Search results for: Satyendra Pratap Singh

Authors: Satyendra Pratap Singh, S. P. Singh

Abstract:

This paper presents a methodology using Gravitational Search Algorithm for optimal placement of Phasor Measurement Units (PMUs) in order to achieve complete observability of the power system. The objective of proposed algorithm is to minimize the total number of PMUs at the power system buses, which in turn minimize installation cost of the PMUs. In this algorithm, the searcher agents are collection of masses which interact with each other using Newton’s laws of gravity and motion. This new Gravitational Search Algorithm based method has been applied to the IEEE 14-bus, IEEE 30-bus and IEEE 118-bus test systems. Case studies reveal optimal number of PMUs with better observability by proposed method.

Keywords: gravitational search algorithm (GSA), law of motion, law of gravity, observability, phasor measurement unit

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Authors: K. Brince Paul, Nagendra Pratap Singh, Shiv Govind Singh, Siva Rama Krishna Vanjari

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In this paper, we report optimized procedure for synthesizing highly oriented, horizontally aligned, Zinc oxide (ZnO) nanowires targeted towards developing highly sensitive piezoelectric transduction elements. The synthesis was carried out using Electrospinning technique, a facile, robust, low cost technique for producing nanowires. The as-synthesized ZnO nanowires were characterized by X-ray powder diffraction (XRD), Field Emission scanning electron microscopy (FESEM) and Energy-dispersive X-ray spectroscopy (EDX).The Piezoelectric behavior of these nanowires was characterized using Peizoelectric Force microscopy (PFM). A very high d33 coefficient of 23.1 pm/V obtained through the PFM measurements is an indicative of its potential application towards developing miniaturized piezoelectric transduction elements for nanoscale devices.

Keywords: electrospinning, piezoelectric, technique, zinc oxide

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Authors: Nirmal Kant Singh, Anshuman Pratap Singh

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In this study the augmentation of heat transfer in a channel with delta winglet type vortex generators is evaluated. Three-dimensional numerical simulations are performed in a rectangular channel with longitudinal triangular vortex generators (LVGs). The span wise averaged Nusselt number and mean temperature are compared with and without vortex generators in the channel. The effect of variation of blade angle (15°, 30°, 45°, and 60°) is studied at a Reynolds number of 10000. The numerical results indicate that the application of LVGs effectively enhances heat transfer in the channel. The Nusselt number and mean outlet temperature were found to be greater using LVGs than in the channel without LVGs. It is observed that heat transfer increases with increase in blade angle at the same Reynolds number.

Keywords: heat transfer, rectangular channel, longitudinal vortex generators, effect of blade angle

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Authors: Vishnu Pratap Singh Kirar

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In the cloud computing hierarchy IaaS is the lowest layer, all other layers are built over it. Thus it is the most important layer of cloud and requisite more importance. Along with advantages IaaS faces some serious security related issue. Mainly Security focuses on Integrity, confidentiality and availability. Cloud computing facilitate to share the resources inside as well as outside of the cloud. On the other hand, cloud still not in the state to provide surety to 100% data security. Cloud provider must ensure that end user/client get a Quality of Service. In this report we describe possible aspects of cloud related security.

Keywords: cloud computing, cloud networking, IaaS, PaaS, SaaS, cloud security

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Authors: Vishnu Pratap Singh Kirar

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Wireless sensor network (WSN) is a network of many interconnected networked systems, they equipped with energy resources and they are used to detect other physical characteristics. On WSN, there are many researches are performed in past decades. WSN applicable in many security systems govern by military and in many civilian related applications. Thus, the security of WSN gets attention of researchers and gives an opportunity for many future aspects. Still, there are many other issues are related to deployment and overall coverage, scalability, size, energy efficiency, quality of service (QoS), computational power and many more. In this paper we discus about various applications and security related issue and requirements of WSN.

