Sohail Anwar, Shoaib Rehman Soomro, Shadi Khan Baloch, Aamir Ali Patoli, Abdul Rahim Kolachi
Cotton is one of the most important agricultural products and is closely linked to the economic development of Pakistan. However, the cotton plant is susceptible to bacterial and viral diseases that can quickly spread and damage plants and ultimately affect the cotton yield. The automated and early detection of affected plants can significantly reduce the potential spread of the disease. This paper presents the implementation and performance analysis of bacterial blight and curl virus disease detection in cotton crops through deep learning techniques. The automated disease detection is performed through transfer learning of six pre-trained deep learning models, namely DenseNet121, DenseNet169, MobileNetV2, ResNet50V2, VGG16, and VGG19. A total of 1362 images of local agricultural fields and 1292 images from online resources were used to train and validate the models. Image augmentation techniques were performed to increase the dataset diversity and size. Transfer learning was implemented for different image resolutions ranging from 32×32 to 256×256 pixels. Performance metrics such as accuracy, precision, recall, F1 Score, and prediction time were evaluated for each implemented model. The results indicate higher accuracy, up to 96%, for DenseNet169 and ResNet50V2 models when trained on the 256×256 pixels image dataset. The lowest accuracy, 52%, was obtained by the MobileNetV2 model when trained on low-resolution, 32×32, images. The confusion matrix analysis indicates the true-positive prediction rates higher than 91% for fresh leaves, 87% for bacterial blight, and 76% for curl virus detection for all implemented models when trained and tested on an image dataset of 128×128 pixels or higher resolution.
{"title":"Performance Analysis of Deep Transfer Learning Models for the Automated Detection of Cotton Plant Diseases","authors":"Sohail Anwar, Shoaib Rehman Soomro, Shadi Khan Baloch, Aamir Ali Patoli, Abdul Rahim Kolachi","doi":"10.48084/etasr.6187","DOIUrl":"https://doi.org/10.48084/etasr.6187","url":null,"abstract":"Cotton is one of the most important agricultural products and is closely linked to the economic development of Pakistan. However, the cotton plant is susceptible to bacterial and viral diseases that can quickly spread and damage plants and ultimately affect the cotton yield. The automated and early detection of affected plants can significantly reduce the potential spread of the disease. This paper presents the implementation and performance analysis of bacterial blight and curl virus disease detection in cotton crops through deep learning techniques. The automated disease detection is performed through transfer learning of six pre-trained deep learning models, namely DenseNet121, DenseNet169, MobileNetV2, ResNet50V2, VGG16, and VGG19. A total of 1362 images of local agricultural fields and 1292 images from online resources were used to train and validate the models. Image augmentation techniques were performed to increase the dataset diversity and size. Transfer learning was implemented for different image resolutions ranging from 32×32 to 256×256 pixels. Performance metrics such as accuracy, precision, recall, F1 Score, and prediction time were evaluated for each implemented model. The results indicate higher accuracy, up to 96%, for DenseNet169 and ResNet50V2 models when trained on the 256×256 pixels image dataset. The lowest accuracy, 52%, was obtained by the MobileNetV2 model when trained on low-resolution, 32×32, images. The confusion matrix analysis indicates the true-positive prediction rates higher than 91% for fresh leaves, 87% for bacterial blight, and 76% for curl virus detection for all implemented models when trained and tested on an image dataset of 128×128 pixels or higher resolution.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917994","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}
Bashayer Alsubhi, Basma Alharbi, Nahla Aljojo, Ameen Banjar, Araek Tashkandi, Abdullah Alghoson, Anas Al-Tirawi
The ability to accurately predict how students will perform has a significant impact on the teaching and learning process, as it can inform the instructor to devote extra attention to a particular student or group of students, which in turn prevents those students from failing a certain course. When it comes to educational data mining, the accuracy and explainability of predictions are of equal importance. Accuracy refers to the degree to which the predicted value was accurate, and explainability refers to the degree to which the predicted value could be understood. This study used machine learning to predict the features that best contribute to the performance of a student, using a dataset collected from a public university in Jeddah, Saudi Arabia. Experimental analysis was carried out with Black-Box (BB) and White-Box (WB) machine-learning classification models. In BB classification models, a decision (or class) is often predicted with limited explainability on why this decision was made, while in WB classification models decisions made are fully interpretable to the stakeholders. The results showed that these BB models performed similarly in terms of accuracy and recall whether the classifiers attempted to predict an A or an F grade. When comparing the classifiers' accuracy in making predictions on B grade, the Support Vector Machine (SVM) was found to be superior to Naïve Bayes (NB). However, the recall results were quite similar except for the K-Nearest Neighbor (KNN) classifier. When predicting grades C and D, RF had the best accuracy and NB the worst. RF had the best recall when predicting a C grade, while NB had the lowest. When predicting a D grade, SVM had the best recall performance, while NB had the lowest.
