Thirumarai Selvi C, Sankara Subbramanian R.S, Muthu Krishnan M, Gnana Priya P
According to the Indian scenario, the majority of reservoirs for holding water are operated independently, which is problematic when there are crises (abnormal inflow, cloudy conditions), which causes the surrounding communities and agricultural areas to be submerged those aquifers. Due to the vast geographic region and depth, it is challenging to manually measure the essential reservoir life metrics. Therefore, this research work suggests a cutting-edge system of reservoir management that includes sensors that are appropriate for measuring variables such as pressure, water level, outflow velocity, inflow velocity, tilt, vibration, etc. The Arduino Uno integrates all of the sensors, and Microsoft Power BI receives the data in real time, where each parameter is shown in an appropriate format for visualization. In case of an emergency water level rise, the alarm is set off. The procedure begins with the collection of data from sensors and concludes with the presentation of that data on a dashboard in a control room situated in a distant place that links to a website where the relevant information can be seen by visitors.
根据印度的情况，大多数用于蓄水的水库都是独立运行的，这在发生危机（异常流入、阴天）时就会出现问题，导致周边社区和农业区被这些含水层淹没。由于地域广阔、水深较深，人工测量重要的水库寿命指标具有挑战性。因此，这项研究工作提出了一种先进的水库管理系统，其中包括适合测量压力、水位、流出速度、流入速度、倾斜、振动等变量的传感器。Arduino Uno 集成了所有传感器，Microsoft Power BI 实时接收数据，并以适当格式显示每个参数，实现可视化。如果水位紧急上升，则会发出警报。该程序从传感器收集数据开始，最后将数据显示在位于远处的控制室的仪表盘上，该仪表盘链接到一个网站，游客可以在该网站上看到相关信息。
{"title":"IoT-Enabled Flood Monitoring System for Enhanced Dam Surveillance and Risk Mitigation","authors":"Thirumarai Selvi C, Sankara Subbramanian R.S, Muthu Krishnan M, Gnana Priya P","doi":"10.54392/irjmt24311","DOIUrl":"https://doi.org/10.54392/irjmt24311","url":null,"abstract":"According to the Indian scenario, the majority of reservoirs for holding water are operated independently, which is problematic when there are crises (abnormal inflow, cloudy conditions), which causes the surrounding communities and agricultural areas to be submerged those aquifers. Due to the vast geographic region and depth, it is challenging to manually measure the essential reservoir life metrics. Therefore, this research work suggests a cutting-edge system of reservoir management that includes sensors that are appropriate for measuring variables such as pressure, water level, outflow velocity, inflow velocity, tilt, vibration, etc. The Arduino Uno integrates all of the sensors, and Microsoft Power BI receives the data in real time, where each parameter is shown in an appropriate format for visualization. In case of an emergency water level rise, the alarm is set off. The procedure begins with the collection of data from sensors and concludes with the presentation of that data on a dashboard in a control room situated in a distant place that links to a website where the relevant information can be seen by visitors.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"100 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140984191","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}
Gowtham M, C. Sivakumar, N. Chandrasekar, Balachandran S, S. N.
A hybrid nanocomposite of zinc vanadate/cobalt oxide (Zn3(VO4)2/CoO at ratios of 90/10, 80/20, 50/50, and 20/80) was obtained using a simple co-precipitation technique, then calcinated for 4 hrs at 400°C. The surface morphological, vibrational, and structural characteristics of the synthesized hybrid nanocomposites were examined. According to the structural study, orthorhombic Zn3(VO4)2 and cubic crystal systems of CoO with space groups Fm-3m were formed. The functional groups of Zinc Vanadate/Cobalt Oxide were examined using FTIR spectroscopy. A scanning electron microscopy (SEM) study reveals the nanosheets structures with the size of 200 nm. The chemical composition and formation of the Zn3(VO4)2/CoO composites were confirmed using X-ray photoelectron spectroscopy (XPS). The electrochemical performance of the hybrid nanocomposites was assessed through CV, GCD and impedance analysis. Among the nanocomposites, Zn3(VO4)2/CoO 80/20 exhibited a high specific capacitance value of 564.36 Fg-1 and retaining 97% of their total capacitance even after 3000 cycles.