Keywords: wireless sensor network (WSN), wireless network attacks, wireless network security, security requirements

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Authors: Varun Singh, Mainak Bandyopadhyay, Maharana Pratap Singh

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The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS) and traffic assignment problem (in transportation planning). Well known applications of evolutionary methods like Genetic Algorithms (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO) have come up to solve complex optimization problems to overcome the shortcomings of existing shortest path analysis methods. It has been reported by various researchers that PSO performs better than other evolutionary optimization algorithms in terms of success rate and solution quality. Further Geographic Information Systems (GIS) have emerged as key information systems for geospatial data analysis and visualization. This research paper is focused towards the application of PSO for solving the shortest path problem between multiple points of interest (POI) based on spatial data of Allahabad City and traffic speed data collected using GPS. Geovisualization of results of analysis is carried out in GIS.

Keywords: particle swarm optimization, GIS, traffic data, outliers

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Authors: Ajay Pratap Singh Lodhi, Deepak Kumar

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Vegetable oil-based environmentally friendly metalworking fluids (MWFs) are formulated. The tribological performance, cytotoxicity, and corrosion resistance of the formulated fluids (FFs) are evaluated and benchmarked with commercial mineral oil-based MWFs (CF). Results show that FFs exhibited better machining characteristics (roughness, cutting forces, and surface morphology) during machining than CF. MTT assay and Live dead cell assay confirm the cytocompatibility nature of the FFs relative to the toxic CF. Electrochemical analysis shows that FFs and CF exhibited comparable corrosion current density.

Keywords: corrosion inhibitors, cytotoxicity, machining, MTT assay, Taguchi method, vegetable oil

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Authors: Riddhi Jangid, Gajendra Pratap Singh

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Petri nets, a formalized modeling language that was introduced back around 50-60 years, have been widely used for modeling discrete event dynamic systems and simulating their behavior. Reachability analysis of Petri nets gives many insights into a modeled system. This idea leads us to study the reachability technique and use it in the reachability problem in the state space of reachable markings. With the same concept, Crisp Boolean Petri nets were defined in which the marking vectors that are boolean are distinct in the reachability analysis of the nets. We generalize the concept and define ‘Crisp’ Petri nets that generate the marking vectors exactly once in their reachability-based analysis, not necessarily Boolean.

Keywords: marking vector, n-vector, Petri nets, reachability

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Authors: Riddhi Jangid, Gajendra Pratap Singh

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Petri nets, a mathematical tool, is used for modeling in different areas of computer sciences, biological networks, chemical systems and many other disciplines. A Petri net model of a given system is created by the graphical representation that describes the properties and behavior of the system. While looking for the behavior of any system, 1-safe Petri nets are of particular interest to many in the application part. Boolean Petri nets correspond to those class in 1- safe Petri nets that generate all the binary n-vectors in their reachability analysis. We study the class by changing different parameters like the token counts in the places and how the structure of the tree changes in the reachability analysis. We discuss here an extended class of Boolean Petri nets that generates n-ary trees in their reachability-based analysis.

Keywords: marking vector, n-vector, petri nets, reachability

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Authors: Dev Pratap Singh, Harshika Hasija, Ashwini S.

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The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computers using hand gestures and voice commands. The system leverages advanced computer vision techniques using the Media Pipe framework and OpenCV to detect and interpret real-time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the speech recognition library allows for seamless execution of tasks like web searches, location navigation, and gesture control in the system through voice commands.

Keywords: gesture recognition, hand tracking, machine learning, convolutional neural networks, natural language processing, voice assistant

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Authors: Vishnu Pratap Singh Kirar

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The main aim of the communication system is to achieve maximum performance. In cognitive radio, any user or transceiver have the ability to sense best suitable channel, while the channel is not in use. It means an unlicensed user can share the spectrum of licensed user without any interference. Though the spectrum sensing consumes a large amount of energy and it can reduce by applying various artificial intelligent methods for determining proper spectrum holes. It also increases the efficiency of Cognitive Radio Network (CRN). In this survey paper, we discuss the use of different learning models and implementation of Artificial Neural Network (ANN) to increase the learning and decision-making capacity of CRN without affecting bandwidth, cost and signal rate.

