Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Uzun, Sevda(67)
- Merdan, Mehmet(59)
- Duran, Celal(49)
- Ozdes, Duygu(46)
- Kaygusuz, Abdullah(45)
- Demir, Selim(37)
- Turan, Ibrahim(37)
- Karsli, Orhan(36)
- Öztürk, Serkan(35)
- Dokuz, Abdurrahman(34)
- Araz, Onur(32)
- Kandemir, Raif(31)
- Kulakaç, Nurşen(29)
- Sipahi, Ferkan(29)
- Arslan, Mehmet(27)
- Baltaci, Cemalettin(27)
- Baltacı, Cemalettin(27)
- Kurt, Kemal(27)
- Topal, Fevzi(27)
- Gücer, Mehmet Ali(25)
- Peker, Iskender(25)
- Aydin, Faruk(24)
- Bayrakdaroglu, Serdar(24)
- Maden, Nafiz(24)
- Ozturk, Samet(24)
- Vural, Alaaddin(24)
- Yolcu, Halil(24)
- Yücel, Cem(24)
- Aliyazicioglu, Yuksel(23)
- Kankal, Murat(23)
- Reis, Hatice Catal(23)
- Ürkek, Bayram(23)
- Günen, Mehmet Akif(22)
- Kurnaz, Muammer(22)
- Topal, Meryem(22)
- Karimullah, Suud Sarim(21)
- Uysal, Ibrahim(21)
- Ashyralyyev, Charyyar(20)
- Aydin, Aydanur(20)
- Bayraktar, Alemdar(20)
Yayınlar
- MakaleYalnız künye2026Scopus SJRQ2Integrating Artificial Intelligence Into Nursing Care Planning: A Comparative Evaluation of Psychosocial Care for Patients With a Stoma
Kulakaç, Nurşen; Tuncer, Metin
This methodological study evaluated and compared the content quality and appropriateness of nursing care plans developed by nurses and artificial intelligence (AI) for addressing the psychosocial problems of patients with stomas. Two nursing care plans addressing the psychosocial needs of a patient with a stoma were developed: 1 generated by nurses and 1 generated by an AI system. The content quality and appropriateness of the care plans were independently evaluated by 12 experts, including nursing academics and clinical nurses, using the DISCERN measurement tool. Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC). The mean DISCERN score of the nurse-generated care plan was significantly higher than that of the AI-generated care plan (P=.008). Inter-rater agreement demonstrated good agreement. The findings indicate that nurse-generated care plans demonstrate superior content quality and overall integrity compared to AI-generated plans. AI-generated care plans exhibit potential as supportive tools in nursing care planning.
- MakaleYayıncıda açık erişim2026Scopus SJRQ3The Effect of Music-Repeating Robotic Parrot Therapy and Different Instrumental Live Music Therapies on Anxiety and Fatigue Levels in Hemodialysis Patients: A Randomized Controlled Trial
Demirağ, Hatice; Cin, Aynur; Aktepe Coşar, Dilan; Uzun, Sevda; Türkmen, Osman Oğulcan
Aim: This study aimed to evaluate the effects of three different music-based interventions on anxiety and fatigue levels in patients undergoing hemodialysis, compared to a concurrent control group receiving standard care. Methods: This randomized controlled trial involved 64 patients receiving maintenance hemodialysis. They were randomly assigned to four equal groups (n = 16 each): live music, live ney sound therapy (using a traditional reed flute known for its soothing, breath-like tones), a music-repeating robotic parrot therapy (an interactive robotic parrot that repeats music or sounds provided by the patient), or a control group (no intervention). The experimental groups received 30-min sessions 3 days per week for 2 months. The “Beck Anxiety Scale” and “Fatigue Severity Scale” were used to measure anxiety and fatigue, respectively. Assessments were performed before the intervention, immediately after the 2-month intervention period, and at 2 months after the end of the intervention period. Results: Before the intervention, there were no significant differences in anxiety or fatigue scores across the four groups (p > 0.05). After the interventions, all three groups exposed to music therapy showed significant reductions in both anxiety and fatigue scores compared to the control group (p < 0.001). Conclusion: All three therapies were effective in managing anxiety and fatigue symptoms in hemodialysis patients. Among them, music-repeating robotic parrot therapy demonstrated the most superior and longest-lasting effects in reducing both anxiety and fatigue levels. Trial Registration: ClinicalTrials.gov identifier: NCT07238374.