{"title":"Exploring Zinc Vanadate/Cobalt Oxide (Zn3(VO4)2/CoO) Nano Hybrid Composites as Supercapacitors for Sustainable Energy Storage Applications","authors":"Gowtham M, C. Sivakumar, N. Chandrasekar, Balachandran S, S. N.","doi":"10.54392/irjmt24310","DOIUrl":"https://doi.org/10.54392/irjmt24310","url":null,"abstract":"A hybrid nanocomposite of zinc vanadate/cobalt oxide (Zn3(VO4)2/CoO at ratios of 90/10, 80/20, 50/50, and 20/80) was obtained using a simple co-precipitation technique, then calcinated for 4 hrs at 400°C. The surface morphological, vibrational, and structural characteristics of the synthesized hybrid nanocomposites were examined. According to the structural study, orthorhombic Zn3(VO4)2 and cubic crystal systems of CoO with space groups Fm-3m were formed. The functional groups of Zinc Vanadate/Cobalt Oxide were examined using FTIR spectroscopy. A scanning electron microscopy (SEM) study reveals the nanosheets structures with the size of 200 nm. The chemical composition and formation of the Zn3(VO4)2/CoO composites were confirmed using X-ray photoelectron spectroscopy (XPS). The electrochemical performance of the hybrid nanocomposites was assessed through CV, GCD and impedance analysis. Among the nanocomposites, Zn3(VO4)2/CoO 80/20 exhibited a high specific capacitance value of 564.36 Fg-1 and retaining 97% of their total capacitance even after 3000 cycles.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994448","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}
The integration of closed-circuit television (CCTV) monitoring is crucial in the field of video processing, which provides an efficient method for comprehensive surveillance. However, a key challenge associated with this practice is its substantial demand for storage space. Typically, surveillance footage is stored in hard disk drives, and due to limited storage spaces, it is deleted after some time. To address this issue, an innovative method for compressing CCTV video, named object detection-based surveillance compression (ODSC), is introduced. Our ODSC model is divided into two steps: -i) depending upon the objects in the video, determine the significant and non-significant frames of surveillance video using the neural network approach YOLOv5s & YOLOv7-tiny and Yolov8s ii) construct the video of significant frames. Following a comprehensive analysis of the experimental outcomes, it is noted that YOLOv8s stands out with a remarkable detection accuracy of 99.7% on the COCO dataset. Our ODSC approach is reducing the storage space greatly and achieving an average compression ratio of up to 96.31% using YOLOv8s, which surpasses the existing state-of-the-art methods.
{"title":"A Novel Approach for Surveillance Compression using Neural Network Technique","authors":"Nikita Mohod, Prateek Agrawal, Vishu Madaan","doi":"10.54392/irjmt2436","DOIUrl":"https://doi.org/10.54392/irjmt2436","url":null,"abstract":"The integration of closed-circuit television (CCTV) monitoring is crucial in the field of video processing, which provides an efficient method for comprehensive surveillance. However, a key challenge associated with this practice is its substantial demand for storage space. Typically, surveillance footage is stored in hard disk drives, and due to limited storage spaces, it is deleted after some time. To address this issue, an innovative method for compressing CCTV video, named object detection-based surveillance compression (ODSC), is introduced. Our ODSC model is divided into two steps: -i) depending upon the objects in the video, determine the significant and non-significant frames of surveillance video using the neural network approach YOLOv5s & YOLOv7-tiny and Yolov8s ii) construct the video of significant frames. Following a comprehensive analysis of the experimental outcomes, it is noted that YOLOv8s stands out with a remarkable detection accuracy of 99.7% on the COCO dataset. Our ODSC approach is reducing the storage space greatly and achieving an average compression ratio of up to 96.31% using YOLOv8s, which surpasses the existing state-of-the-art methods.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140667881","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}
Addressing the class imbalance in classification problems is particularly challenging, especially in the context of medical datasets where misclassifying minority class samples can have significant repercussions. This study is dedicated to mitigating class imbalance in medical datasets by employing a hybrid approach that combines data-level, cost-sensitive, and ensemble methods. Through an assessment of the performance, measured by AUC-ROC values, Sensitivity, F1-Score, and G-Mean of 20 data-level and four cost-sensitive models on seventeen medical datasets - 12 small and five large, a hybridized model, SMOTE-RF-CS-LR has been devised. This model integrates the Synthetic Minority Oversampling Technique (SMOTE), the ensemble classifier Random Forest (RF), and the Cost-Sensitive Logistic Regression (CS-LR). Upon testing the hybridized model on diverse imbalanced ratios, it demonstrated remarkable performance, achieving outstanding performance values on the majority of the datasets. Further examination of the model's training duration and time complexity revealed its efficiency, taking less than a second to train on each small dataset. Consequently, the proposed hybridized model not only proves to be time-efficient but also exhibits robust capabilities in handling class imbalance, yielding outstanding classification results in the context of medical datasets.
