Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Gul, Muhammet(102)
- Güler, Ömer(69)
- Akkurt, Nevzat(68)
- Tekin, H.O.(52)
- ALMisned, Ghada(49)
- Yucesan, Melih(49)
- Celik, Erkan(45)
- Zengin, Gokhan(40)
- Serdar, Osman(39)
- Kocabaş, Mehmet(37)
- Aslan, Naim(31)
- Baskonus, Haci Mehmet(31)
- Bulut, Hasan(31)
- Mete, Suleyman(31)
- Paksoy, Mehmet Yavuz(30)
- Kök, Mediha(27)
- Ince, Muharrem(26)
- Yildirim, Ozal(26)
- Özkul, İskender(26)
- Sulaiman, Tukur Abdulkadir(25)
- Topal, Murat(25)
- Guler, Omer(24)
- Serin, Faruk(23)
- Benzer, Fulya(22)
- Erten, Fusun(22)
- Alataş, Mevlüt(21)
- Güler, Seval Hale(21)
- Baykal, Duygu Sen(20)
- Taşgın, Yahya(20)
- Kutlu, Banu(19)
- Onat, Onur(19)
- Sahin, Kazim(19)
- Tanyol, Mehtap(19)
- Torğut, Gülben(19)
- Yildirim, Numan(19)
- Kahraman, Gökhan(18)
- Orhan, Cemal(18)
- Say, Yakup(18)
- Susam, Selim Orhun(18)
- Utku, Anıl(18)
Yayınlar
- MakaleYalnız künye2023Scopus SJRQ1A data-driven evaluating method on the defrosting effect of the air source heat pump system in Beijing
Guo, Yanhua; Shao, Shuangquan; Geng, Xudong; Li, Hao; Wang, Zhichao; Akkurt, Nevzat
The current demand-based defrosting initiation strategies of air source heat pumps (ASHPs) faces difficulty in achieving a balance between cost and robustness. Therefore, an efficient and innovative demand-based defrosting initiation strategy is proposed in this paper, based on the learned the degradation of heating capacity (DHC) method using data-driven model. Firstly, the DHC method is developed using the initially installed sensors without additional sensors to identify the frosty state. Subsequently, the fully connected neural network (FNN) model is established for predicting frosty state using field measured data from the ASHP system. Finally, the effectiveness of the defrosting initiation strategy is further validated through practical testing. The results demonstrate that the DHC method provides a reliable database for training the FNN model, resulting in an impressive accuracy of 91.43% for predicting the frosty state in the testing set. By adopting the innovative demand-based defrosting initiation strategies, the defrosting frequency, heating loss, and power consumption are respectively decreased by 66.3%, 2.1%, and 6.0% with the SCOP enhanced by 8.6% during a heating season. The promising results highlight that the proposed strategy effectively reduces costs while ensuring the efficiency of demand-based control.
AtıfScopus23WoS22OpenAlex27
İncele - MakaleYalnız künye2023Scopus SJRQ2Investigation of Antimicrobial, Anti-Quorum Sensing, and Cytotoxic Activities of Flavonoids Isolated from Pulicaria armena Boiss. & Kotschy ex Boiss. (Asteraceae)
Başpınar, Yasemin; Gürbüz, Perihan; Dilem Doğan, Şengül; Gözde Gündüz, Miyase; Aleksic, Ivana; Vojnovic, Sandra; Nikodinovic-Runic, Jasmina; Yavuz Paksoy, Mehmet
This is the first report on the separation and biological assessment of all metabolites derived from Pulicaria armena (Asteraceae) which is an endemic species narrowly distributed in the eastern part of Turkey. The phytochemical analysis of P. armena resulted in the identification of one simple phenolic glucoside together with eight flavon and flavonol derivatives whose chemical structures were elucidated by NMR experiments and by the comparison of the spectral data with the relevant literature. The screening of all molecules for their antimicrobial, anti-quorum sensing, and cytotoxic activities revealed the biological potential of some of the isolated compounds. Additionally, quorum sensing inhibitory activity of quercetagetin 5,7,3’ trimethyl ether was supported by molecular docking studies in the active site of LasR which is the primary regulator of this cell-to-cell communication system in bacteria. Lastly, the critical molecular properties indicating drug-likeness of the compounds isolated from P. armena were predicted. As microbial infections can be a serious problem for cancer patients with compromised immune systems, this comprehensive phytochemical research on P. armena with its anti-quorum sensing and cytotoxic compounds can provide a new approach to the treatment.
