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Relative Efficiencies of Farmer Producer Companies in India- Slack-Based Model Approach
The concept of the farmer producer company (FPC) model has received a large momentum especially during the 20202021 farmers' protest in India. This paper examines the relative efficiencies of 46 FPCs in Kerala using non-radial data envelopment analysis (DEA) for the financial year 2018-19. We use a non-oriented slack-based model (SBM) under assumptions of constant and variable returns to scale. The results reveal that 36.96 per cent of the sample FPCs are overall technical efficient and 50 per cent of the FPCs are pure technical efficient. It is found that technical inefficiency is reported for a few FPCs due to scale inefficiency. Among the input and output targets suggested for inefficient FPCs, reduction in the 'number of shareholders' and augmentation of 'profits' reported in most cases to improve their efficiency scores. Based on the findings, we suggest the concerned stakeholders to provide additional financial and non-financial supports to the needy rather than focusing on establishing new FPCs. 2022 IEEE. -
Relevance of psychophysiological and emotional features for the analysis of Human behavior-A Survey
With fresh development in the area of artificial intelligence and machine learning, the analysis of human physiological and psychological behavior has increased greater attention around the world. In this paper, we have provided a detailed survey of the approaches used for human behavior detection considering different modalities with physiological behavior, psychological behavior, and emotion detection with the help of sensors EEG, ECG, GSR, and temperature. At long last, it finishes up with the results of this study and represents the thoughts for future exploration in the zone of human behavior understanding. A rundown and comparison among the ongoing investigations done, that uncovers the currently existing issues and the future work has been examined. The Electrochemical Society -
Reliability analysis of cement manufacturing technique in computerized clinker processing method
Cement production will face severe resource constraints in the future, as they rely on natural resources. Therefore, the industry focuses on raising natural resource requirements at both the development and operational levels. One of the situations left unattended in cement production is modelling reliability on a clinker production device with a defect in its three main components. Bridging this gap, this paper provides a reliability model on the manufacturing method of clinkers. The manufacturing of clinkers is the first step in the cement production process. The clinker manufacturing process comprises three main components: crusher, roller mill, and rotary kiln. Three reliability models are developed in this paper, with failures in its three important components considering three situations. All three components are operative, the first two components are operative, and only the first component is operative. In this paper, the transition probabilities and mean sojourn times and also MTSF are measured. 2023 Author(s). -
Reliable monitoring security system to prevent MAC spoofing in ubiquitous wireless network
Ubiquitous computing is a new paradigm in the world of information technology. Security plays a vital role in such networking environments. However, there are various methods available to generate different Media Access Control (MAC) addresses for the same system, which enables an attacker to spoof into the network. MAC spoofing is one of the major concerns in such an environment where MAC address can be spoofed using a wide range of tools and methods. Different methods can be prioritized to get cache table and attributes of ARP spoofing while targeting the identification of the attack. The routing trace-based technique is the predominant method to analyse MAC spoofing. In this paper, a detailed survey has been done on different methods to detect and prevent such risks. Based on the survey, a new proposal of security architecture has been proposed. This architecture makes use of Monitoring System (MS) that generates frequent network traces into MS table, server data and MS cache which ensures that the MAC spoofing is identified and blocked from the same environment. 2019, Springer Nature Singapore Pte Ltd. -
REMAP: Determination of the inner edge of the dust torus in AGN by measuring time delays
Active galactic nuclei (AGN) are high luminosity sources powered by accretion of matter onto super-massive black holes (SMBHs) located at the centres of galaxies. According to the Unification model of AGN, the SMBH is surrounded by a broad emission line region (BLR) and a dusty torus. It is difficult to study the extent of the dusty torus as the central region of AGN is not resolvable using any conventional imaging techniques available today. Though, current IR interferometric techniques could in principle resolve the torus in nearby AGN, it is very expensive and limited to few bright and nearby AGN. A more feasible alternative to the interferometric technique to find the extent of the dusty torus in AGN is the technique of reverberation mapping (RM). REMAP (REverberation Mapping of AGN Program) is a long term photometric monitoring program being carried out using the 2 m Himalayan Chandra Telescope (HCT) operated by the Indian Institute of Astrophysics, Bangalore, aimed at measuring the torus size in many AGN using the technique of RM. It involves accumulation of suitably long and well sampled light curves in the optical and near-infrared