Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Erten, Fusun(5)
- Sahin, Kazim(5)
- Orhan, Cemal(4)
- Sengur, Abdulkadir(4)
- Zengin, Gokhan(4)
- Akkurt, Nevzat(3)
- Er, Besir(3)
- Ertarğın, Merve(3)
- Gürgenç, Turan(3)
- Karabacak, Abdullah Hasan(3)
- Padigaru, Muralidhara(3)
- Türkoğlu, Muammer(3)
- Uyanık, Hakan(3)
- Zhilevski, Marin(3)
- Çelebi, Müslim(3)
- Çelik, Ertuğrul(3)
- Abderrahmane, Aissa(2)
- Akbulut, Taner(2)
- Akdağ, Mustafa(2)
- Akmaz, Düzgün(2)
- Aksakal, Bünyamin(2)
- Al-Khaleel, Mohammad(2)
- Aslan, Naim(2)
- Aygoren, Caner(2)
- Ayık, Merve(2)
- Beykumul, Aysegul(2)
- Can, Umit(2)
- Cinarli, Fahri Safa(2)
- Demir, Yakup(2)
- Gallo, Monica(2)
- Gašić, Uroš(2)
- Guedri, Kamel(2)
- Guler, Omer(2)
- Guler, Osman(2)
- Gunay, Mihriban(2)
- Ivanov, Marija(2)
- Jugreet, Bibi Sharmeen(2)
- Kilic, Ertugrul(2)
- Küçük, Deniz Bora(2)
- Laidoudi, Houssem(2)
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ2Microstructure and Dry-Sliding Tribology of HVOF-Sprayed NiCrBSi/WC-Co Coatings on AZ91D
Gürgenç, Turan; Macit, Cevher Kürşat; Sömer, Medeni; Aksakal, Bünyamin; Ayık, Merve; Say, Yakup
High-velocity oxy-fuel (HVOF)-sprayed NiCrBSi coatings containing 0, 10, 30, and 50 wt.% WC-Co were evaluated on AZ91D magnesium alloy to determine how the discrete reinforcement level affects surface topography, phase constitution, Vickers microhardness, dry-sliding friction, mass loss, and wear-track microchemistry. As-sprayed surfaces were characterized by three-dimensional profilometry; coating cross-sections and worn surfaces by optical microscopy and SEM/EDS; phase constitution by XRD; and mechanical response by HV0.1 indentation. Dry-sliding tests were performed at 10, 30, and 50 N over 100–1000 m. Increasing WC-Co content raised Sa from 8.8 ± 0.3 to 13.0 ± 0.5 µm and Vickers microhardness from 776 ± 4 to 959 ± 5 HV0.1. XRD indicated a γ-Ni-based matrix containing boride/carbide constituents, while WC, W2C, and Co became increasingly prominent in the reinforced coatings. Boride assignments are based on diffraction evidence, whereas B and C EDS signals were treated semi-quantitatively. The 50 wt.% WC-Co coating exhibited the lowest mass loss and mean coefficient of friction at every load. Its mean friction coefficients were 0.31, 0.35, and 0.41 at 10, 30, and 50 N, corresponding to reductions of 40.1%, 38.9%, and 36.2% relative to AZ91D. At 1000 m, its mass-normalized wear rate indices were 9.0 × 10−4, 4.0 × 10−4, and 5.3 × 10−4 mg N−1 m−1, respectively. Post-wear mapping showed the largest field-scale W-Co-rich fraction in the 50 wt.% coating; however, isolated spectra containing more than 94 wt.% Mg are compatible with local coating penetration/substrate exposure and/or Mg-rich debris. The 50 wt.% composition therefore provided the best combined response among the four tested levels, while intermediate compositions are required to identify a continuous-composition optimum.