AtıfScopus1OpenAlex1
İncele - MakaleYalnız künye2023Scopus SJRQ1In-vitro corrosion and surface properties of PVD-coated β-type TNTZ alloys for potential usage as biomaterials: Investigating the hardness, adhesion, and antibacterial properties of TiN, ZrN, and CrN film
Ali, L.A.; Dikici, B.; Aslan, N.; Yilmazer, Y.; Sen, A.; Yilmazer, H.; Niinomi, M.
In this study, the corrosion and surface properties of non-toxic and non-allergenic β-type Ti–29Nb–13Ta–4.6Zr alloys (TNTZ) coated with TiN, ZrN, and CrN layers using the PVD technique were investigated under in-vitro conditions. The coatings' hardness, adhesion resistance, and antibacterial properties were also studied, with a focus on their potential usage as biomaterials. The corrosion tests were conducted using potentiodynamic polarization, the hardness tests were conducted using the Knoop method, and the adhesion tests were conducted using pull-off tests. The wetting properties of the coatings were examined through contact angle and 3D morphological analysis. The coatings were characterized using SEM-EDS and XRD. The results showed that the adhesion resistance of all coatings was significantly higher than the expected adhesion strength for biomaterial coatings, while their corrosion resistance was higher compared to the uncoated alloy. Besides, the study demonstrates the potential of TiN, ZrN, and CrN coatings on β-type TNTZ for use as a biomaterial with desirable surface and antibacterial properties.
AtıfScopus29OpenAlex27İncele - MakaleAçık Erişim2023Scopus SJRQ1Integrating stratified best–worst method and GIS for landslide susceptibility assessment: a case study in Erzurum province (Turkey)
Konurhan, Zekeriya; Yucesan, Melih; Gul, Muhammet
Landslides are among the most destructive geological disasters that seriously damage human life and infrastructures. Landslides mainly occur in mountainous regions around the world. One of the key processes to reduce these damages is to uncover landslide-exposed areas through different data-driven methods such as Geographical Information System (GIS) and multi-criteria decision-making (MCDM). In the literature, there are many studies developed with these fundamental tools. In this study, unlike the literature, a new landslide susceptibility assessment model is proposed by integrating GIS with the stratified best–worst method (S-BWM). This model has four main dimensions and 16 sub-dimensions under topography, environment-land, location, and hydrological factors, weighted with the S-BWM. A network was created considering the different states that may arise in the importance weights of these dimensions in the future. The transition probabilities of these states were predicted and injected into the classical BWM. Then, maps were created for these dimensions and classifications for each sub-dimension according to the map characteristics. Finally, the most susceptive landslide locations were determined with GIS-based calculations. To demonstrate the model’s applicability, a case study was conducted for the Erzurum region, one of Turkey’s landslide-prone regions. In addition, besides the landslide map, an analysis and discussion about the spatial distribution of susceptibility classes was presented, contributing to the study’s robustness. In the results of landslide susceptibility analysis, landslides are higher in the range of about 1600–2500 m. Approximately 42% (35.59 sq. km) of the study area has high landslide susceptibility, while 58% (64.41 sq. km) has medium and low landslide susceptibility.
