environment–on platforms such as Facebook, Instagram, Twitter, and LinkedIn. A quantitative research method was employed, and data were collected from 512 respondents through an online survey. The data were analyzed using Structural Equation Modeling (SEM). The findings reveal that social media marketing activities, taken as a whole, have a significant and positive effect on both followers’ pro-environmental behaviors and their environmental volunteering intentions. Among the five components, timeliness, awareness, and customization were found to have the strongest influence on volunteering intentions, while timeliness and interaction significantly affected pro-environmental behavior. Additionally, the analysis confirmed that pro-environmental behavior mediates the relationship between social media marketing activities and environmental volunteering intentions. This study contributes to the growing body of literature on environmental communication by demonstrating how specific social media marketing strategies can enhance both environmental engagement and behavioral outcomes among followers. The findings offer practical insights for environmental organizations aiming to strengthen their impact through targeted and effective social media practices.
AtıfScopus1WoS1OpenAlex1İncele - MakaleAçık Erişim2026Scopus SJRQ3Optimization of Rotary Degassing Parameters to Improve Melt Cleanliness and Mechanical Properties in A356 Aluminum Alloy
Kutsal, U.; Çolak, M.; Sağnak, Y.; Bardakci, S.; Ammas, G.; Sarper, G.
Melt cleanliness is a critical factor that directly affects the quality of aluminum castings and becomes even more significant in recycling-oriented production, where high scrap utilization is inevitable. Increasing the proportion of scrap intensifies the risk of oxide films, inclusions, and gas porosity in the molten metal; therefore, efficient refining techniques are essential. In this study, the effects of rotary degassing parameters—including rotor speed, rotation time, and gas flow rate—on melt cleanliness of A356 aluminum alloy were systematically investigated. Experimental melts containing 20% primary and 80% scrap aluminum were treated under various degassing conditions using a commercial flux cleaning flux. Characterization was performed through the Reduced Pressure Test (RPT), bifilm index analysis, fluidity measurements, microstructural examinations, and mechanical testing (tensile test and Brinell hardness measurement). The results demonstrated that increasing the rotor speed from 200 rpm to 500 rpm significantly reduced both the density and bifilm indices, indicating a notable improvement in melt cleanliness. Furthermore, higher gas flow rate (10 L/min) and longer rotation time (10 min) provided additional but secondary improvements. Consequently, the optimization of rotary degassing parameters enhanced not only melt quality but also tensile strength and ductility. These findings offer practical insights for optimizing process control in recycling-based aluminum foundries.
- MakaleYalnız künye2026Scopus SJRQ1Development and Validation of AI Acceptance Scale (AI-AS) for Learners in Higher Education
Kayalı, Bünyami; Yavuz, Mehmet; Üstün, Ayşin Gaye; Uçar, Hasan; Erdoğdu, Erdem; Bozkurt, Aras
The use of artificial intelligence (AI) applications in educational activities is increasing day by day. This situation highlights the need for valid and reliable measurement tools to determine students' levels of acceptance of this technology. This study aims to develop a standard measurement tool to determine students' levels of acceptance of AI. To this end, the systematic scale development steps proposed by DeVellis and Thorpe (2021) were followed. Exploratory factor analysis conducted with 560 participant responses and confirmatory factor analysis conducted with 950 participant responses resulted in a 26-item, 5-factor structure. The factors included in the scale were listed as "usability", "ease of use", "intention and attitude", "facilitating conditions" and "trust and data privacy". The findings revealed that the scale had high internal consistency and exhibited satisfactory model fit indices. The results indicate that the scale is a valid and reliable measurement tool for determining students' levels of AI acceptance. The study offers practical implications for researchers and practitioners and highlights directions for future work on AI adoption in higher education environments.
