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 Erişim2025Scopus SJRQ1Effects of Integrated Extracts of Trigonella foenum-graecum and Asparagus racemosus on Hot Flash-like Symptoms in Ovariectomized Rats
Erten, Fusun; Er, Besir; Ozmen, Ramazan; Tokmak, Muhammed; Gokdere, Ebru; Orhan, Cemal; Morde, Abhijeet A.; Padigaru, Muralidhara; Sahin, Kazim
Vasomotor symptoms, such as hot flashes (HFs), commonly affect women during menopause, leading to a reduced quality of life. The current study evaluates the combined effect of active components Asparagus racemosus (AR) and Trigonella foenum-graecum (TFG) in a single oral formulation (IAT) for alleviating menopausal symptoms in ovariectomized rats. Following bilateral ovariectomy, the animals were randomly assigned to nine groups: (1) Control, (2) Ovariectomy (OVX), (3) OVX+TA1 (TA: Combination of Trigonella and Asparagus; TFG 30 mg/kg + AR 30 mg/kg), (4) OVX+TA2 (TFG 30 mg/kg + AR 15 mg/kg), (5) OVX+TA3 (TFG 15 mg/kg + AR 30 mg/kg), (6) OVX+TA4 (TFG 40 mg/kg + AR 30 mg/kg), (7) OVX+TA5 (TFG 30 mg/kg + AR 40 mg/kg), (8) OVX+IAT1 (IAT: Integrated Asparagus and Trigonella; TFG+AR integrated extract, 30 mg/kg), and (9) OVX+IAT2 (TFG+AR integrated extract, 60 mg/kg). On the 8th day of treatment, tail and skin temperatures were recorded every 30 min for 24 h. Ovariectomized rats exhibited menopausal symptoms, such as hormonal imbalances and elevated skin temperature. Administration of AR, TFG, and IAT significantly decreased serum follicle-stimulating hormone (FSH), luteinizing hormone (LH), and cortisol while increasing estradiol, progesterone, and dopamine (p < 0.0001), effectively alleviating hot flash-like symptoms. Additionally, they mitigated ovariectomy-induced oxidative stress by lowering malondialdehyde (MDA) levels and restoring antioxidant enzyme activity. Ovariectomized rats exhibited increased expression of a proto-oncogene (c-FOS), gonadotropin-releasing hormone (GnRH), Kisspeptin, Neurokinin B (NKB), and Transient receptor potential vanilloid 1 (TRPV1), along with reduced expressing brain-derived neurotrophic factor (BDNF) levels, which were reversed by treatment, especially with the IAT2 combination. The AR and TFG combination, particularly in IAT formulations, showed strong potential in alleviating menopausal symptoms in ovariectomized rats. These findings suggest that the combination of AR and TFG extracts could be a natural alternative for managing postmenopausal symptoms by restoring reproductive hormone levels, regulating lipid profiles, and enhancing antioxidant defense systems.
AtıfScopus8WoS8OpenAlex6
İncele - MakaleAçık Erişim2025Scopus SJRQ2Advancing Air Quality Monitoring: Deep Learning-Based CNN-RNN Hybrid Model for PM2.5 Forecasting
Utku, Anıl; Can, Umit; Alpsülün, Mustafa; Balıkçı, Hasan Celal; Amoozegar, Azadeh; Pilatin, Abdulmuttalip; Barut, Abdulkadir
Particulate matter, particularly PM2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health.
AtıfScopus10WoS6OpenAlex11İncele - MakaleAçık Erişim2025Scopus SJRQ2A Comparative Study of Time–Frequency Representations for Bearing and Rotating Fault Diagnosis Using Vision Transformer
Orhan, Ahmet; Yordanov, Nikolay; Ertarğın, Merve; Zhilevski, Marin; Mikhov, Mikho
This paper presents a comparative analysis of bearing and rotating component fault classification based on different time–frequency representations using vision transformer (ViT). Four different time–frequency transformation techniques—short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert–Huang transform (HHT), and Wigner–Ville distribution (WVD)—were applied to convert the signals into 2D images. A pretrained ViT-Base architecture was fine-tuned on the resulting images for classification tasks. The model was evaluated on two separate scenarios: (i) eight-class rotating component fault classification and (ii) four-class bearing fault classification. Importantly, in each task, the samples were collected under varying conditions of the other component (i.e., different rotating conditions in bearing classification and vice versa). This design allowed for an independent assessment of the model’s ability to generalize across fault domains. The experimental results demonstrate that the ViT-based approach achieves high classification performance across various time–frequency representations, highlighting its potential for mechanical fault diagnosis in rotating machinery. Notably, the model achieved higher accuracy in bearing fault classification compared to rotating component faults, suggesting higher sensitivity to bearing-related anomalies.