Keywords: artificial neural network, cognitive radio, cognitive radio networks, back propagation, spectrum sensing

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Authors: Vani Singhal, Jitendra Parmar, Satyendra Singh Chouhan

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Unseen class discovery has now become an important part of a machine-learning algorithm to judge new classes. Unseen classes are the classes on which the machine learning model is not trained on. With the advancement in technology and AI replacing humans, the amount of data has increased to the next level. So while implementing a model on real-world examples, we come across unseen new classes. Our aim is to find the number of unseen classes by using a hierarchical-based active learning algorithm. The algorithm is based on hierarchical clustering as well as active sampling. The number of clusters that we will get in the end will give the number of unseen classes. The total clusters will also contain some clusters that have unseen classes. Instead of first discovering unseen classes and then finding their number, we directly calculated the number by applying the algorithm. The dataset used is for intent classification. The target data is the intent of the corresponding query. We conclude that when the machine learning model will encounter real-world data, it will automatically find the number of unseen classes. In the future, our next work would be to label these unseen classes correctly.

Keywords: active sampling, hierarchical clustering, open world learning, unseen class discovery

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Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

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Authors: Vishnu Pratap Singh Kirar, Madhuri Saxena

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Advancement in sequence data generation technologies is churning out voluminous omics data and posing a massive challenge to annotate the biological functional features. With so much data available, the use of machine learning methods and tools to make novel inferences has become obvious. Machine learning methods have been successfully applied to a lot of disciplines, including computational biology and bioinformatics. Researchers in computational biology are interested to develop novel machine learning frameworks to classify the huge amounts of biological data. In this proposal, it plan to employ novel machine learning approaches to aid the understanding of how apparently innocuous mutations (in intergenic DNA and at synonymous sites) cause diseases. We are also interested in discovering novel functional sites in the genome and mutations in which can affect a phenotype of interest.

Keywords: genome wide association studies (GWAS), next generation sequencing (NGS), deep learning, omics

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Authors: Amit Kumar, Bhupendra Pratap Singh, Manoj Singh, Monika Punia, Krishan Kumar, V. K. Jain

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Ambient concentrations of volatile organic compounds (VOCs) were investigated in order to find out temporal variations and their ozone forming potentials (OFP) at rural site in Delhi National Capital Region during summer 2013. Sampling was performed for continuous five days, to identify the differences in working days and weekend VOCs concentration levels. Sampling and analytical procedure for VOCs were done using National Institute for Occupational Safety and Health (NIOSH) standard method. On each sampling day, VOCs samples were collected for 3-hours in the morning, afternoon and evening. There has been observed a noticeable contrast in the concentration of VOCs levels between working days and weekend. However, most of the VOCs showed diurnal fluctuations with higher concentrations in the morning and evening as compared to afternoon which might be due to change in meteorology. The results showed that mean toluene/benzene and m-/p-xylene/benzene ratios were higher in the afternoon while it was lower during morning and evening. The relative contribution of the VOCs to ozone formation, total propylene equivalent concentrations and OFP were calculated. Toluene was the most contributing organic contaminant to ozone formation as well as ambient VOCs concentrations. Results obtained in current study demonstrate that ozone formation at rural site in Delhi is probably limited by the emissions of VOCs.

Keywords: VOCs, rural, NIOSH, ozone forming potential, propylene equivalent concentration

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Authors: Vishnu Pratap Singh Kirar

Abstract:

Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: Houda Najeh, Stéphane Ploix, Mahendra Pratap Singh, Karim Chabir, Mohamed Naceur Abdelkrim

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Building systems are highly vulnerable to different kinds of faults and failures. In fact, various faults, failures and human behaviors could affect the building performance. This paper tackles the detection of unreliable sensors in buildings. Different literature surveys on diagnosis techniques for sensor grids in buildings have been published but all of them treat only bias and outliers. Occurences of data gaps have also not been given an adequate span of attention in the academia. The proposed methodology comprises the automatic thresholding for data gap detection for a set of heterogeneous sensors in instrumented buildings. Sensor measurements are considered to be regular time series. However, in reality, sensor values are not uniformly sampled. So, the issue to solve is from which delay each sensor become faulty? The use of time series is required for detection of abnormalities on the delays. The efficiency of the method is evaluated on measurements obtained from a real power plant: an office at Grenoble Institute of technology equipped by 30 sensors.

