Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Uzun, Sevda(7)
- Araz, Onur(6)
- Ozdes, Duygu(6)
- Duran, Celal(5)
- Şahin, Rıdvan(5)
- Kankal, Murat(4)
- Reis, Hatice Catal(4)
- Saydam Eker, Çiğdem(4)
- Şan, Murat(4)
- Alemdag, Selcuk(3)
- Anaç, Halil(3)
- Cakir, Tufan(3)
- Günen, Mehmet Akif(3)
- Ozturk, Kasif Furkan(3)
- Turk, Veysel(3)
- Çelik, Orkun(3)
- Öztürk, Serkan(3)
- Adali, Zafer(2)
- Alemdağ, Selçuk(2)
- Alkan, Hamdi(2)
- Aydin, Aydanur(2)
- Baltacı, Cemalettin(2)
- Bayram, Adem(2)
- Büyüksaraç, Aydın(2)
- Catal Reis, Hatice(2)
- Cebi, S.(2)
- Cevik Onar, S.(2)
- Dağ, Serhat(2)
- Ekici, Lutfiye(2)
- Gül, Abdulkadir(2)
- Kahraman, C.(2)
- Kahraman, Muhammet Mustafa(2)
- Karatas, Seyda Merve(2)
- Kaya, Ayberk(2)
- Khoshelham, Kourosh(2)
- Köseoğlu, Ali(2)
- Melek, Mesut(2)
- Merdan, Mehmet(2)
- Nacar, Sinan(2)
- Ozeken, Sengul Tugba(2)
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ2Computational prioritization of multi-target inhibitors: explainable QSAR and docking-based discovery of dual AChE/BACE1 chemotypes
Bozkır, İsa; İbişoğlu, Merve Seda; Kayıkçıoğlu Bozkır, İlknur; Güler, Halil İbrahim
The discovery of dual acetylcholinesterase (AChE) and β-secretase (BACE1) inhibitors remains a promising strategy against multifactorial Alzheimer’s disease. Here, rigorously curated ChEMBL-derived data were used to develop explainable QSAR (Quantitative structure–activity relationship) models for dual-inhibition prioritization. Molecules were standardized, near-duplicates were removed using a Tanimoto similarity threshold (≥ 0.80), and physicochemical outliers were filtered prior to modeling. Multiple classifiers (including Light Gradient-Boosting Machine, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, k-Nearest Neighbors and Gradient Boosting Decision Trees) and fingerprints (e.g., RDKit fingerprints, Extended Connectivity Fingerprint) were benchmarked under scaffold-based nested cross-validation to prevent data leakage. Class imbalance was handled with SMOTETomek applied strictly within training folds. Model selection relied on F-Score, Area Under the Precision–Recall Curve, Matthews Correlation Coefficient (MCC), and Recall, and performance was accompanied by bootstrap confidence intervals, calibration curves, and Y-randomization controls. In classification, the top model (GBDT + ECFP6) achieved strong generalization (Recall ≈ 1.00, PR-AUC ≈ 0.84, MCC ≈ 0.81, F1 Score ≈ 0.84). Shapley Additive Explanations (SHAP) analysis highlighted aromatic and hydrogen-bonding substructures as key positive contributors. Prospective candidates (e.g., CHEMBL5082250, CHEMBL1651126, CHEMBL1651127) were evaluated by active-site-focused docking against AChE (PDB: 4EY7) and BACE1 (PDB: 2G94) with essential waters retained; docking scores (ΔG, kcal·mol⁻1) were used for relative ranking of the ligands. SwissADME/pkCSM profiling suggested CNS-relevant properties (e.g., MPO, logBB, P-gp liability) and acceptable oral drug-likeness. Collectively, the workflow provides a reproducible and transparent pipeline for prioritizing dual AChE/BACE1 chemotypes and nominates testable scaffolds for experimental validation.