AtıfScopus29İncele - Derleme MakaleAçık Erişim2025Scopus SJRQ2Ghrelin and LEAP2: Their Interaction Effect on Appetite Regulation and the Alterations in Their Levels Following Bariatric Surgery
Alic, Nese; Ayaz, Aylin
Background and Objectives: Ghrelin plays key roles in appetite regulation, involving both homeostatic and hedonic pathways. In obesity, its metabolism is affected by alterations in neural and hormonal responses. Liver-Enriched Antimicrobial Peptide 2 (LEAP2), recently identified as an endogenous ghrelin receptor antagonist, has emerged as a potential regulator of appetite and energy balance, and bariatric surgery is known to induce changes in this system. In this review, we aimed to evaluate the roles of acyl ghrelin, des-acyl ghrelin, and LEAP2 in food intake regulation and summarize findings on the alterations in their levels after bariatric surgery. Materials and Methods: We conducted a narrative review of human and animal studies published in the literature investigating the roles of acyl ghrelin, des-acyl ghrelin, and LEAP2 in food intake regulation, as well as changes in their levels in obesity and following bariatric surgery. Results: Alterations in the ghrelin system, i.e., the acyl ghrelin and des-acyl ghrelin isoforms and LEAP2, in obesity have been reported. Experimental studies have shown that LEAP2 administration reduces food intake and body weight by suppressing ghrelin-induced food intake. Most studies have found marked reductions in fasting acyl ghrelin after sleeve gastrectomy, more so than after Roux-en-Y gastric bypass. Conclusions: In obesity, altered neural and hormonal responses to food also affect ghrelin metabolism, with significant deviations in acyl ghrelin levels and impaired appetite regulation mechanisms. Unlike ghrelin, LEAP2 levels tend to be elevated in obesity. While changes in acyl ghrelin and des-acyl ghrelin isoforms, particularly after sleeve gastrectomy, are well documented, data on LEAP2 remain limited. Further research is needed to better define the role of LEAP2 in ghrelin-mediated food intake and its potential as a therapeutic target in obesity.
AtıfScopus6WoS5OpenAlex7İncele - MakaleAçık Erişim2025Scopus SJRQ2A New, Robust, Adaptive, Versatile, and Scalable Abandoned Object Detection Approach Based on DeepSORT Dynamic Prompts, and Customized LLM for Smart Video Surveillance
Yilmazer, Merve; Karakose, Mehmet
Video cameras are one of the important elements in ensuring security in public areas. Videos inspected by expert personnel using traditional methods may have a high error rate and take a long time to complete. In this study, a new deep learning-based method is proposed for the detection of abandoned objects, such as bags, suitcases, and suitcases left unsupervised in public areas. Transfer learning-based keyframe detection was first performed to remove unnecessary and repetitive frames from the ABODA dataset. Then, human and object classes were detected using the weights of the YOLOv8l model, which has a fast and effective object detection feature. Abandoned object detection is achieved by tracking classes in consecutive frames with the DeepSORT algorithm and measuring the distance between them. In addition, the location information of the human and object classes in the frames was analyzed by a large language model supported by prompt engineering. Thus, an explanation output regarding the location, size, and estimation rate of the object and human classes was created for the authorities. It is observed that the proposed model produces promising results comparable to the state-of-the-art methods for suspicious object detection from videos with success metrics of 97.9% precision, 97.0% recall, and 97.4% f1-score.