bands to measure the time delays between the light curves in different wavebands. These delays are used to determine the radius of the inner edge of the dust torus. REMAP was initiated in the year 2016 and since then about one hour of observing time once every five days (weather permitting) has been allocated at the HCT. Our initial sample carefully selected for this program consists of a total of 8 sources observable using the HCT. REMAP has resulted in the determination of the extent of the inner edge of the dusty torus in one AGN namely H0507+164. Data accumulation for the second source is completed and observations on the third source are going on. We will outline the motivation of this observational program, the observational strategy that is followed, the analysis procedures adopted for this work and the results obtained from this program till now. 2019 Societe Royale des Sciences de Liege. All rights reserved. -
Remote Diabetic Retinopathy Screening with IoT and Machine Learning on Edge Devices
This study presents a novel method of screening for diabetic retinopathy using edge devices the Internet of Things and machine learning. The developed remote screening system ensures broad accessibility as well as affordability by overcoming geographical barriers. While edge computing maximizes real-time analysis, the integration of sophisticated machine learning algorithms improves diagnostic accuracy. The investigation of socio-technical subtleties is guided by the interpretivist philosophy. The outcomes show a strong architecture, effective models, as well as revolutionary effects on accessibility. A critical assessment finds the good points and continuous improvements. Suggestions place a strong emphasis on scaling issues and the ongoing improvement of machine learning models. In order to secure data management and keep up with changing healthcare needs, future research suggests combining blockchain technology with sophisticated imaging modalities. This study advances early detection, enhances accessibility to healthcare, and advances remote screening technologies. 2023 IEEE. -
Removal of arsenic using ecofriendly egg shell and black toner powder
This work is primarily focused on the study of the possible usage of ecofriendly black toner powder and egg shell powder as adsorbent material for the removal of arsenic from industrial effluent. Batch experiments were conducted by varying the concentration, size of the reinforcement particles, time and its pH value. The optimal pH for the effective removal of arsenic was found to be 7. The size of the particles played a significant role in removing the arsenic. Smaller size particles outperformed the bigger size particles and the joint action of intra-particle transfer and pore diffusion mechanism played a major role in the removal of arsenic. 2022 -
Removal of Artifacts from Electroenchaphalography Signal using Multiwavelet Transform
The signal from the brain can be recorded using Electroenchaphalography (EEG). The proposed work summarizes a unique method which is used for the removal of mixed artifacts presented in the electroencephalography signal during the acquisition process. Artifacts comprises of various bio-potential unit such as electrooculogram, electrocardiogram, and electromyogram. These artifacts are referred as a noise sources which is responsible for the complexity of the EEG signal. The artifacts obtained from the EEG signal leads towards improper diagnosis of pathological conditions. The EEG signal which is obtained from the brain is the multi-dimensional signal with the various statistical properties. Time consumption of the EEG signal is not reproducible due to the biological properties of the signal. The information of the EEG signal consists of the data of the neuron levels which is collected for every millisecond with the temporal resolution scale. In account of special cases, EEG signal contains noise and artifacts where information is collected using the extraction of signals. To obtain the information of the artifacts the proposed technique is used to maintain higher accuracy in the extraction process. The proposed technique consists of multiwavelet transform to remove the artifacts from the input EEG signal. In the proposed multiwavelet transform, the signal which consists of noisy features can be decomposed using GHM and thresholding technique. This experimental analysis shows the removal of artifacts from the EEG signals. The pathological conditions are removed which leads to the increase in the accuracy of the system. Also, this research findings shows that the proposed multiwavelet transform based approach outperforms significantly with respect to conventional approaches. The reconstructed EEG signal has the lesser reliability range which is measured in-terms of signal to noise ratio and power spectral density. Published under licence by IOP Publishing Ltd. -
Rendering View of Kitchen Design Using Autodesk 3Ds Max
The method of creating a 3D kitchen design model is clarified, including setting up the sources, working with editable poly, information in the inside of the kitchen design, and applying turbo-smooth and symmetry modifier. The way materials are introduced to the model which is defined in addition to lighting the environment and setting up the renderer. Rendering methods and procedures are also defined. Multiple images were drawn to create the final rendering. The goal of our research is to produce a kitchen design that uses materials to enhance models. Cylinder, sphere, box, plane, and splines were the shapes employed. Editable poly, editable spline, and UVW map are the modifiers. Finally, we enhanced the model using a material editor and target lighting. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Rescue Operation with RF Pose Enabled Drones in Earthquake Zones