- MakaleAçık Erişim2026Scopus SJRQ1Effects of Melatonin Formulations on Caffeine-Evoked Cortical Activity and Pentobarbital-Induced Sleep in Mice
Sahin, Nurhan; Yabas, Mehmet; Er, Besir; Erten, Fusun; Kursun, Oznur Ece Durmaz; Cikim, Ismail Gurkan; Kaplan, Berkan; Erek, Gozde; Ozmen, Busra; Orhan, Cemal
Melatonin has chronobiotic, antioxidant, anti-inflammatory, and neuromodulatory properties, and its oral effects may vary according to formulation. This study compared conventional melatonin with a lipid-multiparticulate (LMP) formulation in two acute pharmacological mouse paradigms. Eighty-four male BALB/c mice were assigned to caffeine-evoked cortical activity or pentobarbital-induced sedative-hypnotic experiments. Mice received caffeine (7.5 mg/kg, i.p.) alone or with standard or LMP-melatonin (10 or 20 mg/kg, oral). ECoG recorded cortical electrical activity under urethane anaesthesia. In a separate cohort, latency to and duration of pentobarbital-induced loss of the righting reflex were measured. Circulating neurochemical and redox markers, as well as neurotransmitter- and inflammation-related gene and protein expression, were evaluated. Caffeine increased spike frequency and altered ECoG amplitude, together with unfavorable changes in circulating neurochemical and redox markers and in neurotransmitter receptor- and inflammation-related outcomes. Both melatonin formulations attenuated several of these changes in a dose-dependent manner, with the strongest effects generally observed in the high-dose LMP-melatonin group. Compared with conventional melatonin, LMP-melatonin produced greater modulation of ECoG activity, circulating biochemical markers, neurotransmitter-receptor expression, and IL-6 and TNF-α-related outcomes. LMP-melatonin also produced a greater reduction in the latency to and a greater prolongation of the duration of pentobarbital-induced loss of the righting reflex. LMP-melatonin produced greater formulation-dependent effects than conventional melatonin on caffeine-evoked cortical electrical activity, pentobarbital-induced sedative-hypnotic responses, and selected biochemical and molecular outcomes. However, pharmacokinetic exposure, physiological sleep stages, sleep architecture, sleep quality, and direct measures of neuronal injury were not assessed. Therefore, the findings do not directly establish enhanced bioavailability, physiological sleep promotion, or neuroprotection.
- MakaleAçık Erişim2026Scopus SJRQ2Comparative Microstructural, Mechanical, and Tribological Evaluation of Cu Matrix Composites Reinforced with B4C, B, Cr, Co, Al2O3, and Graphite via Powder Metallurgy
Macit, Cevher Kursat; Gürgenç, Turan; Aksakal, Bunyamin; Aslan, Naim
Copper and its alloys are widely used in electrical, automotive, aerospace, and energy applications because of their excellent thermal and electrical conductivity. However, the low hardness and poor wear resistance of pure Cu limit its use under tribologically demanding sliding conditions. In this study, Cu matrix composites reinforced with 1 wt.% boron carbide (B4C), boron (B), chromium (Cr), cobalt (Co), alumina (Al2O3), and graphite (Gr) were fabricated by powder metallurgy and comparatively evaluated under identical processing and testing conditions. Phase constitution and microstructural characteristics were analyzed by XRD, SEM, and EDS, while mechanical and tribological behavior was assessed by Vickers hardness and dry sliding wear tests. All reinforcements improved the hardness of the Cu matrix compared with unreinforced Cu. The hardness increase followed the order Cu–B4C (68.91%) > Cu–B (66.43%) > Cu–Gr (63.97%) > Cu–Al2O3 (61.79%) > Cu–Cr (42.69%) > Cu–Co (36.04%). Dry sliding wear tests, performed under a 10 N normal load, 0.05 m s−1 sliding speed, and 1000 m sliding distance against a 316L stainless-steel ball, showed that all reinforced composites exhibited lower mass loss and more stable sliding behavior than pure Cu. Among all samples, Cu–B4C displayed the best wear performance, with a 154.8% improvement in wear resistance relative to pure Cu. SEM analysis of the worn surfaces revealed that reinforcement addition reduced severe plastic deformation, groove formation, and delamination, leading to a more stable wear regime. Graphite- and boron-containing composites benefited from interfacial lubrication and contact stabilization, whereas B4C and Al2O3 improved wear resistance through rigid-particle strengthening and enhanced load-bearing capacity. By comparing ceramic, metalloid, metallic, oxide, and solid-lubricating reinforcements at the same low addition level and under identical processing and testing conditions, this study provides a reinforcement-selection framework for Cu-based composites requiring improved hardness and dry-sliding durability.
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İncele - MakaleAçık Erişim2026Scopus SJRQ2A Strategic Pre-Mechanical Activation Approach for Reducing Acid Consumption and Ion Release on Acid Leaching of Lithium-Bearing Clays
Boylu, Feridun; Obuz Teker, Gülsen; Ersever Angur, Gafure; Özdemir, Orhan; Güven, Onur; Celik, Mehmet S.