AtıfScopus5WoS2OpenAlex8İncele - MakaleAçık Erişim2025Scopus SJRQ2A New Hybrid ConvViT Model for Dangerous Farm Insect Detection
Utku, Anil; Kaya, Mahmut; Canbay, Yavuz
This study proposes a novel hybrid convolution and vision transformer model (ConvViT) designed to detect harmful insect species that adversely affect agricultural production and play a critical role in global food security. By utilizing a dataset comprising images of 15 distinct insect species, the suggested approach combines the strengths of traditional convolutional neural networks (CNNs) with vision transformer (ViT) architectures. This integration aims to capture local-level morphological features effectively while analyzing global spatial relationships more comprehensively. While the CNN structure excels at discerning fine morphological details of insects, the ViT’s self-attention mechanism enables a holistic evaluation of their overall configurations. Several data preprocessing steps were implemented to enhance the model’s performance, including data augmentation techniques and strategies to ensure class balance. In addition, hyperparameter optimization contributed to more stable and robust model training. Experimental results indicate that the ConvViT model outperforms commonly used benchmark architectures such as EfficientNetB0, DenseNet201, ResNet-50, VGG-16, and standalone ViT, achieving a classification accuracy of 93.61%. This hybrid approach improves accuracy and strengthens generalization capabilities, delivering steady performance during training and testing phases, thereby increasing its reliability for field applications. The findings highlight that the ConvViT model achieves high efficiency in pest detection by integrating local and global feature learning. Consequently, this scalable artificial intelligence solution can support sustainable agricultural practices by enabling the early and accurate identification of pests and reducing the need for intensive pesticide use.
AtıfScopus9WoS5OpenAlex11İncele - MakaleAçık Erişim2025Scopus SJRQ2Associations Between Pulmonary Function and Muscle Strength in Turkish National Karate Athletes
Tokgoz, Gurkan; Cinarli, Sena; Akyol, Betül; Aygoren, Caner; Beykumul, Aysegul; Larsen, Malte Nejst; Krustrup, Peter; França, Cíntia; Gouveia, Élvio Rúbio; Cinarli, Fahri Safa
Background: Respiratory efficiency is considered important in karate due to its role in sustaining muscular performance during high-intensity actions. This study examined the association between pulmonary function and isometric muscle strength in national-level karate athletes. Methods: A total of 23 elite karate athletes (mean age: 23.0 ± 2.3 (mean ± SD) years) participated in the study. Pulmonary function was assessed using a digital spirometer, while isometric handgrip, lower back, and leg strength were measured using a dynamometer. The correlation between pulmonary function and isometric strength was analyzed, and linear regression was employed to examine the predictive capacity of pulmonary parameters for muscle strength. Results: The results revealed significant correlations, ranging from large to very large, between pulmonary function and isometric muscle strength, with correlation coefficients from 0.639 to 0.812 (p < 0.01). Pulmonary function was strongly associated with isometric strength, accounting for 27% to 67% of the variance (p < 0.05). Multiple regression analysis revealed that pulmonary function parameters accounted for 71% of the variance in handgrip strength, 47% in leg strength, and 71% in back strength (p < 0.05). Conclusions: These findings highlight the strong associations between pulmonary function and isometric muscle strength in elite karate athletes. The results emphasize the importance of pulmonary health and respiratory muscle function in athletic performance, particularly for sports requiring high-intensity, dynamic movements like karate. Future longitudinal studies are needed to explore the mechanisms underlying the association and potential implications, and for training and performance optimization.