{"title":"Enhanced Classification of Imbalanced Medical Datasets using Hybrid Data-Level, Cost-Sensitive and Ensemble Methods","authors":"Ayushi Gupta, Shikha Gupta","doi":"10.54392/irjmt2435","DOIUrl":"https://doi.org/10.54392/irjmt2435","url":null,"abstract":"Addressing the class imbalance in classification problems is particularly challenging, especially in the context of medical datasets where misclassifying minority class samples can have significant repercussions. This study is dedicated to mitigating class imbalance in medical datasets by employing a hybrid approach that combines data-level, cost-sensitive, and ensemble methods. Through an assessment of the performance, measured by AUC-ROC values, Sensitivity, F1-Score, and G-Mean of 20 data-level and four cost-sensitive models on seventeen medical datasets - 12 small and five large, a hybridized model, SMOTE-RF-CS-LR has been devised. This model integrates the Synthetic Minority Oversampling Technique (SMOTE), the ensemble classifier Random Forest (RF), and the Cost-Sensitive Logistic Regression (CS-LR). Upon testing the hybridized model on diverse imbalanced ratios, it demonstrated remarkable performance, achieving outstanding performance values on the majority of the datasets. Further examination of the model's training duration and time complexity revealed its efficiency, taking less than a second to train on each small dataset. Consequently, the proposed hybridized model not only proves to be time-efficient but also exhibits robust capabilities in handling class imbalance, yielding outstanding classification results in the context of medical datasets.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"45 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140677540","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}
Rapid progress can be observed in the field of computer network technologies. Blockchain technology(BCT) presents a potentially viable alternative for effectively mitigating performance and security issues encountered in distributed systems. Recent studies have focused on exploring a number of exciting new technologies, including BlockChain (BC), Software-Defined Networking (SDN), and the Internet of Things (IoT). Various technologies offer data integrity and secrecy. One such technology that has been utilized for a number of years is cloud computing (CC). Cloud architecture facilitates the flow of confidential information, enabling customers to access remote resources. CC is also accompanied with notable security dangers, concerns, and challenges. In order to tackle these difficulties, we suggest integrating BC and SDN into a CC framework designed for the IoT. The fundamental flexibility and centralized capabilities of SDN facilitate network management, facilitate network abstraction, simplify network evolution, and possess the capacity to effectively handle the IoT network. The utilization of BCT is widely acknowledged as a means to ensure robust security inside distributed SDN (DSDN) and IoT networks, hence enhancing the efficacy of the detection and mitigation procedures.