The increasing demand for lithium and the limited availability of high-grade resources have accelerated interest in lithium-bearing clays as a promising alternative, despite their relatively lower lithium content. Lithium extraction from such clay minerals typically requires thermal treatment or acid leaching to disrupt the clay crystal lattice and enhance lithium solubility. The enrichment tailings from the Kırka Boron Processing Plant in Türkiye consist predominantly of dolomite-rich clay minerals and contain approximately 900–1200 ppm Li. Considering the substantial quantities of these tailings currently stored on-site, recovering lithium and converting these materials into a valuable resource would be of significant economic importance. However, due to their mineralogical composition, conventional acid leaching of these tailings demands relatively high sulfuric acid consumption (1.5–2.0 M H2SO4). This leads to excessively low solution pH and the generation of highly acidic waste streams, while also promoting the co-dissolution of iron (Fe) and aluminum (Al) ions at pH levels below 2, which negatively affects lithium recovery and downstream processing. In this study, mechanical activation was applied to the tailings prior to acid leaching. As a result, the acid requirement to achieve lithium extraction efficiencies of 90% and above was successfully reduced from 1.5 M to 1.0 M H2SO4. Moreover, solution pH was maintained near neutral (~7), and the undesirable dissolution of Fe and Al ions was effectively suppressed and kept under control.
AtıfScopus2WoS2OpenAlex2İncele - MakaleAçık Erişim2026Scopus SJRQ1Multi-Task NILM with Anomaly Detection Using a Hybrid CNN–BilSTM–Transformer Model
Gunay, Mihriban; Demir, Yakup; Zhilevski, Marin
Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics.
AtıfOpenAlex1İncele - MakaleAçık Erişim2026Scopus SJRQ1A Metric-Driven Evaluation Framework for Remaining Useful Life Prognosis with Quantified Uncertainty
Vashishtha, Govind; Chauhan, Sumika; Ertarğın, Merve
This paper introduces a novel metric-driven evaluation framework for Remaining Useful Life (RUL) prognosis in rotating machinery, featuring robust uncertainty quantification. Accurate RUL prediction is vital for optimizing maintenance and preventing failures, but existing methods often struggle with complex nonlinear degradation or lack reliable uncertainty estimates. Our proposed framework integrates a probabilistic Deep State Space Model (DSSM) with a variational inference approach to model complex, non-linear degradation trends and inherent aleatoric uncertainty. A key innovation is the use of the Slime Mold Algorithm (SMA) for efficient hyperparameter optimization, ensuring maximum accuracy. Furthermore, an online adaptation mechanism, governed by a heuristic reinforcement learning agent, allows the model to continuously update its knowledge and adapt to concept drift in real-time. Experimental validation on the IMS bearing dataset demonstrates superior RUL prediction accuracy, evidenced by the lowest Root Mean Square Error (RMSE) of 8.1829 cycles, and a PICP of 0.59416. This dual capability makes the framework highly suitable for real-world predictive maintenance, enhancing safety and reliability.
- MakaleAçık Erişim2026Scopus SJRQ1Evaluation and Classification of Emergency and Disaster Assembly Areas with ORESTE-Sort
Ozdemir, Umit; Mete, Suleyman; Gul, Muhammet
Emergency and Disaster Assembly Areas (EDAA) are designated safe zones where basic needs can be met until temporary shelters are established following natural or man-made disasters like floods, fires, earthquakes, explosions, or chemical incidents. Promptly relocating disaster victims to these areas is crucial for minimizing loss of life and facilitating effective search and rescue operations by maintaining an uninterrupted flow of information. To prepare for disasters like earthquakes, which cause significant material and emotional damage to large populations, sustainable disaster management must be ensured to evaluate site suitability, correct deficiencies, and avoid inappropriate locations. This study will examine the evaluation criteria for EDAAs established by the Tunceli Provincial Disaster and Emergency Management Authority (AFAD) in terms of area, structure, security, and accessibility, taking into account the region’s specific characteristics. Based on a literature review, eleven criteria have been proposed and ranked using the Besson mean ranking method. Areas have been classified into four categories (e.g., adequate, not suitable) using the optimistic, pessimistic, and comprise approaches of the Assignment Rule Driven by Attitudes (ARDA) and the ORESTE-Sort method. The examination of 19 EDAA provides two perspectives: an optimistic view that recommends classifying eleven areas as first class and using all areas as they are, and a pessimistic view that calls for urgent improvements in three areas and states that one area (EDAA 1) is deemed unsuitable due to its assignment to class (Formula presented.). It is also advised that the second area should not be used, despite being rated as class (Formula presented.), due to its proximity to the river and its slope characteristics. The study also performs a sensitivity analysis of the method and provides recommendations for future research.