{"title":"Effective Feature Prediction Models for Student Performance","authors":"Bashayer Alsubhi, Basma Alharbi, Nahla Aljojo, Ameen Banjar, Araek Tashkandi, Abdullah Alghoson, Anas Al-Tirawi","doi":"10.48084/etasr.6345","DOIUrl":"https://doi.org/10.48084/etasr.6345","url":null,"abstract":"The ability to accurately predict how students will perform has a significant impact on the teaching and learning process, as it can inform the instructor to devote extra attention to a particular student or group of students, which in turn prevents those students from failing a certain course. When it comes to educational data mining, the accuracy and explainability of predictions are of equal importance. Accuracy refers to the degree to which the predicted value was accurate, and explainability refers to the degree to which the predicted value could be understood. This study used machine learning to predict the features that best contribute to the performance of a student, using a dataset collected from a public university in Jeddah, Saudi Arabia. Experimental analysis was carried out with Black-Box (BB) and White-Box (WB) machine-learning classification models. In BB classification models, a decision (or class) is often predicted with limited explainability on why this decision was made, while in WB classification models decisions made are fully interpretable to the stakeholders. The results showed that these BB models performed similarly in terms of accuracy and recall whether the classifiers attempted to predict an A or an F grade. When comparing the classifiers' accuracy in making predictions on B grade, the Support Vector Machine (SVM) was found to be superior to Naïve Bayes (NB). However, the recall results were quite similar except for the K-Nearest Neighbor (KNN) classifier. When predicting grades C and D, RF had the best accuracy and NB the worst. RF had the best recall when predicting a C grade, while NB had the lowest. When predicting a D grade, SVM had the best recall performance, while NB had the lowest.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918036","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}
Syed Ibrahim Syed Mahamood Shazuli, Arunachalam Saravanan
Diabetic Retinopathy (DR) is a major source of sightlessness and permanent visual damage. Manual Analysis of DR is a labor-intensive and costly task that requires skilled ophthalmologists to observe and evaluate DR utilizing digital fundus images. The images can be employed for analysis and disease screening. This laborious task can gain a great advantage in automated detection by exploiting Artificial Intelligence (AI) techniques. Content-Based Image Retrieval (CBIR) approaches are utilized to retrieve related images in massive databases and are helpful in many application regions and most healthcare systems. With this motivation, this article develops the new Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification (MRFODL-FIRC) approach for the grading of DR. The suggested MRFODL-FIRC model investigates the retinal fundus imaging effectively to retrieve the relevant images and identify class labels. To achieve this, the MRFODL-FIRC technique uses Median Filtering (MF) as a pre-processing step. The Capsule Network (CapsNet) model is used to produce feature vectors with the MRFO algorithm as a hyperparameter optimizer. For the image retrieval process, the Manhattan distance metric is used. Finally, the Variational Autoencoder (VAE) model is used for recognizing and classifying DR. The investigational assessment of the MRFODL-FIRC technique is accomplished on medical DR and the outputs highlighted the improved performance of the MRFODL-FIRC algorithm over the current approaches.