计算机网络技术领域的发展日新月异。区块链技术（BCT）为有效缓解分布式系统中遇到的性能和安全问题提供了一种潜在的可行替代方案。近期的研究重点是探索一些令人兴奋的新技术，包括区块链（BlockChain，BC）、软件定义网络（Software-Defined Networking，SDN）和物联网（IoT）。各种技术提供了数据完整性和保密性。云计算（CC）就是这样一种已应用多年的技术。云架构促进了机密信息的流动，使客户能够访问远程资源。云计算也伴随着显著的安全危险、问题和挑战。为了解决这些难题，我们建议将 BC 和 SDN 集成到专为物联网设计的 CC 框架中。SDN 具有基本的灵活性和集中化能力，有利于网络管理、促进网络抽象、简化网络演进，并具备有效处理物联网网络的能力。人们普遍认为，利用 BCT 可以确保分布式 SDN（DSDN）和物联网网络的稳健安全性，从而提高检测和缓解程序的功效。
{"title":"BCSDNCC: A Secure Blockchain SDN framework for IoT and Cloud Computing","authors":"Sravan Kumar V, Madhu Kumar V, Chandu Naik Azmea, Karthik Kumar Vaigandla","doi":"10.54392/irjmt2433","DOIUrl":"https://doi.org/10.54392/irjmt2433","url":null,"abstract":"Rapid progress can be observed in the field of computer network technologies. Blockchain technology(BCT) presents a potentially viable alternative for effectively mitigating performance and security issues encountered in distributed systems. Recent studies have focused on exploring a number of exciting new technologies, including BlockChain (BC), Software-Defined Networking (SDN), and the Internet of Things (IoT). Various technologies offer data integrity and secrecy. One such technology that has been utilized for a number of years is cloud computing (CC). Cloud architecture facilitates the flow of confidential information, enabling customers to access remote resources. CC is also accompanied with notable security dangers, concerns, and challenges. In order to tackle these difficulties, we suggest integrating BC and SDN into a CC framework designed for the IoT. The fundamental flexibility and centralized capabilities of SDN facilitate network management, facilitate network abstraction, simplify network evolution, and possess the capacity to effectively handle the IoT network. The utilization of BCT is widely acknowledged as a means to ensure robust security inside distributed SDN (DSDN) and IoT networks, hence enhancing the efficacy of the detection and mitigation procedures.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"155 6‐7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140698663","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}
Smital D. Patil, Pramod J. Deore, Vaishali Bhagwat Patil
Ovarian cancer, a difficult and often asymptomatic malignancy, remains a substantial global health concern in women. An ovary is a female reproductive organ, which lies on each side of the uterus and used to store eggs. Computer-aided diagnosis (CAD) is an approach that involves using computer algorithms and machine learning techniques to assist medical professionals in diagnosing ovarian malignancies, benign tumors or Poly-cystic ovaries (PCOS). The need for models that can effectively predict benign ovarian tumors and ovarian cancer has led to the use of machine learning techniques. Our research objective is to propose a machine learning-based system for accurate and early ovarian mass detection utilizing novel annotated ovarian masses. We have used an actual patient database whose input features were extracted from 187 transvaginal ultrasound images from database. The input image is preprocessed using the Block Matching 3D filter. The process involves employing binary and watershed segmentation techniques, followed by the integration of Gabor, Gray-Level Co-Occurrence Matrix (GLCM), Tamura, and edge feature extraction methods. K-Nearest Neighbors (KNN) and Random Forest (RF) are two classifiers used for classification. Based on our results, we are able to demonstrate that binary segmentation with RF classifiers is more accurate (above 86%) than KNN classifiers (under 84%).