AtıfScopus2WoS1İncele - MakaleAçık Erişim2026Scopus SJRQ2Single-Ended Fault Detection and Fault Location in Transmission Lines Using Approximate Derivative
Akdağ, Mustafa; Mamiş, Mehmet Salih; Akmaz, Düzgün
Fault location in power transmission lines (PTLs) relies on impedance or traveling wave (TW) principles. TW approaches offer superior accuracy and high robustness against fault resistance. While multi-ended methods require precise terminal synchronization, single-ended TW (SETW) methods utilize measurements from one terminal, requiring accurate distinction of reflected waves. This study employs the computationally efficient approximate derivative (AD)—the difference between consecutive samples—for SETW fault detection and location. Normally near zero, the AD of modal signals produces sharp transitions during faults. Comparing AD output to a threshold achieves fault detection. The AD then identifies arrival times of the incident and reflected TWs. When using TW theory to distinguish reflections from the fault point and remote end, the fault distance is calculated from their arrival time difference. Validated through 293 diverse ATP simulated fault scenarios, the approach delivered highly accurate results despite using a lower sampling rate than established methods, utilizing an exceptionally short data window—only 2.03 ms for a 300 km line. Finally, operational boundaries for the signal-to-noise ratio (SNR) in noisy conditions are established.
- MakaleAçık Erişim2026Scopus SJRQ2Translation-Aware Multilingual Sentiment Analysis: A Comparative Study of Machine Translation Systems and Transformer Ensembles
Aslan, Serpil; Kartoğlu, Celal Şamil; Can, Ümit
Multilingual sentiment analysis remains challenging because of cross-linguistic differences, uneven data availability, and potential shifts in meaning introduced during translation. Rather than proposing a new model, this study examines the performance of translation-based pipelines and ensemble Transformer models in multilingual settings. The primary objective is to investigate differences in downstream sentiment classification performance between translation systems within a consistent evaluation framework. Texts in Arabic, Chinese, French, and Italian are standardized through label harmonization and basic preprocessing and subsequently translated into English using Google Translate and the open-source LibreTranslate. Sentiment classification is performed using three Transformer-based models: DeBERTa-v3, RoBERTa, and BERTweet. The experiments are formulated as a binary polarity-classification task comprising positive and negative instances, with neutral samples excluded because their annotations are not directly comparable across the heterogeneous source datasets. Predictions from the individual models are combined through majority voting without introducing a new ensemble method. Experiments on the resulting binary datasets demonstrate that the ensemble achieves the highest mean weighted F1-score, with low fold-to-fold variation under both translation pipelines. Google Translate yields higher and more consistent classification performance across the evaluated subsets, whereas LibreTranslate exhibits greater variation. Given that the datasets differ in domain and genre, these variations cannot be attributed solely to linguistic differences. Overall, the findings reveal differences in downstream sentiment classification performance between the two translation pipelines and indicate that the ensemble combines high mean performance with low fold-to-fold variation within the evaluated binary setting.
- MakaleAçık Erişim2026Scopus SJRQ1Technology Acceptance in Sustainable Distance Education
Akti Aslan, Seda; Çekmez, Erdem; Ayaz, Ahmet; Özyurt, Özcan; Aslan, Alper
This study aims to examine the thematic structure and temporal evolution of the literature on technology acceptance in distance education using the Structural Topic Modeling method. Peer-reviewed articles indexed in the Scopus database between 2016 and 2025 were included in the analysis, yielding 18 consistent thematic clusters. Technology Acceptance Model (TAM) Core Constructs and Structural Equation Modeling (SEM)/Partial Least Squares (PLS) Methodology emerged as the most prevalent themes, reflecting the field’s sustained reliance on quantitative model-testing approaches. Trend analyses revealed a pronounced decline in TAM Core Constructs, while Unified Theory of Acceptance and Use of Technology (UTAUT) and Extensions, Hybrid and Emergency Remote Teaching, and Mobile and AI Applications demonstrated significant upward trends. The period comparison showed that pandemic-related themes became markedly prominent in the 2021–2025 period. Topic correlation analysis indicated the presence of competing temporal niches within the literature. Taken together, the findings suggest that research on technology acceptance in distance education is undergoing a structural transition from single-framework TAM dominance toward a multi-model, context-sensitive research paradigm. These findings carry important implications for the sustainable development of technology-enhanced distance education ecosystems and offer evidence-based guidance for researchers and policymakers committed to building inclusive and adaptive educational systems.
