Pub Date : 2024-05-17DOI: 10.17485/ijst/v17i20.1090
Anil Kumari, Pooja Bhatia
Objectives: The sugar industry comprises various units, including feeding, crushing, evaporation, refining, and crystallization. The feeding system is the most crucial aspect of the sugar mill as the sugar-making process starts from here. So faults which occur here are divided into four categories minor, major, cease faults and walkout faults. This paper sets out to showcase a comprehensive analysis of the system's performance, availability, and profit. The study takes into account how minor, major, and cease faults can potentially impact the system's overall effectiveness. The findings of this research hold significant importance for sugar mills that prioritize optimal performance and profit. Methods: Primary data regarding various failures is collected from Ch. Devilal Co-Operative Sugar Mills Limited, Ahulana, Gohana. To find MTSF, Reliability, Availability, Profit etc., a mathematical model has been created. This model is based on the Semi-Markov process and Regenerative Point Technique and equations are drawn using exponential distribution and solved with Cremer’s rule and Laplace - Stieltjes transformation. Findings: A fault, Bagasse Jamming is considered a cease fault which occurs very frequently in the feeding system. Therefore, it is found that cease faults and major faults have more adverse impacts on the system's performance and availability than minor faults. MTSF, Availability and Profit are inversely proportional to these faults. When a major fault is 0.0035 and the cease fault is 0.0042, MTSF is nearly 100. So, to gain more profit we have to pay more attention to these faults. Novelty: The paper's results will aid in fault removal, increase availability, and optimize maintenance tactics in the sugar industry. Keywords: MTSF, Performance Analysis, Availability, Reliability, Minor, Major, Cease Faults
{"title":"Performance Analysis of Feeding System in Sugar Mill with Consideration of Bagasse Jamming","authors":"Anil Kumari, Pooja Bhatia","doi":"10.17485/ijst/v17i20.1090","DOIUrl":"https://doi.org/10.17485/ijst/v17i20.1090","url":null,"abstract":"Objectives: The sugar industry comprises various units, including feeding, crushing, evaporation, refining, and crystallization. The feeding system is the most crucial aspect of the sugar mill as the sugar-making process starts from here. So faults which occur here are divided into four categories minor, major, cease faults and walkout faults. This paper sets out to showcase a comprehensive analysis of the system's performance, availability, and profit. The study takes into account how minor, major, and cease faults can potentially impact the system's overall effectiveness. The findings of this research hold significant importance for sugar mills that prioritize optimal performance and profit. Methods: Primary data regarding various failures is collected from Ch. Devilal Co-Operative Sugar Mills Limited, Ahulana, Gohana. To find MTSF, Reliability, Availability, Profit etc., a mathematical model has been created. This model is based on the Semi-Markov process and Regenerative Point Technique and equations are drawn using exponential distribution and solved with Cremer’s rule and Laplace - Stieltjes transformation. Findings: A fault, Bagasse Jamming is considered a cease fault which occurs very frequently in the feeding system. Therefore, it is found that cease faults and major faults have more adverse impacts on the system's performance and availability than minor faults. MTSF, Availability and Profit are inversely proportional to these faults. When a major fault is 0.0035 and the cease fault is 0.0042, MTSF is nearly 100. So, to gain more profit we have to pay more attention to these faults. Novelty: The paper's results will aid in fault removal, increase availability, and optimize maintenance tactics in the sugar industry. Keywords: MTSF, Performance Analysis, Availability, Reliability, Minor, Major, Cease Faults","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"53 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140965652","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}
Pub Date : 2024-05-17DOI: 10.17485/ijst/v17i20.629
M. Poojary, Yarramalle Srinivas
