Creating a high-performance and scalable system has always been an issue in tracking systems. Excessive and real-time data intensity is what lies at the bottom of this problem. This article intends to implement big data approaches instead of conventional ones. For the vehicle tracking system architecture, this study went beyond conventional methods and put forward a new design. This validation was compared to the conventional method. A general solution was created and an infrastructure which can be used in similar real-time data transmission was designed.
{"title":"Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment","authors":"Ferhat Koçer, Selim Bayrakli","doi":"10.55525/tjst.1364046","DOIUrl":"https://doi.org/10.55525/tjst.1364046","url":null,"abstract":"Creating a high-performance and scalable system has always been an issue in tracking systems. Excessive and real-time data intensity is what lies at the bottom of this problem. This article intends to implement big data approaches instead of conventional ones. For the vehicle tracking system architecture, this study went beyond conventional methods and put forward a new design. This validation was compared to the conventional method. A general solution was created and an infrastructure which can be used in similar real-time data transmission was designed.","PeriodicalId":516893,"journal":{"name":"Turkish Journal of Science and Technology","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140387835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Obstructive sleep apnea (OSAS), which is one of the leading sleep disorders and can result in death if not diagnosed and treated early, is most often confused with snoring. OSAS disease, the prevalence of which varies between 0.9% and 1.9% in Turkey, is a serious health problem that occurs as a result of complete or partial obstruction of the respiratory tract during sleep, resulting in sleep disruption, poor quality sleep, paralysis and even death in sleep. Polysomnography signal recordings (PSG) obtained from sleep laboratories are used for the diagnosis of OSAS, which is related to factors such as the individual's age, gender, neck diameter, smoking-alcohol consumption, and the occurrence of other sleep disorders. Polysomnography is used in the diagnosis and treatment of sleep disorders such as snoring, sleep apnea, parasomnia (abnormal behaviors during sleep), narcolepsy (sleep attacks that develop during the day) and restless legs syndrome. It allows recording various parameters such as brain waves, eye movements, heart and chest activity measurement, respiratory activities, and the amount of oxygen in the blood with the help of electrodes placed in different parts of the patient's body during night sleep. In this article, the performance of PSG signal data for the diagnosis of sleep apnea was examined on the basis of both signal parameters and the method used. First, feature extraction was made from PSG signals, then the feature vector was classified with artificial neural networks, Support Vector Machine (SVM), K-Nearest Neighbors (k-NN) and Logistic Regression (LR).
{"title":"Performance Comparison of Standard Polysomnographic Parameters Used in the Diagnosis of Sleep Apnea","authors":"Seda Arslan Tuncer, Yakup Çi̇çek, Taner Tuncer","doi":"10.55525/tjst.1419740","DOIUrl":"https://doi.org/10.55525/tjst.1419740","url":null,"abstract":"Obstructive sleep apnea (OSAS), which is one of the leading sleep disorders and can result in death if not diagnosed and treated early, is most often confused with snoring. OSAS disease, the prevalence of which varies between 0.9% and 1.9% in Turkey, is a serious health problem that occurs as a result of complete or partial obstruction of the respiratory tract during sleep, resulting in sleep disruption, poor quality sleep, paralysis and even death in sleep. Polysomnography signal recordings (PSG) obtained from sleep laboratories are used for the diagnosis of OSAS, which is related to factors such as the individual's age, gender, neck diameter, smoking-alcohol consumption, and the occurrence of other sleep disorders. Polysomnography is used in the diagnosis and treatment of sleep disorders such as snoring, sleep apnea, parasomnia (abnormal behaviors during sleep), narcolepsy (sleep attacks that develop during the day) and restless legs syndrome. It allows recording various parameters such as brain waves, eye movements, heart and chest activity measurement, respiratory activities, and the amount of oxygen in the blood with the help of electrodes placed in different parts of the patient's body during night sleep. In this article, the performance of PSG signal data for the diagnosis of sleep apnea was examined on the basis of both signal parameters and the method used. First, feature extraction was made from