{"title":"Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading","authors":"Syed Ibrahim Syed Mahamood Shazuli, Arunachalam Saravanan","doi":"10.48084/etasr.6226","DOIUrl":"https://doi.org/10.48084/etasr.6226","url":null,"abstract":"Diabetic Retinopathy (DR) is a major source of sightlessness and permanent visual damage. Manual Analysis of DR is a labor-intensive and costly task that requires skilled ophthalmologists to observe and evaluate DR utilizing digital fundus images. The images can be employed for analysis and disease screening. This laborious task can gain a great advantage in automated detection by exploiting Artificial Intelligence (AI) techniques. Content-Based Image Retrieval (CBIR) approaches are utilized to retrieve related images in massive databases and are helpful in many application regions and most healthcare systems. With this motivation, this article develops the new Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification (MRFODL-FIRC) approach for the grading of DR. The suggested MRFODL-FIRC model investigates the retinal fundus imaging effectively to retrieve the relevant images and identify class labels. To achieve this, the MRFODL-FIRC technique uses Median Filtering (MF) as a pre-processing step. The Capsule Network (CapsNet) model is used to produce feature vectors with the MRFO algorithm as a hyperparameter optimizer. For the image retrieval process, the Manhattan distance metric is used. Finally, the Variational Autoencoder (VAE) model is used for recognizing and classifying DR. The investigational assessment of the MRFODL-FIRC technique is accomplished on medical DR and the outputs highlighted the improved performance of the MRFODL-FIRC algorithm over the current approaches.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918309","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}
Recent developments in textiles have led to the manufacturing of a variety of fabrics. These developments include spacer fabrics, embroidered fabrics, embedded sensors in fabrics, ECG vests, etc. Electronic components are also being knit within fabrics. The study used a configuration of thermocouples, based on the Seebeck effect, knitted into the main structure using a variety of yarn filaments. The knitted fabric was tested against temperature variation to examine how it affects the impedance of the knitted thermocouples. The testing procedure produced promising results, as it showed that certain combinations of knitting materials may result in positive and negative temperature coefficients of the fabric. The combination of the tested materials provides a guide to developing similar structures for thermoelectric sensor applications.
{"title":"The Incorporation of Thermocouples in Knitted Structures","authors":"Muhammad Tajammal Chughtai","doi":"10.48084/etasr.6183","DOIUrl":"https://doi.org/10.48084/etasr.6183","url":null,"abstract":"Recent developments in textiles have led to the manufacturing of a variety of fabrics. These developments include spacer fabrics, embroidered fabrics, embedded sensors in fabrics, ECG vests, etc. Electronic components are also being knit within fabrics. The study used a configuration of thermocouples, based on the Seebeck effect, knitted into the main structure using a variety of yarn filaments. The knitted fabric was tested against temperature variation to examine how it affects the impedance of the knitted thermocouples. The testing procedure produced promising results, as it showed that certain combinations of knitting materials may result in positive and negative temperature coefficients of the fabric. The combination of the tested materials provides a guide to developing similar structures for thermoelectric sensor applications.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918458","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}
Nickel-base super alloys such as Inconel 625 are preferred in high-temperature and corrosive environments. Since Inconel 625 is expensive and often difficult to machine, it is advantageous to deposit a protective coating of this alloy on a less costly and easily machinable substrate material such as stainless steel. In the present work, coatings were produced on AISI 304 steel substrate by depositing Inconel 625 powder using the laser cladding technique. As-received powder particles of Inconel 625 alloy were characterized using X-Ray Diffraction (XRD) and Field Emission Scanning Electron Microscopy (FESEM). After laser cladding, it becomes important to carry out the microstructural analysis of the cross-sectional areas of the coating and the substrate/coating interface region, for further understanding of the structure-property correlations. In this study, the microstructural features of the coatings and substrate/coating interface were examined using an FESEM equipped with X-ray elemental analysis. The phase analysis of the coating was carried out using XRD. In the coating region, the growth of planar, cellular, columnar dendritic, and equiaxed grains was noticed. It was observed that small amounts of Laves phase were precipitated. Furthermore, the laser-clad Inconel 625 coating showed superior microhardness over the stainless steel substrate.