{"title":"An Intelligent Computer Aided Diagnosis System for Classification of Ovarian Masses using Machine Learning Approach","authors":"Smital D. Patil, Pramod J. Deore, Vaishali Bhagwat Patil","doi":"10.54392/irjmt2434","DOIUrl":"https://doi.org/10.54392/irjmt2434","url":null,"abstract":"Ovarian cancer, a difficult and often asymptomatic malignancy, remains a substantial global health concern in women. An ovary is a female reproductive organ, which lies on each side of the uterus and used to store eggs. Computer-aided diagnosis (CAD) is an approach that involves using computer algorithms and machine learning techniques to assist medical professionals in diagnosing ovarian malignancies, benign tumors or Poly-cystic ovaries (PCOS). The need for models that can effectively predict benign ovarian tumors and ovarian cancer has led to the use of machine learning techniques. Our research objective is to propose a machine learning-based system for accurate and early ovarian mass detection utilizing novel annotated ovarian masses. We have used an actual patient database whose input features were extracted from 187 transvaginal ultrasound images from database. The input image is preprocessed using the Block Matching 3D filter. The process involves employing binary and watershed segmentation techniques, followed by the integration of Gabor, Gray-Level Co-Occurrence Matrix (GLCM), Tamura, and edge feature extraction methods. K-Nearest Neighbors (KNN) and Random Forest (RF) are two classifiers used for classification. Based on our results, we are able to demonstrate that binary segmentation with RF classifiers is more accurate (above 86%) than KNN classifiers (under 84%).","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140698826","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}
Selenium is one of these micronutrients that are essential for animals, plants and microorganisms to remain functional. This review is about the green synthesis of selenium nanoparticles and its application in microbial and cancer therapies. Our hypothesis was that Se NPs produced using plant extracts might offer the biocompatibility and environmental friendliness advantages, and hence be a new prospect for medical applications. To test our hypothesis, we conducted a comprehensive analysis of recent literature, exploring various green synthesis conditions and processes for Se NPs. Various characterisation techniques such as spectroscopy, microscopy and physicochemistry were discussed in order to provide insight into the formation and function of green-synthesised Se NPs. Our findings show that Se NPs produced by green chemistry methods have good properties such as uniform size, shape and stability as detailed examples from recent studies reveal. Furthermore, we discussed the therapeutic and theranostic applications of Se NPs produced in this manner: their potential in antimicrobial and anticancer treatments. Through illustrations of cases where Se NPs inhibit microbial growth and cause apoptosis in cancer cells, the practical significance of our findings was underscored. In summary, our review affirms that using green-mediated synthesis Se NPs improves their biocompatibility and therapeutic efficacy, thus opening up new realms for their application in medical research.
硒是动物、植物和微生物保持功能所必需的微量营养素之一。本综述介绍硒纳米粒子的绿色合成及其在微生物和癌症疗法中的应用。我们的假设是，利用植物萃取物生产的硒纳米粒子可能具有生物相容性和环境友好性等优点，因此在医疗应用方面具有新的前景。为了验证我们的假设，我们对近期的文献进行了全面分析，探索了 Se NPs 的各种绿色合成条件和工艺。我们讨论了光谱学、显微镜和物理化学等各种表征技术，以便深入了解绿色合成 Se NPs 的形成和功能。我们的研究结果表明，通过绿色化学方法制备的硒 NPs 具有良好的特性，如均匀的尺寸、形状和稳定性，近期研究的详细实例也揭示了这一点。此外，我们还讨论了以这种方法制备的硒氮氧化物的治疗和疗法应用：它们在抗菌和抗癌治疗中的潜力。通过 Se NPs 抑制微生物生长和导致癌细胞凋亡的实例，我们强调了研究结果的实际意义。总之，我们的综述肯定了利用绿色介导合成 Se NPs 可改善其生物相容性和治疗效果，从而为其在医学研究中的应用开辟了新的领域。
{"title":"Green Synthesis of Selenium Nanoparticles: Characterization and Therapeutic Applications in Microbial and Cancer Treatments","authors":"Yasodha S, Vickram A.S, R. S","doi":"10.54392/irjmt2432","DOIUrl":"https://doi.org/10.54392/irjmt2432","url":null,"abstract":"Selenium is one of these micronutrients that are essential for animals, plants and microorganisms to remain functional. This review is about the green synthesis of selenium nanoparticles and its application in microbial and cancer therapies. Our hypothesis was that Se NPs produced using plant extracts might offer the biocompatibility and environmental friendliness advantages, and hence be a new prospect for medical applications. To test our hypothesis, we conducted a comprehensive analysis of recent literature, exploring various green synthesis conditions and processes for Se NPs. Various characterisation techniques such as spectroscopy, microscopy and physicochemistry were discussed in order to provide insight