Objectives: The research aims to develop the segmentation model to identify the deformity in the medical images as accurately as possible and plan for better medical treatment. The study is extended to identify the disease before its appearance in the human body through human aura images to support aura imaging in medical diagnosis. Methods: The study used a brain image from the UCI data set and Aura images from the Biowell data set to identify the disease. The segmentation model Bivariate Gaussian Mixture Model (B.G.M.M) was developed. Model parameters are derived using the Expectation Maximization (E.M) Algorithm. The Grasshopper optimization Algorithm (G.O.A) extracts optimal features from the images. The chosen feature is fed as input to the classification model B.G.M.M. Segmentation accuracy is measured using the quality metrics. Findings: The developed approach shows 97% accuracy in identifying the damaged tissues in MRI images and high-intensity energy zones in the aura images, indicating the potential for deformities. Novelty: This study significantly contributes to the field by offering novel solutions for precise and comprehensive image analysis in medical and aura imaging contexts. Keywords: G.O.A, segmentation, G.M.M, E.M, quality metrics, deformity identification, Hue and saturation
{"title":"Image Segmentation Based on G.O.A for Finding Deformities in Medical and Aura Images","authors":"M. Poojary, Yarramalle Srinivas","doi":"10.17485/ijst/v17i20.629","DOIUrl":"https://doi.org/10.17485/ijst/v17i20.629","url":null,"abstract":"Objectives: The research aims to develop the segmentation model to identify the deformity in the medical images as accurately as possible and plan for better medical treatment. The study is extended to identify the disease before its appearance in the human body through human aura images to support aura imaging in medical diagnosis. Methods: The study used a brain image from the UCI data set and Aura images from the Biowell data set to identify the disease. The segmentation model Bivariate Gaussian Mixture Model (B.G.M.M) was developed. Model parameters are derived using the Expectation Maximization (E.M) Algorithm. The Grasshopper optimization Algorithm (G.O.A) extracts optimal features from the images. The chosen feature is fed as input to the classification model B.G.M.M. Segmentation accuracy is measured using the quality metrics. Findings: The developed approach shows 97% accuracy in identifying the damaged tissues in MRI images and high-intensity energy zones in the aura images, indicating the potential for deformities. Novelty: This study significantly contributes to the field by offering novel solutions for precise and comprehensive image analysis in medical and aura imaging contexts. Keywords: G.O.A, segmentation, G.M.M, E.M, quality metrics, deformity identification, Hue and saturation","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963953","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}
Pub Date : 2024-05-17DOI: 10.17485/ijst/v17i20.737
G. Shankarajyothi, G. U. Reddy
Objectives: In the context of graph theory, a litact graph is a specific type of graph. This study introduces a new domination parameter, called split regular domination in litact graphs. Methods: When we talk about split regular domination in a litact graph during this investigation, we think about how to divide the litact graph into partitions that adhere to specific domination principles by taking a minimal split regular dominating set with all vertices of equal degree. We used a few common definitions and the ideas of several domination parameters in G to obtain the results. Findings: Numerous bounds on were found in relation to the different parameters of G like vertices, edges, diameter, vertex covering number, maximum degree and so forth, and its relationship to other dominating parameters of G such as total domination, edge domination, connected domination and so on was also found. Furthermore, outcomes resembling those of Nordhaus-Gaddum were also obtained. Novelty: Graph G was used to find a litact graph. Subsequently, a few findings of a new domination parameter called split regular domination in a litact graph in terms of different parameters of G have been established. Keywords: Graph, Litact Graph, Split Domination Number, Regular Domination Number, Split Regular Domination Number
目的：在图论中，litact 图是一种特殊类型的图。本研究引入了一个新的支配参数，称为 litact 图中的分裂规则支配。研究方法在本研究中，当我们谈论 litact 图中的分裂规则支配时，我们考虑的是如何通过取所有顶点度数相等的最小分裂规则支配集，将 litact 图划分为符合特定支配原则的分区。我们使用了一些常见的定义和 G 中几个支配参数的思想来获得结果。研究结果我们发现了许多与顶点、边、直径、顶点覆盖数、最大度数等 G 不同参数相关的边界，还发现了它与总支配、边支配、连接支配等 G 其他支配参数的关系。此外，还得到了类似于 Nordhaus-Gaddum 的结果。新颖性：图 G 被用来寻找一个 litact 图。随后，根据 G 的不同参数，在 litact 图中建立了一些新的支配参数，称为分裂规则支配。关键词图，Litact 图，分裂支配数，正则支配数，分裂正则支配数