- MakaleAçık Erişim2026Scopus SJRQ2A Genetic Algorithm-Optimized ConvNeXtV2-YOLOv8 Framework for Dental Caries Detection in Panoramic Radiographs
Sobahi, Nebras; Küçük, Deniz Bora; Kılıç, Kazım; İmak, Andaç; Çelebi, Adalet; Alghamedi, Yazyd; Yazicioglu, Cafer; Türkoğlu, Muammer; Şengür, Abdulkadir
Background/Objectives: Dental caries is one of the most common oral diseases worldwide, and early diagnosis is essential for effective treatment. However, detecting carious lesions in panoramic radiographs is challenging because of low image contrast, anatomical complexity, and overlapping structures. This study aimed to develop an improved object detection framework for dental caries localization in panoramic radiographs. Methods: A modified YOLOv8 architecture was developed by integrating ConvNeXtV2 blocks into the Cross-Stage Partial (C2f) modules to enhance feature extraction and gradient propagation. To improve detection performance and reduce overfitting, key training hyperparameters were optimized using a genetic algorithm. The proposed model was evaluated on a dataset of 474 panoramic dental radiographs annotated by experts using bounding boxes. Results: The optimized model achieved a box precision of 78.4%, a recall of 53.6%, and a mean average precision at 50% intersection-over-union threshold (AP50) of 62.6%. Compared with the baseline YOLOv8 model, the proposed approach improved precision and AP50. Visual and quantitative analyses demonstrated that the ConvNeXtV2-enhanced architecture enabled more accurate localization of carious regions. Conclusions: The results indicate that combining ConvNeXtV2-based architectural enhancement with genetic algorithm-based hyperparameter optimization is an effective strategy for dental caries localization in panoramic radiographs. Although recall remains a limitation, the proposed framework shows potential as a computer-aided diagnostic tool for supporting clinical caries assessment.
- MakaleAçık Erişim2026Scopus SJRQ2Effect of Dietary Supplementation with Omega-3 Fatty Acids on Seminal Plasma Arginase Activity and Semen Quality Parameters in Rainbow Trout (Oncorhynchus mykiss Walbaum, 1792)
Kaya, Seyma Ozer; Ozcan, Sinan; Kaya, Emre; Gur, Seyfettin; Koprucu, Kenan; Sonmez, Mustafa; Turk, Gaffari; Erisir, Mine
Background: This study was conducted to determine the effect of various fatty acid ratios added to diets of rainbow trout (Oncorhynchus mykiss, Walbaum) on some sperm parameters and parameters related to arginase activity. Methods: A total of 99 male rainbow trout were used in this study, with the fish averaging 3 years old. Semen was collected by abdominal massage. Four different experimental groups were created in the study: a control group without n-3 series fatty acids added to their diets, and groups with n-3 series fatty acids added at ratios of 1% (D1), 2% (D2), and 3% (D3), respectively. Results: While no significant difference in semen volume was observed between the D1 and D3 groups and the control group, semen volume was significantly higher in the D2 group. Compared with the control group, the time required for sperm motility to decline to both 50% and 0% was significantly prolonged in the D1 and D3 groups. Seminal plasma arginase activity did not differ significantly in the D2 and D3 groups compared with the control group but was significantly higher in the D1 group. Conclusions: Consequently, considering the doses used in the study, fatty acid supplementation was found to lead to changes in sperm quality parameters, specifically volume (only 2%) and arginase activity (only 1%). Given that the rate of decrease in sperm motility was later in groups D1 and D2, it was concluded that the added fatty acids may be beneficial.
- MakaleAçık Erişim2026Scopus SJRQ1Surface EMG-Based Hand Gesture Recognition Using a Hybrid Multistream Deep Learning Architecture
Çelik, Yusuf; Can, Umit
Surface electromyography (sEMG) enables non-invasive measurement of muscle activity for applications such as human–machine interaction, rehabilitation, and prosthesis control. However, high noise levels, inter-subject variability, and the complex nature of muscle activation hinder robust gesture classification. This study proposes a multistream hybrid deep-learning architecture for the FORS-EMG dataset to address these challenges. The model integrates Temporal Convolutional Networks (TCN), depthwise separable convolutions, bidirectional Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) layers, and a Transformer encoder to capture complementary temporal and spectral patterns, and an ArcFace-based classifier to enhance class separability. We evaluate the approach under three protocols: subject-wise, random split without augmentation, and random split with augmentation. In the augmented random-split setting, the model attains 96.4% accuracy, surpassing previously reported values. In the subject-wise setting, accuracy is 74%, revealing limited cross-user generalization. The results demonstrate the method’s high performance and highlight the impact of data-partition strategies for real-world sEMG-based gesture recognition.
