Yayınlar
Koleksiyondaki öğeler
- MakaleAçık Erişim2023Scopus SJRQ3Mapping Tendencies in Curriculum Research on Physical Education and Sports: A Bibliometric Analysis
Şahin, Aykut; Karakaya, Yunus Emre; Dikmen, Melih
This study aims to investigate previous curriculum research on physical education and sports using the bibliometric mapping method. A systematic map was created for the research tendencies in physical education and sports curricula studies conducted between 1975 and 2021. Accordingly, the intention is to fill a gap in the literature by conducting a general situation analysis about the present status of curriculum studies in physical education and sports based on the publication and citation data obtained from the Web of Science (WoS) database. The software VOSviewer was utilised for the bibliometric analysis. After scanning the studies in the database, 454 studies were accessed at the initial stage. A total of 114 studies were covered in the analysis by following certain exclusion criteria. The findings suggested that curriculum studies in physical education and sports were conducted in association with numerous multidisciplinary research areas. Furthermore, in recent years, a linear increase in curriculum studies has occurred on this subject matter. It was also found that scientists from universities in the United States of America were at the centre of the studies. Another striking result was the limited number of journals specialising in sports and education. The results of the bibliometric maps also demonstrated that the researchers in this field investigated a variety of subjects that can be grouped under four main disciplines: ‘health and physical education,’ ‘curriculum reform,’ ‘contents,’ and ‘pre-service teachers.’ Addedl the results revealed that studies on physical education and sports curricula were conducted with a focus on current developments.
AtıfScopus3OpenAlex2
İncele - MakaleAçık Erişim2024Scopus SJRQ2High thermal stability effect of vanadium on the binary CuAl base alloy for a novel CuAlV high-temperature shape memory alloy
Karaduman, Oktay; Özkul, İskender; Güler, Seval Hale; Aksu Canbay, Canan
In this study, two high-temperature shape memory alloys (HTSMAs) of CuAlV with unprecedented chemical compositions were fabricated using the arc melting technique, followed by traditional ice-brine water quenching after the melting process. To characterize the shape memory properties and structure of the alloys, a series of tests including differential calorimetry (DSC and DTA), EDS, optical microscopy, and XRD were conducted. The DSC tests, performed at different heating and cooling rates, demonstrated highly stable reversible martensitic phase transformation peaks at high temperatures, which were also confirmed by the results of DTA tests. Microstructural XRD and optical microscopy tests were conducted at room temperature, revealing the martensitic structure of the alloys in both cases. Based on all the results, the effects of different minor amounts of vanadium additives directly on the CuAlV alloy were discussed.
AtıfScopus2WoS2OpenAlex3
İncele - MakaleYalnız künye2023Scopus SJRQ4Individual and Combined Toxicity of Polycyclic Aromatic Hydrocarbons Phenanthrene and Fluoranthene in Freshwater Amphipod Gammarus pulex (L., 1758) (Amphipoda: Gammaridae)
Yildirim, Nuran Cikcikoglu; Serdar, Osman; Derman, Taner
The combined toxicity of polycyclic aromatic hydrocarbons on freshwater amphipod Gammarus pulex was investigated. The 14-day LC50 values of fluoranthene, phenanthrene and their combination for G. pulex were determined as 5.88±0.23, 8.31±0.57 and 7.00±0.33 mg/L, respectively. To evaluate the single and combined toxicity, sublethal concentrations of fluoranthene, phenanthrene and their combination were exposed to G. pulex for 14 days. Acetylcholinesterase, glutathione-s-transferase and cytochrome P450 1A1 enzymes were measured as biomarkers. Acetylcholinesterase activities showed significant inhibition at the end of 7 and 14 days. No statistically significant change was observed in cytochrome P450 1A1 levels at the end of the 7th day, but a significant increase was detected at the end of the 14th day. A significant inhibition was observed in glutathione-s-transferase activities at the end of the 7th and 14th days. Statistically significant changes were observed in all parameters when the application times are compared. It was determined that G. pulex exposed to different concentrations of phenanthrene and fluoranthene individually and in combination caused significant changes in activites of the all measured enzymes. It was concluded that the cytochrome P450 1A1, glutathione-s-transferase and acetylcholinesterase enzymes in G. pulex are useful biochemical markers for the determination of polycyclic aromatic hydrocarbon toxicity.