PSG signals, then the feature vector was classified with artificial neural networks, Support Vector Machine (SVM), K-Nearest Neighbors (k-NN) and Logistic Regression (LR).","PeriodicalId":516893,"journal":{"name":"Turkish Journal of Science and Technology","volume":" 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140387731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An early prediction of Alzheimer’s disease (AD) progression can help slow down cognitive decline more effectively. Several studies have been devoted to applying different methods based on convolutional neural networks (CNNs) for automated AD diagnosis using resting-state functional magnetic resonance imaging (rs-fMRI). The methods introduced in these studies encounter two major challenges. First, fMRI datasets suffer from being of small size resulting in overfitting. Second, the 4D information of fMRI sessions needs to be efficiently modeled. Some of the studies applied their deep learning methods to functional connectivity matrices generated from fMRI data to model the 4D information, or to fMRI data as separate 2D slices or 3D volumes. However, this results in information loss in both types of methods. In this study, a new model based on Capsule network (CapsNet) and recurrent neural network (RNN) is proposed to model the spatiotemporal (4D) information of fMRI data for AD diagnosis. Experiments were conducted to evaluate the efficiency of the proposed model. According to the results, it has been observed that the proposed model could achieve 94.5% and 61.8% accuracy for the AD versus normal control (NC) and late mild cognitive impairment (lMCI) versus early mild cognitive impairment (eMCI) classification tasks, respectively.
{"title":"Using 3D-CAPSNET and RNN for Alzheimer’s Disease Detection Based on 4D fMRI","authors":"Ali İsmai̇l, Gonca Gokce Menekse Dalveren","doi":"10.55525/tjst.1396312","DOIUrl":"https://doi.org/10.55525/tjst.1396312","url":null,"abstract":"An early prediction of Alzheimer’s disease (AD) progression can help slow down cognitive decline more effectively. Several studies have been devoted to applying different methods based on convolutional neural networks (CNNs) for automated AD diagnosis using resting-state functional magnetic resonance imaging (rs-fMRI). The methods introduced in these studies encounter two major challenges. First, fMRI datasets suffer from being of small size resulting in overfitting. Second, the 4D information of fMRI sessions needs to be efficiently modeled. Some of the studies applied their deep learning methods to functional connectivity matrices generated from fMRI data to model the 4D information, or to fMRI data as separate 2D slices or 3D volumes. However, this results in information loss in both types of methods. In this study, a new model based on Capsule network (CapsNet) and recurrent neural network (RNN) is proposed to model the spatiotemporal (4D) information of fMRI data for AD diagnosis. Experiments were conducted to evaluate the efficiency of the proposed model. According to the results, it has been observed that the proposed model could achieve 94.5% and 61.8% accuracy for the AD versus normal control (NC) and late mild cognitive impairment (lMCI) versus early mild cognitive impairment (eMCI) classification tasks, respectively.","PeriodicalId":516893,"journal":{"name":"Turkish Journal of Science and Technology","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140391700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dijital patoloji, patoloji bilgilerinin elde edilmesi, çıkarılması ve yorumlanmasının hesaplamalı tekniklerle desteklendiği görüntü tabanlı ortamı ifade eder. Teşhis sürecini kolaylaştırma açısından büyük bir potansiyele sahiptir ancak büyük veri boyutu ve geniş depolama alanlarının gerekliliği zorlayıcıdır. Bu nedenle, bu araştırmada, yeniden yapılandırma için veri miktarını azaltmak amacıyla Sıkıştırılmış Algılama (CS) şeması dijital patoloji görüntüleri ile incelenmiştir. CS, başarılı bir kurtarma için sinyallerin seyrekliğini gerektirir; bu, farklı seyrekleştirici bazların nihai performansı değiştirebileceği anlamına gelir. Dijital patoloji görüntülerini seyrekleştirmek için Dalgacık, Contourlet ve Shearlet Dönüşümleri incelenmiştir, Contourlet Dönüşümünün üstün olduğu görülmüştür. Yeniden yapılandırma için Alternatif Yön Çarpan Yöntemi (ADMM) sağlam ve hızlı bir dışbükey optimizasyon yöntemi olduğundan seçilmiştir. Dijital patoloji görüntülerinin klasik görüntülere göre daha az seyrek olmasına rağmen CS geriçatması tatmin edicidir, bu da CS'nin dijital patoloji için potansiyelini vurgulamaktadır. Bu çalışma, dijital patoloji ile CS alanında öncü olabilir ve farklı tipte mikroskoplarla veya farklı dalga boylarında CS tabanlı görüntülemeye yönelik daha ileri çalışmaları teşvik edebilir.