Keywords: building system, time series, diagnosis, outliers, delay, data gap

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Authors: Agastya Pratap Singh

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This review paper examines the emerging applications of generative adversarial networks (GANs) in the fields of image recognition and anomaly detection. With the rapid growth of digital image data, the need for efficient and accurate methodologies to identify and classify images has become increasingly critical. GANs, known for their ability to generate realistic data, have gained significant attention for their potential to enhance traditional image recognition systems and improve anomaly detection performance. The paper systematically analyzes various GAN architectures and their modifications tailored for image recognition tasks, highlighting their strengths and limitations. Additionally, it delves into the effectiveness of GANs in detecting anomalies in diverse datasets, including medical imaging, industrial inspection, and surveillance. The review also discusses the challenges faced in training GANs, such as mode collapse and stability issues, and presents recent advancements aimed at overcoming these obstacles.

Keywords: generative adversarial networks, image recognition, anomaly detection, synthetic data generation, deep learning, computer vision, unsupervised learning, pattern recognition, model evaluation, machine learning applications

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Authors: Alok Kumar Yadav, Saurabh Saraswat, Preeti Sirohi, Manjoo Rani, Sameer Srivastava, Manish Pratap Singh, Nand K. Singh

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The development of antibiotic resistance in bacteria is increasing at an alarming rate, and this is considered as one of the most serious threats in the history of medicine, and an alternative solution should be derived so as to tackle this problem. In many countries, people use the medicinal plants for the treatment of various diseases as these are cheaper, easily available and least toxic. Syzygium cumini is used for the treatment of various kinds of diseases but their mechanism of action is not reported. The antimicrobial activity of Syzygium cumini was tested by the well diffusion assay and zone of inhibition was reported to be 20.06 mm as compared to control with MIC of 0.3 mg/ml. Genomic DNA fragmentation of Bacillus subtilis revealed apoptosis and FE-SEM indicate cell wall cracking on several intervals of time. Propidium iodide staining results showed that few bacterial cells were stained in the control and population of stained cells increase after exposing them for various period of time. Flow cytometric kinetic data analysis on the membrane permeabilization in bacterial cell showed the significant contribution of antimicrobial potential of the seed extract on antimicrobial-induced permeabilization. Two components of Syzygium cumini methanolic seed extract was found to be quite active against four enzymes like PDB ID- 1W5D, 4OX3, 3MFD and 5E2F which have a very crucial role in membrane synthesis in Bacillus subtilis by in silico analysis. Through in silico analysis, lupeol showed highest binding energy for macromolecule 1W5D and 4OX3 whereas stigmasterol showed the highest binding energy for macromolecule 3MFD and 5E2F respectively. It showed that methanolic seed extract of Syzygium cumini can be used for the inhibition of foodborne infections caused by Bacillus subtilis and also as an alternative of prevalent antibiotics.

Keywords: antibiotics, Bacillus subtilis, inhibition, Syzygium cumini

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Authors: Navneetinder Singh, Harprabhjot Singh, Harpreet Singh, Supreet Singh

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Many materials are known to mankind that is widely used for synthesis of corrosion resistant hydrophobic coatings. In the current work, novel hydrophobic composite was synthesized by mixing polytetrafluoroethylene (PTFE) and 20 weight% ceria particles followed by sintering. This composite had same hydrophobic behavior as PTFE. Moreover, composite showed better scratch resistance than virgin PTFE. Pits of plasma sprayed Ni₃Al coating were exploited to hold PTFE composite on the substrate as Superni-75 alloy surface through sintering process. Plasma sprayed surface showed good adhesion with the composite coating during scratch test. Potentiodynamic corrosion test showed 100 fold decreases in corrosion rate of coated sample this may be attributed to inert and hydrophobic nature of PTFE and ceria.