- MakaleAçık Erişim2025Scopus SJRQ2A Novel CuAlMnFe/CeO2 Composite Alloy: Investigating the Wear and Corrosion Features
Doğan, Fatih; Duru, Erhan
Shape memory alloys (SMAs) are known for their exceptional mechanical properties, particularly their superior wear resistance compared to conventional alloys with similar surface hardness. Rare earth oxides are often used as additives to further improve these characteristics. This study investigates the effects of different CeO2 (cerium dioxide) concentrations (0.01 wt.%, 0.1 wt.%, 0.5 wt.%, and 1.0 wt.%) on the properties of CuAlMnFe alloys produced via powder metallurgy (PM). Various analyses were performed, including scanning electron microscopy (SEM), Energy Dispersive Spectroscopy (EDS), X-ray diffraction (XRD), as well as hardness, wear, and corrosion tests. The increase in wear rate is closely related to the formation of precipitates from CeO2 addition. Improvements in wear resistance and hardness are attributed to the effects of grain refinement and solid solution strengthening due to CeO2. Specifically, the wear rate increased from 1.5 × 10−3 mm3/(Nm) to 3.4 × 10−3 mm3/(Nm) with higher CeO2 content. Additionally, the friction coefficient of the CuAlMnFe alloy was reduced with CeO2 addition, indicating enhanced frictional properties. The optimal CeO2 concentration of 0.5% was found to improve grain uniformity, resulting in better wear resistance. Incorporating CeO2 particles into CuAlMnFe alloy enhances hardness and reduces wear rate when used in appropriate amounts. Additionally, it exhibits superior corrosion resistance, as evidenced by a positive shift in corrosion potential in Tafel measurements in solutions and a decrease in corrosion current density. The C0.5 specimen showed the highest corrosion potential (Ecorr, −588 V) and the lowest corrosion current density (icorr, 6.17 μA/cm2) during electrochemical corrosion in 3.5 wt.% NaCl solution.
AtıfScopus2WoS2OpenAlex2İncele - MakaleAçık Erişim2024Scopus SJRQ1Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems
Oyucu, Saadin; Polat, Onur; Türkoğlu, Muammer; Polat, Hüseyin; Aksöz, Ahmet; Ağdaş, Mehmet Tevfik
Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.
AtıfScopus36OpenAlex27İncele - MakaleAçık Erişim2024Scopus SJRQ2The Behavior of Ceramic Fiber Geopolymer Concrete under the Effect of High Temperature
Dalğıç, Aras; Yılmazer Polat, Berivan
Geopolymer concrete (GC), also known as green concrete, contains slag, silica fume, and fly ash as binders. The absence of cement in concrete is critical to protect the world from the environmental impacts of cement production. In addition, exposure to high temperatures is a critical parameter that causes loss of strength in concrete. In this study, Geopolymer concrete samples were prepared with 10 different samples containing different proportions of slag, silica fume, and porous ash and subjected to various physical, mechanical, and optical tests. The sample (GS90) with optimum workability and compressive strength, which also showed high performance in water absorption, freeze-thaw, and UPV tests, was used in high-temperature tests. Portland cement concrete (PCC) was used as a control sample. This study investigated the effect of high temperatures on the physical and mechanical properties of fiber-free GCs containing 2%, 5%, and 10% by volume of ceramic fibers. Therefore, fiber-reinforced, fiber-free, and PCC specimens were subjected to high-temperature tests at 100, 300, 600, and 900 °C. As a result of the observation of crack growth, color changes, and compressive strength parameters in the samples subjected to high-temperature tests, the thermal resistance of the 10% ceramic fiber geopolymer concrete sample was 2.5% higher than other samples. There is no study in the literature that examines the behavior of ceramic fiber-reinforced geopolymer concrete at high temperatures. This research revealed an important finding by proving that ceramic fiber reinforcement increases the compressive strength of geopolymer concretes at a remarkable rate after high-temperature impact.