- MakaleAçık Erişim2026Scopus SJRQ1Place attachment of earthquake victims residing in Antakya (Hatay) after the Kahramanmaraş earthquakes on February 6, 2023
Fakıoğlu, Merve; Mızrak, Sefa
People affected economically, environmentally, physically, and psychologically by the earthquake may be reluctant to move to safer and more comfortable places for various reasons. Investigating the factors that influence people’s place attachment after devastating earthquakes will enable the creation of sustainable communities and cultures. This study examined the impact of earthquake experience, preparedness, place-protective behavior, social support, insecurity, price increase, the surrounding environment, self-efficacy, and risk perception on place attachment among earthquake victims in Antakya following the February 6, 2023, Kahramanmaraş earthquakes. Data were collected via surveys from 453 earthquake victims in the Antakya region of Hatay (Türkiye) province, one year after the earthquakes. Eleven ordered logistic regressions revealed the strength and direction of the factors affecting place attachment. Earthquake victims who were injured, lost relatives, were not evacuated, and were not disturbed by the rubble had higher place attachment. Earthquake preparedness, formal social support, post-earthquake insecurity, post-earthquake price increase, self-efficacy, and earthquake risk perception did not affect place attachment. Sustainability and place-protective behavior increased place attachment. Furthermore, place attachment was higher among men and those who lived longer in Antakya. This study is expected to guide managers, disaster experts, and non-governmental organizations who develop strategies to increase people's place attachment in disaster-affected regions.
- MakaleYalnız künye2026Scopus SJRQ1Effects of earthquake frequency content and isolation thickness on seismic behavior of a structure system resting on geotechnical seismic isolation layer
Ozturk, Kasif Furkan; Cakir, Tufan; Araz, Onur
Geotechnical seismic isolation (GSI) systems aim to partially dissipate the energy before it reaches the foundation of the structure system and are therefore regarded as an important alternative to structural seismic isolation systems. In addition, the use of petroleum-derived waste materials in the GSI systems fills a significant gap in the field of waste recovery. The present study investigates the changes in the dynamic responses of a 5-story building system with and without GSI, using a set of earthquakes classified according to their frequency content. For this purpose, parametric analyses were performed using analytical methods, considering three different earthquake frequency contents and six different GSI thicknesses. Furthermore, an approach for selecting the GSI thickness was suggested. The results generally show that, for structures founded on both natural soil (NS) and GSI systems, the earthquakes with low frequency content produce responses greater than the average responses calculated for all the investigated earthquakes. Although increasing the GSI thickness has a significant effect on attenuating structural responses, increasing this thickness beyond 3 m does not lead to a relatively significant reduction in the dynamic responses of the investigated structural system. Therefore, considering the structural system, NS, rubber-soil mixtures and earthquakes examined in this study, selecting a GSI thickness in the range of 2–3 m (inclusive) may be regarded as a practical approach for mitigating the dynamic responses of the structure since further increments in thickness beyond this range provide only progressively limited improvements in structural responses. Consequently, this research may provide meaningful data for the development of vibration control strategies to improve seismic resilience of structures, and the results are expected to be useful for better understanding of the GSI applications.
- MakaleYalnız künye2026Scopus SJRQ2Reflections of Employees' Digital Addiction Experiences on Workplace Distraction, Safety Perception, and Collective Responsibility: A Phenomenological Study
Doğan, Onur
This study aims to examine how digital addiction based experiences of employees influence attention processes, safety awareness, and collective responsibility in the workplace. It particularly seeks to identify challenges in digitally intensive environments regarding cognitive load, behavioral reflexes, and institutional risk communication. The research employed a phenomenological design with semi- structured interviews conducted with 19 employees from various sectors across Turkey. Criterion sampling was used, and interviews continued until data saturation was reached. All interviews were audio recorded, transcribed verbatim, and analyzed using Colaizzi's (1978) seven step method. Reporting followed the COREQ checklist to ensure rigor and transparency. Thematic analysis revealed three main themes: Cognitive Load and Deterioration in Safety Perception, Behavioral Disconnection and Erosion in Safety Attitudes, and Organizational Silence and Collective Collapse in Risk Awareness. These findings show that digital addiction negatively affects attention, reflexes, safety behaviors, and institutional reporting processes. Digitalization influences not only work efficiency but also employees' safety behaviors and organizational risk communication. Constant digital stimuli cause distraction, disrupt safety practices, and weaken collective responsibility. The study provides insights into how digital addiction transforms workplace safety culture and recommends comprehensive prevention strategies for employers, managers, and policymakers.