Keywords: polytetrafluoroethylene, PTFE, ceria, coating, corrosion

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Authors: Agastya Pratap Singh

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Generative Adversarial Networks (GANs) have emerged as powerful tools in the fields of image recognition and anomaly detection due to their ability to model complex data distributions and generate realistic images. This systematic review explores recent advancements and applications of GANs in both image recognition and anomaly detection tasks. We discuss various GAN architectures, such as DCGAN, CycleGAN, and StyleGAN, which have been tailored to improve accuracy, robustness, and efficiency in visual data analysis. In image recognition, GANs have been used to enhance data augmentation, improve classification models, and generate high-quality synthetic images. In anomaly detection, GANs have proven effective in identifying rare and subtle abnormalities across various domains, including medical imaging, cybersecurity, and industrial inspection. The review also highlights the challenges and limitations associated with GAN-based methods, such as instability during training and mode collapse, and suggests future research directions to overcome these issues. Through this review, we aim to provide researchers with a comprehensive understanding of the capabilities and potential of GANs in transforming image recognition and anomaly detection practices.

Keywords: generative adversarial networks, image recognition, anomaly detection, DCGAN, CycleGAN, StyleGAN, data augmentation

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Authors: Aiswarya C. L, Swatantra Pratap Singh

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In recent years, most desalination plants have been powered by fossil fuels, and to a lesser extent, by green energy. Greenhouse gases emitted by fossil-fuelled plants significantly impact the global climate. So scientists are forced to develop a powerful energy source to protect the environment with greater sustainability due to climate change issues. Nuclear energy can supply much more fresh water than what is currently available. Furthermore, it is more affordable and does not emit any greenhouse gases. This review compares conventional desalination plants with nuclear-powered desalination plants in terms of cost, energy consumption, water recovery, and environmental issues. On the basis of the review conducted, nuclear desalination has been demonstrated to be technically feasible and economically competitive with a variety of fossil fuels, renewable energy sources, and waste heat sources. Nuclear sources have been criticized due to their lack of safety. But studies show, if we were able to handle the issue with care, the problems could be eliminated. Here we're looking at the Seawater Reverse Osmosis Plant (SWROP) at Kudankulam Nuclear Power Plant in Tamil Nadu, India and review the further possibility of implementing nuclear desalination technology in other states of India.

Keywords: energy consumption, environmental impacts, nuclear desalination, water recovery

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Authors: Anjali Singh, Ashwani Raju, Vandana Devi, Mohmad Mohsin Atique, Satyendra Singh, Munendra Singh

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India is the biggest consumer of mercury and, consequently, a major emitter too. The increasing mercury contamination in India’s water resources has gained widespread attention and, therefore, atmospheric deposition is of critical concern. However, little emphasis was placed on the role of precipitation in the aquatic mercury cycle of the Ganga Alluvial Plain which provides drinking water to nearly 7% of the world’s human population. A majority of the precipitation here occurs primarily in 10% duration of the year in the monsoon season. To evaluate the sources and transportation of mercury, water sample analysis has been conducted from two selected sites near Lucknow, which have a strong hydraulic gradient towards the river. 31 groundwater samples from Jehta village (26°55’15’’N; 80°50’21’’E; 119 m above mean sea level) and 31 river water samples from the Behta Nadi (a tributary of the Gomati River draining into the Ganga River) were collected during the monsoon season on every alternate day between 01 July to 30 August 2019. The total mercury analysis was performed by using Flow Injection Atomic Absorption Spectroscopy (AAS)-Mercury Hybride System, and daily rainfall data was collected from the India Meteorological Department, Amausi, Lucknow. The ambient groundwater and river-water concentrations were both 2-4 ng/L as there is no known geogenic source of mercury found in the area. Before the onset of the monsoon season, the groundwater and the river-water recorded mercury concentrations two orders of magnitude higher than the ambient concentrations, indicating the regional transportation of the mercury from the non-point source into the aquatic environment. Maximum mercury concentrations in groundwater and river-water were three orders of magnitude higher than the ambient concentrations after the onset of the monsoon season characterizing the considerable mobilization and redistribution of mercury by monsoonal precipitation. About 50% of both of the water samples were reported mercury below the detection limit, which can be mostly linked to the low intensity of precipitation in August and also with the dilution factor by precipitation. The highest concentration ( > 1200 ng/L) of mercury in groundwater was reported after 6-days lag from the first precipitation peak. Two high concentration peaks (>1000 ng/L) in river-water were separately correlated with the surface flow and groundwater outflow of mercury. We attribute the elevated mercury concentration in both of the water samples before the precipitation event to mercury originating from the extensive use of agrochemicals in mango farming in the plain. However, the elevated mercury concentration during the onset of monsoon appears to increase in area wetted with atmospherically deposited mercury, which migrated down from surface water to groundwater as downslope migration is a fundamental mechanism seen in rivers of the alluvial plain. The present study underscores the significance of monsoonal precipitation in the transportation of mercury to drinking water resources of the Ganga Alluvial Plain. This study also suggests that future research must be pursued for a better understand of the human health impact of mercury contamination and for quantification of the role of Ganga Alluvial Plain in the Global Mercury Cycle.