AtıfScopus3İncele - MakaleAçık Erişim2023Scopus SJRQ1Evaluation of metal pollution related to human health risk in freshwater snail Viviparus contectus (Millet, 1813) as a potential bioindicator species in Lake Habitat (Turkey)
Kutluyer Kocabaş, Filiz; Göktürk Aksu, Emsiye; Kocabaş, Mehmet
Freshwater mollusks are employed as bioindicators for the assessment of water quality in biomonitoring studies since the water quality of natural resources is crucial for humans. The freshwater snail species known as Viviparus contectus (Viviparidae: Gastropoda) is one that people eat. Here, the levels of heavy metals (Cd, Cr, Pb, As, Zn, and Cu) in water and V. contectus samples were determined. An Inductively Coupled Plasma Atomic Emission Spectrometer (ICP-AES, Spectro Arcos, vertical plasma) was used for the analysing the heavy metal concentrations in water and freshwater snail samples. The results of the current investigation demonstrated that freshwater snails accumulated metals in their water and soft tissues in the following orders: Pb > Cr = Zn = Cu = Cd = As and Zn > Cu > Pb > Cd > As = Cr. Autumn was the time of year when higher amounts of heavy metals (As, Cr, Cu, Zn, and Hg) were found. Pb content in the freshwater snail samples was strongly impacted by seasonal fluctuations (P 0.05). For adults, the EDI (estimated daily intake) values were lower than the TDI (tolerable daily intake) values, and the HI (hazard index) values were below 1. Freshwater snail samples had Zn and Pb levels that were over the FAO/WHO, Turkish Food Codex, JECFA, and EC limit values. Except for Pb, the water study shows mean metal concentrations below the USEPA, Turkish Pollution Control Regulation, and World Health Organisation maximum allowed levels. Aquatic ecosystems were negatively impacted by anthropogenic activities overall, and this study can provide a helpful data set for investigations on metallic contamination in water bodies and biomonitoring in freshwater ecosystems.
AtıfScopus7WoS6OpenAlex6
İncele - MakaleAçık Erişim2023Scopus SJRQ4Nurses’ Opinions About Patient Restraint Methods in a Psychiatric Hospital and Their Ethical Sensitivity
Durmaz, Yadigar Çevik; Ersöğütçü, Filiz
Methods of restraint can be used to prevent hospitalized psychiatric patients from injuring themselves or others, but use of restraints causes various ethical problems in psychiatric nursing. This study examined the relationship between nurses’ sociode-mographic characteristics, types of restraint methods used, nurses’ opinions about those methods, and their level of ethical sensitivity toward their use. We found the moral sensitivity of our participants to be moderate, and sta-tistically significant differences were found related to marital status, shift type, and restraint method used. Based on our results, we recommend improving nurses’ working conditions, increas-ing their level of ethical knowledge, and expanding the number of stud-ies on these issues.