{"title":"Digital Pathology Image Reconstruction with Alternating Direction Method of Multipliers (ADMM) using Wavelet, Contourlet and Shearlet Transforms","authors":"Esra Şengün Ermeydan, Ilyas Çankaya","doi":"10.55525/tjst.1367366","DOIUrl":"https://doi.org/10.55525/tjst.1367366","url":null,"abstract":"Dijital patoloji, patoloji bilgilerinin elde edilmesi, çıkarılması ve yorumlanmasının hesaplamalı tekniklerle desteklendiği görüntü tabanlı ortamı ifade eder. Teşhis sürecini kolaylaştırma açısından büyük bir potansiyele sahiptir ancak büyük veri boyutu ve geniş depolama alanlarının gerekliliği zorlayıcıdır. Bu nedenle, bu araştırmada, yeniden yapılandırma için veri miktarını azaltmak amacıyla Sıkıştırılmış Algılama (CS) şeması dijital patoloji görüntüleri ile incelenmiştir. CS, başarılı bir kurtarma için sinyallerin seyrekliğini gerektirir; bu, farklı seyrekleştirici bazların nihai performansı değiştirebileceği anlamına gelir. Dijital patoloji görüntülerini seyrekleştirmek için Dalgacık, Contourlet ve Shearlet Dönüşümleri incelenmiştir, Contourlet Dönüşümünün üstün olduğu görülmüştür. Yeniden yapılandırma için Alternatif Yön Çarpan Yöntemi (ADMM) sağlam ve hızlı bir dışbükey optimizasyon yöntemi olduğundan seçilmiştir. Dijital patoloji görüntülerinin klasik görüntülere göre daha az seyrek olmasına rağmen CS geriçatması tatmin edicidir, bu da CS'nin dijital patoloji için potansiyelini vurgulamaktadır. Bu çalışma, dijital patoloji ile CS alanında öncü olabilir ve farklı tipte mikroskoplarla veya farklı dalga boylarında CS tabanlı görüntülemeye yönelik daha ileri çalışmaları teşvik edebilir.","PeriodicalId":516893,"journal":{"name":"Turkish Journal of Science and Technology","volume":"59 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140398707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ridwan Gbolahan Lateef, M. Lawal, Sarafa Olayide Rasheed
This paper presents the results of the inclusion of Synchronous Static Compensator (STATCOM) power flow models into a hydro-thermal optimal power flow (HTOPF) algorithm. STATCOM basically introduces the voltage magnitude and phase angle of the source converter into the algorithm. For each incorporated STATCOM, an augmented Lagrangian function was formed. The first and second derivatives of these functions were added to the gradient vector and Hessian matrix of an existing algorithm. The modified algorithm was implemented using MATLAB R2018a and tested on 30 and 57 bus systems. Two and three STATCOMs were, respectively, tested on 30 and 57 bus systems. The results obtained showed that one of the STATCOMs used on 30-bus system injected reactive power that ranges from 8.99 MVAR to 28.22 MVAR while the other one injected reactive power in the range of 8.20 MVAR to 33.20 MVAR. For the STATCOMs placed on the 57-bus system, the range of reactive power absorption by the one placed at bus 5 is 7.83 MVAR to 22.86 MVAR while the one at bus 55 absorbed from 5.06 MVAR to 12.92 MVAR. The STATCOM’s reactive power injection at bus 31 ranges from 4.63 MVAR to 10.35 MVAR. All the lower and higher values were obtained at hours 3 and 7, respectively. While the STATCOMs significantly improved the systems’ voltage profile, the impacts of STATCOM on the total systems daily energy loss, daily energy generations (from both plants), daily fuel cost and hydro plant water worth are insignificant.