AtıfScopus19WoS17OpenAlex18İncele - MakaleYalnız künye2023Scopus SJRQ2Ternary hybrid nanofluids flow on a spinning disk with nonlinear thermal radiation
Gohar; Saeed, Anwar; Alyami, Maryam Ahmed; Akkurt, Nevzat; Khan, Tahir Saeed; Gul, Taza
Researchers are interested in the thermophysical properties of nanofluids because of the successful use of nanofluids in state-of-the-art technology and engineering. Hence, hybrid and tri-hybrid nanofluid is used to ensure the required thermal features. An incompressible ternary hybrid nanofluid (NF) flow comprised of (Al2O3) aluminum oxide, (CuO) copper oxide, and (TiO2) titanium dioxide nanoparticles across an impermeable infinite spinning disc are addressed in the present study. The magnetic field and second-degree thermal radiation were used in the proposed model. The main aim of this assessment is to raise the consciousness of energy exhaustion in the industrial and technical sectors. The numerous applicability of ternary nanoparticles (NPs) make the proposed model more plausible, such as, Al2O3 has society-enhancing and life-extending applications, CuO has unique electrical, optical, and magnetic properties, while TiO2 is excessively used in plastic and paper production. For this purpose, the sensations have been expressed in the form of differential equations and the consequences are obtained using the homotopy analysis method (HAM). The graphics and statistical outcomes of the analytical simulation of the proposed model are reported through Figures and Tables.
AtıfScopus14WoS14OpenAlex15İncele - MakaleYalnız künye2023Scopus SJRQ2Antibacterial activity of novel synthesized chitosan-graft-poly(N-tertiary butylacrylamide)/neodymium composites for biomedical application
Yazdıç, Ferit Can; Karaman, Altuğ; Torğut, Gülben; Ayhan, Nagihan Karaaslan
In this present study, composites of chitosan-graft-poly(N-tertiary butylacrylamide) (CH-graft-poly(N‑tert‑BAAm)) copolymer, with Neodymium (Nd), an important rare earth element, were prepared by precipitation technique. Nd was successfully incorporated into the polymer of different weight percentages (0.5%, 1%, and 2%) without any degradation. The effect of neodymium additives on the structural, morphological, and antibacterial activities against gram-positive bacteria and gram-negative bacteria of the polymer was analyzed using various instrument techniques. X-ray diffraction (XRD) results together with Fourier Transform Infrared (FT-IR), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) studies confirmed the morphology of Nd-doped CH-graft-poly(N‑tert‑BAAm) composites without any other impurities. The antibacterial effect of Nd was studied by adding it to the copolymer in a weight ratio of 0.5%–2%. The antibacterial effect of neodymium concentration on four different strains of bacteria was investigated: Escherichia coli (ATCC 25922) (E. coli), Pseudomonas aeruginosa (DSM 50071) (P. aeruginosa), Bacillus subtilis (DSM 1971) (B. subtilis), and Staphylococcus aureus subsp. aureus (ATCC 25923) (S. aureus). The antibacterial activities of the obtained composites were determined using the Agar Well Diffusion Assay Method. Experimental results show that Nd binds well to CH-graft-poly(N‑tert‑BAAm). Activity against E. coli, P. aeruginosa, B. subtilis, and S. aureus subsp. aureus creates a potential for pharmaceutical and biomedical applications.
AtıfScopus8WoS7OpenAlex8İncele - MakaleYayıncıda açık erişim2023Scopus SJRQ3Optimization Approach in Window Function Design for Real-Time Filter Applications
Serbet, Fatmanur; Kaya, Turgay
Eliminating the Gibbs oscillations that occur during the Finite Impulse Response (FIR) digital filter design with the Fourier Series method will ensure correct filtering. For this reason, the development of the window improves the performance of the filter and, therefore, the system. In this study, the cosh window function is designed using Particle Swarm Optimization, which is a preferred optimization method in many areas. Thus, alternatives to the standard results obtained from the existing traditional calculations will be produced, and different windows that perform the same function will be obtained. In addition, exponential and cosh window functions were designed in LabVIEW environment, which is a graphical programming language-based program, and the designed windows were analyzed at different parameter values. LabVIEW provides a fast and easy programming environment, and it provides the opportunity to realize real-Time applications with its external hardware. Utilizing this feature, the amplitude spectrum of cosh window designed in LabVIEW is displayed in real time for different window parameter values. As a result, FIR digital filters were designed using cosh window based on optimization and the cosh window designed in LabVIEW, and the distorted EEG signal was filtered using these filters and displayed in real time.