{"title":"Split Regular Domination in Litact Graphs","authors":"G. Shankarajyothi, G. U. Reddy","doi":"10.17485/ijst/v17i20.737","DOIUrl":"https://doi.org/10.17485/ijst/v17i20.737","url":null,"abstract":"Objectives: In the context of graph theory, a litact graph is a specific type of graph. This study introduces a new domination parameter, called split regular domination in litact graphs. Methods: When we talk about split regular domination in a litact graph during this investigation, we think about how to divide the litact graph into partitions that adhere to specific domination principles by taking a minimal split regular dominating set with all vertices of equal degree. We used a few common definitions and the ideas of several domination parameters in G to obtain the results. Findings: Numerous bounds on were found in relation to the different parameters of G like vertices, edges, diameter, vertex covering number, maximum degree and so forth, and its relationship to other dominating parameters of G such as total domination, edge domination, connected domination and so on was also found. Furthermore, outcomes resembling those of Nordhaus-Gaddum were also obtained. Novelty: Graph G was used to find a litact graph. Subsequently, a few findings of a new domination parameter called split regular domination in a litact graph in terms of different parameters of G have been established. Keywords: Graph, Litact Graph, Split Domination Number, Regular Domination Number, Split Regular Domination Number","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"58 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140964912","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}
Pub Date : 2024-05-14DOI: 10.17485/ijst/v17i19.603
K. L. F. C. Rani, M. P. Anuradha
Objectives: In blockchain, mining is essential for verifying and adding transactions to the chain. Transaction approval time is increasing due to the mining process's limited capacity. To address this issue, this paper aims to reduce the approval time by introducing a new fuzzy logic optimization methodology for dynamic resource allocation of mining capacity based on resource congestion. Method: The proposed methodology does not rely on block size or mining duration and efficiently handles transaction congestion. The proposed fuzzy logic effectively handles the resources in the peak transaction. It allocates the resources dynamically using both horizontal and vertical scaling. It upgrades Transactions Per Second (TPS) and manages difficulty levels considering CPU, memory, and node utilization. Findings: Simulation results demonstrate the efficacy of the proposed methodology in improving blockchain performance compared to traditional blockchain approaches. The analysis includes average active nodes, transaction latency, memory utilization, and transactions per second. Novelty: The proposed work introduces a novel approach to blockchain mining optimization by integrating fuzzy logic for dynamic scaling decisions. This innovative method addresses adaptability and resource efficiency concerns and offers a flexible and efficient solution to blockchain scalability and transaction processing challenges. Keywords: Blockchain, Fuzzy logic, Vertical scaling, Horizontal scaling, Transaction latency
{"title":"Fuzzy Logic-Based Mining Strategy for Transaction Congestion Management in Blockchain Networks","authors":"K. L. F. C. Rani, M. P. Anuradha","doi":"10.17485/ijst/v17i19.603","DOIUrl":"https://doi.org/10.17485/ijst/v17i19.603","url":null,"abstract":"Objectives: In blockchain, mining is essential for verifying and adding transactions to the chain. Transaction approval time is increasing due to the mining process's limited capacity. To address this issue, this paper aims to reduce the approval time by introducing a new fuzzy logic optimization methodology for dynamic resource allocation of mining capacity based on resource congestion. Method: The proposed methodology does not rely on block size or mining duration and efficiently handles transaction congestion. The proposed fuzzy logic effectively handles the resources in the peak transaction. It allocates the resources dynamically using both horizontal and vertical scaling. It upgrades Transactions Per Second (TPS) and manages difficulty levels considering CPU, memory, and node utilization. Findings: Simulation results demonstrate the efficacy of the proposed methodology in improving blockchain performance compared to traditional blockchain approaches. The analysis includes average active nodes, transaction latency, memory utilization, and transactions per second. Novelty: The proposed work introduces a novel approach to blockchain mining optimization by integrating fuzzy logic for dynamic scaling decisions. This innovative method addresses adaptability and resource efficiency concerns and offers a flexible and efficient solution to blockchain scalability and transaction processing challenges. Keywords: Blockchain, Fuzzy logic, Vertical scaling, Horizontal scaling, Transaction latency","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"35 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140979175","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}
Pub Date : 2024-05-14DOI: 10.17485/ijst/v17i19.3264
M. J. Rao, B. Ramakrishna, K. G. D. Prasad, B. Vijay, T. P. Vital, M. Ramanaiah
Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using deep transfer learning and pre-trained neural networks. It analyses breast ultrasound images and identifies important characteristics using pre-trained networks. The goal is to create a more efficient and accurate automated system for breast cancer detection. Methods: The study uses breast ultrasound cancer image data from the Kaggle Data Repository to extract informative features, identify cancer-related characteristics, and classify them into benign, malignant, and normal tissue. Pre-trained Deep Neural Networks (DNNs) extract these features and feed them into a 10-fold cross-validation SVM classifier. The SVM is evaluated using various kernel functions to identify the best kernel for separating data points. This methodology aims to achieve accurate classification of breast cancer in ultrasound images. Findings: The study confirms the effectiveness of deep transfer learning for breast cancer detection in ultrasound images, with Inception V3 outperforming VGG-16 and VGG-19 in extracting relevant features. The combination of Inception V3 and the SVM classifier with a polynomial kernel achieved the highest classification accuracy, indicating its ability to model complex relationships. The study demonstrated an AUC of 0.944 and a classification accuracy of 87.44% using the Inception V3 + SVM polynomial. Novelty: This research demonstrates the potential of deep transfer learning and SVM classifiers for accurate breast cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and VGG-19 for breast cancer detection, demonstrating improved classification accuracy. The combination of Inception V3 and SVM (polynomial) achieved a significant AUC (0.944) and classification accuracy (87.44%), outperforming other models tested. This research underscores the potential of these technologies for accurate breast cancer detection in ultrasound images. Keywords: Breast Cancer, Deep Learning, Feature Extraction, Inception-v3, SVM, Transfer Learning
{"title":"Optimizing Breast Cancer Detection: Deep Transfer Learning Empowered by SVM Classifiers","authors":"M. J. Rao, B. Ramakrishna, K. G. D. Prasad, B. Vijay, T. P. Vital, M. Ramanaiah","doi":"10.17485/ijst/v17i19.3264","DOIUrl":"https://doi.org/10.17485/ijst/v17i19.3264","url":null,"abstract":"Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using deep transfer learning and pre-trained neural networks. It analyses breast ultrasound images and identifies important characteristics using pre-trained networks. The goal is to create a more efficient and accurate automated system for breast cancer detection. Methods: The study uses breast ultrasound cancer image data from the Kaggle Data Repository to extract informative features, identify cancer-related characteristics, and classify them into benign, malignant, and normal tissue. Pre-trained Deep Neural Networks (DNNs) extract these features and feed them into a 10-fold cross-validation SVM classifier. The SVM is evaluated using various kernel functions to identify the best kernel for separating data points. This methodology aims to achieve accurate classification of breast cancer in ultrasound images. Findings: The study confirms the effectiveness of deep transfer learning for breast cancer detection in ultrasound images, with Inception V3 outperforming VGG-16 and VGG-19 in extracting relevant features. The combination of Inception V3 and the SVM classifier with a polynomial kernel achieved the highest classification accuracy, indicating its ability to model complex relationships. The study demonstrated an AUC of 0.944 and a classification accuracy of 87.44% using the Inception V3 + SVM polynomial. Novelty: This research demonstrates the potential of deep transfer learning and SVM classifiers for accurate breast cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and VGG-19 for breast cancer detection, demonstrating improved classification accuracy. The combination of Inception V3 and SVM (polynomial) achieved a significant AUC (0.944) and classification accuracy (87.44%), outperforming other models tested. This research underscores the potential of these technologies for accurate breast cancer detection in ultrasound images. Keywords: Breast Cancer, Deep Learning, Feature Extraction, Inception-v3, SVM, Transfer Learning","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"25 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980496","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}
Pub Date : 2024-05-14DOI: 10.17485/ijst/v17i19.833
Purvi Sankhe, Mukesh Dixit