{"title":"Incorporating STATCOM into a Hydro-Thermal Optimal Power Flow Algorithm","authors":"Ridwan Gbolahan Lateef, M. Lawal, Sarafa Olayide Rasheed","doi":"10.55525/tjst.1341697","DOIUrl":"https://doi.org/10.55525/tjst.1341697","url":null,"abstract":"This paper presents the results of the inclusion of Synchronous Static Compensator (STATCOM) power flow models into a hydro-thermal optimal power flow (HTOPF) algorithm. STATCOM basically introduces the voltage magnitude and phase angle of the source converter into the algorithm. For each incorporated STATCOM, an augmented Lagrangian function was formed. The first and second derivatives of these functions were added to the gradient vector and Hessian matrix of an existing algorithm. The modified algorithm was implemented using MATLAB R2018a and tested on 30 and 57 bus systems. Two and three STATCOMs were, respectively, tested on 30 and 57 bus systems. The results obtained showed that one of the STATCOMs used on 30-bus system injected reactive power that ranges from 8.99 MVAR to 28.22 MVAR while the other one injected reactive power in the range of 8.20 MVAR to 33.20 MVAR. For the STATCOMs placed on the 57-bus system, the range of reactive power absorption by the one placed at bus 5 is 7.83 MVAR to 22.86 MVAR while the one at bus 55 absorbed from 5.06 MVAR to 12.92 MVAR. The STATCOM’s reactive power injection at bus 31 ranges from 4.63 MVAR to 10.35 MVAR. All the lower and higher values were obtained at hours 3 and 7, respectively. While the STATCOMs significantly improved the systems’ voltage profile, the impacts of STATCOM on the total systems daily energy loss, daily energy generations (from both plants), daily fuel cost and hydro plant water worth are insignificant.","PeriodicalId":516893,"journal":{"name":"Turkish Journal of Science and Technology","volume":"53 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140398330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Polylactide (PLA) is a biodegradable polymer derived from natural resources used in various applications ranging from medical to packaging. In this study, biocomposites were developed by combining perlite mineral (PER), a natural filler material, with a biodegradable PLA matrix in incorporated contaminations of 2.5%, 5%, 10%, and 15%. Mixing force measurements, tensile, Shore hardness, impact tests, melt flow indices (MFI), and scanning electron microscopy (SEM) evaluations were carried out on composite samples to determine the processing, mechanical, melt flow, and morphological aspects of the developed composites. When the tensile test data were reviewed, minor decreases in the tensile strength and % elongation parameters were noticed with perlite loadings. The inclusion of perlite powder significantly reduced the impact strength value of PLA. Composites with high amounts of PER displayed elevated hardness values. While the MFI results were analyzed, it was discovered that the addition of PER increased the melt flow characteristics of the PLA polymer. At low PER quantities, SEM micrographs revealed that PER particles were homogeneously distributed in the PLA phase. The particle homogeneity in the composite morphology deteriorated as the PER loading ratio in the composites rose. According to the overall results, the highest performance among composites was achieved in the sample including 2.5% PER, and this sample was considered to be the most suitable option for applications regarding PLA-based biocomposite material purposes.