AtıfScopus8WoS5OpenAlex5İncele - MakaleAçık Erişim2023Scopus SJRQ1Bioaccumulation Pattern and Health Risk Assessment of Heavy Metals in Cirrhinus mrigala at Panjnad Headworks, Bahawalpur, Pakistan
Naz, Saima; Chatha, Ahmad Manan Mustafa; Danabas, Durali; Khan, Muhammad Farhan; Xu, Youhou; Zhu, Peng; Shafique, Laiba
Heavy metal accumulation in freshwater ecosystem has become one of the major aquatic environmental concerns for freshwater flora and fauna due to their higher stability and bioaccumulation as well as bio-magnification properties. Furthermore, passing through the food web, these heavy metals affect human populations ultimately. This study assessed the heavy metal accumulation in Cirrhinus mrigala in spring, autumn, and winter at different locations (I, II, and III) of Panjnad headwork. Furthermore, the human health risk assessment for the consumption of C. mrigala from the sampling locations was also carried out. Fish were collected from upper (I), middle (II), and lower (III) stream of Panjnad on a monthly basis. The current study evaluated the accumulation of Aluminum (Al), Arsenic (As), Barium (Ba), and Lead (Pb) in various fish organs (liver, kidney, gills, fins, skin, muscles and bones) and assessed their potential hazard to human health through health risk assessment indicators. The results demonstrated a significant difference (p < 0.05) in heavy metal accumulation in different fish organs, seasons, and locations. The accumulation of Al, As, Ba, and Pb were considerably higher in liver and kidney as compared to the other body organs and followed a trend of liver > kidney > gills > fins > skin > bones > muscle and the overall mean concentrations of metals in different body tissues of C. mrigala were in the order of Al > As > Ba > Pb. The results also concluded that C. mrigala caught from the Panjnad headwork is not safe for human consumption due to higher values of TTHQIng (3.76), THQIng for Ba (3.27) and CRIng for As (6.4742).
AtıfScopus27OpenAlex25İncele - MakaleYalnız künye2023Scopus SJRQ2New Deep Learning-Based Passenger Flow Prediction Model
Utku, Anıl; Kaya, Sema Kayapinar
The ability to predict passenger flow in transport networks is an important aspect of public transport management. It helps improve transport services, aids those responsible for management to obtain early warning signals of emergencies and unusual circumstances and, in general, makes cities smarter and safer. This paper develops a long short-term memory-based (LTSM-based) deep learning model to predict short-term transit passenger volume on transport routes in Istanbul. This prediction model has been created using a dataset that included the number of people who used different transit routes in Istanbul at one-hour intervals between January and December 2020. The proposed multilayer LSTM-based deep learning model has been compared with popular models such as random forest (RF), support vector machines, autoregressive integrated moving average, multilayer perceptron, and convolutional neural network. The experimental findings showed that the proposed multilayer LSTM-based deep learning model outperformed the other models with regard to prediction for each transfer route. Furthermore, RF, one of the machine learning models used, produced remarkably successful results.