AtıfScopus8WoS10OpenAlex8İncele - MakaleAçık Erişim2024Scopus SJRQ1Exploring Chemical Composition, Antioxidant, Enzyme Inhibitory and Cytotoxic Properties of Glaucium acutidentatum Hausskn. & Bornm. from Turkey Flora: A Novel Source of Bioactive Agents to Design Functional Applications
Yagi, Sakina; Zengin, Gokhan; Uba, Abdullahi Ibrahim; Maciejewska-Turska, Magdalena; Sieniawska, Elwira; Świątek, Łukasz; Rajtar, Barbara; Bahşi, Muammer; Guler, Osman; Dall’Acqua, Stefano
The present study was performed to determine the chemical constituents, cytotoxicity, antioxidant and enzyme inhibition activities of the aerial parts of Glaucium acutidentatum Hausskn. and Bornm. (family Papaveraceae). Methanolic and aqueous extracts were prepared by maceration, homogenizer-assisted extraction (HAE) and infusion. Results showed that the highest total phenolic and flavonoids contents were obtained from the methanol extracts obtained by HAE (53.22 ± 0.10 mg GAE/g) and maceration (30.28 ± 0.51 mg RE/g), respectively. The aporphine, beznyltetrahydroisoquinoline, and protopine types of Glaucium alkaloids have been tentatively identified. Among them, glaucine was identified in all extracts. Flavonoids, phenolic acids, coumarins, organic acids and fatty acids were also detected. Methanolic extract obtained using the HAE method displayed the highest anti-DPPH (41.42 ± 0.62 mg TE/g), total antioxidant (1.20 ± 0.17 mmol TE/g), Cu2+ (113.55 ± 6.44 mg TE/g), and Fe3+ (74.52 ± 4.74 mg TE/g) reducing properties. The aqueous extracts obtained by infusion and HAE methods exerted the best anti-ABTS (103.59 ± 1.49 mg TE/g) and chelating (19.81 ± 0.05 mg EDTAE/g) activities, respectively. Methanolic extract from HAE recorded the highest acetylcholinesterase (2.55 ± 0.10 mg GALAE/g) and α-amylase (0.51 ± 0.02 mmol ACAE/g) inhibition activities, while that obtained by maceration showed the best butyrylcholinesterase (3.76 ± 0.31 mg GALAE/g) inhibition activity. Both extracts revealed the best tyrosinase inhibitory activity (25.15 ± 1.00 and 26.79 ± 2.36 mg KAE/g, p ≥ 0.05). G. acutidentatum maceration-derived aqueous extract showed selective anticancer activity against cells originating from human hypopharyngeal carcinoma. In conclusion, these findings indicated that G. acutidentatum is a promising source of alkaloids and phenolic compounds for variable pharmaceutical formulations.
AtıfScopus13OpenAlex13İncele - MakaleAçık Erişim2024Scopus SJRQ1Impact of a Novel Valerian Extract on Sleep Quality, Relaxation, and GABA/Serotonin Receptor Activity in a Murine Model
Sahin, Kazim; Gencoglu, Hasan; Korkusuz, Ahmet Kayhan; Orhan, Cemal; Aldatmaz, İsmail Ertuğ; Erten, Fusun; Er, Besir; Morde, Abhijeet; Padigaru, Muralidhara; Kilic, Ertugrul
Insomnia is a major global health issue, highlighting the need for treatments that are both effective and safe. Valerian extract, a traditional remedy for sleep problems, offers potential therapeutic options. This research examined the potential sleep-enhancing effects of VA (Valerian Pdr%2) in mice. The study evaluated sleep quality by comparing the impact of the VA extract against melatonin on brain activity, using electrocorticography (ECoG) to assess changes in brain waves. For this purpose, the study utilized two experimental models on BALB/c mice to explore the effects of caffeine-induced insomnia and pentobarbital-induced sleep. In the first model, 25 mice were assigned to five groups to test the effects of caffeine (caffeine, 7.5 mg/kg i.p) alone, caffeine with melatonin (2 mg/kg), or caffeine with different doses of valerian extract (100 or 300 mg/kg) given orally on brain activity, assessed via electrocorticography (ECoG) and further analyses on the receptor proteins and neurotransmitters. In the second model, a different set of 25 mice were divided into five groups to examine the impact of pentobarbital (42 mg/kg) alone, with melatonin, or with the valerian extract on sleep induction, observing the effects 45 min after administration. The study found that ECoG frequencies were lower in groups treated with melatonin and two doses of valerian extract (100 and 300 mg/kg), with 300 mg/kg showing the most significant effect in reducing frequencies compared to the caffeine control group, indicating enhanced sleep quality (p < 0.05). This was supported by increased levels of serotonin, melatonin, and dopamine and higher levels of certain brain receptors in the melatonin and valerian extract groups (p < 0.05). Modulatory efficacy for the apoptotic markers in the brain was also noted (p < 0.05). Additionally, melatonin and both doses of VA increased sleep duration and reduced sleep onset time compared to the pentobarbital control, which was particularly notable with high doses. In conclusion, the findings suggest that high doses (300 mg/kg) of valerian extract enhance both the quantity and quality of sleep through the GABAergic pathway and effectively increase sleep duration while reducing the time to fall asleep in a pentobarbital-induced sleep model in mice.