AtıfScopus2WoS2OpenAlex5İncele - MakaleYalnız künye2026Scopus SJRQ2Multi level throttled attention for accurate and efficient classification of retinal diseases in OCT images
Celik, Kemal; Celik, Fatih
The classification of retinal diseases using Optical Coherence Tomography images has gained significant attention in recent years. However, due to the heterogeneity of retinal disorders, achieving high classification accuracy remains challenging. While deep learning has improved the identification of various ophthalmic conditions, accurate and early diagnosis is still critical for effective clinical decision-making. Additionally, reducing computational complexity is essential for building scalable diagnostic systems. To address these challenges, we propose a novel Multi-level Throttled Attention (MLTA) model that first applies attention to extract important features, followed by throttling to filter out redundant or less useful features. This process is applied at multiple stages, operating on both Convolutional Neural Network layers and Transformer architectures, and then fuses them finally. This approach optimizes feature selection by preserving the most informative retinal features while discarding redundant ones from multi-level feature maps. As a result, the model enhances classification performance while reducing computational overhead. We evaluate our proposed model on two benchmark OCT datasets: OCT2017 and OCTID, achieving accuracy scores of 99.59 ± 0.04% and 99.71 ± 0.03%, respectively. Our method outperforms existing CNN-based and Vision Transformer-based models, demonstrating superior accuracy and efficiency through this hierarchical attention.
- MakaleAçık Erişim2026Scopus SJRQ2Experimental and Modeling Study of Fluidity in Ductile Iron (GGG40)
Coşar, Mehmet; Şüküroğlu, Ebru Emine; Güngör, Kadir; Çolak, Murat
Ductile irons (spheroidal graphite cast irons, SGI) are widely used in automotive and energy systems due to their high strength, ductility, and excellent machinability. The casting quality of these alloys largely depends on the flow and filling behavior of the molten metal, namely its fluidity. In this study, the effects of pouring temperature, filling time, and metallurgical quality on the fluidity of GGG40 ductile iron were investigated through both experimental and numerical approaches. Experimental trials were conducted at pouring temperatures of 1350 °C, 1400 °C, and 1450 °C; metallurgical quality levels of 10%, 50%, and 90%; and filling times of 5 s and 10 s. The fluidity lengths were measured and statistically evaluated. The results revealed that increasing the pouring temperature and metallurgical quality significantly improved fluidity, while shorter filling times allowed the molten metal to travel longer distances before solidification. Modeling studies performed using a commercial casting simulation software showed strong correlation with the experimental data. Microstructural examinations indicated that higher pouring temperatures and improved metallurgical quality led to a more homogeneous distribution of graphite nodules and an increased ferrite fraction in the matrix. The findings provide valuable insights into the influence of process parameters on the fluidity behavior of ductile iron and offer critical design data for enhancing casting quality and productivity in industrial foundry applications.
AtıfScopus1İncele - MakaleYalnız künye2026Scopus SJRQ1Stationarity or nonlinear nonstationarity? Assessing standard duration extreme rainfall under Türkiye’s complex topography and climate variability
Şahin, Nagehan; Şan, Murat; Kankal, Murat
The impact of climate change on extreme rainfall regimes requires a reassessment of the design processes of traditional hydrological structures. For this purpose, conducting an in-depth analysis of the nonstationary nature of extreme rainfall in regions with complex topography and climate is of great importance. This study examines the stationary and nonstationary states of standard duration (5 min–24 h) annual maximum rainfall data across Türkiye. For this purpose, the intensity-duration-frequency relationship was analyzed using data at 100 homogeneous stations during 1980–2020. The analyses were performed using the generalized extreme value (GEV) distribution with eight linear and non linear location and scale parameters, based on stationary and nonstationary models. Time was included as a covariate in nonstationary models. The corrected akaike information criterion and likelihood ratio test were used to select the appropriate GEV distribution. Findings indicate that stationarity is generally dominant, but nonstationarity is relatively higher in a nonlinear manner in cold climates and complex topography. The effect of location and scale parameters was found to be individual in the short duration and combined in the long duration. It was observed that nonstationarity was relatively higher in complex topography than in flatter regions up to 8 h. Still, the diversity of nonlinear models decreased over the long duration. In conclusion, this study emphasizes the need to consider nonstationary approaches in designing hydrological structures, especially in climate change.