AtıfScopus1WoS1OpenAlex2İncele - MakaleYalnız künye2024Scopus SJRQ2Sonochemical removal of Pb (II) ions from the water medium using Bi2S3 nanostructres
Kurnaz Yetim, Nurdan; Berberoğlu, Elif Aybike; Aslan, Naim; Koç, Mümin Mehmet; Özcan, Cemile
Removal of heavy metals becomes increasingly crucial for public health and the environment. In this regard, Bi2S3 nanoflowers were produced for heavy metal ion removal applications. Nanoflowers are fabricated using hydrothermal synthesis, and then tested as adsorbents for the removal of lead (Pb2+) ions from aqueous systems. To assess the morphological and chemical characteristics, Bi2S3 nanoflowers were investigated using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS) techniques. Flame atomic adsorption spectroscopy (FAAS) was used in the assessment of heavy metal ion recovery. To determine the optimum conditions of the adsorption system, the effect of different parameters such as pH, eluent type and amount, adsorbent dose, adsorbent time, etc. were investigated. For lead removal, the optimum pH value was determined as 5.0, eluent dose and type were found 1 M HNO3. For 20 mL, the optimum adsorbent dose and time were found 50.0 mg, and 30 min, respectively. The adsorption capacity of the Bi2S3 nanoflowers was found to be quite higher than the results reported in similar works. It was illustrated that Bi2S3 nanoflowers have great potential to be used in water remediation applications as sorbent material.
AtıfScopus4WoS5OpenAlex5İncele - MakaleAçık Erişim2023Scopus SJRQ1A study on the combustion performance and NOx emissions characteristics of a novel U-type radiant tube based on MILD combustion theory
Fan, Huanbao; Feng, Junxiao; Bai, Wei; Wang, Hui; Akkurt, Nevzat; Li, Weidong; Gao, Jun; AliAkbaria, Omid; Xu, Qian
Based on the theory of moderate or intense low-oxygen dilution (MILD) combustion, a 3D steady-state CFD model coupling dual flue gas recirculation and dispersion combustion was developed to describe the flow field, combustion temperature field, wall temperature distribution, and NOx concentration field during the operation of a novel U-type radiant tube (N-URT). A user-defined function (UDF) method was implemented to simulate the dilution of O2 concentration in the combustion air due to external flue gas recirculation (E-FGR). The effects of the E-FGR ratio on combustion characteristics and NOx emission were evaluated. The simulated results were consistent with experimental results. The simulated NOx concentration at the outlet was reduced to 105.5 mg/m3 at 8% O2, which was about 35.2% lower than the conventional combustion method. The study found that when the E-FGR ratio was increased from 0 to 25%, the standard deviation of wall temperature decreased from 30.3 °C to 23.3 °C, and the non-uniformity coefficient decreased from 0.2598 to 0.2183, indicating an improvement in temperature uniformity. However, the thermal efficiency decreased from 82.1% to 76.2%. Moreover, at higher E-FGR ratios, the volume of the high-temperature region (above 1500 ℃) gradually decreased, and both the maximum flame temperature and the outlet NOx concentration decreased sharply. Regression equations for the E-FGR ratio (β) on the maximum combustion temperature (Tmax) and the outlet NOx concentration (CNOx) were obtained, and an E-FGR ratio greater than 13.4% was recommended to meet the emission requirements.
AtıfScopus14OpenAlex14İncele - MakaleAçık Erişim2023Scopus SJRQ1Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries
Utku, Anil
COVID-19 has a disease and health phenomenon and has sociological and economic adverse effects. Accurate prediction of the spread of the epidemic will help in the planning of health management and the development of economic and sociological action plans. In the literature, there are many studies to analyse and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyse the cross-country spread in the world's most populous countries. In this study, it was aimed to predict the spread of the COVID-19 epidemic. The motivation of this study is to reduce the workload of health workers, take preventive measures and optimize health processes by predicting the spread of the COVID-19 epidemic. A hybrid deep learning model was developed to predict and analyse COVID-19 cross-country spread and a case study was carried out for the world's most populous countries. The developed model was tested extensively using RMSE, MAE and R2. The experimental results showed that the developed model was more successful in predicting and analysis of COVID-19 cross-country spread in the world's most populous countries than LR, RF, SVM, MLP, CNN, GRU, LSTM and base CNN-GRU. In the developed model, CNN performs convolution and pooling operations to extract spatial features from the input data. GRU provides learning of long-term and non-linear relationships inferred by CNN. The developed hybrid model was more successful than the other models compared, as it enabled the effective features of the CNN and GRU models to be used together. The prediction and analysis of the cross-country spread of COVID-19 in the world's most populated countries can be presented as a novelty of this study.
