DSpace @ Recep Tayyip Erdoğan Üniversitesi Akademik Açık Arşiv
Not a member yet
12203 research outputs found
Sort by
Synergistic effects of ionic liquid and redox species for improved aqueous-based Zn-ion capacitor
No full textAs the demand for high-performance energy storage devices grow, aqueous zinc-ion hybrid capacitors (ZICs) have gained significant attention for their ability to combine the high energy density of zinc-ion batteries (ZIBs) with the high-power density of supercapacitors (SCs). However, their application is limited by poor stability caused by zinc dendrite growth from uneven Zn deposition/stripping. Ionic liquids (ILs) and redox species in ZICs are an emerging area of research focused on improving the performance and efficiency of energy storage devices. The combination of IL and redox species can enhance the charge storage capacity, stability, and cycling performance of ZICs, potentially providing high energy and power densities with long-term durability. Herein, synergistic effects of 1-Ethyl-3-methylimidazoliumtriflate (EMImTfO) and 1-Ethyl-3-methylimidazolium iodide (EMImI) were investigated on aqueous electrolyte of Zn(TfO)2. The Zn/graphene ZIC delivers capacities of 82 and 96 mAh g−1 at 0.5 A g−1 in Zn(TfO)2 and Zn(TfO)2/ EMImTfO electrolytes, respectively, while the redox additive of EMImI boosts the capacity to 182 mAh g−1 under the same conditions. Moreover, even at a high current density of 5 A g−1, the capacity was found to be 100 mAh g−1, indicating improved rate capability. These findings offer a promising strategy for the development of redox-active electrolytes tailored for next-generation sustainable energy storage systems
Evaluation of serum and urine GDF-15 levels in patients with ureteral stones
No full textBackground: Acute renal colic, most often caused by ureteral stones, is a common cause of emergency admissions. While non-contrast computed tomography (CT) is the diagnostic gold standard, its use is limited by radiation exposure, cost, and accessibility. Growth Differentiation Factor-15 (GDF-15) is a stress-induced cytokine elevated in various acute pathologies. This study investigated the diagnostic and predictive value of serum and urine GDF-15 in patients with acute renal colic due to ureteral stones. Methods: In this prospective observational study (January 2024–March 2025), 76 patients presenting with sudden-onset flank pain were enrolled. A total of 41 patients with radiologically confirmed ureteral stones formed the stone-positive group, and 35 patients without urinary pathology served as controls. Serum and urine GDF-15 levels were measured by ELISA, along with routine laboratory tests. CT was used to assess stone characteristics, hydronephrosis grade, and ureteral wall thickness. Group comparisons were performed using the Mann–Whitney U test, correlations with Spearman’s test, and diagnostic performance with ROC analysis. Results: Both serum and urine GDF-15 levels were significantly higher in stone-positive patients (p 1 mm and proximal stones. No significant association was found with spontaneous stone passage (p > 0.05). Conclusions: Urine GDF-15 shows promising diagnostic accuracy for ureteral stones and may serve as a non-invasive adjunctive tool when imaging is limited. While associated with inflammation and stone location, it did not predict spontaneous stone passage. These findings support its potential as a clinical biomarker, though further large-scale validation is required
A novel decision-making approach based on regret theory under bipolar Z-number information
No full textThis study introduces a new set theory called bipolar fuzzy Z-number set that makes it possible to express an information both bipolar from positive and negative perspectives, and with the reliability of it. Then, besides basic set-theoretical operations and the score function, a new distance measure is also presented. Furthermore, a decision method integrating with regret theory is proposed. Additionally, to compute the weights of criteria under bipolar Z-number information, a method in which objective and subjective criterion weights are considered simultaneously is introduced by combining the weights found by SWARA and MEREC. Also, a model with standard deviation is developed to obtain decision maker’s weights. Finally, the problem of selection of paper raw material is handled to demonstrate stability and advantage of the proposed method
Çay Bitkisinde Bazı Besin Element Eksikleri ve Toksisite Belirtileri
No full textÇay Tarımı Dersi 12. Hafta Ders Notu Konusu : Çay Bitkisinde Bazı Besin Element Eksikleri ve Toksisite Belirtiler
Association between mitral annular calcification and ventricular tachycardia in patients with reduced and mildly reduced ejection fraction