AtıfScopus2WoS1OpenAlex1
İncele - MakaleAçık Erişim2026Scopus SJRQ1A comparative interventional study on the effectiveness of acceptance and commitment therapy, emotion-focused therapy, and attachment-based therapy in improving emotion regulation among adolescents with digital game addiction
Güngör, Sunay
Background: Digital game addiction is a rapidly developing social and mental health concern that is closely related to emotion regulation difficulties. The present study aimed to compare the effectiveness of acceptance and commitment therapy (ACT), emotion-focused therapy (EFT), and attachment-based therapy (ABT) in improving emotion regulation among adolescents with digital game addiction. Methods: The present study was an interventional study employing a pretest–posttest design and a control group. The sample included 100 adolescents aged 13–18 years who were diagnosed with gaming disorder according to the 11th Revision of the International Classification of Diseases (ICD-11) criteria. Participants were selected using purposive sampling and were assigned to four groups of 25 individuals each, including three experimental groups and one wait-list control group. The 7-item Digital Game Addiction Scale (DGAS-7) and the Regulation of Emotions Questionnaire among Adolescents (REQ-A) were used to gather data. ACT and EFT were administered in eight sessions, whereas ABT was delivered in 10 sessions. Data were analyzed using repeated measures ANOVA in SPSS. Results: It was found that the three therapeutic strategies produced a significant increase in functional emotion regulation scores (internal and external) and a significant decrease in dysfunctional emotion regulation scores compared to the control group (p < 0.001). However, the effect sizes for the ACT and EFT groups were very large compared to the ABT group. There was no significant difference between ACT and EFT; nevertheless, both interventions were significantly more effective compared to ABT at both the posttest and follow-up stages. Conclusion: ACT and EFT are more effective interventions for improving emotion regulation among adolescents with digital game addiction because of their emphasis on psychological flexibility and emotionally profound processing. Although ABT was found to be moderately effective, it can be used as an adjunctive therapy, particularly in cases involving relational problems.
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İncele - MakaleYalnız künye2026Scopus SJRQ2Germination as a Pre-isolation Modification Method for Common Buckwheat Protein Isolate
Gülbahar, Esma; Tunç, Merve Tuğçe; Akdoğan, Arda; Baltacı, Cemalettin; Sarıcaoğlu, Furkan Türker; Parlak, Mahmut Ekrem; Aydın, Engin; Odabaş, Halil İbrahim
This study investigated germination as a pre-isolation modification method to enhance the techno-functional and nutritional properties of common buckwheat (Fagopyrum esculentum Moench) protein. Germination was conducted at two different temperatures (20 and 30 °C) for 24, 48, and 72 h, and isolated proteins were analyzed for their chemical, structural, and functional properties. Results showed that the highest protein yield was achieved at 30 °C for 24 h, though durations exceeding 48 h reduced purity. SDS-PAGE and FTIR analyses confirmed that endogenous protease activity degraded high-molecular-weight proteins and increasing random coil proportions, indicating partial unfolding. SEM imaging revealed that germination transformed the dense protein surface into a porous morphology. Increasing germination time and temperature led to decreased lightness (L*) and hue angle, along with moderate increases in redness (a*), indicating notable changes in the visual appearance of the protein isolates. DSC results showed a decrease in thermal stability (Td) due to structural changes. In addition, germination significantly improved solubility, emulsifying activity, foaming capacity, and water/oil absorption, particularly at the 24 h of germination. Most notably, germination at 30 °C for 24 h increased in vitro protein digestibility from 75.67 to 82.19%. The results show that the germination is a promising approach to enhance the functionality and digestibility of buckwheat protein for potential application in food industry.