The main objective of this research is to use machine learning algorithms to locate people stranded by an earthquake or other big disasters. Disasters are often unpredictable, they can result in significant economic loss, and the survivors may struggle with despair and other mental health issues. The time, the victim's precise location, the possible condition of the victim, the resources and manpower on hand are the main challenges the rescue team must deal with. This article examines a model that gathers data and, using that data, predicts risk analysis and probability of finding the shortest distance to reach the person in need. Using a drone equipped with RF-pose technology and EHT sensors, it will be able to locate any individuals trapped inside a collapsed structure. To determine the dataset's extreme points and the shortest route to the victim's location by using the Dijkstra's algorithms. The primary aim of this article is to discuss the idea of applying these ML (Machine Learning) algorithms and creating a model that aids in rescuing those trapped beneath collapsed buildings. Devices that are part of the Internet of Things (IoT) have grown in popularity over the past few years as a result of their capacity for data collection and transmission. Particularly in disaster management, search and rescue operations, and other related disciplines, drones have shown to be useful IoT devices. These tools are perfect for emergency response circumstances because they can be utilized to access locations that are hard to get to or too dangerous for humans. Drones with cameras and other sensors can be used in disaster management to gather data in real-time on the severity of the damage caused by earthquakes and other disasters. The afflicted area may be mapped out with their help, and they can also be used to find survivors and spot dangerous places that should be avoided. The rescue operation can then be planned and the resource allocation made more efficient using this information. Drones can be used in search and rescue operations to find and follow people who are stuck or lost. Drones can be equipped with the RF-pose sensors used in the research described in the abstract to assist in locating people who are buried under debris. Thermal camera-equipped drones can also be used to locate people in low-light or night-time conditions by detecting their body heat. The capacity of drones to offer real-time data is one of the benefits particularly disaster management. 2023 IEEE. -
Research Advancements In Autism Spectrum Disorder Using Neuroimaging
Autism Spectrum Disorder (ASD) is a complex neurological condition that manifests as a spectrum of symptoms at varying levels of severity.. Insufficient data and heterogeneous characteristics of ASD are the primary causes of it being a complex, challenging, and intriguing field of research. ASD is declared one of the fastest-growing mental disorders affecting the normal life of subjects at various levels of severity and stages of age. Recent research work observed a significant change in brain structure, functional connectivity, and network using neuroimaging resources. Each autistic brain is as unique as a fingerprint for typically developed subjects. Magnetic Resonance Imaging (MRI) is accepted as an excellent diagnostic technology for numerous disorders with a satisfactory amount of information by medical experts. Cognitive deficits brain MRI modalities contain microscopic information, which is time-consuming and needs experts to interpret. Artificial intelligence (AI) strategies (Machine Learning and Deep Learning) are implemented with various imaging modalities to decrypt the information for diagnosis and to support computer-added solutions for appropriate treatment. The research aims to discover the various evolutionary impacts of artificial intelligence for the diagnosis of Autism syndrome disorder using neuroimaging. To automate the diagnosis using artificial intelligence methodologies, medical imaging has proved to be of immense use. Though neuroimaging and AI produced satisfactory diagnostic solutions for many mental disorders, research is required to explore the autistic brain for more neuroimaging information to be used for further investigation. Some of the Internet of Things (IoT) solutions for detection and training are also invented but not with the use of Neuroimaging. Autism is a neurological condition that affects the brain, and hence more research is advised using imaging and AI techniques to support the community to enjoy a normal life. 2023 American Institute of Physics Inc.. All rights reserved. -
Research challenges in self-driving vehicle by using internet of things (IoT)
This article summarizes the benefits, safety hazards, and limitations of owning a self-driving vehicle. Finding a way to use an SDV(Self Driving Vehicle) is minimizing the risk for an accident is important for public and road safety. The actual rate of accidents for self-driving vehicles are lower than that for regular vehicles since the total number of miles of self-driving vehicles combined is nowhere close to that of regular fossil-fueled vehicles. Even though there is no proof that self-driving vehicles will not cause accidents, it is important to know that self-driving vehicles weren't the cause in all the cases they have been involved. That is, it will not be purely considered as the machine's mistake. The safety level of self-driving vehicles has been proven to be one of the best and that has led to the number of serious accident-related wounds in self-driving vehicles to remain lower than the standard level. Nevertheless, Internet of Things plays a major role in developing the self-driving vehicle concept. 2021 IEEE. -
