Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Dertli, Enes(114)
- Kaya, Yunus(48)
- Şahin, Engin(48)
- İspirli, Hümeyra(47)
- Özen, Soner(38)
- Şenay, Volkan(37)
- Pat, Suat(36)
- Kutlu, Naciye(32)
- Artar, Musa(31)
- Can, Zehra(31)
- Korkmaz, Şadan(31)
- Yilmaz, Mustafa Tahsin(31)
- Öner, Erdal(31)
- Bayram, Sinan(30)
- Cuce, Erdem(30)
- Ertugrul, Mehmet(29)
- Hasar, Ugur Cem(29)
- Ecem Bayram, Nesrin(28)
- Mercan, Emin(28)
- Ayata, Ümit(27)
- Bayraktar, Bülent(27)
- Çolak, Murat(23)
- Özdemir, Akın(23)
- Ayazoglu, Rabil(22)
- Cuce, Pinar Mert(22)
- Sert, Durmuş(22)
- Bayram, Ozkan(21)
- Kamiloğlu, Aybike(21)
- Dirican, Ebubekir(20)
- Kadakal, Mahir(20)
- Abdioglu, Murat(19)
- Birinci, Ahmet(19)
- Tozlu, Alperen(19)
- Gerçek, Yusuf Can(18)
- Taylan, Osman(18)
- Cinar, Mehmet(17)
- Gul, Volkan(16)
- Hasar, Ugur C.(16)
- Kadakal, Huriye(16)
- Kara, Yakup(16)
Yayınlar
- 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.
AtıfScopus1OpenAlex2
İncele - 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.
AtıfScopus3WoS2OpenAlex3
İncele - 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.
AtıfWoS1OpenAlex2İncele - Derleme MakaleAçık Erişim2026Scopus SJRQ1Artificial Intelligence Supported Online Assessment and Evaluation in Higher Education: A Systematic Review With SWOT Analysis
Üstün, Ayşin Gaye; Kayalı, Bünyami; Yavuz, Mehmet
Artificial intelligence-supported online assessment and evaluation (AI-SOAE) systems have the potential to transform assessment and evaluation processes in higher education; however, there is a need for a comprehensive evaluation of the pedagogical, technical, and ethical dimensions of these systems. In this regard, the study systematically examined the characteristics of AI-SOAE systems within the framework of a SWOT analysis. A systematic literature review method was used in the research. Ninety-eight studies published between 2018 and 2025 in the Web of Science (WoS), Scopus, and Education Resources Information Centre (ERIC) databases were analysed in accordance with PRISMA guidelines. The findings show that the strengths of AI-SOAE systems include personalised feedback, advanced assessment processes, increased efficiency, and data-driven decision support mechanisms. In contrast, technical infrastructure limitations, algorithmic biases, data management issues, and negative user experiences were identified as the weaknesses of the systems. The analysis also reveals that AI-SOAE systems offer significant opportunities in terms of pedagogical transformation, digitalisation, and institutional competitiveness; however, they face threats such as data security, ethical concerns, risks to academic integrity, and limited empirical evidence. Consequently, AI-SOAE systems offer context- and application-dependent potential for supporting assessment and evaluation practices in higher education. Effectively realising this potential depends on strengthening technical infrastructure, managing algorithmic risks, and developing transparent, ethical, and institutional policy frameworks. By synthesising the literature in the field of AI-SOAE in a structured manner, this study provides a comprehensive assessment for researchers, educators, and policymakers. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
AtıfScopus1WoS1OpenAlex3İncele - MakaleAçık Erişim2026Scopus SJRQ2Dual-Band Electromagnetic Shielding of Concrete Block Walls Using Concentric Double Square-Ring Frequency-Selective Surfaces near the n78 and n79 5G Frequency Regions
Cakir, Mehmet
Featured Application: This study demonstrates a concrete-integrated dual-band frequency-selective shielding approach for selectively attenuating the main sub-6 GHz 5G n78 and n79 frequency regions in building walls without increasing wall thickness or altering conventional concrete block geometry. This paper presents a dual-band shielding approach in which two concentric square rings, fabricated on a single 0.2 mm copper sheet, are embedded in the mortar joint of prefabricated concrete blocks. The outer ring targets n78 (design center 3.5 GHz) and the inner ring targets n79 (design center 4.7 GHz). Both rings are dimensioned from the effective wavelength inside the concrete medium (εr = 5.0): the outer ring has an outer side of 9.93 mm and the inner ring an outer side of 7.43 mm, with a 180° gap offset between the two elements to suppress inter-ring coupling. CST Microwave Studio simulations predicted two shielding peaks of 37.2 dB at 3.48 GHz and 35.8 dB at 4.72 GHz, with −10 dB bandwidths of 580 MHz and 490 MHz, respectively. Computer numerical control (CNC)-milled copper panels cast into ordinary Portland cement (OPC) concrete blocks achieved measured peak SE of 34.9 dB (n78) and 33.4 dB (n79), with bandwidths of 510 MHz and 420 MHz. The 2.3–2.4 dB shortfall relative to simulation is attributed to coupling-gap drift during milling and spacer-induced surface voids. A permittivity sweep from εr = 4.5 to 6.0 showed that peak SE remains above 30 dB throughout.