- MakaleYalnız künye2026Scopus SJRQ2Utilizing Pollutant Waste Cheese Whey into Low-cost Broth for Bacterial Growth and Protein Production
Alkowlani, Amir Towfiq Hasan; Beldüz, Ali Osman; Çanakçi, Sabriye; Bektaş, Kadriye İnan; Ay, Fulya; Özyurt, Taner; Yıldız, Oktay
Cheese whey (CW), an abundant and nutrient-rich byproduct of cheese production, accounts for approximately 85% of milk volume and is generated globally in quantities reaching 145–200 million tons annually. Despite its biochemical complexity and high organic load, CW remains underutilized in microbial biotechnology. Its pollutant potential is up to 100 times higher than domestic sewage due to extreme biological oxygen demand (BOD), chemical oxygen demand (COD), high nutrient content, and acidity. Untreated discharge causes severe oxygen depletion, damages aquatic ecosystems, degrades soil, and contributes to climate change. This study evaluates CW as a sustainable and cost-effective alternative to synthetic bacterial growth media. Ten bacterial strains were cultured for 17 h in different forms of CW fresh, aged (up to two years), and dried and compared with standard Luria Bertani (LB) medium. Bacterial growth was assessed using optical density at 600 nm (OD₆₀₀), and protein yields were measured in µg/µL. CW supported growth ranged from OD₆₀₀ 2.36–4.46, comparable to 2.53–4.16 in LB, with protein yields between 0.19 and 0.43 µg/µL versus 0.18–0.44 µg/µL in LB. These findings demonstrate that CW can effectively support bacterial proliferation and protein production, offering a novel valorization route for a major agro-industrial pollutant while reducing environmental harm and fermentation costs.
- MakaleAçık Erişim2026Preliminary Study on the Characterization of a Novel Polysaccharide from Enterococcus faecium SL60 Isolated from Siyez Sourdough; [Siyez Ekşi Mayasından İzole Edilen Enterococcus faecium SL60'tan Elde Edilen Yeni Bir Polisakkaritin Karakterizasyonu Üzerine Ön Çalışma]
Genç, Berna
This study reports the production, structural characterization, and preliminary cytotoxicity evaluation of an extracellular polysaccharide (EPS) synthesized by Enterococcus faecium SL60, a strain isolated from traditional Siyez (einkorn) sourdough. Analytical techniques including GC-MS, FTIR, XRD, zeta potential, and elemental analysis were applied to characterize the EPS. The purified EPS was found to be a water-soluble, negatively charged, amorphous heteropolysaccharide predominantly composed of mannose and glucose, with minor proportions of rhamnose, arabinose, galactose, and xylose. The total carbohydrate yield was 7.6 g/L. FTIR confirmed the presence of typical carbohydrate functional groups, while zeta potential analysis indicated a moderate surface charge (−9.71 mV), implying colloidal stability. Cytotoxicity evaluation using the CCD-1079Sk fibroblast cell line showed no significant toxicity at concentrations up to 500 µg/mL. These findings highlight the potential of SL60-derived EPS as a safe bioactive compound with possible applications in functional food development and health-related products.
- Editöre MektupAçık Erişim2026Scopus SJRQ4Reply to the Letter to the Editor: Assessing the Predictive Value of Kolmogorov–Arnold Networks for the No–Reflow Phenomenon in ST–Segment Elevation Myocardial Infarction: A Comparative Machine Learning Study; [Editöre Mektup Yanıtı: ST Segment Yükselmesi Miyokard Enfarktüsünde No–Reflow Fenomeni için Kolmogorov–Arnold Ağlarının Öngörü Değerinin Değerlendirilmesi: Karşılaştırmalı Makine Öğrenimi Çalışması]
Taşolar, Hakan; Bayramoğlu, Adil; Günen, Mehmet Akif; Levent, Sümeyye; Güral, Yunus; Halisdemir, Nurhan
- MakaleAçık Erişim2026Scopus SJRQ4Association Between Health Literacy and Mediterranean Diet Adherence Among Adult Women in Türkiye
Çam, Alper Veli; Şik, Elif
Objective: Health literacy is widely recognized as an important determinant of informed health-related decision-making. However, evidence regarding whether health literacy translates into healthy eating behaviors, particularly adherence to the Mediterranean diet, remains limited. This study aimed to examine the association between health literacy levels and adherence to the Mediterranean diet among adult women living in Türkiye. Methods: This cross-sectional study was conducted with 400 women aged 18 years and older residing in Türkiye. Data were collected using a sociodemographic information form, the Turkish Health Literacy Scale–Short Form, and the Mediterranean Diet Quality Index (KIDMED). Health literacy levels were categorized as inadequate, problematic–limited, adequate, and excellent, while Mediterranean diet adherence was classified as low, moderate, or optimal. Ordinal logistic regression analysis was performed to examine the association between health literacy and adherence to the Mediterranean diet, adjusting for sociodemographic variables (age, education, income, and occupation). Findings: Among the participants, 25.3% had inadequate health literacy, 31.8% had problematic–limited health literacy, 34.0% had adequate health literacy, and 9.0% had excellent health literacy. Mediterranean diet adherence was low in 34.5% of participants, moderate in 52.3%, and optimal in 13.3%. The ordinal logistic regression analysis revealed that, after adjusting for sociodemographic variables, one dimension of health literacy (Cognitive Health Literacy) was significantly associated with Mediterranean diet adherence, whereas no significant association was observed for the other dimension. Conclusion: The findings suggest that certain dimensions of health literacy may be associated with adherence to the Mediterranean diet when sociodemographic factors are taken into account. These results highlight the importance of considering both individual knowledge and contextual factors in understanding dietary behavior. Future research should further explore the role of nutrition-specific literacy and structural determinants in shaping healthy eating practices.