into the formation and function of green-synthesised Se NPs. Our findings show that Se NPs produced by green chemistry methods have good properties such as uniform size, shape and stability as detailed examples from recent studies reveal. Furthermore, we discussed the therapeutic and theranostic applications of Se NPs produced in this manner: their potential in antimicrobial and anticancer treatments. Through illustrations of cases where Se NPs inhibit microbial growth and cause apoptosis in cancer cells, the practical significance of our findings was underscored. In summary, our review affirms that using green-mediated synthesis Se NPs improves their biocompatibility and therapeutic efficacy, thus opening up new realms for their application in medical research.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"43 s7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700272","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}
Raj vigneshwar R, Rohith S.M, Ravi Shankar K, Mahi Kaarthik G, Shanthi P
A Heavy-duty cargo truck manufactured by the Chinese company SHACMAN X3000 is designed and analyzed in this paper. Here, this paper developed a Chassis Health Monitoring System (CHMS). The objective of the system is improving the safety measures by combining computational techniques using FEA on static structural and Modal analysis followed by experimental work by implementation of IoT for monitoring and validation purposes. In this paper, for analysis purpose, we selected four critical points based on the survey and underwent the analysis by computational tool. The CHMS consists of a Force sensor, a Flux sensor, and RGB with Arduino, which is to collect and analyze to monitor the frame. The analyzed results give the optimal value in the frame near the critical areas, which results the crack. The CHMS, it a pre-alert system and safe guard the chassis.
{"title":"Mitigating Frame Cracks in Off-Highway Vehicle: A Combined Approach of Finite Element Analysis and IoT-based Chassis Health Monitoring System","authors":"Raj vigneshwar R, Rohith S.M, Ravi Shankar K, Mahi Kaarthik G, Shanthi P","doi":"10.54392/irjmt2431","DOIUrl":"https://doi.org/10.54392/irjmt2431","url":null,"abstract":"A Heavy-duty cargo truck manufactured by the Chinese company SHACMAN X3000 is designed and analyzed in this paper. Here, this paper developed a Chassis Health Monitoring System (CHMS). The objective of the system is improving the safety measures by combining computational techniques using FEA on static structural and Modal analysis followed by experimental work by implementation of IoT for monitoring and validation purposes. In this paper, for analysis purpose, we selected four critical points based on the survey and underwent the analysis by computational tool. The CHMS consists of a Force sensor, a Flux sensor, and RGB with Arduino, which is to collect and analyze to monitor the frame. The analyzed results give the optimal value in the frame near the critical areas, which results the crack. The CHMS, it a pre-alert system and safe guard the chassis.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737046","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}
Avishek Khanal, Pasang Sherpa, Prakriti Chataut, A. Khanal, Giri Suja
The production of electronic waste (e-waste) has reached alarming levels globally, posing significant environmental, economic, and health risks. This review paper comprehensively analyzes the challenges, impacts, and potential solutions associated with e-waste management in developing nations. It highlights the urgent need for proper regulations, infrastructure development, and public awareness to address the growing problem of e-waste. The paper identifies gaps in current research, such as the lack of concrete recommendations and practical solutions, and aims to provide a foundation for future studies to propose strategies for improving e-waste management practices. The findings emphasize the environmental effects of e-waste and the negative consequences on disadvantaged communities, particularly in underdeveloped regions. Furthermore, the review highlights the importance of transitioning to a circular economy and the economic opportunities presented by e-waste, which contains valuable metals that can be recovered and recycled. The paper calls for the formulation of specific policies focusing on the 3Rs (Reduction, Reuse, and Recycle) and the implementation of provisions such as pollution taxes to reduce e-waste and promote responsible consumption. By addressing these challenges and offering sustainable solutions, effective e-waste management can mitigate environmental risks, protect human health, and contribute to a circular economy.