Objective: To create an AI-powered recommendation system that is designed for IT professionals to help them choose the best software development approaches. Through the use of specified data parameters. Methods: The recommendation system will make use of machine learning algorithms and data analysis methods to examine team dynamics, project needs, and other variables. The technology will enable developers to improve the quality of products and speed up the development process by recommending suitable development methodologies. Data parameters considered for the development of the recommendation model fall into four categories: requirements, user involvement, development team, type of project, and risk associated with it. Findings: Existing recommendation systems developed by different researchers are applicable for only requirement elicitation and to recommend different phases of the development process, whereas systems that will help select development methodology are not available in the existing systems. Among the five machine learning algorithms applied in the recommender system building process, the DecisionTree Classifier and RandomForest Classifier exhibit superior performance, achieving 100% accuracy, while the Kneighbors Classifier indicates 94.74% accuracy. Novelty: This study of systems introduces a novel approach to software development methodology, a recommender system, which helps IT developers select the best appropriate development approach for the development of a software product or project based on the type of project to be built and other data parameters. Keywords: Agile, Development, Requirements, Methodology, User, Customer
目标：创建一个面向 IT 专业人员的人工智能推荐系统，帮助他们选择最佳的软件开发方法。通过使用指定的数据参数。方法：该推荐系统将利用机器学习算法和数据分析方法来检查团队动态、项目需求和其他变量。该技术将通过推荐合适的开发方法，帮助开发人员提高产品质量，加快开发进程。开发推荐模型时考虑的数据参数分为四类：需求、用户参与、开发团队、项目类型和相关风险。研究结果由不同研究人员开发的现有推荐系统仅适用于需求征询和推荐开发流程的不同阶段，而有助于选择开发方法的系统在现有系统中并不存在。在推荐系统构建过程中应用的五种机器学习算法中，决策树分类器和随机森林分类器表现优异，准确率达到 100%，而 Kneighbors 分类器的准确率为 94.74%。新颖性：本系统研究引入了一种新颖的软件开发方法--推荐系统，帮助 IT 开发人员根据待建项目的类型和其他数据参数，为软件产品或项目的开发选择最合适的开发方法。关键词敏捷 开发 需求 方法 用户 客户
{"title":"Analysis of Traditional and Agile Software Development Process for Developing Recommender Model using Machine Learning","authors":"Purvi Sankhe, Mukesh Dixit","doi":"10.17485/ijst/v17i19.833","DOIUrl":"https://doi.org/10.17485/ijst/v17i19.833","url":null,"abstract":"Objective: To create an AI-powered recommendation system that is designed for IT professionals to help them choose the best software development approaches. Through the use of specified data parameters. Methods: The recommendation system will make use of machine learning algorithms and data analysis methods to examine team dynamics, project needs, and other variables. The technology will enable developers to improve the quality of products and speed up the development process by recommending suitable development methodologies. Data parameters considered for the development of the recommendation model fall into four categories: requirements, user involvement, development team, type of project, and risk associated with it. Findings: Existing recommendation systems developed by different researchers are applicable for only requirement elicitation and to recommend different phases of the development process, whereas systems that will help select development methodology are not available in the existing systems. Among the five machine learning algorithms applied in the recommender system building process, the DecisionTree Classifier and RandomForest Classifier exhibit superior performance, achieving 100% accuracy, while the Kneighbors Classifier indicates 94.74% accuracy. Novelty: This study of systems introduces a novel approach to software development methodology, a recommender system, which helps IT developers select the best appropriate development approach for the development of a software product or project based on the type of project to be built and other data parameters. Keywords: Agile, Development, Requirements, Methodology, User, Customer","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"10 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140982019","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}
Pub Date : 2024-05-14DOI: 10.17485/ijst/v17i19.881
Sajeesh S Nair, Saral Kumar Gupta, N. S. Shine, K. T. Thomas, P. R. Bijumon, Stanly George, Sarath S Nair, Anu K George
Objectives: The significant rise in modern cath lab units has led to a proportionate increase in cath lab procedures and subsequent radiation environment may elevate the occupational radiation exposure to staff. This study aimed to assess the collective impact of fundamental radiation safety devices in the cath lab on decreasing occupational radiation exposure to staff. Methods: This study was conducted in our cath lab room, equipped with a Siemens Artis cath lab unit. Measurements were performed using RaySafe X2 detectors and Thermo Luminescent Dosimeters (TLDs). Dose assessments were conducted without safety measures and then found a considerable reduction of dose by adding basic radiation safety measures. Findings: The use of lead aprons resulted in a substantial reduction( 92%) in radiation dose. The effect of time and distance versus dose was plotted. The