The presence of a natural gas leak within a household carries the potential for fires and poses a risk of natural gas poisoning. Similar to how we approach other hazardous energy sources such as ...electricity and gasoline, it is crucial to exercise caution when dealing with natural gas exposure. To prevent potential hazards and dangerous situation, identified a smart gas detection system for rapid and accurate detection of gas leaks. The proposed gas leakage detection system combines advanced sensor technology, real-time monitoring, and automated alert mechanisms to ensure timely identification and response to gas leaks. The MQ2 sensor helps in detection of gas leakage. The MQ2 sensor possesses the capability to detect a wide range of gases, including methane, propane, carbon monoxide, and hydrogen. This versatility makes it an invaluable tool in ensuring safety and protecting against potential hazards and early identification of gas leaks. The collected data is analyzed using sophisticated algorithms to distinguish between normal background gas levels and potential leaks. NodeMCU, equipped with its Wi-Fi capabilities, functions as the central control unit of the system. The NodeMCU gathers real-time data from the gas sensors, constantly monitoring gas levels. It processes this data and sends it to the cloud or a central server using its internet connection. The system's ability to monitor in real-time ensures that any gas leaks detected are quickly reported to the relevant personnel or authorities through automated alerts. The fast notification system allows for quick actions, reducing the risks of accidents, saving lives, preventing property damage, and mitigating harm to the environment.
Healthcare systems around the world have faced challenges due to the Coronavirus disease (COVID-19) epidemic, which has taken resources and attention away from long-term diseases like liver cancer. ...To identify the effect of COVID-19 on liver cancer, proposed an effective research work with Extreme Gradient Boosting (XGBoost) classifier. The identification of the most suitable feature is executed through the utilization of SelectKBest, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) techniques. A comparative analysis is carried out to ascertain the more precise feature selection, employing Decision Tree, K-Nearest Neighbors (KNN), CatBoost, LightGBM, and XGBoost algorithms. Further, the proposed work undertakes an analysis of the impact of COVID-19 on liver cancer, encompassing both the pandemic and pre-pandemic periods. In comparison to other feature selection techniques and models, SelectKBest feature selection with Xgboost classifier provided an accuracy of 0.92. The proposed research work provides valuable insights to healthcare professionals and policy makers, providing a better understanding of the challenges faced by liver cancer patients during the pandemic. By meticulously exploring feature selection techniques and employing advanced machine learning models, this study quantifiably demonstrates the effectiveness of the proposed methodology in addressing the impact of COVID-19 on liver cancer, contributing to a more comprehensive understanding of this critical issue.
► In sodium heated steam generators, hydrogen is produced in the water side. ► Hydrogen permeates into the sodium side and becomes a major impurity. ► Hydrogen mass flux from water to sodium side, is ...a very important design data. ► We experimentally quantified hydrogen flux through a Mod 9Cr1Mo steam generator. ► It is compared with literature data available for 2.25Cr1Mo steam generator.
Heat, produced in a SFR (Sodium cooled Fast Reactor) is exchanged to a sodium circuit, where it is further transferred from sodium to water in a steam generator (SG). SG, typically made of ferritic steel, is a shell and tube heat exchanger with sodium on shell side and water/steam on tube side. Water/steam, flowing through the SG tube, reacts with the tube material, producing magnetite and hydrogen. Most of the hydrogen, formed in the water side, permeates into sodium through the SG tube wall and becomes the major impurity in sodium. The mass flux of hydrogen, permeating into sodium, is particularly important in the design of cold trap (CT) which is used for purifying sodium. Experiments were performed with a model SG made of Mod 9Cr1Mo having 19 tubes, in Steam Generator Test Facility (SGTF) at Indira Gandhi Centre for Atomic Research (IGCAR), to quantify hydrogen flux. Material, structural layout and operating conditions of SG of Prototype Fast Breeder Reactor (PFBR) were simulated. This paper brings out the data and experiences gained through the experiments.