AtıfOpenAlex1İncele - MakaleAçık Erişim2025Scopus SJRQ2Investigation of the Effects of Filling Speed, Casting Temperature and Metallurgical Quality on Fluidity of Lamellar Graphite Cast Iron at Different Section Thicknesses
Köse, Sami; Çolak, Murat; Şüküroğlu, Ebru Emine
In this study, the fluidity properties of the alloy were investigated at different casting temperatures, different section thicknesses, and varying casting parameters of lamellar graphite cast iron materials. To achieve our goal, we utilized sand molds that were created with specific parameters including pouring temperature, metallurgical quality, section thickness, and fluidity test model. These molds were used for casting. Thus, the effect of fluidity properties in changing casting conditions and liquid metal advance distances at determined section thicknesses was investigated. Modeling was carried out with FlowCast casting simulation software by determining the liquid metal advance distance depending on the section thickness in the castings made in sand molds under changing casting conditions. The fluidity and advance distance of the liquid metal was determined comparatively with experimental and modeling techniques under the changing casting conditions in the parameters determined in this study. When the outcomes were examined; it was observed that different liquid metal advance distances occur at different cross-section thicknesses depending on the changing conditions.
AtıfScopus8WoS8OpenAlex7İncele - MakaleAçık Erişim2025Scopus SJRQ2Optimal Ratio of Steel and Polyester Fibers on the Mechanical Behavior of Alkali-Activated Concrete Produced by Industrial Waste Minerals
Aksüt, Yavuz Selim; Çullu, Mustafa; Yetgin, Şükrü
Alkali Activation Concretes (AAC) have many superior properties compared to Ordinary Portland Cement Concrete (OPC). However, it shows traditional concrete behavior with its brittleness. In this case, the tensile strength and flexural strength which are weak in addition to ductility is being studied to be increased by using various artificial and natural fibers. In this context, it is inevitable to conduct multifaceted research to improve the behavior of Fiber Alkali Activated Concrete (FAAC). In this study, 13 different mixes were made using steel and polyester fibers in different proportions by volume (0%; 0.25%; 0.50%; 0.75%; 1%) separately and hybrid. The mechanical properties of cubic (150 × 150 × 150 mm), cylindirical (100 × 200 mm) and prismatic (150 × 150 × 600 mm) specimens were determined including compressive strength, modulus of elasticity and poison ratio, splitting tensile strength and flexural strength (toughness-energy absorption capacity and modulus of rupture). crack formation in concrete occurs early and a significant decrease in strength is observed due to the high flexibility and low flow resistance of polyester fibers. Steel fibers with high strength and higher yield strength were used in hybrid form to solve this problem. It was revealed that the blend ratio that contributed the most to the mechanical behavior of the specimens was the case where the fibers were used as hybrid at a ratio of 1% by volume evaluation from both the experimental results and the optimization.
AtıfScopus4OpenAlex6İncele - Kitap BölümüYalnız künye2025Scopus SJRQ4The Robust Method for Solving the Conformable Time-Fractional Klein–Gordon Equation
Anaç, Halil
The present study used the conformable fractional q-Shehu homotopy analysis transform method to investigate the conformable time-fractional Klein–Gordon equations with proportional delay. The numerical answers to this problem are graphed using Maple software. The efficacy and consistency of the proposed method have been demonstrated through numerical simulations.
- Kitap BölümüYalnız künye2025Scopus SJRQ4Investigation of Fractional Order Genesio-Tesi System Using Laplace Transformation
Bekiryazici, Zafer; Sengul, Suleyman; Merdan, Mehmet
In this paper the fractional-order Genesio-Tesi chaotic system is investigated with Laplace transformation method. Genesio-Tesi system is a three-dimensional equation system with quadratic nonlinearity and is frequently used for exemplifying chaotic systems. In this study, Caputo fractional derivative is used to obtain the fractional system and Laplace transformation is applied in order to analyze the explicit solutions of the system.
- MakaleAçık Erişim2025Scopus SJRQ1DAPNet++: density adaptive PointNet + + for airborne laser scanning data
Akbulut, Zeynep; Karsli, Fevzi
Addressing the challenges arising from the irregularity and varying density of Airborne Laser Scanning (ALS) point clouds, which particularly affect the performance and generalization ability of 3D Deep Neural Networks (DNNs), is essential for their effective application in direct semantic segmentation tasks. In this study, we investigated the underexplored aspect of adapting PointNet + + for semantic segmentation of ALS point clouds. We introduced Density-Adaptive PointNet++ (DAPNet++) to enhance robustness against variable point densities through modifications made to the original PointNet + + architecture. Our methodology includes controlled block partitioning based on point density replacing the original batching strategy. Another key advancement in the study is the automation of the initial value of the neighborhood search radius by taking into account the characteristics of the dataset. This approach optimizes receptive field determination, crucial for effective semantic segmentation. The effectiveness of DAPNet + + is validated through extensive experiments on various datasets, including ISPRS Vaihingen, DALES, subsampled DALES, and OpenGF. Notable improvements include up to 11% increase in weighted mean Intersection-over-Union (mIoU) on the highly variable OpenGF test dataset and 3% increase in mIoU on the subsampled DALES dataset. Furthermore, the generalization capability of the DAPNet + + was tested, revealing an approximately 5% improvement in evaluation metrics compared to PointNet++. In summary, DAPNet + + minimizes trial-and-error in the selection of parameters for block partitioning and radius, and it enhances robustness against variable point density. With the proposed approach, it has been observed that there is a significant improvement in accuracy for underrepresented classes, mitigating class imbalance in ALS point clouds.
