The study's focus is on lung nodules, which are frequently connected to lung cancer, the world's most common cause of cancer-related deaths. In clinical practice, a timely and precise diagnosis of these nodules is essential, albeit difficult. For diagnosis, the study used CT scans from the Lung Image Database Consortium and the LIDC-IDRI dataset. Noise reduction with a Gaussian Smoothing (GS) Filter and contrast enhancement were part of the preprocessing. With a Dual-path Multi-scale Attention Fusion Module (DualMAF) and a Multi-scale Normalized Channel Attention Module (MNCA), the study presented the LNS-DualMAGNet model for lung nodule segmentation. These modules improve interdependence across channels and semantic understanding by utilizing novel approaches such as Depthwise Separable Convolutions and attention mechanisms. For increased performance, the model also incorporates DSConv and a Resnet34 block. The Dung Beetle Optimization Algorithm (DBOA) was used for tuning the hyperparameter of the proposed classifier. Findings indicated that the proposed model performed better than the existing approaches, attaining a 0.99 accuracy and DSC, indicating its potential to enhance lung nodule segmentation for clinical diagnosis.
{"title":"Enhanced Lung Nodule Segmentation using Dung Beetle Optimization based LNS-DualMAGNet Model","authors":"Sathyamoorthy K, Ravikumar S","doi":"10.54392/irjmt2416","DOIUrl":"https://doi.org/10.54392/irjmt2416","url":null,"abstract":"The study's focus is on lung nodules, which are frequently connected to lung cancer, the world's most common cause of cancer-related deaths. In clinical practice, a timely and precise diagnosis of these nodules is essential, albeit difficult. For diagnosis, the study used CT scans from the Lung Image Database Consortium and the LIDC-IDRI dataset. Noise reduction with a Gaussian Smoothing (GS) Filter and contrast enhancement were part of the preprocessing. With a Dual-path Multi-scale Attention Fusion Module (DualMAF) and a Multi-scale Normalized Channel Attention Module (MNCA), the study presented the LNS-DualMAGNet model for lung nodule segmentation. These modules improve interdependence across channels and semantic understanding by utilizing novel approaches such as Depthwise Separable Convolutions and attention mechanisms. For increased performance, the model also incorporates DSConv and a Resnet34 block. The Dung Beetle Optimization Algorithm (DBOA) was used for tuning the hyperparameter of the proposed classifier. Findings indicated that the proposed model performed better than the existing approaches, attaining a 0.99 accuracy and DSC, indicating its potential to enhance lung nodule segmentation for clinical diagnosis.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"21 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140493815","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}
This paper aims to quantify the subbasin’s potable water supply demand forecast from 2023 to 2050 under various scenarios of climate change and socioeconomic development. The variability of the climate and the resulting problems with urbanization threaten the availability of water resources, especially in less developed countries like Ethiopia. Thus, the main objective of this study is showing the necessary to determine the amount of water needed in advance, in order to comply with the availability of water resources within a specified future period under different scenarios. Our indicator-based approach used a multicriteria decision-making technique. Accordingly, several important variables were considered, including climatological, anthropological, demographic, socioeconomic, and economic variables, in addition to water engineering-related factors (e.g. Water losses). The method also considered a number of factors, such as unexpected and extreme temperature changes, and forecasting factors studied by the Ethiopian Ministry of Water and Energy. The projected population in the subbasin is estimated at 2.52 million, so the total projected water supply demand i.e., for domestic, non-domestic, industrial, commercial, public, and institutional is approximately 126.53 MCM/yr by 2050. Our results revealed how changes in both climatic and socioeconomic factors strongly influence future water resource system performance, and this will help the water services provider better prioritize the refurbishment of existing infrastructure and investment in new infrastructure, and more importantly, manage the subbasin effectively by introducing resilient adaptation options.