{"title":"Inconel 625 Coatings on AISI 304 Steel using Laser Cladding: Microstructure and Hardness","authors":"Vadakke Parambil Vijeesh, Motagondanahalli Rangarasaiah Ramesh, Aroor Dinesh Anoop","doi":"10.48084/etasr.6297","DOIUrl":"https://doi.org/10.48084/etasr.6297","url":null,"abstract":"Nickel-base super alloys such as Inconel 625 are preferred in high-temperature and corrosive environments. Since Inconel 625 is expensive and often difficult to machine, it is advantageous to deposit a protective coating of this alloy on a less costly and easily machinable substrate material such as stainless steel. In the present work, coatings were produced on AISI 304 steel substrate by depositing Inconel 625 powder using the laser cladding technique. As-received powder particles of Inconel 625 alloy were characterized using X-Ray Diffraction (XRD) and Field Emission Scanning Electron Microscopy (FESEM). After laser cladding, it becomes important to carry out the microstructural analysis of the cross-sectional areas of the coating and the substrate/coating interface region, for further understanding of the structure-property correlations. In this study, the microstructural features of the coatings and substrate/coating interface were examined using an FESEM equipped with X-ray elemental analysis. The phase analysis of the coating was carried out using XRD. In the coating region, the growth of planar, cellular, columnar dendritic, and equiaxed grains was noticed. It was observed that small amounts of Laves phase were precipitated. Furthermore, the laser-clad Inconel 625 coating showed superior microhardness over the stainless steel substrate.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918755","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}
Pham Van Bach Ngoc, Le Huy Hoang, Le Minh Hieu, Ngoc Hai Nguyen, Nguyen Luong Thien, Van Tuan Doan
Mobile robots have many industrial applications, including security, food service, and fire safety. Detecting smoke and fire quickly for early warning and monitoring is crucial in every industrial safety system. In this paper, a method for early smoke and fire detection using mobile robots equipped with cameras is presented. The method employs artificial intelligence for trajectory planning and navigation, and focus is given to detection and localization techniques for mobile robot navigation. A model of a mobile robot with Omni wheels and a modified YOLOv5 algorithm for fire and smoke detection is also introduced, which is integrated into the control system. This research addresses the issue of distinct objects of the same class by assigning each object a unique identification. The implementation not only detects fire and smoke but also identifies the position of objects in three-dimensional space, allowing the robot to map its environment incrementally for mobile navigation. The experimental results demonstrate the high accuracy achieved by the proposed method in identifying smoke and fire.
{"title":"Real-Time Fire and Smoke Detection for Trajectory Planning and Navigation of a Mobile Robot","authors":"Pham Van Bach Ngoc, Le Huy Hoang, Le Minh Hieu, Ngoc Hai Nguyen, Nguyen Luong Thien, Van Tuan Doan","doi":"10.48084/etasr.6252","DOIUrl":"https://doi.org/10.48084/etasr.6252","url":null,"abstract":"Mobile robots have many industrial applications, including security, food service, and fire safety. Detecting smoke and fire quickly for early warning and monitoring is crucial in every industrial safety system. In this paper, a method for early smoke and fire detection using mobile robots equipped with cameras is presented. The method employs artificial intelligence for trajectory planning and navigation, and focus is given to detection and localization techniques for mobile robot navigation. A model of a mobile robot with Omni wheels and a modified YOLOv5 algorithm for fire and smoke detection is also introduced, which is integrated into the control system. This research addresses the issue of distinct objects of the same class by assigning each object a unique identification. The implementation not only detects fire and smoke but also identifies the position of objects in three-dimensional space, allowing the robot to map its environment incrementally for mobile navigation. The experimental results demonstrate the high accuracy achieved by the proposed method in identifying smoke and fire.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918761","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}
Damien Raj Felicia Rose Anandhi, Selvarajan Sathiamoorthy
The detection of diseases in rice plants is an essential step in ensuring healthy crop growth and maximizing yields. A real-time and accurate plant disease detection technique can assist in the development of mitigation strategies to ensure food security on a large scale and economical rice crop protection. An accurate classification of rice plant diseases using DL and computer vision could create a foundation to achieve a site-specific application of agrochemicals. Image investigation tools are efficient for the early diagnosis of plant diseases and the continuous monitoring of plant health status. This article presents an Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion for Rice Plant Disease Detection and Classification (ESHODL-MFRPDC) technique. The proposed technique employed a DL-based fusion process with a hyperparameter tuning strategy to achieve an improved rice plant disease detection performance. The ESHODL-MFRPDC approach used Bilateral Filtering (BF)-based noise removal and contrast enhancement as a preprocessing step. Furthermore, Mayfly Optimization (MFO) with a Multi-Level Thresholding (MLT) based segmentation process was used to recognize the diseased portions in the leaf image. A fusion of three DL models was used for feature extraction, namely Residual Network (ResNet50), Xception, and NASNet. The Quasi-Recurrent Neural Network (QRNN) was used for the recognition of rice plant diseases, and its hyperparameters were set using the ESHO method. The performance of the ESHODL-MFRPDC method was validated using the rice leaf disease dataset from the UCI database. An extensive comparison study demonstrated the promising performance of the proposed method over others.