AtıfScopus23WoS20OpenAlex31İncele - MakaleAçık Erişim2024Scopus SJRQ1A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes
Doğan, Gürkan; Ergen, Burhan
Semantic segmentation is the most important stage of making sense of the visual traffic scene for autonomous driving. In recent years, convolutional neural networks (CNN)-based methods for semantic segmentation of urban traffic scenes are among the trending studies. However, the methods developed in the studies carried out so far are insufficient in terms of accuracy performance criteria. In this study, a new CNN-based semantic segmentation method with higher accuracy performance is proposed. A new module, the Attentional Atrous Feature Pooling (AAFP) Module, has been developed for the proposed method. This module is located between the encoder and decoder in the general network structure and aims to obtain multi-scale information and add attentional features to large and small objects. As a result of experimental tests with the CamVid data set, an accuracy value of approximately 2% higher was achieved with a mIoU value of 70.59% compared to other state-of-art methods. Therefore, the proposed method can semantically segment objects in the urban traffic scene better than other methods.
AtıfScopus19WoS10İncele - MakaleYayıncıda açık erişim2024Scopus SJRQ1Exposure and connectedness to natural environments: An examination of the measurement invariance of the Nature Exposure Scale (NES) and Connectedness to Nature Scale (CNS) across 65 nations, 40 languages, gender identities, and age groups
Swami, Viren; White, Mathew P.; Voracek, Martin; Tran, Ulrich S.; Aavik, Toivo; Ranjbar, Hamed Abdollahpour; Adebayo, Sulaiman Olanrewaju; Afhami, Reza; Ahmed, Oli; Aimé, Annie
Detachment from nature is contributing to the environmental crisis and reversing this trend requires detailed monitoring and targeted interventions to reconnect people to nature. Most tools measuring nature exposure and attachment were developed in high-income countries and little is known about their robustness across national and linguistic groups. Therefore, we used data from the Body Image in Nature Survey to assess measurement invariance of the Nature Exposure Scale (NES) and the Connectedness to Nature Scale (CNS) across 65 nations, 40 languages, gender identities, and age groups (N = 56,968). While multi-group confirmatory factor analysis (MG-CFA) of the NES supported full scalar invariance across gender identities and age groups, only partial scalar invariance was supported across national and linguistic groups. MG-CFA of the CNS also supported full scalar invariance across gender identities and age groups, but only partial scalar invariance of a 7-item version of the CNS across national and linguistic groups. Nation-level associations between NES and CNS scores were negligible, likely reflecting a lack of conceptual clarity over what the NES is measuring. Individual-level associations between both measures and sociodemographic variables were weak. Findings suggest that the CNS-7 may be a useful tool to measure nature connectedness globally, but measures other than the NES may be needed to capture nature exposure cross-culturally.
AtıfScopus35OpenAlex41İncele - 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 - BildiriAçık Erişim2025Deep Learning-Based Approaches for Short-Term Residential Electricity Consumption Prediction: A Comparative Study of LSTM, CNN-LSTM, and CNN-GRU Models
Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup
The rapid increase in the world population has increased electricity consumption and demand for electricity. In order for electricity distribution companies to determine electricity demand, household electricity consumption must be predicted. In this study, an open access dataset was used to predict individual household electricity consumption. For this purpose, deep learning-based LSTM, CNN-LSTM and CNN-GRU models were developed and a comparative analysis was performed. The performance of the models was evaluated using four evaluation metrics commonly used in prediction model performance evaluation. According to the results obtained, it was seen that the models used in the study were successful in predicting short-term electricity consumption. In particular, the CNNLSTM model was found to have a lower error rate compared to the other two models.