AtıfScopus9WoS5OpenAlex6İncele - MakaleAçık Erişim2025Scopus SJRQ1Advanced brain tumor analysis: a novel strategy for segmentation and classification using modern computational methods
Reis, Hatice Catal; Turk, Veysel
Despite their low incidence, brain tumors are one of the most invasive cancer types, constituting a significant burden of death and disease in all age groups. Early and accurate diagnosis of brain tumors plays a vital role in reducing mortality rates. The heterogeneous nature of brain tumors and the diversity of tumor lesions may make it difficult for radiologists to make the right decision in the manual diagnosis process. This study proposes the use of machine learning methods for the classification of brain tumors (pituitary, meningioma, and glioma) and the use of metaheuristic algorithms graph theory, and random walker algorithms in the segmentation of brain tumors. The classification performed with the proposed method obtained an overall accuracy rate of 98.33%. In addition, the classification accuracy of 99.50%, 99.50%, 98.67%, and 99.00% was achieved for no tumor, pituitary, meningioma, and glioma, respectively. Experiments in the segmentation process show that metaheuristic algorithms and max-flow graph cut approach produce successful results.
AtıfScopus6OpenAlex5İncele - MakaleYalnız künye2025Scopus SJRQ1Discontinuities identification from rock outcrop using auto-encoder and point clouds
Günen, Mehmet Akif; Aliyazıcıoğlu, Şener
Rock slopes and geotechnical structures often exhibit discontinuity planes, which significantly influence their mechanical behavior and stability. The precise identification of these discontinuities is critical for ensuring the safety and cost-effectiveness of construction projects. However, previous methods are often limited by subjectivity, low automation, and challenges in inaccessible or hazardous environments. This study introduces a novel seven-step approach utilizing auto-encoders for estimating the orientation parameters of individual discontinuity sets from point cloud data. The steps include: data collection, filtering point clouds to eliminate outliers, determining neighborhood size, feature extraction, training a stacked auto-encoder for feature learning, estimating discontinuity sets using density-based clustering, and calculating individual discontinuities through the least squares method for orientation parameter estimation. In addition, the methodology incorporates a planarity-based approach for estimating neighborhood size. The statistical performance has been validated through evaluations in two real case studies and one synthetic case study. The classification achieves an accuracy exceeding 95%, highlighting the high efficacy of the approach. Results for orientation parameter estimation show consistency with existing methods and in situ measurements, with average orientation values for discontinuities within 5 degrees for individual sets. This framework represents a considerable advancement in rock discontinuity analysis, offering a robust and automated solution that is particularly effective in hazardous and hard-to-reach areas.
AtıfScopus10WoS10OpenAlex11İncele - MakaleAçık Erişim2025Scopus SJRQ2The effect of psychotherapeutic interventions on pain and quality of life in endometriosis: a systematic review and meta-analysis study
Özcan, Handan; Çuvadar, Ayşe; Uzun, Sevda
Aim: This study aimed to determine the effect of psychotherapeutic interventions on pain, quality of life and health profile in women diagnosed with endometriosis. Methods: For this systematic review and meta-analysis study, studies were obtained by searching PubMed, Web of Science, EBSCOhost, Google Scholar, and YÖK Thesis Center databases between August and October 2024 without any year limitation. After the review, 10 studies were included in the study. The data were synthesized by meta-analysis and narrative methods. Results: In this systematic review and meta-analysis, psychotherapeutic interventions for the treatment and management of endometriosis were found to be effective on quality of life (SMD = 0.515, 95% CI = 0.035 to 0.995; Z = 2.104, I2 = 67.523%, p < 0.05), pain (SMD = − 0.454, 95% CI = − 1.600 to − 0.179; Z = − 1.566, I2 = 84.463, p > 0.05), and health profile (SMD = 0.041, 95% CI = − 0.363 to 0.281; Z = − 0.250759, I2 = 56.876%, p > 0.05). In subgroup analyses, it was determined that the types of psychotherapeutic interventions were not effective, but the number of sessions was effective. Conclusion: Psychotherapeutic interventions to reduce symptoms in endometriosis are effective in improving quality of life but not pain and health profile.