{"title":"Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion Technique for Rice Plant Disease Segmentation and Classification","authors":"Damien Raj Felicia Rose Anandhi, Selvarajan Sathiamoorthy","doi":"10.48084/etasr.6324","DOIUrl":"https://doi.org/10.48084/etasr.6324","url":null,"abstract":"The detection of diseases in rice plants is an essential step in ensuring healthy crop growth and maximizing yields. A real-time and accurate plant disease detection technique can assist in the development of mitigation strategies to ensure food security on a large scale and economical rice crop protection. An accurate classification of rice plant diseases using DL and computer vision could create a foundation to achieve a site-specific application of agrochemicals. Image investigation tools are efficient for the early diagnosis of plant diseases and the continuous monitoring of plant health status. This article presents an Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion for Rice Plant Disease Detection and Classification (ESHODL-MFRPDC) technique. The proposed technique employed a DL-based fusion process with a hyperparameter tuning strategy to achieve an improved rice plant disease detection performance. The ESHODL-MFRPDC approach used Bilateral Filtering (BF)-based noise removal and contrast enhancement as a preprocessing step. Furthermore, Mayfly Optimization (MFO) with a Multi-Level Thresholding (MLT) based segmentation process was used to recognize the diseased portions in the leaf image. A fusion of three DL models was used for feature extraction, namely Residual Network (ResNet50), Xception, and NASNet. The Quasi-Recurrent Neural Network (QRNN) was used for the recognition of rice plant diseases, and its hyperparameters were set using the ESHO method. The performance of the ESHODL-MFRPDC method was validated using the rice leaf disease dataset from the UCI database. An extensive comparison study demonstrated the promising performance of the proposed method over others.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918764","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}
Improving and maintaining the stability of a power system is a major focus of modern technology and research. However, due to financial issues, environmental concerns, and health risks associated with electric and magnetic fields, the growth of the current transmission system is constrained. Transmission line problems can be resolved by the effective use of reactive power compensation based on Flexible AC Transmission (FACT) devices. The effectiveness of these devices in regulating active and reactive powers as well as dampening oscillations in the transient phase of the power system is examined using a Static Synchronous Series Compensator (SSSC) with Artificial Neural Network (ANN) control. When compared to the traditional Proportional Integral (PI) controller, the suggested ANN controller offers better dynamic performance. For the suggested test system, this study utilized modeling and simulation using the MATLAB/Simulink software. The observations show that by using ANN in disturbed situations, power oscillations are quickly damped and power flow is enhanced.
{"title":"Enhancement of Power System Security by the Intelligent Control of a Static Synchronous Series Compensator","authors":"Sapana Arun Bhande, Vinod Kumar Chandrakar","doi":"10.48084/etasr.6150","DOIUrl":"https://doi.org/10.48084/etasr.6150","url":null,"abstract":"Improving and maintaining the stability of a power system is a major focus of modern technology and research. However, due to financial issues, environmental concerns, and health risks associated with electric and magnetic fields, the growth of the current transmission system is constrained. Transmission line problems can be resolved by the effective use of reactive power compensation based on Flexible AC Transmission (FACT) devices. The effectiveness of these devices in regulating active and reactive powers as well as dampening oscillations in the transient phase of the power system is examined using a Static Synchronous Series Compensator (SSSC) with Artificial Neural Network (ANN) control. When compared to the traditional Proportional Integral (PI) controller, the suggested ANN controller offers better dynamic performance. For the suggested test system, this study utilized modeling and simulation using the MATLAB/Simulink software. The observations show that by using ANN in disturbed situations, power oscillations are quickly damped and power flow is enhanced.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917850","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}
Several studies have investigated the process of alteration of ilmenite, especially in black sand. To predict the mechanisms of ilmenite alteration and the role of some minor element oxides in the alteration process, separated non-magnetic altered ilmenite grains were examined using a binocular microscope and a Cameca SX-100 microprobe instrument. Twenty intergrown phases of alteration products were concluded in three postulated scenarios for the following alteration processes, carried out after forming the most stable lowest Leached pseudorutile (LPSR) phase FeTi3O6(OH)3. Most of the alteration phases of pseudorutile (PSR) and LPSR have real Ti/(Ti+Fe) ratios between 0.6 and 0.75. Some misleading calculations of definite analyzed ilmenite alteration spots showed that the analyzed TiO2 percentage is contained within the chemical formula of the analyzed LPSR phase. In these cases, the false Ti/(Ti+Fe) ratios attain up to 0.9, the false included total number of anions (O, OH) ranges between 7 and 8.5, and the associated molecular water ranged between half and two water molecules (0.5-2 H2O). In these cases, the structure of the remaining LPSR phase may be intergrown with a separated individual triple rutile phase, which appears to have the same X-Ray Diffraction (XRD) pattern as the single PSR phase, or intergrown with a cryptocrystalline TiO2 phase. Some molecular formulas of PSR or Hydroxylian PSR (HPSR) from previous studies were discussed and explained following the proposed approach.