Research Initiative on Sustainable Education System: Model of Balancing Green Computing and ICT in Quality Education
Green Computing Practices (GCP) convey the revolutionary changes of the modern education system. The education system is transforming into a hybrid mode of operations in effective teaching and learning procedure. In the modern era, computer devices are playing a foremost role in performing ICT based teaching and learning (ICT-BTL). The GCP and ICT-BTL are the creative and innovative practices that can ensure the eco-friendly enactment and safeguard from various harmful environmental impacts. The motive of projecting the present research outcome is to address the impact of GCP on ICT-BTL activities. The creative and innovative practices of ICT-BTL support the implementation of GCP towards a sustainable education system. A sustainable education system interconnects the teachers, learners, institutions, and industrial experts through eco-friendly electronic and computer devices that ensure maximum efficiency in education with minimum environmental impacts. 2022 IEEE. -
Research on Unmanned Artificial intelligence Based Financial Volatility Prediction in International Stock Market
This study digs into the area of unmanned artificial intelligence (AI) for financial volatility prediction in the worldwide stock market, delivering unique insights into the deployment of cutting-edge technology to handle the multifarious issues of market dynamics. Our research uses Long Short-Term Memory (LSTM) networks as the AI model of choice, showing its usefulness in capturing temporal relationships in financial data by analyzing past stock price data, trading volumes, and a variety of technical indicators. Our findings suggest a potential capacity to reliably predict financial market volatility after extensive data pretreatment, feature engineering, and model training. A powerful instrument for investors, fund managers, and financial institutions to make better informed and accurate investment choices, the model's low Root Mean Squared Error (RMSE) and high (R2) values highlight its practical usefulness. Beyond the purely technical, our study considers the ethical, regulatory, risk reduction, and optimization implications for the financial sector. Financial decision-making and risk management are being transformed by the increasingly globalized market environment, and the results given here provide a concrete roadmap towards the appropriate integration of unmanned AI systems. 2024 IEEE. -
Residual stress analysis on functionally graded 8% Y2O3-ZrO2 and NiCrAlY thermal barrier coatings
Thermal Barrier Coatings (TBCs) protect metallic components that operate in high temperature environments and enhance their service life. The conventional two-layered TBC system consists of a duplex ceramic top coat (TC) fabricated from 8 wt% yttria stabilized zirconia (8-YSZ) and an underlying bond coat (BC) comprised of intermetallic layers such as NiAl or MCrAlY (M = Co, Ni) etc. In the present study, functionally graded material (FGM) TBCs were fabricated by introducing a third blend layer of 8-YSZ and NiCrAlY, in between the BC and TC in order to enhance the thermal fatigue life of the TBC. The blend layer in FGM TBCs provides a smoother transition in thermal expansion properties between the metallic substrate and the top ceramic coat (8YSZ) which have widely different thermal expansion characteristics compared with each other. In service, thermal fatigue introduces severe tensile stresses between the coated layers and the substrates leading to ultimate detachment of the coatings from the substrates. In this work, residual stress analysis by Cos ? method was carried out as a non-destructive assessment tool to foresee the likelihood of onset of failure in the TBCs, well before the damage was visible. The two-layered (conventional) and three-layered (FGM) TBCs were synthesized on Inconel 718 substrates by atmospheric plasma spray (APS) technique. The TBCs were subjected to thermal fatigue tests between 1200? (by using gas flame) and ambient temperature and evaluated for residual stress analysis at different stages of thermal fatigue testing. The goal was to assess if residual stress analysis could be used to determine if the TBC was about to fail well before the delamination occurred and the catastrophic failure could be avoided. The tests conducted and results obtained are presented. 2022 -
Residual stresses analysis on thermal barrier coatingsndt tool for condition assessment