聚乳酸（PLA）是一种从自然资源中提取的可生物降解聚合物，可用于从医疗到包装等各种领域。在这项研究中，通过将天然填充材料珍珠岩矿物（PER）与可生物降解的聚乳酸基体相结合，开发出了生物复合材料，其掺入量分别为 2.5%、5%、10% 和 15%。对复合材料样品进行了混合力测量、拉伸、肖氏硬度、冲击试验、熔体流动指数（MFI）和扫描电子显微镜（SEM）评估，以确定所开发复合材料的加工、机械、熔体流动和形态等方面。拉伸测试数据显示，随着珍珠岩含量的增加，拉伸强度和伸长率参数略有下降。珍珠岩粉末的加入大大降低了聚乳酸的冲击强度值。含有大量 PER 的复合材料显示出较高的硬度值。在对 MFI 结果进行分析时发现，添加 PER 增加了聚乳酸聚合物的熔体流动特性。在低 PER 量时，SEM 显微照片显示 PER 颗粒均匀地分布在聚乳酸相中。随着 PER 在复合材料中添加比例的增加，复合材料形貌中的颗粒均匀性有所下降。从总体结果来看，PER 含量为 2.5% 的样品的复合材料性能最高，因此该样品被认为是最适合应用于聚乳酸基生物复合材料的样品。
{"title":"Expanded perlite mineral as a natural additive used in polylactide-based biodegradable composites","authors":"Erkan Aksoy, Süha Tirkeş, Ümit Tayfun, S. Tirkeş","doi":"10.55525/tjst.1348926","DOIUrl":"https://doi.org/10.55525/tjst.1348926","url":null,"abstract":"Polylactide (PLA) is a biodegradable polymer derived from natural resources used in various applications ranging from medical to packaging. In this study, biocomposites were developed by combining perlite mineral (PER), a natural filler material, with a biodegradable PLA matrix in incorporated contaminations of 2.5%, 5%, 10%, and 15%. Mixing force measurements, tensile, Shore hardness, impact tests, melt flow indices (MFI), and scanning electron microscopy (SEM) evaluations were carried out on composite samples to determine the processing, mechanical, melt flow, and morphological aspects of the developed composites. When the tensile test data were reviewed, minor decreases in the tensile strength and % elongation parameters were noticed with perlite loadings. The inclusion of perlite powder significantly reduced the impact strength value of PLA. Composites with high amounts of PER displayed elevated hardness values. While the MFI results were analyzed, it was discovered that the addition of PER increased the melt flow characteristics of the PLA polymer. At low PER quantities, SEM micrographs revealed that PER particles were homogeneously distributed in the PLA phase. The particle homogeneity in the composite morphology deteriorated as the PER loading ratio in the composites rose. According to the overall results, the highest performance among composites was achieved in the sample including 2.5% PER, and this sample was considered to be the most suitable option for applications regarding PLA-based biocomposite material purposes.","PeriodicalId":516893,"journal":{"name":"Turkish Journal of Science and Technology","volume":"66 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140398319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ensuring a secure network environment is crucial, especially with the increasing number of threats and attacks on digital systems. Implementing effective security measures, such as anomaly detection can help detect any abnormal traffic patterns. Several statistical and machine learning aproaches are used to detect network anomalies including robust statistical methods. Robust methods can help identifying abnormal traffic patterns and distinguish them from the normal traffic accurately. In this study, a robust Principal Component Analysis (PCA) method called ROBPCA which is known for its extensive use in the literature of chemometrics and genetics is utilized for detecting network anomalies and compared with another robust PCA method called PCAGRID. The anomaly detection performances of these methods are evaluated by injecting synthetic traffic volume into a well-known traffic matrix. According to the application results, when the normal subspace contaminated with large anomalies the ROBPCA method provided much better performance in detecting anomalies.
