Volume 10 | Issue 6 |

 Abstract

Urbanization and Industrial growth have been identified as the primary sources of air pollution in India's metropolitan cities. One of the most important challenges in urban areas has been highlighted as air pollution. Particulate matter is still one of the most prominent forms of air pollution in cities, with serious health repercussions for both acute and chronic exposures. The presence of particulate matter is mostly determined by meteorological conditions. Citizens and governments around the world have become increasingly concerned about the effects of air pollution on human health, and have proposed sustainable development solutions to address air pollution challenges caused by current industrialization, which include liquid droplets, solid particles, and gas molecules dispersed throughout the atmosphere. The high concentration of particulate matter of sizes PM10 and PM2.5 has a major negative impact on human health. The determination of particulate matter concentration in atmospheric air for the betterment of human being well of primary importance. So, in this paper, a machine learning model were used for predicting particulate matter concentration in atmospheric air is investigated on Vapi Air Quality Monitoring data sets, which were obtained from 2017 to 2021. These models were compared on their performance in particulate matter prediction and the best model was found for predicting particulate matter.

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 Keywords

Particulate Matter, Meteorological factors, Random Forest, Decision Tree, and Prediction.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Sustainability; Competitive Advantage; Five Star Hotels; Sustainability; New Delhi.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Non Cognizable offences, Juvenile, Crime, child rights, child care institutions, Amendment 2021, Enforcement

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Clean, clean beauty, green, sustainable, natural, organic, cosmetics

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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This project describes the detail analysis of transient stability and voltage stability enhancement of the hybrid system. In this paper we are using the synchronous generator and photovoltaic (PV) system. These two systems are connected to the grid through the transmission line. In this project we are proposing the control scheme for PV inverters to improve the transient stability of the system. Here we are going to install a dc link capacitor to absorb the exceeding active power, which cannot be absorbed by the faulty grid. Now-a-days the power system has gone through a significant increase in the penetration of Renewable energy (RE) sources, that are always in connection with the power grid through power converters (such as inverters). To maintain and enhance the voltage stability we are going to inject the reactive power into the system. Here the FRT is installed in order to improves the voltage stability and its post-fault recovery through the delivery of reactive power into the grid. The control system of a PV plant also possesses current limiters, DC link voltage control and making the FRT capability able to face any type of fault.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Photovoltaic generation, synchronous machine, transient stability, voltage stability, FRT, PMU and PDC

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Salesforce Health Cloud, Healthcare, CRM, EHR

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Creative Commons Attribution 4.0 and The Open Definition

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OLEDs are solid-state devices made up of thin films of organic molecules that emit light when electricity is applied. OLEDs have the potential to deliver brighter, sharper displays on electronic gadgets while using less electricity than today's light emitting diodes (LEDs). An OLED, like an LED, is a solid-state semiconductor device with a thickness of 100 to 500 nanometers, or about 200 times that of a human hair. Organic material can be layered in two or three layers in OLEDs. The following components make up an OLED: 1.The substrate, 2.The anode, 3.The organic layers, and 4.The cathode OLEDs are currently found in small-screen devices including mobile phones, PDAs, and digital cameras. OLED research and development is moving quickly right now, and it could lead to future uses in heads-up displays.

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 Keywords

200

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Creative Commons Attribution 4.0 and The Open Definition

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Ultraviolet (UV) light is used for the purpose of disinfection or sterilization of rooms and surfaces. UV-C is employed as it has germicidal properties, in particular - bacteria and viruses, but it is detrimental to human beings as well. So, for the purpose of disinfection without human interference, a UV Robot has been designed and implemented that follows a predefined path. It was equipped with three 20W UV lamps which radiate light in all directions. Given that UV light can be dangerous to humans, an embedded system based on Arduino along with PIR sensors is employed on top of the robot that detects human or animal motion and presence. So, one of the effective ways to avoid getting infected with SARS-COV-2 (Coronavirus) is by sterilizing rooms using a UV robot.

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Creative Commons Attribution 4.0 and The Open Definition

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Ultraviolet Light, UV Disinfection

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Creative Commons Attribution 4.0 and The Open Definition

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Due to a multi-fold growth in the diffusion of multimedia data through the open and unprotected Internet, content authentication of digital photographs has caught the attention of forensic professionals and security researchers. Attackers who are astute come up with new approaches to test state-of-the-art forensic tools for detecting forgeries in digital photographs. On benchmarked datasets, feature engineering techniques have produced accuracy of up to 97 percent. Deep learning algorithms have showed promise in a variety of picture classification tasks, but they are unable to discover hidden patterns in digital images that can be used to consistently detect image forgeries. Deep learning techniques for forgery detection have a state-of-the-art accuracy of up to 98 percent on benchmarked datasets. The proposed approach aims to improve detection accuracy even more, bringing it close to 100%. To mine patterns responsible for accurate forgery detection, this work uses a synergy of created features based on colour attributes and deep features using the image's luminance channel. The first Stream computes 648-D Markov-based features from the image's quaternion discrete cosine transform. In the second Stream, the image's Local Binary Pattern is extracted using the YCbCr colorspace's luminance channel. Local binary feature maps are also input into the pre-trained ResNet-18 model to get a 512-D feature vector named 'ResFeats' from the model's convolutional base portion's last layer. An 1160-D feature vector is created by combining handcrafted features from Stream I and ResFeats from Stream II. The method is also tested on the CASIA v1 and CASIA v2 datasets, and classification is done with a shallow neural network. On benchmark datasets, the suggested fusion-based technique has a 99.3 percent accuracy.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Image Processing, Foregery Image Detection

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Creative Commons Attribution 4.0 and The Open Definition