{"title":"Future potable water supply demand projection under climate change and socioeconomic scenarios: A case of Gshba subbasin, Northern Ethiopia","authors":"Mehari Gebreyohannes Hiben, Admasu Gebeyehu Awoke, Abraha Adugna Ashenafi","doi":"10.54392/irjmt2415","DOIUrl":"https://doi.org/10.54392/irjmt2415","url":null,"abstract":"This paper aims to quantify the subbasin’s potable water supply demand forecast from 2023 to 2050 under various scenarios of climate change and socioeconomic development. The variability of the climate and the resulting problems with urbanization threaten the availability of water resources, especially in less developed countries like Ethiopia. Thus, the main objective of this study is showing the necessary to determine the amount of water needed in advance, in order to comply with the availability of water resources within a specified future period under different scenarios. Our indicator-based approach used a multicriteria decision-making technique. Accordingly, several important variables were considered, including climatological, anthropological, demographic, socioeconomic, and economic variables, in addition to water engineering-related factors (e.g. Water losses). The method also considered a number of factors, such as unexpected and extreme temperature changes, and forecasting factors studied by the Ethiopian Ministry of Water and Energy. The projected population in the subbasin is estimated at 2.52 million, so the total projected water supply demand i.e., for domestic, non-domestic, industrial, commercial, public, and institutional is approximately 126.53 MCM/yr by 2050. Our results revealed how changes in both climatic and socioeconomic factors strongly influence future water resource system performance, and this will help the water services provider better prioritize the refurbishment of existing infrastructure and investment in new infrastructure, and more importantly, manage the subbasin effectively by introducing resilient adaptation options.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"37 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140495984","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}
Agriculture plays a vital role in India's economy, and the health of crops is critical for maximizing yield. In particular, cucumber, a key salad ingredient known for its health benefits, is susceptible to various diseases such as water mold, bacterial wilt, angular leaf spot, anthracnose, and powdery mildew. These diseases not only affect the quality of cucumbers but also significantly reduce their yield. Early detection of these diseases is crucial for successful cultivation, but traditional manual methods of disease identification by farmers or diagnosticians are time-consuming and prone to misidentification. To address these challenges, we explore advanced artificial intelligence techniques. We implement and compare various machine learning algorithms, including ResNet, AlexNet, and VGG-16, for disease classification in cucumbers. However, these methods often struggle with issues such as noise, irrelevant features, and the generation of pertinent characteristics. To overcome these limitations, we propose a novel approach using a GLCM (Gray Level Co-occurrence Matrix) feature extraction method combined with a Random Forest classifier. This new algorithm aims to improve the accuracy and efficiency of disease detection. Our dataset comprises four distinct categories: Healthy, Anthracnose, Aphids, and CYSDV. It is sourced from diverse platforms, including online repositories like kaggle and direct collection from cucumber farms. The initial phase of our methodology involves noise reduction by converting images into the LAB color space and isolating specific regions using the k-means clustering algorithm. Subsequently, we extract texture features from the diseased leaf images using the GLCM algorithm, and classification is performed using the Random Forest model. Comparative analysis shows that our proposed Random Forest algorithm outperforms previous models like LGBM (Light Gradient Boosting Machine) and QSVM (Quantum-Support Vector Machine) in predicting disease presence in cucumber plants with higher accuracy rate of 98.62%, Precision 98.77%, Recall 98.48% and also F1 Score 98.62%.
{"title":"Cucumber Leaf Disease Detection using GLCM Features with Random Forest Algorithm","authors":"Nancy C, Kiran S","doi":"10.54392/irjmt2414","DOIUrl":"https://doi.org/10.54392/irjmt2414","url":null,"abstract":"Agriculture plays a vital role in India's economy, and the health of crops is critical for maximizing yield. In particular, cucumber, a key salad ingredient known for its health benefits, is susceptible to various diseases such as water mold, bacterial wilt, angular leaf spot, anthracnose, and powdery mildew. These diseases not only affect the quality of cucumbers but also significantly reduce their yield. Early detection of these diseases is crucial for successful cultivation, but traditional manual methods of disease identification by farmers or diagnosticians are time-consuming and prone to misidentification. To address these challenges, we explore advanced artificial intelligence techniques. We implement and compare various machine learning algorithms, including ResNet, AlexNet, and VGG-16, for disease classification in cucumbers. However, these methods often struggle with issues such as noise, irrelevant features, and the generation of pertinent characteristics. To overcome these limitations, we propose a novel approach using a GLCM (Gray Level Co-occurrence Matrix) feature extraction method combined with a Random Forest classifier. This new algorithm aims to improve the accuracy and efficiency of disease detection. Our dataset comprises four distinct categories: Healthy, Anthracnose, Aphids, and CYSDV. It is sourced from diverse platforms, including online repositories like kaggle and direct collection from cucumber farms. The initial phase of our methodology involves noise reduction by converting images into the LAB color space and isolating specific regions using the k-means clustering algorithm. Subsequently, we extract texture features from the diseased leaf images using the GLCM algorithm, and classification is performed using the Random Forest model. Comparative analysis shows that our proposed Random Forest algorithm outperforms previous models like LGBM (Light Gradient Boosting Machine) and QSVM (Quantum-Support Vector Machine) in predicting disease presence in cucumber plants with higher accuracy rate of 98.62%, Precision 98.77%, Recall 98.48% and also F1 Score 98.62%.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"78 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139613003","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}
Rohini Patil, Janhvi Pawar, Kamal Shah, Disha Shetty, A. Ajith, Sakshi Jadhav
Wildfires are among the world's most pressing issues, and they are getting more prevalent as global warming and other environmental conditions deteriorate. These wildfires might be caused by humans or by natural causes. Wildfires are one of the factors contributing to the extinction of rare flora and wildlife that serve to maintain our planet's ecological balance. In this paper, a comparative analysis of various machine learning classifier models for predicting forest fires was undertaken using two separate datasets. The suggested system's processing is dependent on a few characteristics such as temperature, humidity, oxygen, and wind. Several machine learning classification techniques, including logistic regression, support vector classifier, decision tree, k neighbors and random forest, were used in this study. For further optimization of the model, K-fold cross validation method and hyperparameter tuning were implemented. The system reveals Support Vector Machine as the best strategy for the forest fire dataset, with 96.88% accuracy. Random Forest method was found to be the best for the Cortez and Morais dataset, with 90.24% accuracy.