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İncele - MakaleAçık Erişim2026Scopus SJRQ1Comprehensive Analysis of Phytochemical Compounds in Seeds of Indigenous Grape Varieties Cultivated in Southeastern Anatolia: Fatty Acids, Tocopherols, Phytosterols, Vitamins, and Antioxidant Properties
Odabaşioğlu, Mehmet İlhan; Çakır, Atilla; Karaca Sanyürek, Nesrin; İşlek, Fırat
Aim: In this study, the phytochemical composition of the seeds of twelve different local grape (Vitis vinifera L.) varieties traditionally cultivated in the Southeastern Anatolia Region (Diyarbakır, Türkiye) was investigated using a holistic approach. The main objective of the study was to evaluate the associations of the effects of berry skin colour, the ripening stage and utilization type on the fatty acid profile, tocopherol and phytosterol contents, vitamin K and D levels, antioxidant capacity (DPPH) and lipid peroxidation (MDA) of grape seeds. Method: The seeds of grape varieties belonging to four different colour groups, grown on their own roots, were harvested during the 2017 growing season and analysed. Fatty acid composition was determined by gas chromatography (GC), vitamin and phytosterol contents by HPLC-UV, MDA content by the thiobarbituric acid reactive substance (TBARS) method, and antioxidant capacity by the DPPH radical scavenging assay. Findings: Linoleic acid was identified as the predominant fatty acid in all varieties (53.68–67.06%), followed by oleic acid (13.80–27.32%). α-Tocopherol was identified as the predominant tocopherol form (1.597–4.703 mg kg−1), whilst β-sitosterol was the phytosterol component present at the highest level (915.67–3340.42 µg g−1). Statistically significant varietal differences were detected for most measured phytochemical parameters. Group-wise differences associated with berry skin colour and ripening stage were observed for selected traits. Overall, varietal differences appeared to be more pronounced than those associated with berry skin colour or ripening stage. It was observed that the differences in phytochemical composition between the various commercial classifications of grapes were very limited. Result: The findings reveal that the seeds of the local grape varieties studied exhibit a rich phytochemical profile and represent an important source of bioactive compounds that could be utilised, particularly in the functional food, nutraceutical and cosmetics industries. The ‘Tahannebi’ variety stood out as the one with the highest values across many parameters. Differences observed among samples of the same cultivar collected from different vineyards suggest that vineyard-specific conditions may contribute to variation in selected phytochemical traits. Future multi-year and multi-location studies are needed to confirm the stability of these phytochemical profiles and to further clarify genotype × environment interactions.
- DüzeltmeAçık Erişim2026Scopus SJRQ1Correction: 2D MHD Mixed Convection in a Zigzag Trapezoidal Thermal Energy Storage System Using NEPCM (Nanomaterials, (2022), 12, 19, (3270), 10.3390/nano12193270)
Abderrahmane, Aissa; Younis, Obai; Al-Khaleel, Mohammad; Laidoudi, Houssem; Akkurt, Nevzat; Guedri, Kamel; Marzouki, Riadh
There were errors in the original publication [1]. In Abstract and Introduction, any reference to “heat transfer (HT) irreversibility,” “fluid friction (FF) irreversibility,” or “Bejan number” is erroneous and should be removed. Any reference to “wave number” should be corrected to “zigzag number,” which is the relevant parameter in the study. The corrected Abstract and the corrected sentence in the last paragraph of the Introduction appear below, respectively. In a magnetic field, two-dimensional (2D) mixed convection is investigated within a zigzagged trapezoidal chamber. The lower side of the trapezoidal chamber is irregular, in particular, a zigzagged wall with different zigzag numbers N. The fluid particles move in the room due to the motion of the upper wall, while the porosity-enthalpy approach represents the melting process. The thermal parameters of the fluid are enhanced by what is called a nano-encapsulated phase change material (NEPCM) consisting of polyurethane as the shell and a nonadecane as the core, while water is used as the base fluid. In order to treat the governing equations, the well-known Galerkin finite element method (GFEM) is applied. In addition, the heat transfer (HT) and the dynamic behavior of flow are treated in terms of average Nusselt number and contours of streamlines. The main results show that the melt band curve of heat capacity behaves almost parabolically at smaller values of Reynolds number (Re) and larger values of Hartmann number (Ha). Moreover, minimizing the zigzag number is better in order to obtain a higher heat transfer rate. In addition, a comparative study of the heat transfer and the dynamic behavior of flow in terms of average Nusselt number and contours of streamlines has been presented. In Section 2.1. Mathematical Modeling and Equations, Paragraph 3 and Paragraph 4, the term of porosity must be added to the momentum equations. The Equations (2), (3), (8) and (9) should be updated to the following: (Formula presented.) (Formula presented.) (Formula presented.) (Formula presented.) In Section 2.1. Mathematical Modeling and Equations, Paragraph 4, Paragraph 5 and Paragraph 9, there is no cylinder inside the cavity, and any reference to a “cylinder” or “diameter D” is incorrect and should be disregarded. The pressure in Equation (6), the Reynolds Number in Equation (11) and the definition for the average Nusselt number at the zigzag wall in Equation (16) should be updated to the following: (Formula presented.) (Formula presented.) (Formula presented.) The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.