- MakaleYalnız künye2025Scopus SJRQ2Studies on the improvement of thermal, dielectric and optical properties of poly(2,2,2-trifluoroethyl methacrylate) by the addition of graphite oxide as fillers
Barim, Esra
In this study, poly(2,2,2-trifluoroethyl methacrylate) homopolymer (PTFEMA) was synthesized via free radical polymerization method, and its composites with graphite oxide (GO) were prepared to improve its dielectric and thermal properties. Thermogravimetric evaluation revealed that graphite oxide acts to limit thermal diffusion throughout the PTFEMA loaded graphite oxide (GO) composite, and hence, thermal stability of composites improved. While the initial decomposition temperature of pure PTFEMA was found to be 233°C from thermogravimetric analysis measurements, the initial decomposition temperature of PTFEMA containing 5 wt% graphite oxide was found to be 251°C. The glass transition temperatures of the composites increased from 76°C to 86°C due to the increase in the amount of graphite oxide added to PTFEMA. The dielectric properties of composites were investigated from 100 Hz to 10 kHz for different temperatures. While the dielectric constant, ε′, of pure PTFEMA at 1 kHz at room temperature was found to be 3.00, that of the PTFEMA containing 5 wt% GO was found 10.32. Therefore, graphite oxide doped PTFEMA composites may be suitable candidates for energy storage applications. The dc conductivity and dielectric loss factor of PTFEMA containing 5 wt% graphite oxide were seen to increase with temperature.
AtıfScopus1OpenAlex1İncele - MakaleAçık Erişim2025Scopus SJRQ1Evaluation of the seismic response of reinforced concrete buildings in the light of lessons learned from the February 6, 2023, Kahramanmaraş, Türkiye earthquake sequences
Yön, Burak; Dedeoğlu, İbrahim Özgür; Yetkin, Musa; Erkek, Hakan; Calayır, Yusuf
On 6 February 2023, two significant seismic events occurred in the southeastern region of Türkiye. The seismic activity, which was perceptible in numerous countries beyond Türkiye, resulted in a considerable number of fatalities. A considerable number of individuals lost their lives and were rendered homeless as a result of the disaster. Two of the most significant factors contributing to the occurrence of these tragedies are the magnitude of the earthquake and structural deficiencies. The present study is concerned with a detailed analysis of these two factors. This study initially considers the seismicity of the region where the earthquakes that occurred on 6 February 2023 took place, as well as the seismic characteristics of these earthquakes. Subsequently, the findings of the field studies conducted in Hatay, Adıyaman, Kahramanmaraş and Malatya, the cities where the earthquakes caused the most destruction, are presented. The objective of the field study is to ascertain the collapse patterns, structural damages and the factors influencing these damages in reinforced concrete structures in the region. The primary causes of damage to structures can be attributed to several factors, including the presence of a strong beam-weak column mechanism, the soft story-weak story mechanism, the pounding effect, the short column damage, the long cantilever and overhangs, the short beam damage, the buckling damage, the torsion effect, the quality of the materials, the insufficient transverse reinforcement, the compressive failure due to over-reinforcement, the corrosion effect, the damage to reinforced concrete shear walls, the infill wall damage, and the damage caused by the soil and foundation system. These causes have been evaluated and recommendations have been formulated to prevent structural damage.
AtıfScopus55WoS50OpenAlex61İncele - MakaleAçık Erişim2023Scopus SJRQ2Binary PCL–waste photopolymer blends for biodegradable food packaging applications
Gürler, Nedim; Pekdemir, Mustafa Ersin; Torğut, Gülben; Kök, Mediha
The aim of this study is to prepare waste photopolymer (WPP) / poly ɛ-caprolactone (PCL) blends that can be used in food packaging with different ratios (5%, 10%, 20% and 30%) WPP. The structural properties of the blend films were characterized by attenuated total reflection-fourier transform infrared spectroscopy (ATR-FTIR), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Opacity, surface color, water vapor permeability and shape memory properties of these films were examined. Pure PCL film exhibited more thermal stability compared to PCL/WPP blend films. Therewithal, the glass transition temperature of the pure PCL film showed a shift. Water vapor permeability improved with the addition of WPP. L*, a* and transmittance values reduced with increasing WPP, but b* value increased. It was thought that these polymer blends would enable to be used as packaging material in terms of compatibility and waste evaluation.