impact of lead flaps and the use of a ceiling suspension shield quantify reductions in scattered doses. The cumulative impact of each safety measure was calculated, and the outcome indicates a 99% reduction in dose. The importance of utilizing all available protective measures when working with radiation cannot be overstated. It is essential for maximizing safety, minimizing risks, and fostering a culture of safety within radiation environments like cath labs. Novelty: This is a thorough assessment of different radiation protection strategies in the specific setting of a Cath lab. It not only evaluates individual measures but also considers their combined impact and the calculation based on the exit dose from the patient. Keywords: Cardiologist, Radiation Dose, Interventional Radiology, Radiation protection, Cath lab
{"title":"Evaluating the Cumulative Effects of Fundamental Radiation Safety Measures on Health Professionals in Cath Lab","authors":"Sajeesh S Nair, Saral Kumar Gupta, N. S. Shine, K. T. Thomas, P. R. Bijumon, Stanly George, Sarath S Nair, Anu K George","doi":"10.17485/ijst/v17i19.881","DOIUrl":"https://doi.org/10.17485/ijst/v17i19.881","url":null,"abstract":"Objectives: The significant rise in modern cath lab units has led to a proportionate increase in cath lab procedures and subsequent radiation environment may elevate the occupational radiation exposure to staff. This study aimed to assess the collective impact of fundamental radiation safety devices in the cath lab on decreasing occupational radiation exposure to staff. Methods: This study was conducted in our cath lab room, equipped with a Siemens Artis cath lab unit. Measurements were performed using RaySafe X2 detectors and Thermo Luminescent Dosimeters (TLDs). Dose assessments were conducted without safety measures and then found a considerable reduction of dose by adding basic radiation safety measures. Findings: The use of lead aprons resulted in a substantial reduction( 92%) in radiation dose. The effect of time and distance versus dose was plotted. The impact of lead flaps and the use of a ceiling suspension shield quantify reductions in scattered doses. The cumulative impact of each safety measure was calculated, and the outcome indicates a 99% reduction in dose. The importance of utilizing all available protective measures when working with radiation cannot be overstated. It is essential for maximizing safety, minimizing risks, and fostering a culture of safety within radiation environments like cath labs. Novelty: This is a thorough assessment of different radiation protection strategies in the specific setting of a Cath lab. It not only evaluates individual measures but also considers their combined impact and the calculation based on the exit dose from the patient. Keywords: Cardiologist, Radiation Dose, Interventional Radiology, Radiation protection, Cath lab","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"27 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980320","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}
Pub Date : 2024-05-14DOI: 10.17485/ijst/v17i19.868
Vijaymala Ghuge, T. L. Holambe, Bhausaheb Sontakke, G. Shrimangale
Objective: The aim of this research is to gain a comprehensive understanding of radon diffusion equation in water. Methods: A time fractional radon diffusion equation with Caputo sense is employed to find diffusion dynamics of radon in water medium. The fractional order explicit finite difference technique is used to find its numerical solution. A Python software is used to find numerical solution. Findings: The effect of fractional-order parameters on the distribution and concentration profiles of radon in water has been investigated. Furthermore, we study stability and convergence of the explicit finite difference method. Novelty: The fractional order explicit finite difference method can be used to estimate approximate solution of such fractional order differential equations. Keywords: Radon Diffusion Equation, Finite Difference Method, Caputo, Fractional Derivative, Python
{"title":"Solving Time-fractional Order Radon Diffusion Equation in Water by Finite Difference Method","authors":"Vijaymala Ghuge, T. L. Holambe, Bhausaheb Sontakke, G. Shrimangale","doi":"10.17485/ijst/v17i19.868","DOIUrl":"https://doi.org/10.17485/ijst/v17i19.868","url":null,"abstract":"Objective: The aim of this research is to gain a comprehensive understanding of radon diffusion equation in water. Methods: A time fractional radon diffusion equation with Caputo sense is employed to find diffusion dynamics of radon in water medium. The fractional order explicit finite difference technique is used to find its numerical solution. A Python software is used to find numerical solution. Findings: The effect of fractional-order parameters on the distribution and concentration profiles of radon in water has been investigated. Furthermore, we study stability and convergence of the explicit finite difference method. Novelty: The fractional order explicit finite difference method can be used to estimate approximate solution of such fractional order differential equations. Keywords: Radon Diffusion Equation, Finite Difference Method, Caputo, Fractional Derivative, Python","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"47 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980950","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}