- MakaleAçık Erişim2026Scopus SJRQ1Bio-inspired passive cooling of photovoltaic modules using Sphagnum palustre moss: An experimental study
Cuce, Pinar Mert; Abay, Gökhan; Cuce, Erdem; Guclu, Tamer; Alvur, Emre
This study investigates the potential of Sphagnum palustre moss as a completely natural, passive evaporative cooling medium for photovoltaic (PV) modules. Unlike synthetic hydrogels or complex biomimetic structures, the proposed system utilises the innate porous structure and high water-retention capacity (up to 20 times its dry weight) of natural moss to facilitate rapid heat dissipation. Experimental evaluations conducted under effective solar irradiances of 510.4–820.9 W/m2 demonstrate that the moss-cooled configuration achieves a remarkable temperature reduction of 27.4–39.8 °C compared to uncooled standalone panels. These thermal improvements translate into relative power gains of 6.10% to 11.66% and an exceptional evaporative cooling power of up to 598 W/m2. The results establish Sphagnum palustre as a highly effective, biodegradable, and low-cost alternative for PV thermal management, offering performance competitive with advanced engineered cooling materials whilst ensuring long-term environmental sustainability.
AtıfOpenAlex1İncele - MakaleYayıncıda açık erişim2026Scopus SJRQ1Optimization of extraction conditions of Micromeria fruticosa and use of optimized extract in beef patties production
Anlar Daldal, Pınar; Cinar Topcu, Kübra; Çakır, Özlem
This study aimed to optimize the conditions for obtaining an ethanol extract from Micromeria fruticosa (M. fruticosa) and to determine the antioxidant activity and phenolic compound content of the resulting extract. Subsequently, the use of the optimized extract in beef patties was evaluated, and the effects of the cooking process on the physicochemical properties of the product were examined. In this context, the highest efficiency was obtained in terms of total phenolic contents (TPC), as well as DPPH (2,2-diphenyl-1-picrylhydrazyl) and ferric reducing antioxidant power (FRAP) values, during the ethanol extraction of the plant. The optimum extraction conditions were determined to be 12 g of sample, 20 min, and 75% ultrasonic power. Under these conditions, the TPC of the extract was found to be 37.128 mg GAE/g dry weight, the DPPH value was 42.865, and the FRAP value was 254.776 mg TE/g dry weight. In the second phase of the study, the optimized extract was used at different concentrations (0%, 0.5%, 1.0%, and 1.5%) to produce beef patties, which were then analyzed for pH, moisture content, TBARS (thiobarbituric acid-reactive substances), and cooking yield. The use of the extract did not cause a statistically significant difference in the moisture content and TBARS values of the samples (p > 0.05). However, it was determined that the pH values of the samples containing 0% and 0.5% extract were lower than those containing 1.0% and 1.5% extract (p < 0.01). In addition, it was observed that the use of the extract had a positive effect on cooking yield, and that cooking yield increased with increasing extract concentration. As a result of the study, it was concluded that M. fruticosa holds promising potential as a natural additive in meat and meat products, offering a functional ingredient that can contribute to the development of value-added, health-oriented food products.
- MakaleAçık Erişim2026Scopus SJRQ3Testing the Relationship between Investor Risk Appetite and Country Risk: The Case of Turkey
Kaya, Abdulkadir; Gulhan, Unal; Gungor, Bener
Summary: Country risk is an important factor affecting the risk appetite of investors who want to create an optimal portfolio by minimizing their risks. Investor risk appetite can affect country risk as a factor contributing to the development of financial markets and the country’s economy. The aim of this study is to determine the relationship between investor risk appetite and country risk. Hatemi-J cointegration and Hatemi-J asymmetric causality tests were used to determine the relationship between CDS premium, which is frequently preferred to represent country risk, and domestic and foreign investor risk appetite variables. As a result of the analysis, it has been determined that CDS premium and domestic and foreign investor risk appetite variables are cointegrated, a positive causality from increases in both local and foreign investor risk appetite to CDS premium and a negative or positive causality from decreases in CDS variables to both local and foreign investor risk appetite.
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