AtıfScopus14WoS13OpenAlex14İncele - MakaleYalnız künye2025Scopus SJRQ4Deep learning-based hybrid traffic flow prediction model for freight transport
Utku, Anıl; Kaya, Sema Kayapinar; Can, Umit
Urban freight transportation is crucial to sustaining domestic and international trade and fulfilling the regular requirements of community companies and customers. Therefore, this study developed an efficient convolutional neural network (CNN) and long short-term memory (LSTM) based hybrid deep learning model for predicting traffic flow in freight transportation. The number of freight trucks that would travel along 663 Street in Anderlecht, Belgium, and their average speed was predicted. The created model was compared with support vector machine (SVM), linear regression (LR), multilayer perceptron (MLP), random forest (RF), LSTM, and CNN using the success measures. The critical finding of this study is that combining the feature extraction ability of the 1D CNN model with the learning ability of the LSTM model from multivariate traffic data increases the traffic density prediction success compared to other traditional methods. Based on the results of the experiments, the CNN-LSTM model outperformed all the other models employed in this study. With this model, freight density can be successfully predicted, assisting in planning freight transport activities.
- MakaleYalnız künye2024Scopus SJRQ1Effect of oxidation on radiation shielding capacity of ZrNbTaMoW Refractory High Entropy Alloys (RHEA) for nuclear reactor applications: Experimental and theoretical assessment
Sakar, E.; Guler, O.; Alim, B.; Guler, S.H.; Dikici, B.; Lampke, T.
This study presents the production and comprehensive assessment of a Refractory High Entropy Alloy (RHEA) with a ZrNbTaMoW composition for possible usage nuclear reactor applications. The alloy was synthesized using the mechanical alloying (MA) method, and its radiation properties were examined. Elemental powders of Zr, Nb, Ta, Mo, and W were utilized in equimolar quantities during the 120-hour MA process, conducted under a high-purity Ar atmosphere to prevent oxidation. The obtained HEA was then subjected to a mechano-thermal process to transform it into Refractory High-Entropy Oxide (RHEO) for comparing their radiation-shielding performance under oxide conditions. The characterization of the produced alloys through X-ray diffraction (XRD), scanning electron microscopy (SEM), and electron dispersive spectroscopy (EDS). The photon shielding parameters of the alloys were experimentally obtained for various energies emitted from a 133Ba radioactive source, employing a Canberra UltraLEGe semiconductor detector. Furthermore, theoretical calculations were performed to validate the experimental data and to evaluate the alloys' photon shielding capabilities comprehensively. For this purpose, Phy-X/PSD software was employed to determine mass attenuation coefficients, half-value layer, effective atomic number, and effective electron density. Fast neutron absorption capabilities were determined through the calculation of average absorption cross-sections in the 2–12 MeV energy range. The results showed that the oxidation process significantly diminished the photon absorption capacity of RHEA.