野火是世界上最紧迫的问题之一，而且随着全球变暖和其他环境条件的恶化，野火越来越普遍。这些野火可能是人为的，也可能是自然的。野火是导致珍稀植物和野生动物灭绝的因素之一，而珍稀植物和野生动物正是维持地球生态平衡的重要力量。本文使用两个独立的数据集，对预测森林火灾的各种机器学习分类器模型进行了比较分析。建议系统的处理过程取决于温度、湿度、氧气和风力等一些特征。本研究使用了几种机器学习分类技术，包括逻辑回归、支持向量分类器、决策树、k 邻居和随机森林。为进一步优化模型，采用了 K 折交叉验证法和超参数调整法。系统显示，支持向量机是森林火灾数据集的最佳策略，准确率为 96.88%。随机森林方法是 Cortez 和 Morais 数据集的最佳方法，准确率为 90.24%。
{"title":"Machine Learning based Forest Fire Prediction: A Comparative Approach","authors":"Rohini Patil, Janhvi Pawar, Kamal Shah, Disha Shetty, A. Ajith, Sakshi Jadhav","doi":"10.54392/irjmt2413","DOIUrl":"https://doi.org/10.54392/irjmt2413","url":null,"abstract":"Wildfires are among the world's most pressing issues, and they are getting more prevalent as global warming and other environmental conditions deteriorate. These wildfires might be caused by humans or by natural causes. Wildfires are one of the factors contributing to the extinction of rare flora and wildlife that serve to maintain our planet's ecological balance. In this paper, a comparative analysis of various machine learning classifier models for predicting forest fires was undertaken using two separate datasets. The suggested system's processing is dependent on a few characteristics such as temperature, humidity, oxygen, and wind. Several machine learning classification techniques, including logistic regression, support vector classifier, decision tree, k neighbors and random forest, were used in this study. For further optimization of the model, K-fold cross validation method and hyperparameter tuning were implemented. The system reveals Support Vector Machine as the best strategy for the forest fire dataset, with 96.88% accuracy. Random Forest method was found to be the best for the Cortez and Morais dataset, with 90.24% accuracy.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"10 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139616879","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}
This study employs computational simulations to comprehensively investigate the molecular properties of 1,4-Dinitrosopiperazine-2-carboxylic acid. Through rigorous analysis, the research explores the compound's structural characteristics, vibrational assignments, chemical shifts, electronic properties, donor-acceptor interactions, Mulliken atomic charges, molecular electrostatic potential surface (MESP), and thermodynamic parameters. The findings provide intricate insights into the behavior of the compound, unveiling potential applications in diverse chemical contexts. This thorough examination contributes significantly to our understanding of the fundamental properties of 1,4-Dinitrosopiperazine-2-carboxylic acid, offering invaluable knowledge for both further research endeavors and practical applications. The detailed elucidation of these properties holds promise for advancements in various fields, from pharmaceuticals to materials science, marking a significant stride towards harnessing the full potential of this compound in contemporary chemistry.