- MakaleAçık Erişim2026Scopus SJRQ1A leakage-free analytics framework for subject-wise electrocardiogram-based sleep apnea detection using machine learning
Melek, Negin
Sleep apnea is associated with impaired sleep quality and elevated cardiovascular risk, creating a need for reliable automated screening approaches. Although electrocardiogram (ECG)-based detection methods have shown promising results, reported performance values can be affected by evaluation strategies that allow information leakage between training and testing data. This study investigates ECG-based sleep apnea detection under strict subject-wise conditions using the PhysioNet Apnea-ECG database. Thirty-five annotated single-lead ECG recordings sampled at 100 Hz were divided into non-overlapping 60-s segments according to minute-wise apnea labels. Statistical ECG descriptors and time-domain heart rate variability features were extracted and evaluated using k-nearest neighbors (KNN), support vector machine (SVM), and Random Forest (RF) classifiers. In addition, a lightweight one-dimensional convolutional neural network (1D-CNN) was trained directly on filtered ECG segments. Repeated holdout validation with subject-level train-test separation was adopted, and normalization parameters were estimated exclusively from training data in each repetition to prevent information leakage. Model performance was assessed using accuracy, balanced accuracy, precision, recall, specificity, F1-score, ROC-AUC, Cohen's kappa, and Golden Distance (GD). In the representative subject-wise evaluation, Random Forest achieved the most balanced performance, yielding the highest balanced accuracy (0.70) together with the lowest GD (0.56). Across the repeated subject-wise holdout experiments, SVM achieved the strongest average overall performance, producing the highest mean accuracy (0.72), balanced accuracy (0.69), ROC-AUC (0.77), and Cohen's kappa (0.39), and the lowest mean GD value (0.58). The lightweight 1D-CNN converged stably during training, but its performance on unseen subjects remained lower than that of the handcrafted feature-based models.
- MakaleAçık Erişim2026Scopus SJRQ1The Effect of Fatigue During Search and Rescue Efforts in Debris on the Quality of Cardiopulmonary Resuscitation
Çavuş, Kadir; Tiryaki, Oğuzhan; Tiryaki, Elif; Çelik, Suat; Saçar, Hüseyin Bora
Objectives: Cardiopulmonary resuscitation (CPR), which is used in cases of life-threatening cardiopulmonary arrest, is a physically exhausting procedure. Adding to that, sometimes, even before performing CPR, interventions to rescue the injured person from a challenging environment have caused significant fatigue. In this study, taking a novel research approach, we generated a scenario of fatigue during a rescue from earthquake debris and aimed to measure the effect of that fatigue on the quality of CPR performed by paramedics. Methods: The research followed an experimental design with 2 groups (experimental/control) and 2 measurements (pretest/post-test). The study population was selected using power analysis. The sample, consisting of 84 paramedic students, was randomly divided into 42 control and 42 experimental participants. Current American Heart Association (AHA 2020) and European Resuscitation Council (ERC 2021) guidelines were strictly followed when performing CPR. In order to assess the accuracy of CPR, a General Doctor GD-CPR200S-A (2010 standard) simulator was utilized. The participants were fatigued by practicing the process of extracting and transporting earthquake victims from rubble. A personal information form with 20 questions and a CPR measurement form were used to obtain the data. Results: In the analysis performed to measure the differences between the CPR indicators for the control and experimental groups in the post-test and pretest, the difference in compression (control: 6.5 ± 50.1 and experimental: −10.3 ± 46.0) was not significant. Meanwhile, we found that the difference in ventilation (control: 0.3 ± 5.4 vs. experiment: 8.1 ± 4.6) and the difference in CPR completion times (control: 0.2 ± 1.2 vs. experiment: −0.7 ± 0.7) between the post-test and pretest were significant. Conclusions: There was no significant difference in correct compressions between the control and experimental groups, but there was a significant difference in ventilation and CPR completion times. For this reason, it is recommended to focus on the effect of fatigue on CPR quality, especially on the ventilation process. It is also recommended to include fatigue scenarios in CPR trainings.