AtıfScopus3WoS3OpenAlex4İncele - MakaleAçık Erişim2025Scopus SJRQ1Adoption of AI-based order picking in warehouse: benefits, challenges, and critical success factors
Rad, Fakhreddin Fakhrai; Oghazi, Pejvak; Onur, İzmir; Kordestani, Arash
This research assesses the adoption of artificial intelligence (AI)-based batch order picking in a warehouse, focusing on benefits, challenges, and critical success factors. Using customer order data and simulation, the study employs a quantitative approach, combining mathematical and statistical estimations with qualitative examinations centered on interviews with the warehouse staff, its Enterprise Resource Planning (ERP) developer, and the AI developer. The findings of this mixed-method study reveal that the AI-based order-picking system (AI-based system) has improved order-picking efficiency by reducing travel distance and time. Nevertheless, challenges hinder maximum utilization of the system. In addition, the research highlights critical success factors and other benefits of adopting the system tailored to warehouse management. Understanding the lessons learned in this research is essential for businesses seeking to adopt AI to enhance their efficiency.
AtıfScopus13WoS5OpenAlex5İncele - MakaleAçık Erişim2025Scopus SJRQ2Enhancing part-based gait recognition via ensemble learning and feature fusion
Yaprak, Büşranur; Gedikli, Eyüp
Gait, a behavior-based biometric feature, has gained increasing popularity in human identification, particularly in surveillance systems, due to its ability to function without physical contact or explicit consent. Traditional silhouette-based methods have demonstrated that different body parts exhibit distinct movement patterns during walking, thereby enhancing recognition accuracy. In this study, we propose an improved part-based gait recognition approach by leveraging ensemble learning on local body regions. The Gait Energy Image (GEI) is segmented into five horizontal parts, and ensemble learning is applied to the convolutional neural network (CNN) responsible for their processing. A separate MetaModel is trained for each body part to integrate the part-based features obtained from ensemble learning and synthesize the most discriminative ones. Additionally, a part-removal process is introduced to mitigate the effects of appearance-based variations by analyzing absolute differences between images with and without variations. The aggregated most distinctive features contribute to robust recognition. We evaluate our proposed approach on the CASIA-B, CASIA-C, and Outdoor-Gait datasets, and experimental results indicate that ensemble learning significantly enhances part-based gait recognition performance under various appearance variations, outperforming several state-of-the-art methods. The datasets and source code are available at https://github.com/busrakckugurlu/Enhancing-Part-based-Gait-Recognition-via-Ensemble-Learning-and-Feature-Fusion/tree/main.
AtıfScopus9WoS3OpenAlex7İncele - MakaleAçık Erişim2025Scopus SJRQ1Post-seismic structural assessment: advanced crack detection through complex feature extraction using pre-trained deep learning and machine learning integration
Reis, Hatice Catal; Turk, Veysel; Ustuner, Mustafa; Kaya Yildiz, Cagla Melisa; Tatli, Ramazan
Earthquakes can often cause significant damage to buildings. After an earthquake, experts/managers need to make quick and accurate damage assessments. Traditionally, manual analysis processes have been widely used in damage assessment studies. The fact that these methods are time-consuming and based on human observation leads to certain limitations in damage assessment studies. In recent years, artificial intelligence techniques such as deep learning and machine learning have frequently been preferred in damage detection studies, and significant success has been achieved. This study aimed to automatically detect cracks/damages in the buildings in Diyarbakir city after the February 6, 2023 Kahramanmaras, Turkey earthquake. Our experimental dataset was collected by the researchers and named Kahramanmaras-Diyarbakir Earthquake Building Crack Dataset (KDBECD-2023). The data set consists of four categories in terms of damage level: undamaged, slightly damaged, moderately damaged, and heavily damaged buildings. DenseNet201 deep learning architecture and popular machine learning algorithms, Support Vector Machine, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) were used to classify cracks at different damage levels. In the experimental phase, feature extraction was performed with the DenseNet201 architecture. In addition, dimensional reduction was applied with the Principal Component Analysis method to reduce the computational complexity of the proposed hybrid study. According to the experimental results, the DenseNet201-KNN hybrid model gave the most successful result with an accuracy value of 94.62%. The results of this study can make important contributions to decision makers and experts in detecting cracks and damages in buildings after an earthquake.