No full textObjective: This study aimed to evaluate the association between mitral annular calcification (MAC) and ventricular tachycardia (VT) in patients with reduced and mildly reduced ejection fraction and to identify independent predictors of VT. Materials and Methods: A total of 143 patients with heart failure and left ventricular ejection fraction (LVEF) under 50% were included in this retrospective cross-sectional study. Patients were classified into two groups according to the presence of VT. Clinical, biochemical, and echocardiographic variables were compared between groups. Independent predictors of VT were identified using multivariable logistic regression analysis. Results: MAC was significantly more prevalent in the VT group compared with controls (43.6% vs. 17.4%, p < 0.001) and was the strongest independent predictor of VT (OR: 2.74; 95% CI: 1.13–6.65; p = 0.026). Higher inflammatory activity, lower serum albumin levels, increased left atrial volume, renal dysfunction, and elevated diastolic filling pressures were also associated with VT. Conclusions: MAC is a strong and independent predictor of ventricular tachycardia in patients with reduced and mildly reduced ejection fraction. Incorporating MAC into the overall arrhythmic risk profile alongside inflammatory, metabolic, and structural parameters may improve risk stratification in this population
Non-specific immune responses of rainbow trout, Oncorhynchus mykiss, to dietary cottonseed meal and probiotic Saccharomyces cerevisiae (PTCC 5052) inclusion
No full textThe aim of this study was to evaluate the effects of adding cottonseed meal and yeast, Saccharomyces cerevisiae (strain PTCC 5052), to diet on humoral and mucosal immunological responses, as well as bactericidal activities in juvenile rainbow trout. Fish (approximately 31 g) were distributed into 12 tanks across three treatments with four replications. Each treatment was fed with either a control diet without cottonseed meal (CTL), a diet containing 150 g/kg cottonseed meal (CSM), or a diet containing 150 g/kg cottonseed meal plus 1 × 108 CFU/g of yeast (CSMY), for 8 weeks. The results indicated no significant effects of dietary cottonseed meal on plasma lysozyme, alternative complement, and bactericidal activity against Yersinia ruckeri and Streptococcus iniae. Dietary cottonseed meal also showed no significant effects on skin mucus lysozyme, immunoglobulin, and bactericidal activity against Aeromonas hydrophila, gut lysozyme, alternative complement activities and immunoglobulin level. However, the CSMY treatment significantly increased the above-mentioned parameters in plasma, skin mucus and gut. Skin mucus bactericidal activity against S. iniae significantly increased in fish fed diets containing cottonseed meal and cottonseed meal plus yeast, compared to the control group. In conclusion, adding 150 g/kg cottonseed meal to the diet has no negative effects on fish immunological parameters. Furthermore, adding 1 × 108 CFU/g of yeast improves various humoral, mucosal, and intestinal immunological parameters, potentially increasing fish resistance against opportunistic pathogens
Machine learning-based prediction and feature attribution analysis of contrast-associated acute kidney injury in patients with acute myocardial infarction
No full textBackground and Objectives: Contrast-associated acute kidney injury (CA-AKI) is a frequent and clinically significant complication in patients with acute myocardial infarction (AMI) undergoing coronary angiography. Early and accurate risk stratification remains challenging with conventional models that rely on linear assumptions and limited variable integration. This study aimed to evaluate and compare the predictive performance of multiple machine learning (ML) algorithms with traditional logistic regression and the Mehran risk score for CA-AKI prediction and to explore key determinants of risk using explainable artificial intelligence methods. Materials and Methods: This retrospective, single-center study included 1741 patients with AMI who underwent coronary angiography. CA-AKI was defined according to KDIGO criteria. Multiple ML models, including gradient boosting machine (GBM), random forest (RF), XGBoost, support vector machine, elastic net, and standard logistic regression were developed using routinely available clinical and laboratory variables. A weighted ensemble model combining the best-performing algorithms was constructed. Model discrimination was assessed using area under the receiver operating characteristic curve (AUC), along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Model interpretability was evaluated using feature importance and SHapley Additive exPlanations (SHAP). Results: CA-AKI occurred in 356 patients (20.4%). In multivariable logistic regression, lower left ventricular ejection fraction, higher contrast volume, lower sodium, lower hemoglobin, and higher neutrophil-to-lymphocyte ratio (NLR) were independently associated with CA-AKI. Among ML approaches, the weighted ensemble model demonstrated the highest discriminative performance (AUC 0.721), outperforming logistic regression and the Mehran risk score (AUC 0.608). Importantly, the ensemble model achieved a consistently high NPV (0.942), enabling reliable identification of low-risk patients. Explainability analyses revealed that inflammatory markers, particularly NLR, along with sodium, uric acid, baseline renal indices, and contrast burden, were the most influential predictors across models. Conclusions: In patients with AMI undergoing coronary angiography, interpretable ML models, especially ensemble and gradient boosting-based approaches, provide superior risk stratification for CA-AKI compared with conventional methods. The high negative predictive value highlights their clinical utility in safely identifying low-risk patients and supporting individualized, risk-adapted preventive strategies