{"title":"Transforming E-Waste Management: Challenges and Opportunities","authors":"Avishek Khanal, Pasang Sherpa, Prakriti Chataut, A. Khanal, Giri Suja","doi":"10.54392/irjmt2429","DOIUrl":"https://doi.org/10.54392/irjmt2429","url":null,"abstract":"The production of electronic waste (e-waste) has reached alarming levels globally, posing significant environmental, economic, and health risks. This review paper comprehensively analyzes the challenges, impacts, and potential solutions associated with e-waste management in developing nations. It highlights the urgent need for proper regulations, infrastructure development, and public awareness to address the growing problem of e-waste. The paper identifies gaps in current research, such as the lack of concrete recommendations and practical solutions, and aims to provide a foundation for future studies to propose strategies for improving e-waste management practices. The findings emphasize the environmental effects of e-waste and the negative consequences on disadvantaged communities, particularly in underdeveloped regions. Furthermore, the review highlights the importance of transitioning to a circular economy and the economic opportunities presented by e-waste, which contains valuable metals that can be recovered and recycled. The paper calls for the formulation of specific policies focusing on the 3Rs (Reduction, Reuse, and Recycle) and the implementation of provisions such as pollution taxes to reduce e-waste and promote responsible consumption. By addressing these challenges and offering sustainable solutions, effective e-waste management can mitigate environmental risks, protect human health, and contribute to a circular economy.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"14 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436058","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}
V. G, J. Chohan, Rupa B, Priyankka A.L, Thirunavukarasu P, Abinaya M, Jaswanth V, Matcha Doondi Venkata Kodanda Sai Anvesh
The utilization of composite materials in construction has recently exerted a significant impact on society, particularly concerning ecological responsibility and environmental considerations. On a daily basis, proposals advocating the use of emerging materials crafted from discarded or repurposed items are put forth to transcend the limitations posed by conventional resources. One notable aspect of this movement revolves around textile components, encompassing fibres such as wool, cotton, cannabis, and flax. Over the past decade, there has been a heightened focus on worn clothing, as it represents an unprocessed product that holds both commercial viability and ecological benefits. Approximately 1.5 percent of the global waste generated daily comprises textile scraps, with blue jeans, crafted from cotton, standing out as the most prevalent type of apparel worldwide. Textile scraps find new life through recycling, serving various purposes such as the creation of electrical wires, the production of pulverized substances for temperature and acoustic insulation materials, and the incorporation as filler or reinforcement in concrete construction. This paper delves into multiple themes, covering (i) the adverse environmental impacts stemming from the extensive use of clothing; (ii) the recycling and reclamation of textile waste; and (iii) the utilization of waste and reclaimed materials from textiles as building components.
{"title":"A Brief Analysis of The Production of Building Materials Utilizing Waste-Based Reinforcements and Recycled Textiles","authors":"V. G, J. Chohan, Rupa B, Priyankka A.L, Thirunavukarasu P, Abinaya M, Jaswanth V, Matcha Doondi Venkata Kodanda Sai Anvesh","doi":"10.54392/irjmt24210","DOIUrl":"https://doi.org/10.54392/irjmt24210","url":null,"abstract":"The utilization of composite materials in construction has recently exerted a significant impact on society, particularly concerning ecological responsibility and environmental considerations. On a daily basis, proposals advocating the use of emerging materials crafted from discarded or repurposed items are put forth to transcend the limitations posed by conventional resources. One notable aspect of this movement revolves around textile components, encompassing fibres such as wool, cotton, cannabis, and flax. Over the past decade, there has been a heightened focus on worn clothing, as it represents an unprocessed product that holds both commercial viability and ecological benefits. Approximately 1.5 percent of the global waste generated daily comprises textile scraps, with blue jeans, crafted from cotton, standing out as the most prevalent type of apparel worldwide. Textile scraps find new life through recycling, serving various purposes such as the creation of electrical wires, the production of pulverized substances for temperature and acoustic insulation materials, and the incorporation as filler or reinforcement in concrete construction. This paper delves into multiple themes, covering (i) the adverse environmental impacts stemming from the extensive use of clothing; (ii) the recycling and reclamation of textile waste; and (iii) the utilization of waste and reclaimed materials from textiles as building components.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"23 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140435377","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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