AtıfScopus19OpenAlex19İncele - MakaleAçık Erişim2024Scopus SJRQ2On LP-Kenmotsu Manifold with Regard to Generalized Symmetric Metric Connection of Type (α, β)
Prakasha, Doddabhadrappla Gowda; Bin Turki, Nasser; Veerabhadraswamy Deepika, Mathad; Ünal, İnan
In the current article, we examine Lorentzian para-Kenmotsu (shortly, LP-Kenmotsu) manifolds with regard to the generalized symmetric metric connection (Formula presented.) of type (Formula presented.). First, we obtain the expressions for curvature tensor, Ricci tensor and scalar curvature of an LP-Kenmotsu manifold with regard to the connection (Formula presented.). Next, we analyze LP-Kenmotsu manifolds equipped with the connection (Formula presented.) that are locally symmetric, Ricci semi-symmetric, and (Formula presented.) -Ricci symmetric and also demonstrated that in all these situations the manifold is an Einstein one with regard to the connection (Formula presented.). Moreover, we obtain some conclusions about projectively flat, projectively semi-symmetric and (Formula presented.) -projectively flat LP-Kenmotsu manifolds concerning the connection (Formula presented.) along with several consequences through corollaries. Ultimately, we provide a 5-dimensional LP-Kenmotsu manifold example to validate the derived expressions.
AtıfScopus3OpenAlex1İncele - MakaleAçık Erişim2024Scopus SJRQ2Photocatalytic Hydrogen Evolution of TiZrNbHfTaOx High-Entropy Oxide Synthesized by Mechano-Thermal Method
Güler, Ömer; Boyrazlı, Mustafa; Albayrak, Muhammet Gökhan; Güler, Seval Hale; Ishihara, Tatsumi; Edalati, Kaveh
One of the most promising solutions to slow down CO2 emissions is the use of photocatalysis to produce hydrogen as a clean fuel. However, the efficiency of the photocatalysts is not at the desired level, and they usually need precious metal co-catalysts for reactions. In this study, to achieve efficient photocatalytic hydrogen production, a high-entropy oxide was synthesized by a mechano-thermal method. The synthesized high-entropy oxide had a bandgap of 2.45 eV, which coincided with both UV and visible light regions. The material could successfully produce hydrogen from water under light, but the main difference to conventional photocatalysts was that the photocatalysis proceeded without a co-catalyst addition. Hydrogen production increased with increasing time, and at the end of the 3 h period, 134.76 µmol/m2 h of hydrogen was produced. These findings not only introduce a new method for producing high-entropy photocatalysts but also confirm the high potential of high-entropy photocatalysts for hydrogen production without the need for precious metal co-catalysts.
AtıfScopus33WoS30İncele - MakaleAçık Erişim2024Scopus SJRQ1Determining the Levels of Cortisol, Testosterone, Lactic Acid and Anaerobic Performance in Athletes Using Various Forms of Coffee
Gür, Melda; Çınar, Vedat; Akbulut, Taner; Bozbay, Kenan; Yücedal, Polat; Aslan, Mehdi; Avcu, Gökçe; Padulo, Johnny; Russo, Luca; Rog, Joanna
Background: Coffee is considered one of the most preferred and consumed beverage types in the world, and caffeine is known to increase physical performance due to its ergogenic properties. The aim of this study is to examine the effects of coffee consumption in different forms on cortisol, testosterone, lactic acid and anaerobic performance levels. Methods: A total of 15 licensed male football players participated in the research voluntarily. The research was implemented in a single-blind, counterbalanced, randomized and crossover study design. Participants were given caffeinated coffee (CK), decaffeinated coffee (placebo) (DK), powdered caffeine (in a gelatin capsule) (PC) and powdered placebo (maltodextrin in a capsule) (PM) on different days, and the Wingate test protocol was performed after the warm-up protocol. Blood samples were collected post-test. Cortisol, testosterone and lactic acid levels in the serum samples taken were determined by the ELISA method. Results: As a result, it was revealed that caffeinated coffee given to participants who exercise increased anaerobic power. However, it was observed that lactic acid levels were higher in placebo and decaffeinated coffee. The highest level of cortisol was found in caffeinated coffee and powdered caffeine compared to the placebo. Testosterone values were observed to be highest in caffeinated coffee and decaffeinated coffee compared to a placebo. Conclusions: The study suggests that the type of caffeine is a factor that affects absorption rate, which impacts performance and hormone levels.