{"title":"An Application of Robust Principal Component Analysis Methods for Anomaly Detection","authors":"Kubra Bagci, H. E. Çelik","doi":"10.55525/tjst.1293057","DOIUrl":"https://doi.org/10.55525/tjst.1293057","url":null,"abstract":"Ensuring a secure network environment is crucial, especially with the increasing number of threats and attacks on digital systems. Implementing effective security measures, such as anomaly detection can help detect any abnormal traffic patterns. Several statistical and machine learning aproaches are used to detect network anomalies including robust statistical methods. Robust methods can help identifying abnormal traffic patterns and distinguish them from the normal traffic accurately. In this study, a robust Principal Component Analysis (PCA) method called ROBPCA which is known for its extensive use in the literature of chemometrics and genetics is utilized for detecting network anomalies and compared with another robust PCA method called PCAGRID. The anomaly detection performances of these methods are evaluated by injecting synthetic traffic volume into a well-known traffic matrix. According to the application results, when the normal subspace contaminated with large anomalies the ROBPCA method provided much better performance in detecting anomalies.","PeriodicalId":516893,"journal":{"name":"Turkish Journal of Science and Technology","volume":"23 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140401466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, high entropy FeCoNiMnMoV and FeCoNiMn (Ferro Mo-Ferro-V) alloys were produced by arc melting method.After the arc melting process, the samples were annealed at 1000ᵒC under argon atmosphere for 15 hours.Physical and thermodynamic calculations were performed to determine the properties of the alloy.In the study, both alloys were characterized. For characterization, XRD, SEM, EDS and Micro hardness were taken from the samples.The aim of my study is to examine the effect of using low-priced starting materials on the microstructure of HEA alloy.For this purpose, ferro alloys were added to the alloy.As a result, similar properties were obtained for the microstructure of both alloys.However, it was determined that after heat treatment, the hardness decreased more due to the chemical composition of the ferro alloy.
在本研究中，采用电弧熔炼法生产了高熵铁钴镍锰钼钒合金和铁钴镍锰合金（铁钼-铁钒合金），电弧熔炼过程结束后，样品在 1000ᵒC 的氩气环境下退火 15 小时。在表征过程中，对样品进行了 XRD、SEM、EDS 和显微硬度测量。我的研究旨在考察使用低价原材料对 HEA 合金微观结构的影响。
{"title":"Effect of Ferro-Alloys on the Properties of High Entropy Alloy with FeCoNiMnMoV Composition Produced by Arc-Melting Method","authors":"S. H. Güler","doi":"10.55525/tjst.1401275","DOIUrl":"https://doi.org/10.55525/tjst.1401275","url":null,"abstract":"In this study, high entropy FeCoNiMnMoV and FeCoNiMn (Ferro Mo-Ferro-V) alloys were produced by arc melting method.After the arc melting process, the samples were annealed at 1000ᵒC under argon atmosphere for 15 hours.Physical and thermodynamic calculations were performed to determine the properties of the alloy.In the study, both alloys were characterized. For characterization, XRD, SEM, EDS and Micro hardness were taken from the samples.The aim of my study is to examine the effect of using low-priced starting materials on the microstructure of HEA alloy.For this purpose, ferro alloys were added to the alloy.As a result, similar properties were obtained for the microstructure of both alloys.However, it was determined that after heat treatment, the hardness decreased more due to the chemical composition of the ferro alloy.","PeriodicalId":516893,"journal":{"name":"Turkish Journal of Science and Technology","volume":"204 S621","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140428281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Abubakar, Mustafa Kaya, Mustafa Eri̇ş, Mohammed Mansur Abubakar, Serkan Karakuş, Khalid Jibril Sani̇
Tuberculosis, a contagious lung ailment, stands as a prominent global mortality factor. Its significant impact on public health in Nigeria necessitates comprehensive intervention strategies. Detecting, preventing, and treating this disease remains imperative. Chest X-ray (CXR) images hold a pivotal role among diagnostic tools. Recent strides in deep learning have notably improved medical image analysis. In this research, we harnessed publicly available and proprietary CXR image datasets to construct robust models. Leveraging pre-trained deep neural networks, we aimed to enhance tuberculosis detection. Impressively, our experimentation yielded remarkable outcomes. Notably, f1-scores of 98% and 86% were attained on the respective public and private datasets. These results underscore the potency of deep neural networks in effectively identifying tuberculosis from CXR images. The study emphasizes the promise of this technology in combating the disease's spread and impact.