- MakaleYalnız künye2026Scopus SJRQ1Structural and physicochemical characterisation of branched dextrans produced by an active α-(1→2) branching sucrase from Apilactobacillus kunkeei PDER37
İspirli, Hümeyra; Dertli, Enes
Recently, branching sucrases encoded in the genomes of certain Lactic Acid Bacteria (LAB) strains have become novel enzymes to obtain branched α-glucans. In this study an active α-(1 → 2) branching sucrase from Apilactobacillus kunkeei PDER37 was expressed, characterised and distinct branched dextrans was obtained with reactions under different sucrose: dextran ratio. Structural characterisation by 1H and 13C NMR analysis demonstrated the branching of the dextran with (1 → 2)-linked α- d -glucose units with no alteration in the final structure depending on sucrose: dextran ratio (D0) but this ratio was effective for the determination of the molecular weights of the branched dextrans (D1, D2 and D3). FTIR analysis further supported the dextran structures and suggested the higher accumulation of the α-Glc units in the branched dextrans. Thermal characterisation of the branched dextrans obtained by TGA and DSC analysis suggested the increased hygroscopicity of the branching units. Both SEM and AFM analysis demonstrated more porous chain like structures in the branched dextrans. This study provides valuable information on the role of active α-(1 → 2) branching sucrase (BS37) for the production of branched dextrans with potential increased physicochemical status applicable for food and other industries.
- MakaleAçık Erişim2026Scopus SJRQ3DFT-based structural and energetic characterisation of nitrogen-rich tetrazine/triazine derivatives with Kamlet-Jacobs performance predictions
Ekincioğlu, Yavuz; Kepceoğlu, Abdullah; Çakir, Zehra; Şentürk, Şükrü
Nitrogen-rich heterocycles are attractive candidates for next-generation energetic materials because they can deliver high heats of formation while producing relatively benign, N2-rich decomposition products. In this work, three nitrogen-rich frameworks 3,6-bis(tetrazol-5-yl)−1,2,4,5-tetrazine (H2BTTz), 2,4,6-tris(tetrazol-5-yl)−1,3,5-triazine (H3TTT), and 2,3,5,6-tetra(tetrazol-5-yl)pyrazine (H4TTP) were investigated using a combined conformational sampling and density functional theory (DFT) approach. Initial conformational searches were carried out with the MMFF method, and the lowest-energy structures were optimised at the DFT/B3LYP/6-311++G(d,p) level a widely used compromise between accuracy and computational cost for nitrogen-rich energetic molecules. Electronic structure analyses included frontier molecular orbitals, Mulliken and natural population charges, and molecular electrostatic potential maps. Energetic performance was evaluated through heats of formation, detonation velocity and pressure, and impact sensitivity estimates. Topological descriptors such as ELF, LOL, and NCI were analyzed using Multiwfn program. The calculated HOMO–LUMO gaps (3.54-4.90 eV) indicate electronically stable molecular frameworks. Gas-phase formation enthalpies were converted to condensed-phase values using a sublimation correction, and detonation properties were estimated via the Kamlet–Jacobs equations. The predicted performance follows the order H4TTP > H2BTTz > H3TTT. Although their detonation performance is lower than that of FOX-7 and CL-20, these nitrogen-rich systems remain promising candidates for reduced-sensitivity energetic materials and merit further experimental investigation.
AtıfScopus1WoS1OpenAlex1İncele - MakaleYalnız künye2026Scopus SJRQ1Upcycling marble waste into construction materials and heavy metal adsorbents via hot-pressed geopolymerization technique
Öksüzer, Nurullah; Uslu, Emin; Gökçe, H. Süleyman
This study presents a sustainable approach to utilizing marble dust waste by producing bifunctional metakaolin-based geopolymers via a synchronized hot-pressing technique. In this study, the environmental footprint was minimized by designing mixtures in which marble waste constituted 50% of the total volume. Three stoichiometric series were investigated by varying the activator modulus (SiO2/Na2O = 3 and 4) and the Si/Al ratio (2 and 3) under two curing temperatures (150 and 300 °C) and three compaction stresses (5, 25, and 50 MPa). Thus, a high-volume industrial byproduct was transformed into a functional component. In this context, it investigated the transformation of industrial products into next-generation materials by using them to produce cementless composite geopolymers for civil engineering and to adsorb heavy metal (Cu2+). As a result of the research, an early compressive strength of 58 MPa was obtained through heat treatment and pressing applied at 300 °C and 50 MPa. This optimum strength was achieved in the SiO2/Na2O = 3, Si/Al = 3 series, whereas excess silica (SiO2/Na2O = 4) and excess aluminum (Si/Al = 2) limited the strength to 16.8 and 35.2 MPa, respectively. Microstructural analyses (SEM/EDS and FT-IR) revealed that marble dust was used as a filler material in the microstructure to produce an N-A-S-H-containing matrix. The produced materials were then ground after their service life and used to purify water containing heavy metals. More than 99.75% of Cu2+ was removed through ion exchange between copper ions used as heavy metals. The residual Cu2+ concentration fell below 0.25 mg/L from an initial concentration of 100 mg/L, and the released Na+-to-adsorbed Cu2+ molar ratio of approximately 1.8–2.3 confirmed an ion-exchange-dominated uptake mechanism. This structural and environmental impact demonstrates that marble waste, which is otherwise discarded as waste, is considered inert, offering an alternative model for the circular economy and environmental applications.