AtıfScopus9OpenAlex12İncele - MakaleAçık Erişim2024Scopus SJRQ2Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
Korkmaz, Adem; Agdas, Mehmet Tevfik; Kosunalp, Selahattin; Iliev, Teodor; Stoyanov, Ivaylo
The increasing global population and environmental changes pose significant challenges to food security and sustainable agricultural practices. To overcome these challenges, protecting farm animals and effectively detecting potential environmental threats is critical for economic and ecological sustainability. In this context, the current study examined the animal detection capabilities and efficiency of advanced deep learning models, such as YOLOv8, Yolo-NAS, and Fast-RNN, across a dataset of 2462 images encompassing various animal species that could pose a risk to farm animals. After converting the images into a standardized format, they were divided into three sets for training, validation, and testing, and each model was evaluated on this dataset during the analysis process. The findings indicated that the YOLOv8 model demonstrated superior performance, with 93% precision, 85.2% recall, and 93.1% mAP50 values, while Yolo-NAS was particularly noteworthy for its high recall value, indicating a remarkable detection ability. The Fast-RNN model also offered significant efficiency with balanced performance. The results reveal the considerable potential of deep learning-based object detection technologies in protecting farm animals and enhancing farm security. Additionally, this study provides valuable insights for future model optimization and customization research.
AtıfScopus9WoS7OpenAlex7İncele - MakaleAçık Erişim2024Scopus SJRQ2Leptin, Ghrelin, Irisin, Asprosin and Subfatin Changes in Obese Women: Effect of Exercise and Different Nutrition Types
Bengin, Elif; Kırtepe, Abdurrahman; Çınar, Vedat; Akbulut, Taner; Russo, Luca; Aydemir, İsa; Yücedal, Polat; Aydın, Süleyman; Migliaccio, Gian Mario
Background and Objectives: In this study, the effects of a six-week training program and various diets on subfatin, asprosin, irisin, leptin, ghrelin and the lipid profile were investigated in overweight women. Materials and Methods: A total of 78 women voluntarily participated in the study. Groups: The study was divided into eight groups: Healthy Control, Obese Control, Obese + Vegetarian, Obese + Ketogenic, Obese + Intermittent Fasting, Obese + Exercise + Vegetarian, Obese + Exercise + Ketogenic and Obese + Exercise + Intermittent Fasting. While there was no intervention in the healthy and obese control groups, the other groups followed predetermined exercise and diet programs for 6 weeks. Blood samples were taken from the participants in the research group twice (before and after the interventions). An autoanalyzer was used to determine the lipid profile in the blood samples taken, and the ELISA method was used to analyze other parameters. Results: Overall, a significant difference was found in the values of weight, BMI, subfatin, ghrelin, leptin, cholesterol, triglyceride, HDL and LDL as a result of the exercise and diet interventions (p < 0.05). There was no significant difference in asprosin and irisin values (p > 0.05). Conclusions: In conclusion, regular exercise and dietary interventions in obese women can regulate lipid profile, ghrelin, leptin and asprosin levels, and increasing irisin with exercise can activate lipid metabolism and support positive changes in lean mass.