{"title":"Computational study on the structural features, vibrational aspects, chemical shifts, and electronic properties of 1,4-Dinitrosopiperazine-2-carboxylic acid: Insights into donor-acceptor interactions and thermodynamic properties","authors":"S. S","doi":"10.54392/irjmt2411","DOIUrl":"https://doi.org/10.54392/irjmt2411","url":null,"abstract":"This study employs computational simulations to comprehensively investigate the molecular properties of 1,4-Dinitrosopiperazine-2-carboxylic acid. Through rigorous analysis, the research explores the compound's structural characteristics, vibrational assignments, chemical shifts, electronic properties, donor-acceptor interactions, Mulliken atomic charges, molecular electrostatic potential surface (MESP), and thermodynamic parameters. The findings provide intricate insights into the behavior of the compound, unveiling potential applications in diverse chemical contexts. This thorough examination contributes significantly to our understanding of the fundamental properties of 1,4-Dinitrosopiperazine-2-carboxylic acid, offering invaluable knowledge for both further research endeavors and practical applications. The detailed elucidation of these properties holds promise for advancements in various fields, from pharmaceuticals to materials science, marking a significant stride towards harnessing the full potential of this compound in contemporary chemistry.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"102 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998632","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}
Water resource evaluation, management, and conservation at the local, national, and international levels depend on an accurate understanding of the hydrological processes. In data-poor environments and topographically complicated areas like the Ghba subbasin in the headwaters of the Nile River, the function of hydrological models is crucial. The primary goal of this study is to use the WEAP model to simulate the hydrology of the Ghba basin. This is because recent hydrological behaviour has changed significantly and resulted in a serious water deficit. The minimal satisfactory performance limit for the monthly stream flow variable was strongly attained by the multi-variable calibration scenario (R2 = 0.82, NSE = 0.82, IA= 0.80 RSR = 0.87 and PBIAS = 9 % for calibration scenario; and R2 = 0.78, NSE = 0.81, IA= 0.70 RSR = 0.80 and PBIAS = 11.5 % for validation scenario). Evapotranspiration makes up 63.4% of the water balance, according to the model simulation, while surface runoff, interflow, baseflow and groundwater recharge accounting for 11.1 %, 11.8%, 5.4% and 8.3 %, respectively. The simulated average annual streamflow at the subbasin outlet is 16.33 m3/s. The simulated monthly minimum flow occurs in January with an average flow of 1.78 m3/s and a coefficient of dispersion of 0.45. Maximum flows occur in July and August, with an average flow of 53.57 m3/s and a coefficient of dispersion of 0.19. The main rainy season was shown to have a larger spatial distribution of simulated runoff, and the average annual recharge value is 53.5 mm. The study's conclusions indicated that both surface water harvesting and groundwater extraction might be used for reliable water distribution to the subbasin's continuously increasing sectoral water demand.
{"title":"Hydrological Modeling and Evaluation of Water Balance Over the Complex Topography of Nile Basin Headwaters: The Case of Ghba River, Northern Ethiopia","authors":"Mehari Gebreyohannes Hiben, Admasu Gebeyehu Awoke, Abraha Adugna Ashenafi","doi":"10.54392/irjmt2363","DOIUrl":"https://doi.org/10.54392/irjmt2363","url":null,"abstract":"Water resource evaluation, management, and conservation at the local, national, and international levels depend on an accurate understanding of the hydrological processes. In data-poor environments and topographically complicated areas like the Ghba subbasin in the headwaters of the Nile River, the function of hydrological models is crucial. The primary goal of this study is to use the WEAP model to simulate the hydrology of the Ghba basin. This is because recent hydrological behaviour has changed significantly and resulted in a serious water deficit. The minimal satisfactory performance limit for the monthly stream flow variable was strongly attained by the multi-variable calibration scenario (R2 = 0.82, NSE = 0.82, IA= 0.80 RSR = 0.87 and PBIAS = 9 % for calibration scenario; and R2 = 0.78, NSE = 0.81, IA= 0.70 RSR = 0.80 and PBIAS = 11.5 % for validation scenario). Evapotranspiration makes up 63.4% of the water balance, according to the model simulation, while surface runoff, interflow, baseflow and groundwater recharge accounting for 11.1 %, 11.8%, 5.4% and 8.3 %, respectively. The simulated average annual streamflow at the subbasin outlet is 16.33 m3/s. The simulated monthly minimum flow occurs in January with an average flow of 1.78 m3/s and a coefficient of dispersion of 0.45. Maximum flows occur in July and August, with an average flow of 53.57 m3/s and a coefficient of dispersion of 0.19. The main rainy season was shown to have a larger spatial distribution of simulated runoff, and the average annual recharge value is 53.5 mm. The study's conclusions indicated that both surface water harvesting and groundwater extraction might be used for reliable water distribution to the subbasin's continuously increasing sectoral water demand.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"31 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135873130","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 objective of this study is to enhance the efficiency of biodiesel production from Bauhinia monandra seeds through the application of response surface methodology (RSM). The subsequent evaluation will focus on the fuel characterisation and properties measurement. The process was optimised by adjusting the methanol-oil molar ratio (MOR), reaction (RTe), and reaction time (RTm). The ASTM set the standards for conducting the property measurements, and the fuel characterization was performed using Fourier transform infrared spectroscopy (FTIR). The optimisation analysis revealed that the highest yield of BMB was achieved by employing an MOR of 7.4:1, keeping a temperature of 80 °C, and allowing the reaction to occur for a duration of 64 minutes. In optimal circumstances, the yield rate of BPB is recorded at 89.3%. According to FTIR, the BMB consists of carbon-based components of superior quality, and the measured physicochemical properties of fuel meet the required standards.