AtıfScopus19WoS12OpenAlex16İncele - MakaleAçık Erişim2023Scopus SJRQ1Numerical study for battery thermal management system improvement with air channel in electric vehicles
Tuğan, Volkan; Yardımcı, Uğurcan
In order to ensure the safe and efficient operation of the battery system in electric vehicles, the heat generated in the batteries must be rapidly removed from the system. Therefore, a battery thermal management system is needed to ensure that the battery pack operates at appropriate temperatures. This study focuses on improving the battery thermal management system in electric vehicles. For this purpose, two different types of battery modules with new air channel design were created. The temperature distribution, maximum temperature difference and pressure drop in the battery modules were numerically investigated with computational fluid dynamics. In addition, the effect of three different air velocities (1 m/s, 2 m/s and 3 m/s) on the battery temperature was evaluated. The results showed that the maximum temperature and maximum temperature difference in the battery decreased with the increase of the air inlet velocity. It was observed that the maximum temperature difference obtained for design 2 and design 3 decreased in the range of 15.01–17.93 % and 7.25–12.22 %, respectively, compared to Design 1.
AtıfScopus34WoS35İncele - MakaleAçık Erişim2023Scopus SJRQ2ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images
Imak, Andaç; Çelebi, Adalet; Polat, Onur; Türkoğlu, Muammer; Şengür, Abdulkadir
Objective: Impacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years, major advances have been made in medical imaging segmentation using deep convolutional neural network-based networks. In this study, we report on the development of an artificial intelligence system for the automatic identification of impacted tooth from panoramic dental X-ray images. Methods: Among existing networks, in medical imaging segmentation, U-Net architectures are widely implemented. In this article, for dental X-ray image segmentation, blocks and convolutional block structures using inverted residual blocks are upgraded by taking advantage of U-Net’s network capacity-intensive connections. At the same time, we propose a method for jumping connections in which bi-directional convolution long short-term memory is used instead of a simple connection. Assessment of the proposed artificial intelligence model performance was evaluated with accuracy, F1-score, intersection over union, and recall. Results: In the proposed method, experimental results are obtained with 99.82% accuracy, 91.59% F1-score, 84.48% intersection over union, and 90.71% recall. Conclusion: Our findings show that our artificial intelligence system could help with future diagnostic support in clinical practice.
AtıfScopus21OpenAlex23İncele - MakaleYalnız künye2023Scopus SJRQ2Numerical investigation of the effect of using louver baffles on thermal performance in solar air collectors
Tuğan, Volkan; Işık, Erdem
Conventional flat plate solar air collectors have advantages such as low cost and simple design. However, their thermal efficiency is lower than that of other designs. The thermal efficiency of these collectors can be increased by detailed examination of parameters such as the shape, size, arrangement and material of the air duct. In this study, it is aimed to improve the thermal efficiency of solar air collectors. The effect of louver baffles, which were not previously used in solar air collectors, on thermal efficiency was investigated numerically via computational fluid dynamics. Three different solar air collectors with flat plates, segmented baffles, and louver baffles were compared in terms of thermal efficiency. In addition, pressure drops in solar air collectors were investigated. The mass flow rate of the air in all collectors is assumed to be 0.04 kg/s. Analyzes were carried out between 09:00 and 16:00. The thermal efficiency values obtained during the day for solar air collectors with flat plate, segmented baffles and louver baffles are 27.66%–56.35%, 29.57%–58.82% and 30.09%–59.47%, respectively. The daily thermal efficiency values obtained for solar air collectors with flat plate, segmented baffles and louver baffles are 46.98%, 49.18% and 49.87%, respectively. The pressure drop for solar air collectors with flat plate, segmented baffles and louver baffles were found to be 4.62, 18.82 and 5.96 Pa, respectively. Based on the data obtained from this study, louver baffles are recommended for use in solar heating applications due to their high thermal efficiency and low pressure drop.
AtıfScopus5WoS5OpenAlex5İncele - MakaleYayıncıda açık erişim2023Scopus SJRQ1Soil-dependent β and γ shape parameters of the Haverkamp infiltration model for 3D infiltration flow
Yilmaz, D.; Lassabatere, L.; Moret-Fernandez, D.; Rahmati, M.; Angulo-Jaramillo, R.; Latorre, B.