Prediction of urban water consumption using AI-based multiple modeling based on deep learning
No full textAccurate prediction of urban water consumption supports sustainable resource management and aligns with the United Nations Sustainable Development Goals. This study applies the inclusive multiple modeling using deep learning models, implemented in MATLAB, to improve prediction robustness in the Mahabad water distribution network in West Azerbaijan Province, Iran. At the first level, Long Short-Term Memory and Group Method of Data Handling models predicted water consumption using precipitation, temperature, and population data. The second level employed a deep neural network, taking both inputs and outputs from the first-level models. Model performance was evaluated using the Nash–Sutcliffe efficiency, root mean square error, residual homoscedasticity, Taylor diagram, and peak demand analysis. The results indicate that the inclusive multiple modeling framework enhances prediction robustness by leveraging model strengths and reducing individual errors. The deep neural network at the second level outperformed other models, achieving an overall Nash–Sutcliffe efficiency of 0.956 and root mean square error of 0.046, compared to Long Short-Term Memory (Nash–Sutcliffe efficiency = 0.887, root mean square error = 0.074) and Group Method of Data Handling (Nash–Sutcliffe efficiency = 0.875, root mean square error = 0.078). Residual analysis confirmed stable error distribution for the deep neural network and Group Method of Data Handling, while Long Short-Term Memory showed heteroscedasticity. The Taylor diagram confirmed higher correlation and better standard deviation match for the deep neural network. The approach also accurately predicted extreme demand peaks, demonstrating its potential as a robust and transferable tool for urban water management under changing climatic and demographic conditions
The effect of behavioral factors related to cashlessness on the perception of green finance
No full textThis study aims to examine the effects of behavioral factors shaping cashless payment behaviors in Turkey on green finance perception. The study focuses on subconscious factors such as functional benefit, economic benefit, psychological benefit, attitude toward technology, and perceived behavioral control. Data were collected from 520 participants aged 18 and older via an online survey and analyzed using Structural Equation Modeling (SEM). The findings reveal that functional benefit, perceived behavioral control, and attitudes toward technology significantly and positively influence perceptions of green finance, while economic and psychological benefits do not have a significant effect on behavioral control. The results reveal that behavioral factors such as ease of use and attitudes toward technology play a more significant role in individuals’ adoption of cashless payment systems than rational economic gain expectations. The study contributes to the literature by showing that cashless payment behaviors contribute to the development of sustainable finance and environmental awareness. It also provides practical recommendations for policymakers and financial institutions on improving digital payment infrastructure and user experience to promote green finance
Turkish real-life atrial fibrillation in clinical practice: 2-year clinical outcomes of the TRAFFIC study
No full textBackground: Atrial fibrillation (AF) is a major public health issue associated with thromboembolism and mortality. Real-world data from Tü rkiye are limited despite expanding use of non-vitamin K antagonist oral anticoagulants (NOACs). The Turkish Real Life Atrial Fibrillation in Clinical Practice (TRAFFIC) study aimed to characterize the demographic features, risk profiles, treatment patterns, and 2-year clinical outcomes of patients with non-valvular AF (NVAF) in Tü rkiye. Methods: TRAFFIC was a national, prospective, multicenter, observational registry enrolling 1659 NVAF patients from 36 centers with 6-monthly follow-up for 24 months. Baseline data included demographics, comorbidities, CHA2DS2-VASc, HAS-BLED, AF subtype, European Heart Rhythm Association (EHRA) score, and antithrombotictherapy. Outcomes were ischemic stroke/systemic embolism (SE), major bleeding, and all-cause mortality. Predictors of mortality were evaluated using adjusted Cox regression, and associations of risk scores were explored using univariate Cox models with restricted cubic splines. Results: Median age was 70 years, 48% female, with intermediate CHA2DS2-VASc (most 2-5) and low-to-intermediate HAS-BLED scores (most 0-2). Permanent AF wasthe most common subtype (48%). Antithrombotic therapy largely reflected risk profiles, with NOACs being the dominant treatment (65%). Over 2 years, all-cause mortality was 8.9%, ischemic stroke/SE 2.4%, and major bleeding 1.3%. In adjusted analysis, age, congestive heart failure, and diabetes mellitus were independent predictors of mortality. Both CHA2DS2-VASc and HAS-BLED scores showed threshold effects for mortality and thromboembolic risk but notfor bleeding. Conclusion: TRAFFIC provides contemporary Turkish NVAF data, showing lower event rates than historical cohorts. Outcomes are comparable with international registries; persistent mortality burden highlights the needforAF care beyond anticoagulation