AtıfScopus6WoS4OpenAlex5İncele - MakaleYalnız künye2025Scopus SJRQ2Remanent magnetization and magnetic relaxation in Bi:2223/ag tapes fabricated by the Powder-in-Tube (PIT) method at 30 K
Düzgün, İbrahim; Çelebi, Selahattin
In this study, the remanent magnetization and magnetic relaxation behaviour of Bi-2223/Ag superconducting tapes fabricated using the Powder-In-Tube (PIT) method were investigated. Measurements were conducted at a fixed temperature of 30 K, both under various field cycling amplitudes and in constant magnetic field conditions to analyse time-dependent magnetization. The remanent magnetization values obtained after full magnetic cycles were normalized and compared with theoretical predictions based on the extended critical state model. Additionally, magnetic relaxation curves were recorded at 30 K to evaluate flux creep characteristics. A clear logarithmic decay in magnetization was observed, indicating the coexistence of at least two distinct pinning regimes. These results contribute to a deeper understanding of flux dynamics and magnetic stability in Bi-2223/Ag PIT tapes for practical superconducting applications.
- MakaleAçık Erişim2025Scopus SJRQ2Green synthesis of iron and silver nanoparticles from aqueous extract of buckwheat husk waste: antibacterial, cytotoxic, and dye decolorization properties
Karpuz, Ömer; Baltacı, Cemalettin; Gül, Abdulkadir; Gülen, Jale; Bozbeyoğlu, Pınar; Aydoğan, Nebahat
Iron oxide (BHFeNP) and silver nanoparticles (BHAgNP) were produced using a green synthesis method through the extract of buckwheat husk (BH) waste to remove dye pollutants from water bodies in this study. The BHFeNP and BHAgNP were characterized by TEM, SEM, XRD, EDX, and FTIR techniques. Then, the methylene blue (MB) removal performance of the synthesized nanoparticles (NPs) was investigated. MB adsorption experiments showed that BHFeNPs were more dependent on temperature, adsorbent amount, and pH, while BHAgNPs notably depended on adsorbent amount, initial MB concentration, and pH. BHAgNPs had higher adsorption rates than BHFeNPs, and the Langmuir isotherm was more suitable for the adsorption process. BHAgNP and BHFeNP were shown to have the maximum adsorption capacities of 96.3 and 53.4 mg/g at pH 8 and 25 °C, respectively, in the presence of 0.25 g/L NP. Kinetic and thermodynamic calculations revealed that MB adsorption was spontaneously endothermic and supported the pseudo-second-order kinetic model. The antibacterial potentials of BHFeNPs and BHAgNPs were investigated against two gram-negative (Escherichia coli and Pseudomonas aeruginosa) and two gram-positive (Enterococcus faecalis and Staphylococcus aureus) bacterial strains. BHAgNPs exhibited a higher antibacterial activity against the strains used. In cytotoxicity studies with ARPE-19, A549, HeLa, and DU-145 cell lines, it was observed that BHAgNP had a greater cell growth inhibition effect than BHFeNP and BH extract. Examining the morphological changes in the cells exhibited that BHAgNP significantly inhibited A549 and ARPE-19. Reusability studies of the generated NPs revealed MB removal capabilities of 92.5% and above after the fifth stage. The present study demonstrates that BHAgNP and BHFeNPs are viable nanoadsorbents for the elimination of environmental pollution due to their excellent removal performances and stable adsorbent characteristics.