Estimating of soil sorptivity ((Figure presented.)) and saturated hydraulic conductivity ((Figure presented.)) parameters by field infiltration tests are widespread due to the ease of the experimental protocol and data treatment. The analytical equation proposed by Haverkamp et al. (1994) allows the modelling of the cumulative infiltration process, from which the hydraulic parameters can be estimated. This model depends on both initial and final values of the soil hydraulic conductivity, initial soil sorptivity, the volumetric water content increase ((Figure presented.)) and two infiltration constants, the so-called (Figure presented.) and (Figure presented.) parameters. However, to reduce the number of unknown variables when inverting experimental data, constant parameters such as (Figure presented.) and (Figure presented.) are usually prefixed to 0.6 and 0.75, respectively. In this study, the values of these constants are investigated using numerical infiltration curves for different soil types and initial soil water contents for the van Genuchten-Mualem (vGM) soil hydraulic model. Our new approach considers the long-time expansions of the Haverkamp model, the exact soil properties such as (Figure presented.), (Figure presented.) and initial soil moisture to derive the value of the (Figure presented.) and (Figure presented.) parameters for each specific case. We then generated numerically cumulative infiltration curves using Hydrus-3D software and fitted the long-time expansions to derive the value of the (Figure presented.) and (Figure presented.) parameters. The results show that these parameters are influenced by the initial soil water content and the soil type. However, for initially dry soil conditions, some prefixed values can be proposed instead of the currently used values. If an accurate estimate of (Figure presented.) and (Figure presented.) is the case, then for coarse-textured soils such as sand and loamy sand, we propose the use of 0.9 for both constants. For the remaining soils, the value of 0.75 can be retained for (Figure presented.). For (Figure presented.) constant, 0.75 and 1.5 values can be considered for, intermediate permeable soils (sandy loam and loam) and low permeable soils (silty loam and silt), respectively. We clarify that the results are based on using the vGM model to describe the hydraulic functions of the soil and that the results may differ, and the assumptions may change for other models.
AtıfScopus12OpenAlex10İncele - MakaleYalnız künye2023Scopus SJRQ1An efficient hybrid weather prediction model based on deep learning
Utku, A.; Can, U.
Weather events directly affect human activities. In particular, extreme weather events with global warming, forest fires, and high air temperatures that cause drought make human life difficult. Effective and accurate weather prediction models are needed to take precautions against such climatic events. Therefore, it is essential to develop models that make precise weather predictions. Technological developments contributed significantly to developing successful deep learning-based weather prediction models. With a high success rate, this study proposed a hybrid weather prediction model based on Convolutional Neural Networks and Recurrent Neural Networks models. The proposed hybrid model was applied to the Jena dataset, which contains 14-parameter, large-scale meteorological data that were never utilized for weather prediction. The experimental results were compared with popular deep learning, machine learning, and statistical methods such as Auto-Regressive Integrated Moving Average, Convolution Neural Networks, Long-Short Term Memory, Multilayer Perceptron, Random Forest, Recurrent Neural Networks, and Support Vector Machine. As a result of these comparisons, the proposed hybrid model obtained the best prediction result for all metrics. For example, according to the weather prediction results for Jena, Germany, the proposed hybrid model got the results of Mean Squared Error: 0.035, Root-Mean-Squared Error: 0.189, Mean Absolute Error: 0.126, and R-Squared: 0.987.