Pub Date : 2024-05-14DOI: 10.17485/ijst/v17i19.379
Aditya Sharma, D. K. Palwalia
Objectives: This work focuses on the stability analysis of grid connected microgrids. It considers the impact of load disturbance and grid voltage change on voltage and current levels, as well as reactive and active power responses, is analysed. Methods: A comprehensive small-signal state-space model is developed for an inverter-based microgrid, incorporating submodules of inverters, phase-locked loops (PLLs), and LCL filters. The model is linearized around a stable operating point, and eigenvalue analysis is performed and validated through MATLAB/Simulink simulations. A current controller operating in the d-q frame is proposed to enhance stable power conversion and maintain microgrid stability. Findings: The proposed model and control strategy demonstrate the microgrid's ability to maintain transient voltage stability under severe dynamic conditions. During a 10% grid voltage fluctuation, the microgrid exhibits stable active and reactive power responses, with currents and voltages at the point of common coupling stabilizing within 0.2 seconds. Furthermore, when a 25 kVA active load is disconnected, the microgrid effectively manages the power transition, maintaining stable operation with minimal deviations in key parameters. The current controller simplifies AC current control, integrating active power management from solar input, DC-link voltage stability, and reactive power control. Novelty: The novelty lies in the comprehensive analysis of transient voltage stability in grid-connected microgrids under grid voltage fluctuations and load disturbances, areas that have received limited attention in previous research. By developing a detailed small-signal state-space model incorporating PLL and LCL filter dynamics and proposing a robust control strategy with the current controller, this study offers new insights into enhancing the resilience and reliability of grid-connected microgrids during transient events. Keywords: Microgrid, Small Signal Stability, Voltage Source Inverter, State Space model, Eigen Values
{"title":"Small Signal Modelling and Stability Analysis of a Grid Connected Inverted Based Microgrid","authors":"Aditya Sharma, D. K. Palwalia","doi":"10.17485/ijst/v17i19.379","DOIUrl":"https://doi.org/10.17485/ijst/v17i19.379","url":null,"abstract":"Objectives: This work focuses on the stability analysis of grid connected microgrids. It considers the impact of load disturbance and grid voltage change on voltage and current levels, as well as reactive and active power responses, is analysed. Methods: A comprehensive small-signal state-space model is developed for an inverter-based microgrid, incorporating submodules of inverters, phase-locked loops (PLLs), and LCL filters. The model is linearized around a stable operating point, and eigenvalue analysis is performed and validated through MATLAB/Simulink simulations. A current controller operating in the d-q frame is proposed to enhance stable power conversion and maintain microgrid stability. Findings: The proposed model and control strategy demonstrate the microgrid's ability to maintain transient voltage stability under severe dynamic conditions. During a 10% grid voltage fluctuation, the microgrid exhibits stable active and reactive power responses, with currents and voltages at the point of common coupling stabilizing within 0.2 seconds. Furthermore, when a 25 kVA active load is disconnected, the microgrid effectively manages the power transition, maintaining stable operation with minimal deviations in key parameters. The current controller simplifies AC current control, integrating active power management from solar input, DC-link voltage stability, and reactive power control. Novelty: The novelty lies in the comprehensive analysis of transient voltage stability in grid-connected microgrids under grid voltage fluctuations and load disturbances, areas that have received limited attention in previous research. By developing a detailed small-signal state-space model incorporating PLL and LCL filter dynamics and proposing a robust control strategy with the current controller, this study offers new insights into enhancing the resilience and reliability of grid-connected microgrids during transient events. Keywords: Microgrid, Small Signal Stability, Voltage Source Inverter, State Space model, Eigen Values","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"29 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980145","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}