AtıfScopus2WoS2OpenAlex2İncele - MakaleAçık Erişim2026Scopus SJRQ2Enhanced cationic dye adsorption using Stenotrophomonas indicatrix -modified MnFe2O4: performance evaluation and adsorption mechanism
Ozdes, Duygu; Dede, Alper; Turfan, Fatma Asya; Seker, Serdal
In this study, MnFe2O4 magnetic nanoparticles were modified with the Stenotrophomonas indicatrix bacteria for the first time in the literature to develop an effective and novel bio-adsorbent for the remediation of toxic cationic dyes, specifically Rhodamine 6G (R6G) and Methylene Blue (MB), from contaminated aqueous media. MnFe2O4 and bacteria-loaded MnFe2O4 were thoroughly characterized using FTIR, XRD, and SEM-EDX analyses. XRD results confirmed that the cubic spinel structure of MnFe2O4 was preserved after bacterial modification, while FTIR spectra evidenced the successful incorporation of biological functional groups, including –NH, –OH, amide, and phosphate moieties, onto the nanoparticle surface. Adsorption performance of the dyes was evaluated in terms of parameters including aqueous solution pH, equilibrium time, bacteria-loaded MnFe2O4 dosage, and initial R6G and MB concentration. After investigating the effect of salt on adsorption efficiency, the applicability of the proposed adsorbent was assessed using real water samples. According to the experimental results, the optimum pH and equilibrium period for the uptake of both dyes were determined to be 8.0 and 60 min, respectively. Kinetic analyses showed that the pseudo-second order kinetic model exhibited a superior fit in representing the adsorption rates of both dyes, while isotherm studies indicated that both Langmuir and Freundlich isotherm models fit the experimental data well. While the adsorption capacities of pure MnFe2O4 for MB and R6G were 19.5 mg g− 1 and 15.3 mg g− 1, respectively, these values reached 67.6 mg g− 1 and 59.5 mg g− 1 for the bacteria-loaded MnFe2O4. The obtained data revealed that bacterial modification significantly increased the adsorption capacity of MnFe2O4, suggesting that the developed bio-adsorbent stands out as an effective and promising candidate for the remediation of dye-contaminated effluents.
- MakaleYalnız künye2026Scopus SJRQ1Phytochemical profiling, biological activities, and molecular docking analysis of Dorycnium pentaphyllum subsp. haussknechtii
Ayna, Adnan; Hakan, Oguzhan; Yapıcı, İsmail; İzol, Ebubekir; Yılmaz, Mustafa Abdullah; Necip, Adem; Yapar, Yakup
BACKGROUND: In the current study, the biological activity fingerprint of Dorycnium pentaphyllum subsp. haussknechtii was investigated by an interdisciplinary approach through analysis of its phytochemical content, antioxidant capacity, enzyme inhibitory potential and molecular docking. RESULTS: The analysis of the phytochemical content by liquid chromatography–tandem mass spectrometry was characterized by the presence of flavan-3-ols, such as epicatechin, epigallocatechin, and catechin, as well as quercetin-derived flavonoids such as isoquercitrin. Analysis of the antioxidant capacity by 2,2-diphenyl-1-picrylhydrazyl, 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), ferric reducing antioxidant power, Fe3+–Fe2+ reducing and cupric reducing antioxidant capacity methods indicated the strong antioxidant capacity of the plant extract. In addition, the plant extract was able to modulate biological enzyme activity effectively, with IC50 values of 111.78 μg mL−1 for acetylcholinesterase, 22.07 μg mL−1 for butyrylcholinesterase, 1.39 μg mL−1 for α-glucosidase, 7.34 μg mL−1 for carbonic anhydrase I, and 17.41 μg mL−1 for carbonic anhydrase II. To have a better understanding of the molecular mechanism for the plant extract's properties, the major phenolic compounds present in the plant extract were investigated for molecular docking with their respective enzymes. As such, the results revealed a strong binding affinity and conformational stability, with strong hydrogen bonding and π–π interactions. CONCLUSION: Generally, the properties revealed by the plant extract indicate that the phenolic content, strong antioxidant properties, and enzyme inhibition activity of D. pentaphyllum subsp. haussknechtii have shown great potential for being a useful natural source for therapeutic lead compounds.