{"title":"Derin Sinir Ağlarını Kullanan Göğüs Röntgenleri ile Otomatik Tüberküloz Sınıflandırması Örnek Çalışma: Nijerya Halk Sağlığı","authors":"M. Abubakar, Mustafa Kaya, Mustafa Eri̇ş, Mohammed Mansur Abubakar, Serkan Karakuş, Khalid Jibril Sani̇","doi":"10.55525/tjst.1222836","DOIUrl":"https://doi.org/10.55525/tjst.1222836","url":null,"abstract":"Tuberculosis, a contagious lung ailment, stands as a prominent global mortality factor. Its significant impact on public health in Nigeria necessitates comprehensive intervention strategies. Detecting, preventing, and treating this disease remains imperative. Chest X-ray (CXR) images hold a pivotal role among diagnostic tools. Recent strides in deep learning have notably improved medical image analysis. In this research, we harnessed publicly available and proprietary CXR image datasets to construct robust models. Leveraging pre-trained deep neural networks, we aimed to enhance tuberculosis detection. Impressively, our experimentation yielded remarkable outcomes. Notably, f1-scores of 98% and 86% were attained on the respective public and private datasets. These results underscore the potency of deep neural networks in effectively identifying tuberculosis from CXR images. The study emphasizes the promise of this technology in combating the disease's spread and impact.","PeriodicalId":516893,"journal":{"name":"Turkish Journal of Science and Technology","volume":"65 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Drought is a prolonged period of inadequate rainfall, such as one season, one year or several years, on a statistical multi-year average for a region. Drought is a natural disaster effective on several socio-economic activities from agriculture to public health and leads to deterioration of the environment sustainability. The drought starts with meteorological drought, continues with agricultural and hydrological drought, and when it is in the socioeconomic dimension, the effects begin to be observed. Generally, drought studies are based on drought indices in the literature. This study applied long-term precipitation, temperature and evaporation data from Samsun, Tokat, Merzifon, Çorum and Amasya meteorological stations from 1961 to 2022 to investigate the drought in the Yeşilırmak basin of Turkey. The present study applied Standardized Precipitation Index (SPI), and Effective Drought Index (EDI), China Z- Index (CZI) and Standardized Precipitation Evapotranspiration Index (SPEI) based on daily, monthly, seasonal and annual time periods to evaluate drought. The Sen slope and Mann-Kendall test were employed for data analysis. The results revealed that the monthly drought indices for the study area were almost identical for the study area. Although there were particularly severe and wet periods, generally normal drought levels were observed
{"title":"Drought assessment of Yeşilırmak Basin using long-term data","authors":"V. Kartal","doi":"10.55525/tjst.1392199","DOIUrl":"https://doi.org/10.55525/tjst.1392199","url":null,"abstract":"Drought is a prolonged period of inadequate rainfall, such as one season, one year or several years, on a statistical multi-year average for a region. Drought is a natural disaster effective on several socio-economic activities from agriculture to public health and leads to deterioration of the environment sustainability. The drought starts with meteorological drought, continues with agricultural and hydrological drought, and when it is in the socioeconomic dimension, the effects begin to be observed. Generally, drought studies are based on drought indices in the literature. This study applied long-term precipitation, temperature and evaporation data from Samsun, Tokat, Merzifon, Çorum and Amasya meteorological stations from 1961 to 2022 to investigate the drought in the Yeşilırmak basin of Turkey. The present study applied Standardized Precipitation Index (SPI), and Effective Drought Index (EDI), China Z- Index (CZI) and Standardized Precipitation Evapotranspiration Index (SPEI) based on daily, monthly, seasonal and annual time periods to evaluate drought. The Sen slope and Mann-Kendall test were employed for data analysis. The results revealed that the monthly drought indices for the study area were almost identical for the study area. Although there were particularly severe and wet periods, generally normal drought levels were observed","PeriodicalId":516893,"journal":{"name":"Turkish Journal of Science and Technology","volume":"33 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140457680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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