{"title":"Optimising Process Parameters for Bauhinia Monandra Biodiesel Production and Characterization","authors":"Suresh Vellaiyan","doi":"10.54392/irjmt2361","DOIUrl":"https://doi.org/10.54392/irjmt2361","url":null,"abstract":"The objective of this study is to enhance the efficiency of biodiesel production from Bauhinia monandra seeds through the application of response surface methodology (RSM). The subsequent evaluation will focus on the fuel characterisation and properties measurement. The process was optimised by adjusting the methanol-oil molar ratio (MOR), reaction (RTe), and reaction time (RTm). The ASTM set the standards for conducting the property measurements, and the fuel characterization was performed using Fourier transform infrared spectroscopy (FTIR). The optimisation analysis revealed that the highest yield of BMB was achieved by employing an MOR of 7.4:1, keeping a temperature of 80 °C, and allowing the reaction to occur for a duration of 64 minutes. In optimal circumstances, the yield rate of BPB is recorded at 89.3%. According to FTIR, the BMB consists of carbon-based components of superior quality, and the measured physicochemical properties of fuel meet the required standards.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136358521","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 the modern world cyber-physical production systems are increasingly used. They allow you to control the flow of the technological process in production in real time. But the use of such an approach is greatly complicated by the fact that the equipment of many enterprises is old and cannot support the necessary functions. This is primarily due to the lack of the necessary sensors, as well as the corresponding software. Since the complete replacement of production equipment is very expensive, the task is to create separate monitoring systems. They must be able to integrate into the necessary parts of the production process. And they should also be cheap. In this work, we propose to build a model of such a monitoring and visualization system. The main attention in the work is focused on the hardware implementation of the proposed system and the relationship of its individual elements.
{"title":"Automated Monitoring and Visualization System in Production","authors":"Vyacheslav Lyashenko, Amer Tahseen Abu-Jassar, Vladyslav Yevsieiev, Svitlana Maksymova","doi":"10.54392/irjmt2362","DOIUrl":"https://doi.org/10.54392/irjmt2362","url":null,"abstract":"In the modern world cyber-physical production systems are increasingly used. They allow you to control the flow of the technological process in production in real time. But the use of such an approach is greatly complicated by the fact that the equipment of many enterprises is old and cannot support the necessary functions. This is primarily due to the lack of the necessary sensors, as well as the corresponding software. Since the complete replacement of production equipment is very expensive, the task is to create separate monitoring systems. They must be able to integrate into the necessary parts of the production process. And they should also be cheap. In this work, we propose to build a model of such a monitoring and visualization system. The main attention in the work is focused on the hardware implementation of the proposed system and the relationship of its individual elements.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136358824","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}
Hematite (α-Fe2O3) nanoparticles have been prepared by the conventional chemical precipitation synthesis technique. The prepared samples were subjected to structural, morphological, optical, magnetic and antibacterial behaviours. The diffraction analysis implies that the measured crystallite size of α- Fe2O3 nanoparticles is found to be 39 nm. The UV-visible absorption spectroscopy exhibits a strong absorption around 560 nm which is characteristics of Fe2O3 nanoparticles and the calculated bandgap value is found to be 2.07 eV. The presence of iron oxide polymorphs can be demonstrated by displaying phonon modes in Raman spectroscopy. Fourier-transform infrared spectroscopy (FTIR) study is used to identify the existence of functional groups and chemical structure of the synthesised Fe2O3 nanoparticles. Magnetic analysis displays hysteretic behaviour at room temperature with saturation magnetization Ms = 0.0036 emu/gm, the remanent magnetization Mr = 0.000698 emu/gm, and coercivity Hc = –0.27 Oe, respectively. The antibacterial activities of these α-Fe2O3 nanoparticles were investigated on pathogenic bacteria Pseudomonas aeruginosa, Bacillus cereus, Staphylococcus aureus, and E. coli by a zone of inhibition method.
