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The surge in interest surrounding renewable energy stems from concerns regarding pollution and the finite supply of nonrenewable resources. Solar PV and wind hybrid renewable energy systems (HRES) are increasingly recognized as practical and cost-effective solutions, particularly in remote areas. However, the intermittent nature of solar and wind power presents a challenge. To address this, incorporating a hydrogen source into the system has been proposed. This study focuses on modelling and sizing a hybrid energy system tailored for remote areas, accommodating both home and electric vehicle loads. The simulation is conducted for Siliguri, West Bengal, India, with the goal of optimizing productivity, minimizing expenses, and considering economic factors using HOMER Pro software. The integration of green hydrogen-based power generation with photovoltaic and wind HRES emerges as an effective solution. Solar power, in particular, showcases promising opportunities for the electrolysis process and HRES systems. The presented work facilitates the modelling of a green hydrogen-based green energy system, taking into account capacity, cost, and emission constraints. Various case studies are conducted to enhance system efficiency and reduce the costs of energy (COE). In this paper, three cases of grid-connected and three cases of off-grid or grid-disconnected systems are considered for highlighting the benefits of hydrogen energy incorporation in both types of systems. This research contributes to sustainable energy solutions, advancing a greener and more efficient energy landscape, especially in addressing the recent development in load combinations of home and electric vehicle loads in both grid-connected as well as grid-disconnected system. [ABSTRACT FROM AUTHOR]

One of the highly acclaimed innovations is the mobile wallet. Mobile wallets ensure that customers can make purchases even if they forget their wallets at home. The use of these wallets has increased significantly with the spread of the COVID-19 pandemic as a preventive measure. However, with COVID-19 restrictions lifted and adaptation to the pandemic, customers are no longer forced to use mobile wallets. That is, things have returned to normal as before the pandemic, where the customer is left free to choose the method of payment. In this regard, businesses need to retain their customers and make sure that those customers will reuse their own mobile wallets even during this period (post-COVID-19). Hence, it is critical to explore customer loyalty determinants toward mobile wallet services in such a period. Therefore, using the Indian context, this paper sought to explore the determinants of customer loyalty toward mobile wallet services post-COVID-19. This study also investigated the role of trust as a moderator. Based on a sample size of 243 customers in India, the relationships in the proposed model were tested using SmartPLS statistical technology. The results revealed that service quality, privacy and security, and trust are the key determinants in gaining customer loyalty toward mobile wallets. Yet, the results did not support perceived usefulness and ease of use as determinants of customer loyalty. As for trust as a moderator, the results supported only one of the four proposed hypotheses. Specifically, the results supported that "trust has a significant moderating impact on the relationship between service quality and customer loyalty". The results offer a more precise comprehension of the relationships between customer loyalty and customer loyalty determinants in post-COVID-19 and enable managers to make better management decisions. [ABSTRACT FROM AUTHOR]

Canals are a very imperative source of irrigation for the agricultural sector in India. Seepage causes major water loss in canals, and hence, the installation of liners becomes necessary. Compacted clay soils are commonly used as liners in the canals. This structure will most probably be subjected to salinization and desalinization cycles throughout its life. Because of the interaction between the pore liquid and clay particles, physico-chemical influences considerably impact the behavior of clay barriers. In this paper, the effect of interacting fluid on volume change, consolidation parameters, and hydraulic conductivity of compacted clay soil is investigated with the help of a one-dimensional consolidation test. The compacted clay specimens were immersed alternatively with distilled water (DW) and sodium chloride (NaCl) solutions (SW) at constant loading of 10 kPa, which replicates the load conditions in the field canal due to 1 m head of water and incremental loading as per IS 2720 part 15 standards. The experimental results proved that there is a percentage volume change increase of about two times for each stage inundated with 4M NaCl solution than its preceding stages inundated with distilled water at constant loading of 10 kPa. The consolidation rate was accelerated with 4M NaCl solution than the normal consolidation at incremental loading. The permeability coefficient in the salt water-induced sample increased by 217% more than the distilled water-induced sample at incremental loading. Therefore, the soil specimen subjected to alternate salinization and desalinization cycles significantly affects the volumetric and consolidation behavior, leading to decreased life of clay barrier structures. [ABSTRACT FROM AUTHOR]