Keywords: drinking water resources, Ganga alluvial plain, india, mercury

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Authors: Shivendra Pratap Singh, Rishabh Sharma

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The widespread adoption of mobile technology in recent years has revolutionized the way we communicate and access information. The evolution of mobile systems has been rapid and impactful, shaping our lives and changing the way we live and work. However, despite its significant influence, the history and development of mobile technology are not well understood by the general public. This research paper aims to examine the history, technology and future of mobile systems, exploring their evolution from early mobile phones to the latest smartphones and beyond. The study will analyze the technological advancements and innovations that have shaped the mobile industry, from the introduction of mobile internet and multimedia capabilities to the integration of artificial intelligence and 5G networks. Additionally, the paper will also address the challenges and opportunities facing the future of mobile technology, such as privacy concerns, battery life, and the increasing demand for high-speed internet. Finally, the paper will also provide insights into potential future developments and innovations in the mobile sector, such as foldable phones, wearable technology, and the Internet of Things (IoT). The purpose of this research paper is to provide a comprehensive overview of the history, technology, and future of mobile systems, shedding light on their impact on society and the challenges and opportunities that lie ahead.

Keywords: mobile technology, artificial intelligence, networking, iot, technological advancements, smartphones

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Authors: Gurmeet Singh Cheema, Navjotinder Singh, Gurjinder Singh, Amardeep Singh

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Aluminium and its alloys play have excellent corrosion resistant properties, ease of fabrication and high specific strength to weight ratio. In this investigation an attempt has been made to study the effect of different post weld heat treatment methods on the mechanical and metallurgical properties of TIG welded joints of the commercial aluminium alloy. Three different methods of post weld heat treatments are, solution heat treatment, artificial aged and combination of solution heat treatment and artificial aging are given to TIG welded aluminium joints. Mechanical and metallurgical properties of as welded and post weld treated joints of the aluminium alloys was examined.

Keywords: aluminium alloys, TIG welding, post weld heat treatment

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Authors: Ashish Kumar Pathak, Ritika Sharma, Vishal Nath, Sudhir Pratap Singh, Rakesh Tuli

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Litchi is a sub-tropical fruit crop with genotypes bearing delicious juicy fruits with variable seed size (bold to rudimentary size). Small seed size is a desirable trait in litchi, as it increases consumer acceptance and fruit processing. The biochemical activities in mid- stage ovules (e.g. 16, 20, 24 and 28 days after anthesis) determine the fate of seed and fruit development in litchi. Comprehensive ovule-specific transcriptome analysis was performed in two litchi genotypes with contrasting seed size to gain molecular insight on determinants of seed fates in litchi fruits. The transcriptomic data was de-novo assembled in 1,39,608 trinity transcripts, out of which 6,325 trinity transcripts were differentially expressed between the two contrasting genotypes. Differential transcriptional pattern was found among ovule development stages in contrasting litchi genotypes. The putative genes for salicylic acid, jasmonic acid and brassinosteroid pathway were down-regulated in ovules of small-seeded litchi. Embryogenesis, cell expansion, seed size and stress related trinity transcripts exhibited altered expression in small-seeded genotype. The putative regulators of seed maturation and seed storage were down-regulated in small-seed genotype.