AtıfScopus15WoS7İncele - MakaleYalnız künye2025Scopus SJRQ2Oriental beech (Fagus orientalis L.) steaming condensate: phenolic composition and functional activities
Akmeşe, Osman; Sivrikaya, Hüseyin; Baltacı, Cemalettin
This study presents a comprehensive investigation of the bioactive properties of the condensate formed during the steaming treatment of beech wood (Fagus orientalis L.). The phenolic compound profile, antioxidant capacity, enzyme inhibition, and antimicrobial activity of the condensate were evaluated using multiple analytical methods. Rich in total phenolic content (339.20 mg GAE/g) and flavonoid levels (79.74 mg QE/g), the condensate exhibited strong reducing power in the ferric reducing antioxidant power assay (FRAP), while demonstrating moderate radical scavenging activity in the DPPH (2,2-diphenyl-1-picrylhydrazyl) and ABTS (2,2′-azinobis (3-ethylbenzothiazoline 6-sulfonic acid)) tests. In antimicrobial assessments, the condensate was found to be effective against Gram-positive bacteria, Staphylococcus aureus and Bacillus cereus as well as Candida albicans as a pathogen fungus, while showing lower activity against Gram-negative bacteria. Its antifungal efficacy was found to be notable when compared to nystatin. Although the IC₅₀ value of the condensate in α-amylase inhibition assays (1320.00 µg/mL) was weaker than that of the reference inhibitor acarbose (33.26 µg/mL), the results indicate its potential as a plant-based enzyme inhibitor candidate. Fourier transform infrared (FTIR) spectroscopy revealed characteristic peaks associated with lignin derivatives, carbohydrates, and ester bonds in the condensate. LC–MS/MS analyses identified chlorogenic acid (1581.65 µg/g) as the major phenolic compound, along with ferulic acid and rosmarinic acid. Beech steaming condensate stands out as a valuable bioactive resource in the context of industrial by-product valorization. With its antioxidant, antimicrobial, and enzyme-inhibitory properties, it holds promising potential for applications in the food preservation, pharmaceutical, and cosmetic industries. These findings may contribute to the sustainability of the forest industry from a circular economy perspective.
AtıfOpenAlex1İncele - MakaleYalnız künye2025Scopus SJRQ1The effect of testicular self-examination education on health beliefs in adult men: a systematic review and meta-analysis
Korkmaz, Mehmet; Yildirim, Tugba; Altun, Elif; Uzun, Sevda; Kayalar, Aycan
Purpose: Testicular cancer is primarily detected by the patient or their sexual partner and is most commonly diagnosed in men between the ages of 15 and 35. This study aims to assess the impact of self-examination education on health beliefs among adult men. Materials and methods: This study, which is a systematic review and meta-analysis, obtained studies by searching PubMed, Web of Scıence, EBSCOhost, Google Scholar, ProQuest and YOK Thesis Centre databases without a year limitation. After the reviews, 12 studies were included in the study. Data were synthesised by meta-analysis and narrative methods. Results: The findings of this systematic review and meta-analysis demonstrate that self-examination education significantly influences health beliefs in adult men (SMD: 1.790; 95% CI: 0.954 to 2.624; Z = 4.198, p = 0.00; I² = 99.109%). Conclusion: This study highlights that self-examination education may play an influential role in the shaping of adult men’s health beliefs, potentially contributing to the early detection and prevention of testicular cancer.
- MakaleAçık Erişim2025Scopus SJRQ1Hematology patients’ metaphorical perceptions of the disease and psychosocial support needs in the treatment process: a phenomenological study from a rural region of Türkiye
Uzun, Sevda; Ediz, Çiçek; Mohammadnezhad, Masoud
Purpose: Although health services and access to these services have increased worldwide, there are still major barriers to access to health services, especially for rural, poor, and disabled individuals. The aim of this study was to evaluate the metaphorical perceptions of hematology patients living in rural areas in Türkiye about the disease and their psychosocial support needs during the treatment process with a phenomenological approach. Methods: In this study, in which the phenomenological research method was used, semi-structured in-depth interviews were conducted with 14 hematology patients receiving treatment in a state hospital in a province in the eastern region of Türkiye. Criterion sampling method, one of the purposive sampling methods, was used to reach the sample group. Interviews continued until data saturation was achieved. The data of the study were evaluated using thematic analysis. The study was conducted and reported according to the COREQ checklist. Results: Data analysis revealed two main themes including the following: metaphorical perceptions towards hematologic cancer with the sub-themes of emotional turmoil, social alienation and stigma and physical debilitation, and pathways to resilience with the sub-themes of disease process management, inner resilience and faith, and psychosocial support. Conclusion: This study revealed that the adaptation process of patients to hematologic cancer is quite difficult, and psychosocial support is an indispensable requirement for them in their lives. In order for patients and their families to cope with this very difficult disease process, it is thought that providing them with a higher level of psychological support will be beneficial in coping with the disease effectively.
AtıfScopus4WoS5İncele