AtıfScopus15WoS11OpenAlex11İncele - MakaleAçık Erişim2023Scopus SJRQ1Adding New Scientific Evidences on the Pharmaceutical Properties of Pelargonium quercetorum Agnew Extracts by Using In Vitro and In Silico Approaches
Chiavaroli, Annalisa; Libero, Maria Loreta; Di Simone, Simonetta Cristina; Acquaviva, Alessandra; Nilofar; Recinella, Lucia; Leone, Sheila; Brunetti, Luigi; Cicia, Donatella; Izzo, Angelo Antonio
Pelargonium quercetorum is a medicinal plant traditionally used for treating intestinal worms. In the present study, the chemical composition and bio-pharmacological properties of P. quercetorum extracts were investigated. Enzyme inhibition and scavenging/reducing properties of water, methanol, and ethyl acetate extracts were assayed. The extracts were also studied in an ex vivo experimental model of colon inflammation, and in this context the gene expression of cyclooxygenase-2 (COX-2) and tumor necrosis factor α (TNFα) were assayed. Additionally, in colon cancer HCT116 cells, the gene expression of transient receptor potential cation channel subfamily M (melastatin) member 8 (TRPM8), possibly involved in colon carcinogenesis, was conducted as well. The extracts showed a different qualitative and quantitative content of phytochemicals, with water and methanol extracts being richer in total phenols and flavonoids, among which are flavonol glycosides and hydroxycinnamic acids. This could explain, at least in part, the higher antioxidant effects shown by methanol and water extracts, compared with ethyl acetate extract. By contrast, the ethyl acetate was more effective as cytotoxic agent against colon cancer cells, and this could be related, albeit partially, to the content of thymol and to its putative ability to downregulate TRPM8 gene expression. Additionally, the ethyl acetate extract was effective in inhibiting the gene expression of COX-2 and TNFα in isolated colon tissue exposed to LPS. Overall, the present results support future studies for investigating protective effects against gut inflammatory diseases.
AtıfScopus35OpenAlex35İncele - Derleme MakaleYalnız künye2023Scopus SJRQ1A review of advances in mechanical behaviors of the underground energy transmission pipeline network under loads
Zheng, Qiang; Xu, Qian; Shu, Zekai; Yang, Di; Chen, Weiwang; Akkurt, Nevzat; Zhang, Hui; Lin, Lin; Zhang, Xinxin; Ding, Yulong
The safety of underground energy transmission pipeline network is threatened by traffic, earthquakes, explosions, and other stresses. To solve the problem of damage to the underground energy transmission pipeline networks by external loads, this study analyzed the mechanical properties of the Pipe-Soil Coupling System (PSCS) under external loads from a mechanical perspective. The pipeline constitutive model, the soil constitutive model, the internal effect of the PSCS, the influence of external stresses on the PSCS, and the dynamic response of the PSCS under various research methodologies were all studied. First, different mechanical features of the pipes and soil were employed to investigate the pipe and soil constitutive models. Second, based on the different force directions within the PSCS, the earth pressure and axial friction of the PSCS were investigated to clarify the interaction between the pipe and soil. For the purpose of to better understand the degradation of the underground pipeline state, the impacts of man-made and natural stresses on the PSCS failure were explored. Finally, the results of the investigation of the dynamic response of the PSCS were examined for their applicability using three research methods: theoretical analysis, numerical simulation, and experimental analysis. It is found that the existing constitutive models of pipeline and soil are lack of basis, which increases the difficulty of the selection of constitutive models in the process of research. In the analysis of load effect, earth pressure plays a dominant role in affecting the local stability of pipeline, but the calculation accuracy of soil pressure needs to be improved. Due to the lack of experimental guidance for complex damage phenomena under external loads, it is impossible to accurately determine the limit state parameters of pipelines under complex damage modes. It is critical to create and refine the theory underpinned by numerous experimental data in order to address the issue of pipeline dynamic response under multi-field coupling, including explosion and earthquake. This work is critical for analyzing existing research findings, identifying future research problems, and enhancing buried pipeline safety management.