Keywords: Litchi, seed, transcriptome, defence

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Authors: Jasbinder Singh, Harjit Pal Singh, S. A. Khan

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The work, in this paper, presents the comparison of encoded speech signals by different VoIP narrow-band and wide-band codecs for different modulation schemes. The simulation results indicate that codec has an impact on the speech quality and also effected by modulation schemes.

Keywords: VoIP, coders, modulations, BER, MOS

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Authors: Anshuman Srivastava, Shiv Singh, Sheelendra Pratap Singh

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A simple, sensitive and effective gas chromatography tandem mass spectrometry (GC-MS/MS) method was developed for simultaneous analysis of multi pesticide residues (organophosphate, organochlorines, synthetic pyrethroids and herbicides) in food commodities using phenolic resin based activated carbon fibers (ACFs) as reversed-dispersive solid phase extraction (r-DSPE) sorbent in modified QuEChERS (Quick Easy Cheap Effective Rugged Safe) method. The acetonitrile-based QuEChERS technique was used for the extraction of the analytes from food matrices followed by sample cleanup with ACFs instead of traditionally used primary secondary amine (PSA). Different physico-chemical characterization techniques such as Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray diffraction and Brunauer-Emmet-Teller surface area analysis were employed to investigate the engineering and structural properties of ACFs. The recovery of pesticides and herbicides was tested at concentration levels of 0.02 and 0.2 mg/kg in different commodities such as cauliflower, cucumber, banana, apple, wheat and black gram. The recoveries of all twenty-six pesticides and herbicides were found in acceptable limit (70-120%) according to SANCO guideline with relative standard deviation value < 15%. The limit of detection and limit of quantification of the method was in the range of 0.38-3.69 ng/mL and 1.26 -12.19 ng/mL, respectively. In traditional QuEChERS method, PSA used as r-DSPE sorbent plays a vital role in sample clean-up process and demonstrates good recoveries for multiclass pesticides. This study reports that ACFs are better in terms of removal of co-extractives in comparison of PSA without compromising the recoveries of multi pesticides from food matrices. Further, ACF replaces the need of charcoal in addition to the PSA from traditional QuEChERS method which is used to remove pigments. The developed method will be cost effective because the ACFs are significantly cheaper than the PSA. So the proposed modified QuEChERS method is more robust, effective and has better sample cleanup efficiency for multiclass multi pesticide residues analysis in different food matrices such as vegetables, grains and fruits.

Keywords: QuEChERS, activated carbon fibers, primary secondary amine, pesticides, sample preparation, carbon nanomaterials

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Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

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Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation

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Authors: Jeevan Jyoti, Bhanu Pratap Singh, S. R. Dhakate

Abstract:

In the present study, multiwalled carbon nanotubes (MWCNTs) and reduced graphene oxide (RGO) were synthesized by chemical vapor deposition and Improved Hummer’s method, respectively and their composite with acrylonitrile butadiene styrene (ABS) were prepared by twin screw co rotating extrusion technique. The electromagnetic interference (EMI) shielding effectiveness of graphene oxide carbon nanotube (GCNTs) hybrid composites was investigated and the results were compared with EMI shielding of carbon nanotube (CNTs) and reduced graphene oxide (RGO) in the frequency range of 12.4-18 GHz (Ku-band). The experimental results indicate that the EMI shielding effectiveness of these composites is achieved up to –21 dB for 10 wt. % loading of GCNT loading. The mechanism of improvement in EMI shielding effectiveness is discussed by resolving their contribution in absorption and reflection loss. The main reason for such a high improved shielding effectiveness has been attributed to the significant improvement in the electrical conductivity of the composites. The electrical conductivity of these GCNT/ABS composites was increased from 10-13 S/cm to 10-7 S/cm showing the improvement of the 6 order of the magnitude. Scanning electron microscopic (SEM) and high resolution transmission electron microscopic (HRTEM) studies showed that the GCNTs were uniformly dispersed in the ABS polymer matrix. GCNTs form a network throughout the polymer matrix and promote the reinforcement.

Keywords: ABS, EMI shielding, multiwalled carbon nanotubes, reduced graphene oxide, graphene, oxide-carbon nanotube (GCNTs), twin screw extruder, multiwall carbon nanotube, electrical conductivity

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