AtıfScopus1WoS1OpenAlex3İncele - MakaleYalnız künye2026Scopus SJRQ2Improving the hydrothermal reduced graphene oxide supercapacitor with heat-moisture-treated potato starch as binder
Daulay, Amru; Yassaroh, Yassaroh; Samada, Lukmanul Hakim; Amrulloh, M. Hanif; Kumaş, Cavit; Nariswari, Mentari Kirana; Putri, Devi Mariska; Chambioso, Yugo
Alternative binders that can be processed in water are still in the early stages of development for more efficient and sustainable supercapacitors. However, their electrochemical performance is still below average. This study uses heat-moisture-treated starch (starch HMT) as a binder for reduced graphene oxide (rGO) supercapacitors. Starch HMT has been fabricated at 120 °C. The XRD diffractogram exhibits peaks at 2θ angles of 15°, 17°, 22°, and 24°. The 2θ values indicate that potato starch is of type B. FTIR spectra show the formation of OH, C–O–C, C-O, C–O–C, and C-H bonds. SEM images show cracks in the morphology. Electrochemical testing shows a specific capacity of 230 F/g at a current density of 1 A/g. The charge–discharge stability of the electrodes at 5000 cycles is 90%. These results offer a framework for developing electrode materials and binders for supercapacitors.
- MakaleAçık Erişim2026Scopus SJRQ1Toward Sustainable Financial Behavior: The Role of Financial Socialization and FinTech in Enhancing Green Financial Literacy
Soydaş, Şafak Sönmez; Özbek, Adem; Çam, Alper Veli; Radulescu, Magdalena; Alofaysan, Hind
This study examines how financial literacy and financial socialization influence green financial literacy through the mediating role of FinTech usage within a sustainability-oriented framework. Green financial literacy is treated as a proxy for sustainable financial behavior rather than a direct behavioral measure. Based on financial socialization theory, the study proposes an integrated model linking individual competencies, social learning processes, and digital financial technologies. Data were collected from 539 individuals using a structured questionnaire and analyzed with structural equation modeling (SEM) employing a robust weighted least squares estimator. The findings show that financial literacy (β = 0.76, p < 0.001) and financial socialization (β = 0.23, p < 0.001) significantly increase FinTech usage. While financial literacy does not directly affect green financial literacy (β = −0.13, p > 0.05), FinTech usage has a strong positive impact (β = 0.88, p < 0.001), indicating a full mediation effect. Bootstrapping results further confirm the robustness of the mediation effects. Financial socialization also has a direct positive effect (β = 0.20, p < 0.01). The results suggest that financial knowledge alone is insufficient to support sustainability-oriented financial awareness unless it is supported by digital financial technologies. The study highlights the key role of FinTech in facilitating the integration of financial knowledge and social learning into sustainability-oriented financial awareness and decision-making and offers practical implications for promoting sustainable finance.
AtıfOpenAlex1İncele - MakaleYalnız künye2026Scopus SJRQ1Multi-scale deep fusion architecture for brain tumor detection via local-contextual feature integration
Reis, Hatice Catal; Turk, Veysel
Early detection of brain tumors is crucial for enhancing treatment strategies and improving clinical prognosis. However, current conventional methods limit diagnostic accuracy and slow down diagnostic processes due to subjective interpretations and dependence on experts. Deep learning-based approaches have the potential to enhance diagnostic accuracy and facilitate early diagnosis by mitigating current limitations in automatic learning techniques for feature extraction and modeling processes. In this study, an advanced deep learning model, Multi-Scale Local-Context Fusion Network (MSLCFNet), is proposed for automatic diagnosis of brain tumors within the scope of oncological diseases. The proposed model evaluates local spatial features, global patterns, and long-range contextual relationships holistically through multi-layered representations, enabling effective identification of complex structures of tumor morphology. This architecture has the potential to increase diagnostic consistency and minimize the risk of misdiagnosis by reducing inter-observer variability, especially in cases where limited contrast and unclear boundaries lead to diagnostic uncertainty. The proposed model achieved accuracy rates of 96.51% and 98.17% on two different MRI brain tumor datasets, respectively. Additionally, a hybrid model based on MSLCFNet+ResNet50 was proposed in the study to obtain high-level feature representations for brain tumor diagnosis. The model offers a significant improvement in the diagnosis process by reducing misdiagnosis rates to 98.86% and 99.50% accuracy. The proposed methods make a significant contribution to enhancing diagnostic accuracy in clinical decision support systems, as they enable the efficient processing of complex morphological structures.