AtıfScopus34OpenAlex37İncele - MakaleAçık Erişim2025Scopus SJRQ1Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches
Yilmaz, Mehmet; Yalcin, Erkut; Demir, Fatih; Ozdemir, Ahmet Munir; Atar, Muhammed; Gunes, Aysegul; Furtana Yalcin, Beyza; Cambay, Ertugrul
This study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. Unlike traditional manual inspections, which are time-consuming and error-prone, our approach leverages advanced segmentation techniques to ensure accurate, pixel-level classification of various crack types. The main challenge of this study was determining the damage caused to highways by earthquakes with magnitudes greater than 7.0, which occur approximately once every 200 years. The most crucial step in the automatic detection of these damages is the reliable preparation of a high-accuracy dataset. To achieve this, pixel-based labels were created by experts in the construction field by analyzing each pixel value. Following two major earthquakes, a unique dataset for segmenting roadway deterioration was created through intensive and detailed studies. This study aims to present the performance results of popular deep learning-based segmentation models in an unbiased manner, providing a feasible infrastructure for future real-time applications. The innovative aspect of this research lies in the creation of a unique post-earthquake dataset, collected and labeled from highways affected by the February 6, 2023 earthquakes in Turkey (Mw = 7.7 and Mw = 7.6). Deep learning models, including SegNet, Attention SegNet, U-Net, FCN (8s), and DeepLab, were trained and tested on this dataset. Among these, the SegNet model achieved the best performance with an average accuracy of 86.72%, precision of 92.99%, and sensitivity of 78.45%. By demonstrating superior performance metrics compared to existing methods, this study provides a robust framework for future infrastructure monitoring and maintenance strategies, ensuring safer and more resilient transportation networks in disaster-prone regions.
AtıfScopus6OpenAlex4İncele - MakaleAçık Erişim2024Scopus SJRQ3Effects of vibration conditions on machinability of aisi 52100 bearing steel
Agar, Salih; Tosun, Nihat; Mahmat, Aybars
This study focused on determining the effects of the cooling condition in the conventional and ultrasonic-assisted turning of AISI 52100 steel. AISI 52100 steel has been widely used in the bearing, mold, and automotive industries because of its superior wear resistance and mechanical properties. However, its high wear resistance caused poor machinability in conventional machining methods. Low surface quality, rapid tool wear, high cutting temperature, and high cutting force are the main difficulties in the conventional machining of AISI 52100 steel. On the other hand, ultrasonic-assisted machining has become widely used in the past decades for the machining of materials that have low machinability in conventional methods. Conventional turning and ultrasonic-assisted turning were selected as machining methods. Ultrasonic-assisted turning experiments were conducted under 20 and 30 kHz vibration frequencies. Cubic Boron Nitride (CBN) cutting tools were used in machining experiments. In machining operations 50, 100, and 150 m/min cutting speeds were selected, and feed rate (0.1 mm/rev) and depth of cut (0.5 mm) were kept constant in experiments. Minimum quantity lubrication technique which used 1% Al2O3 nanoparticle additives cutting fluid and dry cooling methods were used. CBN insert was used as a cutting tool. The effects of cutting speed, cutting method, and cooling condition on surface quality, cutting force, cutting zone temperature, and cutting tool wear were studied experimentally. Analysis of Variance (ANOVA) was carried out to determine the significance of the effects of input variables on process outputs. As a result of this study, it is determined that combined UAT and nanoMQL have significant effects on the process outputs.
AtıfScopus3WoS3OpenAlex3İncele - MakaleAçık Erişim2024Scopus SJRQ2Fabrication of CuAlVMg High-Temperature Shape Memory Alloy Containing Low-Soluble Vanadium Addition
Karaduman, Oktay; Boyrazli, Mustafa; Canbay, Canan Aksu; Özkul, İskender; Çelik, Emrah; Başbağ, Güneş
For the first time, CuAlVMg high-temperature shape memory alloy (HTSMA) with unprecedented alloy composition and with extended solubility of vanadium element was fabricated as-cast ingot by arc melting method, then homogenization of the alloy in high β-phase temperature region and quenching in iced-brine water were performed, respectively. The characteristic forward martensite to austenite martensitic transformation (MT) temperatures were detected at around 390 °C, which put the novel alloy in the category of HTSMAs. The thermal response of the alloy at high-temperatures was observed by DTA test as coherent with usual Cu-based HTSMAs. The residual sub-eutectoid precipitations emerging at around 500 °C hindered the direct martensitic transformation. The martensite structure of the alloy was revealed by XRD, SEM and optical microscopy tests.