{"title":"Exploration on the Structural, Optical, Morphological and Magnetic Properties of Hematite Nanoparticles and Their Antibacterial Activity","authors":"Rajapandi P, Viruthagiri G","doi":"10.54392/irjmt2353","DOIUrl":"https://doi.org/10.54392/irjmt2353","url":null,"abstract":"Hematite (α-Fe2O3) nanoparticles have been prepared by the conventional chemical precipitation synthesis technique. The prepared samples were subjected to structural, morphological, optical, magnetic and antibacterial behaviours. The diffraction analysis implies that the measured crystallite size of α- Fe2O3 nanoparticles is found to be 39 nm. The UV-visible absorption spectroscopy exhibits a strong absorption around 560 nm which is characteristics of Fe2O3 nanoparticles and the calculated bandgap value is found to be 2.07 eV. The presence of iron oxide polymorphs can be demonstrated by displaying phonon modes in Raman spectroscopy. Fourier-transform infrared spectroscopy (FTIR) study is used to identify the existence of functional groups and chemical structure of the synthesised Fe2O3 nanoparticles. Magnetic analysis displays hysteretic behaviour at room temperature with saturation magnetization Ms = 0.0036 emu/gm, the remanent magnetization Mr = 0.000698 emu/gm, and coercivity Hc = –0.27 Oe, respectively. The antibacterial activities of these α-Fe2O3 nanoparticles were investigated on pathogenic bacteria Pseudomonas aeruginosa, Bacillus cereus, Staphylococcus aureus, and E. coli by a zone of inhibition method.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135039706","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}
Tamilarasi A, Sivabalaselvamani D, Rahunathan L, Adhithyaa N
Vehicular ad-hoc Networks (VANETs) play a significant role in Intelligent Transportation Systems (ITS) Design. Intelligent Transportation Systems are the first mandatory requirements for any smart city. Researchers are vigorously working on ITSs for smart cities and so VANETs have received a lot of attention. In VANETs, Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) message transmissions are carried out using wireless access technologies like IEEE 802.11p and IEEE 1609 WAVE family of standards. The crucial challenge in the implementation of VANETs involves the task of deciding the routing protocol because unlike MANETs, handover in VANETs is extremely high. In this paper, a novel routing technique, One Caption for 3 Nodes (1C3N) algorithm is proposed. This algorithm is implemented along with other topology-based existing routing protocols for the implementation of VANETs in the Coimbatore-Urban Area (Indian Smart City). The performance evaluation is carried out by comparing metrics like goodput, Overhead, Packet delivery ratio (PDR), Packet loss ratio (PLR) and end-to-end delay for four existing VANET routing protocols. The results show that a proper combination of packet loss model with routing protocol enhances the goodput and reduces the overhead for a fixed VANET. It is observed that the proposed 1C3N routing technique provides 60-65% better goodput than the other four algorithms.
车辆自组织网络(VANETs)在智能交通系统(ITS)设计中发挥着重要作用。智能交通系统是任何智慧城市的首要强制性要求。研究人员正在大力研究智能城市的信息通信系统，因此vanet受到了广泛的关注。在VANETs中，车辆对车辆(V2V)和车辆对基础设施(V2I)信息传输使用无线接入技术，如IEEE 802.11p和IEEE 1609 WAVE系列标准进行。vanet实现的关键挑战涉及确定路由协议的任务，因为与manet不同，vanet中的切换非常高。本文提出了一种新的路由技术——一标题3节点(One Caption for 3 Nodes, 1C3N)算法。该算法与其他基于拓扑的现有路由协议一起实现，用于在哥印拜陀市区(印度智慧城市)实现vanet。通过比较四种现有VANET路由协议的goodput、Overhead、Packet delivery ratio (PDR)、Packet loss ratio (PLR)和端到端时延等指标来进行性能评估。结果表明，适当地将丢包模型与路由协议相结合，可以提高固定VANET的性能，降低开销。结果表明，所提出的1C3N路由技术比其他四种算法的goodput性能提高了60-65%。
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