AtıfScopus15OpenAlex14İncele - MakaleAçık Erişim2024Scopus SJRQ2Enhancing Fault Location Accuracy in Transmission Lines Using Transient Frequency Spectrum Analysis: An Investigation into Key Factors and Improvement Strategies
Akdağ, Mustafa; Mamiş, Mehmet Salih; Akmaz, Düzgün
Fault location estimation in transmission lines is critical for power system reliability. Various methods have been developed for this purpose, among which transient frequency spectrum analysis (TFSA) stands out as a recent method based on travelling wave (TW) theory. TFSA determines the fault location by analyzing the frequency spectrum of transient currents and/or voltages at the instant of the fault, offering advantages such as independence from fault impedance and the ability to locate faults with one-side measurements. Despite its success in fault location, TFSA has several considerations that warrant detailed investigation. This study explores the effects of source inductance, series compensation, fault arc, and current transformer (CT) characteristics on transient frequencies. Additionally, the impact of noise on TFSA results is examined. The new proposed source inductance compensation method can reduce the error of 6.55% to 0.88%, where the same error can be reduced to 3.45% with the compensation method given in previous study. Strategies to enhance accuracy are discussed and compared to previous studies, including a proposed detection approach providing appropriate data size and precise wave propagation speed calculations. These findings contribute to a deeper understanding of TFSA’s limitations and inform practical improvements for fault location accuracy in power transmission systems.
AtıfScopus6WoS5OpenAlex3İncele - MakaleAçık Erişim2024Scopus SJRQ2BSC-Based Digital Transformation Strategy Selection and Sensitivity Analysis
Oner, Mahir; Cebeci, Ufuk; Dogan, Onur
In today’s digital age, businesses are tasked with adapting to rapidly advancing technology. This transformation is far from simple, with many companies facing difficulties navigating new technological trends. This paper highlights a key segment of a comprehensive strategic model developed to address this challenge. The model integrates various planning and decision-making tools, such as the Balanced Scorecard (BSC), Objectives and Key Results (OKR), SWOT analysis, TOWS, and the Spherical Fuzzy Analytic Hierarchy Process (SFAHP). Integrating these tools in the proposed model provides businesses with a well-rounded pathway to manage digital transformation. The model considers human elements, uncertainty management, needs prioritization, and flexibility, aiming to find the optimal balance between theory and practical applications in real-world business scenarios. This particular study delves into the use of SFAHP, specifically addressing the challenge of effectively selecting the most suitable strategy among various options. This approach not only brings a new perspective to digital transformation but also highlights the importance of choosing the right strategy. This choice is crucial for the overall adaptation of businesses. It shows how carefully applying the SFAHP method is key. Combining this with a successful digital transformation strategy is essential. Together, they provide practical and efficient solutions for businesses in a fast-changing technological environment.
AtıfScopus5İncele - MakaleAçık Erişim2023Scopus SJRQ2Automated Hypertension Detection Using ConvMixer and Spectrogram Techniques with Ballistocardiograph Signals
Ozcelik, Salih T. A.; Uyanık, Hakan; Deniz, Erkan; Sengur, Abdulkadir
Blood pressure is the pressure exerted by the blood in the veins against the walls of the veins. If this value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). This health problem which often referred to as the “silent killer” reduces the quality of life and causes severe damage to many body parts in various ways. Besides, its mortality rate is very high. Hence, rapid and effective diagnosis of this health problem is crucial. In this study, an automatic diagnosis of HPT has been proposed using ballistocardiography (BCG) signals. The BCG signals were transformed to the time-frequency domain using the spectrogram method. While creating the spectrogram images, parameters such as window type, window length, overlapping rate, and fast Fourier transform size were adjusted. Then, these images were classified using ConvMixer architecture, similar to vision transformers (ViT) and multi-layer perceptron (MLP)-mixer structures, which have attracted a lot of attention. Its performance was compared with classical architectures such as ResNet18 and ResNet50. The results obtained showed that the ConvMixer structure gave very successful results and a very short operation time. Our proposed model has obtained an accuracy of 98.14%, 98.79%, and 97.69% for the ResNet18, ResNet50, and ConvMixer architectures, respectively. In addition, it has been observed that the processing time of the ConvMixer architecture is relatively short compared to these two architectures.
AtıfScopus21WoS16OpenAlex21İncele