- Derleme MakaleAçık Erişim2026Scopus SJRQ1Nutraceuticals in Uro-Oncology: A Structured Expert Review and Precision-Oriented Framework
Erten, Fusun; Kalemoglu, Ecem; Kucuk, Omer; Sahin, Kazim
Urologic malignancies (UMs), including prostate cancer (PCa), bladder cancer (BCa), renal cell carcinoma (RCC), and testicular germ cell tumors (TGCT), are governed by interconnected molecular pathways that regulate proliferation, angiogenesis, metabolism, invasion, immune escape, and treatment resistance. Key pathways include PI3K/AKT/mTOR, VEGF/VEGFR, EGFR, FGFR, c-MET, androgen receptor signaling, DNA damage response, inflammatory transcriptional programs, and regulation of the tumor microenvironment. This structured expert review evaluates mechanistic, translational, epidemiological, and clinical evidence on food- and botanical-derived nutraceuticals that may influence cancer-related signaling, redox balance, inflammation, metabolic adaptation, and host–tumor interactions. Nutraceuticals are considered investigational adjunctive exposures rather than anticancer treatments or alternatives to standard care. Relevant literature was identified through a structured, non-systematic search of PubMed/MEDLINE, Scopus, Web of Science Core Collection, and Embase from database inception to 17 June 2026, supplemented by backward and forward citation searches. Eligible evidence comprised preclinical, observational, interventional, pharmacokinetic, formulation, safety, and drug–nutraceutical interaction studies. Evidence was evaluated by cancer type, compound class, molecular context, formulation, exposure, translational maturity, and safety, and was classified into five stages: prevention signal, mechanistic plausibility, bioavailability and exposure feasibility, exposure-linked biomarker activity, and clinical benefit. An exposure–species–compartment framework was used to assess whether parent compounds and relevant metabolites reached systemic, urinary, or target-tissue concentrations compatible with the proposed effects. Findings based solely on supraphysiological concentrations of unconjugated parent compounds were considered hypothesis-generating unless supported by human exposure or tissue-distribution data. Curcumin, green tea catechins, isoflavones, carotenoids, flavonols, stilbenes, and triterpenoids affect several cancer-related pathways, primarily in experimental models. Translation to clinical practice is constrained by inconsistent formulations, limited bioavailability, inadequate target-tissue exposure data, few biomarker-linked studies, and possible interactions with anticancer therapies. PCa and BCa provide the most suitable settings for exposure-verified mechanistic studies. In RCC, safety and treatment interactions should be prioritized, whereas in TGCTs, non-interference with curative cisplatin-based therapy must be demonstrated. Future studies should use chemically defined formulations, verify clinically relevant exposure, incorporate mechanism-matched biological-response endpoints, and confirm compatibility with established treatment. Current evidence does not support nutraceuticals as treatments for urologic malignancies.
- MakaleAçık Erişim2026Scopus SJRQ2A Deep Learning Framework for Local Earthquake Magnitude Estimation Using Three-Component Waveforms
Çelik, Yusuf
This study presents a two-stage deep learning framework for accurate and generalizable estimation of local earthquake magnitudes from three-component seismic waveforms, within the context of ground-based remote sensing systems. In the first stage, phase transition boundaries are identified at the sample level to enable consistent temporal alignment of the signals. In the second stage, earthquake magnitude estimation is performed using 30 s waveform segments aligned with the early portion of the signal and enriched with spectral and statistical features. The model was initially trained on the globally diverse dataset STEAD and later fine-tuned using a subset of KOERI waveforms, and its performance was evaluated on an independent KOERI test set. The results demonstrate high prediction accuracy, with a mean absolute error of approximately 0.09 and a coefficient of determination (R2) of about 0.95, indicating strong agreement between predicted and true magnitudes. The model maintains stable performance across varying signal characteristics and geographic regions, highlighting its strong transferability. These findings suggest that seismic sensor networks can be effectively utilized as remote sensing systems for rapid and reliable earthquake characterization.
- MakaleAçık Erişim2026Scopus SJRQ2Thickness- and Binder-Chemistry-Dependent Structural, Mechanical, and Tribological Performance of HVOF-Sprayed WC-Co and WC-Co-Cr Coatings on High-Speed Steel
Macit, Cevher Kursat; Aksakal, Bünyamin; Ayık, Merve; Gürgenç, Turan; Aslan, Naim
High-velocity oxy-fuel (HVOF) spraying was used to deposit WC-Co and WC-Co-Cr coatings on high-speed steel in order to determine how binder chemistry and coating thickness jointly control load support, friction, and dry sliding wear resistance. Nominal 50 and 100 µm coatings were compared within the same substrate framework by XRD, FT-IR, SEM/EDX, cross-sectional image analysis, Vickers hardness testing, dry sliding wear testing, and post-wear SEM/EDX. The coatings retained WC as the dominant crystalline phase, while weak overlapping features were associated with binder-rich regions and limited decarburization products. Cross-sectional observations confirmed continuous coating build-up close to the nominal thicknesses and low apparent dark-feature/porosity fractions. All WC-based coatings increased the effective hardness and reduced mass loss relative to uncoated HSS. Among the tested conditions, WC-Co-Cr-100 provided the highest effective hardness and the lowest mass loss after 1000 m sliding, whereas WC-Co-100 produced the lowest mean coefficient of friction. The results show that low friction and high wear resistance are not governed by identical mechanisms: WC-Co favours interfacial shear reduction, while the thicker Cr-containing coating provides superior resistance to material removal through improved carbide retention, binder stability, and coating-scale load support.