- MakaleYalnız künye2026Scopus SJRQ1AI-enhanced disaster education in middle schools: A mixed study from social studies classes
Yetişensoy, Okan
This research aims to examine the educational potential of AI-assisted chatbots, the popular AI technology of recent years, in middle school disaster education. In this regard, six chatbot characters in human form, each with fictional stories, were developed for various disasters taught within the scope of 5th-grade Social Studies in Türkiye. While the practitioner teacher instructed on disaster-related curricular outcomes in the experimental group through activities that included the use of the relevant chatbots, the same curricular outcomes in the control group were taught using conventional pedagogical methods. The experimental results showed that the disaster knowledge of the experimental group students was significantly higher than that of the control group students. However, no significant difference was observed in students’ disaster awareness levels. In the semi-structured interviews, students expressed that chatbots promoted a more effective, interesting, and student-centered disaster education with their distinctive design and pedagogical features. However, they noted that chatbots had certain inadequacies in developing concrete experiences, as well as psychomotor skills necessary to address disaster risks. Findings from the study indicate that the relevant AI technology is effective in promoting disaster education processes, particularly in facilitating more effective learning. Nevertheless, achieving more complex competencies for mitigating risks requires a higher level of technological advancement. At this point, it is suggested that undertaking initiatives to combine chatbots with extended reality environments will be beneficial in making them a more effective tools in disaster education processes.
AtıfScopus2İncele - MakaleAçık Erişim2026Scopus SJRQ2Production of Gamma-Aminobutyric Acid by Levilactobacillus brevis AK19B: Strain Characterization and Process Optimization Using Box–Behnken Design
Memiş, Habibe; Kamiloğlu, Aybike
Levilactobacillus brevis AK19B strain demonstrated promising probiotic potential as a hydrophilic lactic acid bacterium with valuable phytase activity. It also exhibited strong antioxidant properties and maintained viability in bile salt-rich environments, suggesting robustness in gastrointestinal conditions. To maximize gamma-aminobutyric acid (GABA) production, a Box–Behnken design was applied to assess the effects of monosodium glutamate (MSG) concentration, NaCl concentration, pH, and temperature. The results showed that these factors significantly influenced GABA synthesis, with their interactions well-modeled by a quadratic response surface. Optimal conditions were identified as pH 5.136, MSG concentration of 297 mM, temperature at 26 °C, and NaCl concentration of 2.82%, under which the predicted GABA production reached 43.85 mg/mL. These findings highlight AK19B as a promising candidate for functional food applications and industrial GABA biosynthesis.
- MakaleAçık Erişim2026Scopus SJRQ2Bioinspired Sternal Implant Design for Generic Anatomical Reconstruction: An In Silico Framework for Material Selection and Biomechanical Validation
Kutbay, Işıl; Gerdan, Zeynep; Çolak, Murat; Tabak, Yasemin; Şensoy, Abdullah Tahir
The sternum protects the intrathoracic organs and contributes to chest wall mechanics, which makes reconstruction after tumor resection, trauma, or infection a demanding biomechanical problem. This study presents an in silico workflow for preselecting materials for sternal implants before physical prototyping. After a virtual resection, an anatomically conformal implant was designed and candidate biomaterials were screened in CES Selector using density, elastic modulus, fatigue strength, fracture toughness, toxicity, medical grade suitability, and MRI safety. A representative subset of the screened candidates was then compared by finite element modeling in terms of stress transfer and deformation. Seventeen candidates met the screening criteria. Ti-13Nb-13Zr showed an elastic modulus of about 80 GPa, and the titanium-based candidates showed deformation values of about 0.96 to 1.03 mm, whereas GF PEEK reached about 1.74 mm. The stress shielding index also showed that titanium-based materials remained on the implant-dominant side, while polymer-based materials shifted stress transfer toward bone. Taken together, the findings suggest that Ti-13Nb-13Zr offers the best overall balance for load-bearing sternal reconstruction, whereas PEEK-based systems may be more suitable within the present model for hybrid or adjunct designs. The proposed workflow can support early implant planning and guide future experimental and clinical studies.