{"title":"A New Approach on the Egyptian Black Sand Ilmenite Alteration Processes","authors":"Mohamed Moustafa","doi":"10.48084/etasr.6026","DOIUrl":"https://doi.org/10.48084/etasr.6026","url":null,"abstract":"Several studies have investigated the process of alteration of ilmenite, especially in black sand. To predict the mechanisms of ilmenite alteration and the role of some minor element oxides in the alteration process, separated non-magnetic altered ilmenite grains were examined using a binocular microscope and a Cameca SX-100 microprobe instrument. Twenty intergrown phases of alteration products were concluded in three postulated scenarios for the following alteration processes, carried out after forming the most stable lowest Leached pseudorutile (LPSR) phase FeTi3O6(OH)3. Most of the alteration phases of pseudorutile (PSR) and LPSR have real Ti/(Ti+Fe) ratios between 0.6 and 0.75. Some misleading calculations of definite analyzed ilmenite alteration spots showed that the analyzed TiO2 percentage is contained within the chemical formula of the analyzed LPSR phase. In these cases, the false Ti/(Ti+Fe) ratios attain up to 0.9, the false included total number of anions (O, OH) ranges between 7 and 8.5, and the associated molecular water ranged between half and two water molecules (0.5-2 H2O). In these cases, the structure of the remaining LPSR phase may be intergrown with a separated individual triple rutile phase, which appears to have the same X-Ray Diffraction (XRD) pattern as the single PSR phase, or intergrown with a cryptocrystalline TiO2 phase. Some molecular formulas of PSR or Hydroxylian PSR (HPSR) from previous studies were discussed and explained following the proposed approach.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917858","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 promising experiments, boric acid has been tested as a safe and environmentally friendly insecticide for controlling Ceratitis capitata Wiedeman, a mediterranean fruit fly diptera belonging the Tephritidae family. Obtaining encouraging results can partially solve insecticidal pollution caused by chemical insecticides. Boric acid was applied in five baits that were, water, 5 and 10% sugar solutions, and 2.5 and 5% protein solutions on just emerged and 24-hour-old flies. For each bait, boric acid was presented by successive concentrations of 0.5%, 1%, 1.5%, and 2%. After 24 hours, the aged-fly death percentage ranged from 12.2 to 69.4 % and from 48 to 99.4% after 48 hours for just-emerged flies. However, for 24-hour-old flies, the percentage of death ranged from 32.6 to 90.4% after 24 hours and 65 to 99.6% after 48 hours. The current study shows the existence of a a direct proportionality between death percentage and the concentration of boric acid in the five baits, as death percentage increased with boric acid concentration. In addition, different baits had some effect on death percentage, but without a noticeable correlation. To avoid direct contact with the host plant and the boric acid-based baits, it is strongly encouraged to utilize boric acid in medfly control methods like the mass trapping technique.
{"title":"Boric Acid as a Safe Insecticide for Controlling the Mediterranean Fruit Fly Ceratitis Capitata Wiedemann (Diptera: Tephritidae)","authors":"Naimah Asid Alanazi","doi":"10.48084/etasr.6305","DOIUrl":"https://doi.org/10.48084/etasr.6305","url":null,"abstract":"In promising experiments, boric acid has been tested as a safe and environmentally friendly insecticide for controlling Ceratitis capitata Wiedeman, a mediterranean fruit fly diptera belonging the Tephritidae family. Obtaining encouraging results can partially solve insecticidal pollution caused by chemical insecticides. Boric acid was applied in five baits that were, water, 5 and 10% sugar solutions, and 2.5 and 5% protein solutions on just emerged and 24-hour-old flies. For each bait, boric acid was presented by successive concentrations of 0.5%, 1%, 1.5%, and 2%. After 24 hours, the aged-fly death percentage ranged from 12.2 to 69.4 % and from 48 to 99.4% after 48 hours for just-emerged flies. However, for 24-hour-old flies, the percentage of death ranged from 32.6 to 90.4% after 24 hours and 65 to 99.6% after 48 hours. The current study shows the existence of a a direct proportionality between death percentage and the concentration of boric acid in the five baits, as death percentage increased with boric acid concentration. In addition, different baits had some effect on death percentage, but without a noticeable correlation. To avoid direct contact with the host plant and the boric acid-based baits, it is strongly encouraged to utilize boric acid in medfly control methods like the mass trapping technique.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917867","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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