- MakaleAçık Erişim2026Scopus SJRQ1Failure of a Code-Compliant Reinforced Concrete Building: Damage Patterns and Nonlinear Seismic Response
Onat, Onur; Karaşin, İbrahim Baran; Yön, Burak; Varolgüneş, Sadık; Öncü, Mehmet Emin; Uslu, Ali
This study investigates the seismic performance limitations of a newly constructed reinforced concrete building that collapsed during the 6 February 2023 Kahramanmaraş–Elbistan earthquake despite formal compliance with current seismic design requirements. Beyond the specific earthquake event, the study addresses a broader scientific problem: the limited understanding of the relationship between observed damage mechanisms and nonlinear dynamic response in mid-rise reinforced concrete buildings. The first part classifies recurring structural and non-structural damage patterns identified in newly constructed RC residences. The second part presents a nonlinear fiber-based static and dynamic analysis of a collapsed mid-rise building. Nonlinear dynamic analyses were conducted using ground motion records scaled to match the site-specific elastic design spectrum defined by TBDY 2018, corresponding to predefined seismic performance levels rather than an incremental dynamic analysis framework. The results indicate that an extremely low shear wall–to–floor area ratio (0.0357%) combined with asymmetric vertical element distribution significantly amplified torsional response and local shear demands. Nonlinear dynamic analyses showed that critical shear walls exceeded Collapse Prevention limits under DD2-level excitation, while system-level shear contribution limits remained within code-defined thresholds. Dynamic base shear demand corresponded to approximately 30% of the maximum nonlinear capacity obtained from pushover analysis, indicating that localized member failure rather than global strength deficiency governed the collapse mechanism. The analytically identified critical members were consistent with the observed collapse configuration, particularly at the soft ground story. The findings demonstrate that prescriptive code compliance alone may not ensure satisfactory seismic performance when structural irregularities, torsional amplification, and detailing deficiencies coexist. The results are consistent with damage patterns reported in other recent destructive earthquakes and contribute to improving the understanding of collapse mechanisms in code-compliant RC buildings.
AtıfScopus4OpenAlex7İncele - MakaleAçık Erişim2026Scopus SJRQ2A Unified and Interpretable Benchmark of Classification Models for DGA-Based Power Transformer Fault Diagnosis
Doğan, Kıvanç; Ertarğın, Merve; Çalışkan, Abuzer; Bakay, Ayşenur
Power transformers are among the most critical components of electric power transmission and distribution systems, and unexpected failures can lead to substantial economic losses and prolonged power outages. Dissolved Gas Analysis (DGA) is the most widely used diagnostic technique for transformer fault diagnosis and involves interpreting gases dissolved in insulating oil. However, conventional interpretation methods, such as the Rogers ratio, the Doernenburg ratio, the IEC 60599 ratio method, and the Duval Triangle, rely heavily on expert knowledge, may produce inconsistent diagnoses for the same oil sample, and may fail to provide a diagnosis in certain cases. In this study, 12 classification models were evaluated using the publicly available Power Transformers Fault Detection and Diagnosis (FDD) and Remaining Useful Life (RUL) dataset and a unified evaluation protocol. Model performance was assessed using Accuracy, Balanced Accuracy, and Macro-F1 score, while model interpretability was investigated through Shapley Additive Explanations (SHAP) analysis. The results showed that ensemble tree-based methods achieved the best overall performance. LightGBM and Random Forest both attained an Accuracy of 0.969 and a Macro-F1 score of 0.924, while LightGBM further achieved the highest Balanced Accuracy of 0.940. XGBoost exhibited the most stable performance under cross-validation. SHAP analysis revealed that engineered relative concentration features, particularly the CO/H2 ratio and the combined gas ratio, were among the most influential features for fault classification. These findings demonstrate that, for datasets of this scale, ensemble tree-based models combined with well-designed features provide strong and interpretable performance for imbalanced DGA-based fault diagnosis, highlighting the effectiveness of feature-based ensemble learning for small- to medium-sized datasets.