AtıfScopus15WoS13OpenAlex14İncele - Kitap BölümüYalnız künye2023When ideology is all that matters!: Exploring non-violent islamism through fetullah Gulen and Taqiuddin an-nabhani
Orofino, Elisa; Çobanoğlu, Yavuz
AtıfScopus1İncele - MakaleYalnız künye2023Scopus SJRQ1A long short-term memory-based hybrid model optimized using a genetic algorithm for particulate matter 2.5 prediction
Utku, Anıl; Can, Ümit; Kamal, Mustafa; Das, Narasingha; Cifuentes-Faura, Javier; Barut, Abdulkadir
Beijing, Shanghai, Singapore, and London are regions with high population density and industrial activities. In this sense, accurate prediction of the rate of particulate matter 2.5 (PM_2.5), one of the most important air pollutants, is very important for creating a healthy ecological system. This study proposes a hybrid method to predict PM_2.5 with high success. This method aims to optimize the Long Short-Term Memory (LSTM) deep learning model, which successfully solves time series problems with a Genetic Algorithm (GA). Here, the hyperparameters of the LSTM model, such as the number of layers, number of neurons, number of epochs, and kernel size, are optimized to achieve the highest prediction rate. Against this backdrop, the present study uses for the first time a hybrid GA-LSTM model to predict PM_2.5 concentrations in Beijing, Shanghai, Singapore, and London. The Beijing dataset consists of 13 attributes, the Shanghai dataset consists of 10 attributes, the London dataset consists of 5 attributes, and the Singapore dataset consists of 7 attributes. In this study, input variables such as PM_2.5, DEWP, TEMP, PRESS, WS, WD, PRE, Snowfall hours, Rain hours, and Season were used. To assess the effectiveness of this hybrid method, the developed GA-LSTM model was compared with k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNN), LSTM, and Gated Recurrent Units (GRU). Experimental results showed that GA-LSTM outperformed the compared models. In addition, one of the best results was obtained compared to other models in the literature.
AtıfScopus17OpenAlex18İncele - MakaleYalnız künye2023Scopus SJRQ4The Effect of Self-Esteem on Loneliness in Individuals With Schizophrenia
Üşenmez, Tülay Yıldırım; Budak, Funda Kavak; Ayhan, Mehmet Osman
The aim of this study was to evaluate the effect of self-esteem on loneliness in persons with schizophrenia. This study was conducted between August 2020 and August 2021 at a community mental health center affiliated with a research and training hospital in Turkey. This was a cross-sectional study that included 100 individuals with schizophrenia. A Descriptive Characteristics Form (DCF), the Rosenberg Self-Esteem Scale (RSES), and the University of California Los Angeles Loneliness Scale (UCLA-LS) were used for data collection. The individuals’ total mean scores were 2.47 ± 1.24 for RSES and 56.42 ± 10.00 for UCLA-LS. A moderate negative correlation was found between these scores (r =-0.526, P < 0.01). The results revealed that the persons with schizophrenia had a moderate level of self-esteem and a high level of loneliness, possibly indicating that as self-esteem increases, loneliness levels may decrease.
- MakaleAçık Erişim2023Scopus SJRQ1Magneto-couple stress of tri-hybrid metallic oxide nanomaterials in porous media with nonlinear properties for thermal technology advancement
Salawu, S.O.; Obalalu, A.M.; Fatunmbi, E.O.; Disu, A.B.; Akkurt, Nevzat
The rising industrial demand for technological advancement in improving their working fluids and thermal augmentation has prompted studies on various non-Newtonian fluids and nanofluids. These play momentous role in many engineering devices and electronics development. Therefore, this study seeks to examine the thermal performance of ternary hybridized magneto-couple stress nanofluids in permeable media with nonlinear thermofluidic properties. The dispersion of suspended cobalt ferrite CoFe2O4, titanium dioxide TiO2 and magnesium oxide MgO nanoparticles occurs in H2O conventional solvent. With isothermal energy, the tri-hybrid nanofluid is controlled by stretchy velocity, magnetic field, gravity and Ohmic heating. An invariant transmutation of the partial derivative model is offered using similarity variables. The complete solutions to the model are obtained via Chebyshev technique coupled with integrated collocation method. From the graphically presented results, it is revealed that strong heat transfer is provided by ternary CoFe2O4+Ti02+MgO nanofluids compared to CoFe2O4+Ti02 and CoFe2O4 nanofluids. Also, the nanofluid concentration volume fraction enhanced thermal distribution and performance in the system.
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