Objectives: To analyze the critical postures of the CNC milling machine operators by RULA (Rapid Upper Limb Assessment) scores and develop an ANN (Artificial Neural Network) prediction model. Methods: The methodology includes a postural analysis of 40 male CNC milling machine operators across Bangladesh, employing both manual (using manual RULA assessment worksheet) and digital (using CATIA V5R21 software) RULA methods complemented by an ANN prediction model. Finally, Digital RULA scores through DHM (Digital Human Modeling) and ANN predicted RULA scores would be compared. Findings: Digital RULA analysis reveals that lifting, carrying, and positioning are the most crucial ergonomic postures, and the most prominent high-risk category limbs are wrist and arm. The overall initial RULA score for lifting, carrying, and positioning are 7, 6, and 7, respectively, and reduced to 3, 3 and 4 respectively for ergonomically designed posture. The ANN model, structured with input, hidden, and output layers of 7, 10, and 1 nodes, significantly refines ergonomic risk prediction by aligning predicted scores closely with actual outcomes during the first stage, emphasized for training. It demonstrates a perfect correlation (R=1) in training, testing, validation, and overall performance for using manual RULA scores. The model's accuracy is further evidenced by minimal prediction offsets across all datasets for digital RULA score in the second stage, with correlation coefficients of 0.87003 (training), 0.93676 (validation), 0.89113 (testing), and (0.88395) for overall. This study contributes significant advancements in ergonomic risk assessment, highlighting the adoption of improved postures to reduce musculoskeletal disorders. Novelty: Employing both manual and DHM methods for RULA score calculation combined with ANN model, which can predict postural risk as floating number and fit a wider range of parameters. Keywords: ANN, CNC, Digital Human Modeling (DHM), Ergonomics, RULA
{"title":"Comparative Ergonomic Posture Analysis of CNC Milling Machine Workers through Digital Human Modeling and Artificial Neural Networks","authors":"Rakesh Roy, Md. Mahafuj Anam Murad, Masum Billah, Subrata Talapatra, Md Mahfuzur Rahman, Sarojit Kumar Biswas","doi":"10.17485/ijst/v17i19.912","DOIUrl":"https://doi.org/10.17485/ijst/v17i19.912","url":null,"abstract":"Objectives: To analyze the critical postures of the CNC milling machine operators by RULA (Rapid Upper Limb Assessment) scores and develop an ANN (Artificial Neural Network) prediction model. Methods: The methodology includes a postural analysis of 40 male CNC milling machine operators across Bangladesh, employing both manual (using manual RULA assessment worksheet) and digital (using CATIA V5R21 software) RULA methods complemented by an ANN prediction model. Finally, Digital RULA scores through DHM (Digital Human Modeling) and ANN predicted RULA scores would be compared. Findings: Digital RULA analysis reveals that lifting, carrying, and positioning are the most crucial ergonomic postures, and the most prominent high-risk category limbs are wrist and arm. The overall initial RULA score for lifting, carrying, and positioning are 7, 6, and 7, respectively, and reduced to 3, 3 and 4 respectively for ergonomically designed posture. The ANN model, structured with input, hidden, and output layers of 7, 10, and 1 nodes, significantly refines ergonomic risk prediction by aligning predicted scores closely with actual outcomes during the first stage, emphasized for training. It demonstrates a perfect correlation (R=1) in training, testing, validation, and overall performance for using manual RULA scores. The model's accuracy is further evidenced by minimal prediction offsets across all datasets for digital RULA score in the second stage, with correlation coefficients of 0.87003 (training), 0.93676 (validation), 0.89113 (testing), and (0.88395) for overall. This study contributes significant advancements in ergonomic risk assessment, highlighting the adoption of improved postures to reduce musculoskeletal disorders. Novelty: Employing both manual and DHM methods for RULA score calculation combined with ANN model, which can predict postural risk as floating number and fit a wider range of parameters. Keywords: ANN, CNC, Digital Human Modeling (DHM), Ergonomics, RULA","PeriodicalId":13296,"journal":{"name":"Indian journal of science and technology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140979603","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：