Improvement in the engine efficiency follows reduction in fuel consumption which is possible by increasing the engine combustion temperature. Coating the piston of diesel engine with a high temperature-resistant material, known as thermal barrier coating, generally 68% Y2O3 stabilized ZrO2, is a popular method to reduce the temperature it experiences in service and to increase engine efficiency. Whether bare or coated component, fabrication and different thermal expansion coefficients of the ceramic coating and piston metal cause generation of residual stresses in them. These hidden residual stresses (tensile or compressive) play a significant role in governing the failure mechanism of the different sections of the components and their important role (also developed in service) is mostly neglected in engineering practices. Residual stresses analysis of components in service may throw light on the condition of the components without destroying them. In this work, portable X-ray residual stress analyzer was used to evaluate the condition of AlSi alloys piston flat plates that were coated with 250-m-thick 68% Y2O3 stabilized ZrO2 and subjected to thermal treatments. The analysis revealed (a) residual stress-free pattern for uncoated AlSi substrate, (b) compressive residual stress at the substrate (AlSi)coating (TBC) interface and (c) tensile residual stress at the substrate (AlSi)coating (TBC) interface of a thermal shocked coated substrate. The analysis method exhibited good possibility for using this as a tool for non-destructive testing for predicting the onset of failure at the coating substrate interface, without destroying the component in service. Springer Nature Singapore Pte Ltd 2020. -
Residual-Based Statistical Process Control Charts in the Presence of Multicollinearity: an EWMA Framework with (RK) Estimator
Reliability monitoring of financial health requires strong control mechanisms, and the residual chart is an invaluable instrument to perform it. One of the key problems statisticians face while modeling is the problem of multicollinearity which arises when there is a strong correlation between independent variables leading to imprecise coefficient estimates and poor outcomes. To solve this problem and to make sure that the control chart works even with correlated data, we integrated a Weighted Moving Average Exponential smoothing chart within the modeling technique. The theoretical approach assures long-term variability and consistency of the residual control chart. These control charts are used to understand the process and the performances in various sectors. The charts can be used as analytical instruments to help recognize patterns, variations, or anomalies in economic indicators specifically in budget deficit data and facilitate rapid identification of any changes or inconsistencies in the fiscal deficit by policymakers. Further advances in statistical process control are rendered feasible by this study, which deepens the understanding and awareness of the potential uses and implications of the Weighted Moving Average Exponential smoothing chart for fiscal deficit data in the Economic realm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Resource Aware Weighted Least Connection Load Balancing Technique in Cloud Computing
Cloud computing became a pivotal for the most of the real time applications. In cloud computing, the customer demands the services with the best performance even when the application is expanding rapidly. Therefore, it is essential to manage the resources effectively because the number of users and services growing proportionately. The main aim of the load balancing technique is to allocate the customers' requests with the large pool of resources efficiently. The problem is how to evenly distribute the load of requests among the compute nodes according to their capacity. Therefore, there is a need for an effective load balancing technique for smooth continuity of operations in a distributed environment with a heterogeneous server configuration. This paper presents a novel load balancing technique, namely, Resource aware weighted least connection load balancing which addresses the above said problem efficiently. The essence of this work is to assign the requests across multiple servers based on the requested resource and the status of the number of connections presently served by each server. This work used standard score technique to enumerate the weight of each node. Experiments were conducted using Cloud Analyst, a famous cloud simulator breed from CloudSim. Appropriate performance parameters were analysed to measure the effectiveness of the proposed technique. Future directions for the extension of the implemented technique also identified. 2023 IEEE. -
Resource Curse - Impact of Renewable Natural Resources on Economic Growth in the U.S. using ARDL Approach
The analyses of the resource paradox in the United States of 29 years are conducted by the econometric model of ARDL. The dataset taken for the study is from the source of World Bank. After testing the stationarity and cointegration of 4 independent variable and one dependent variables of Gross Domestic product, this study will be giving the conclusion of long term and short-term relations of the variables to show the existence of Resource curse in the US within the 29 years of dataset. Causation test shows that there doesn't exist any particular causal relations between the variables and hence there need to be thorough study in this phenomenon. 2024 IEEE. -
Response surface optimization and process design for glycidol synthesis using potassium modified rice husk silica
Glycerol, an inexpensive by-product from biodiesel production can be converted into many useful products notably glycidol, which has a wide range of uses. In this study, glycidol synthesis has been done using a biowaste mediated catalyst in a single step process. Silica and potassium incorporated silica were synthesized from biowaste rice husk. These catalysts were characterized by different spectroscopic techniques. Basic sites in the catalysts were estimated using temperature-programmed desorption study. Four operational parameters were optimized using Box Behnken Design (BBD) of response surface methodology (RSM). Potassium incorporated rice husk was found to be one of the best catalysts for glycidol production with 60.8% glycerol conversion and 62.9% selectivity within one hour of reaction time. 2020 Elsevier Ltd. All rights reserved.