Millions of lives are at risk annually due to traffic accidents, and the need for a proactive and efficient accident detection system is paramount. The traditional accident detection system faces ...some challenges such as limited scalability, connectivity, power consumption, and reliability of sensors. So proposed an accident detection system with Internet of Things (IoT) technologies to enhance road safety and emergency response mechanisms. The proposed system integrates advanced components such as Global Positioning System(GPS) modules, and Global System for Mobile Communication(GSM) modules to enable real-time monitoring, precise location tracking, and immediate communication with emergency services. The novelty of this work lies in the implementation of a cost-effective accident detection system that accurately provides alerts to the intended party. The system delivers alert messages containing longitude and latitude coordinates. Additionally, it initiates alerts in the form of phone calls, fostering timely communication with accident victims. This prompt interaction has the potential to save lives by facilitating on-demand treatment and immediate assistance to those in need. This methodology has the potential for future applications in industries such as Traffic Management and Analysis, Road Safety Enhancement, and Emergency Response Optimization.
Metal-organic framework-derived materials are now considered potential next-generation electrode materials for supercapacitors. In this present investigation, Co
3
O
4
@MnO
2
nanosheets are ...synthesized using ZIF-67, which is used as a sacrificial template through a facile hydrothermal method. The unique vertically grown nanosheets provide an effective pathway for rapidly transporting electrons and ions. As a result, the ZIF-67 derived Co
3
O
4
@MnO
2
-3 electrode material shows a high specific capacitance of 768 C g
−1
at 1 A g
−1
current density with outstanding cycling stability (86% retention after 5000 cycles) and the porous structure of the material has a good BET surface area of 160.8 m
2
g
−1
. As a hybrid supercapacitor, Co
3
O
4
@MnO
2
-3//activated carbon exhibits a high specific capacitance (82.9 C g
−1
) and long cycle life (85.5% retention after 5000 cycles). Moreover, a high energy density of 60.17 W h kg
−1
and power density of 2674.37 W kg
−1
has been achieved. This attractive performance reveals that Co
3
O
4
@MnO
2
nanosheets could find potential applications as an electrode material for high-performance hybrid supercapacitors.
Metal-organic framework-derived materials are now considered potential next-generation electrode materials for supercapacitors.
One of the dangerous disease pneumonia, which can affect one or both lungs, is frequently brought on by viruses, fungi or bacteria. Radiotherapists with advanced training are required to evaluate ...chest X-rays used to diagnose pneumonia. Brain tumours are another fatal disease that can have a profound impact on a patient's quality of life and alter everything for them and their loved ones. Therefore, creating an automatic system for diagnosing pneumonia would help treat the disease quickly, especially in remote areas. The work proposes a general framework for MRI and CT images that can automatically detect diseases such as brain tumours and pneumonia with single framework using VGG16. The proposed work focusing on developing streamlit web application for deployment. The proposed work gained an accuracy of 97.87% compared to Inception, Xception and ResNet for brain tumor detection and 94.3% for Pnemonia detection model. The training and testing data are compared using a CNN-based classifier to determine the optimal outcome.
Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask ...identification software and applications have already been released by a few businesses, and others have promised to do the same for the service. The proposed work examines face mask detection accuracy using CNN networks. Mask wear is now required in many developed and developing countries worldwide when leaving the house or entering public spaces. It will be difficult to maintain touchless access control in buildings while recognising faces wearing masks on any surveillance systems. Masks covering faces has made face detection algorithms and performance difficult. The proposed work detect face mask labeled no mask or mask with detection accuracy. The work train the system to click images of a face and provide labeled data. The work is classified using Convolution Neural Network (CNN), a Deep learning technique, to classify the input image with the help of the classification algorithm MobileNetV2. The trained system shows whether a person in the video frame is wearing a mask or not.
Friction stir welding is a method used to weld together materials considered challenging by fusion welding. FSW is primarily a solid phase method that has been proven efficient due to its ability to ...manufacture low-cost, low-distortion welds. The quality of weld and stresses can be determined by calculating the amount of heat transferred. Recently, many researchers have developed algorithms to optimize manufacturing techniques. These machine learning techniques have been applied to FSW, which allows it to predict the defect before its occurrence. ML methods such as the adaptive neurofuzzy interference system, regression model, support vector machine, and artificial neural networks were studied to predict the error percentage for the friction stir welding technique. This article examines machine learning applications in FSW by utilizing an artificial neural network (ANN) to control fracture failure and a convolutional neural network (CNN) to detect faults. The ultimate tensile strength is predicted using a regression and classification model, a decision tree model, a support vector machine for defecting classification, and Gaussian process regression (UTS). Machine learning implementation mainly promotes uniformity in the process and precision and maximally averts human error and involvement.
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