- MakaleAçık Erişim2026Scopus SJRQ2Synthesis, Characterization, Biological Properties, and Sensor Applications of Novel Thiosemicarbazone Derivatives
Gezegen, Hayreddin; Özbek, Oguz; Topal, Meryem; Şahin, Hatice; Koçyiğit, Ümit M.
In this study, novel thiosemicarbazone derivative molecules (5a–j) were synthesized. The characterization of the synthesized molecules was carried out using 1H–, 13C–NMR, FT–IR, and Q–TOF spectroscopic techniques. Then, the inhibitory effect of these compounds was examined for human carbonic anhydrase I and II (hCA I, and II) and acetylcholinesterase (AChE). The hCA I II, and AChE were efficiently inhibited by synthesized thiosemicarbazone derivatives, with Ki values in the range of 0.293–1.666 µM for hCA I, 0.298–1.332 µM for hCA II, and 52.51–138.63 µM for AChE, respectively. Finally, the sensor properties of the synthesized thiosemicarbazone derivatives were investigated. The newly synthesized thiosemicarbazone derivative molecules exhibited high selectivity towards Pb2+ ions. The sensor prepared with 5a from these molecules had a Nernstian behavior of 28.8 ± 1.5 mV/decade and a low detection limit of 2.86 × 10−6 M in a wide concentration range of 1.0 × 10−1–1.0 × 10−5 M. The newly proposed sensor also had a wide pH working range (5.0–10.0), a very fast response time (5 s), and good reproducibility. The newly fabricated sensors were used as indicator electrodes for the potentiometric titration of Pb2+ ions with ethylenediaminetetraacetic acid (EDTA) and were successfully applied for the determination of Pb2+ ion content in different water samples.
AtıfScopus7WoS7OpenAlex5İncele - MakaleYalnız künye2026Scopus SJRQ3Burned area detection using Sentinel-1 and Sentinel-2 features and analysis of ensemble models with explainable AI
Günen, Mehmet Akif; Türk, Salih
Wildfires are serious environmental disasters that threaten ecosystem sustainability, reduce biodiversity, and disrupt the global carbon cycle. This study aimed to improve the accuracy and interpretability of burned area mapping in the Bodrum, Köyceğiz, and Marmaris districts of Muğla province, Türkiye, by integrating multi-sensor satellite data with ensemble machine learning and explainable artificial intelligence (XAI). Sentinel-1 and Sentinel-2 imagery were processed to derive 50 optical and radar-based features, which were classified using four ensemble methods: AdaBoost.M1, Random Forest, GentleBoost, and RUSBoost. The SHapley Additive exPlanations framework was applied to quantify the contribution of each feature and guide feature selection. Results indicated that Sentinel-2 spectral indices, particularly NBR, NBRSWIR, and bands B12 and B8, were the most decisive variables for burned area classification, while Sentinel-1 features contributed less strongly overall but the IVH_ratio variable emerged as the most informative radar-based indicator. Among the classifiers, AdaBoost.M1 achieved the most stable and accurate performance, Random Forest showed strong sensitivity to spectral variability, GentleBoost performed effectively with reduced feature sets, and RUSBoost addressed class imbalance but showed weaker spatial continuity. Importantly, classification accuracy saturated after approximately 15 features, demonstrating that high accuracy can be achieved with fewer inputs and lower computational demand. Moreover, McNemar’s statistical test confirmed that the observed differences among classifiers were significant, highlighting GentleBoost’s clear superiority over other methods. Overall, the integration of optical and radar data with XAI provides a transparent, efficient, and replicable framework for operational burned area monitoring in Mediterranean ecosystems.
AtıfScopus2WoS3OpenAlex2İncele