The availability of sophisticated instrumental techniques for adulteration detection in milk is a distant dream for rural areas in India. To address this constraint, a simple chromatographic paper‐based test strip for the detection of urea in milk is developed. The said strip was prepared by dipping a chromatographic paper for 10 min in an admixture of a supernatant (coarse ground soybean powder soaked overnight in water) and an acid‐base indicator. The strip utilizes the urease naturally present in soybean for the production of ammonia from urea which consequently raises the pH enabling the change in color of the strip. The limit of detection of urea by developed strip is 0.07%. The response time of the strip ranges from 3 to 5 min. The strip was found to be stable for 6 months when stored at ambient temperature with reproducible results. Novelty impact statement: A simple, low cost and sensitive strip based test for detection of urea in milk is developed. The strip works on the principle of use of natural urease present in soybean slurry. The limit of detection observed in the test for urea detection in milk is 0.07%. [ABSTRACT FROM AUTHOR]

This paper is devoted to answering some questions using a mathematical model by analyzing India's first and second phases of the COVID-19 pandemic. A new mathematical model is introduced with a nonmonotonic incidence rate to incorporate the psychological effect of COVID-19 in society. The paper also discusses the local stability and global stability of an endemic equilibrium and a disease-free equilibrium. The basic reproduction number is evaluated using the proposed COVID-19 model for disease spread in India based on the actual data sets. The study of nonperiodic solutions at a positive equilibrium point is also analyzed. The model is rigorously studied using MATLAB to alert the decision-making bodies to hinder the emergence of any other pandemic outbreaks or the arrival of subsequent pandemic waves. This paper shows the excellent prediction of the first wave and very commanding for the second wave. The exciting results of the paper are as follows: (i) psychological effect on the human population has an impact on propagation; (ii) lockdown is a suitable technique mathematically to control the COVID spread; (iii) different variants produce different waves; (iv) the peak value always crosses its past value. [ABSTRACT FROM AUTHOR]

The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke. Most of the literature work is based on the MRI and CT scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes. In India where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes. Hence, this problem has motivated us to conduct the study presented in this paper. A noninvasive approach for the early diagnosis of the strokes is proposed. The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of EEG. Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed EEG data. This paper is proposing time series-based approaches such as LSTM, biLSTM, GRU, and FFNN that can handle time series-based predictions to make useful decisions. The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with 95.6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. The experimental outcome is able to measure the brain waves to predict the signs of strokes. The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients. [ABSTRACT FROM AUTHOR]

This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate. [ABSTRACT FROM AUTHOR]

This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in southern parts of Rajasthan state of India. This is achieved by optimal deployment of optimally sized renewable energy (RE) generators using GOMOHSA. Performance indexes such as active power loss minimization index (APMLI), the reactive power loss minimization index (RPMLI), and the total voltage deviation improvement index (TVDII) are introduced to evaluate the health of the test network with different load scenarios. Performance of proposed GOMOHSA has been tested for five different operating scenarios of loads and RE generation. It is established that the proposed GOMOHSA finds the optimal deployment of optimally sized RE generators, and the investment cost of deployment of these RE generators can be recovered within a time period that is less than 5 years. Performance of GOMOHSA is superior compared to a conventional genetic algorithm (GA) in terms of performance indexes, RE generator capacity, payback period, and parameter sensitivity. Study is performed using MATLAB software for loading scenario of base year 2021 and projected year 2031. [ABSTRACT FROM AUTHOR]

Rural and remote area electrification is of grave concern around the globe. Therefore, well-planned and cost-effective microgrids integrating renewable energy sources are emerging as effective solutions. However, the microgrid's stable operation and its future deployment is affected by the perturbations caused due to uncertainity in renewable sources, dependency on the battery state of charge, and load variation. So, considering the possible concerns affecting the planning and development of a microgrid for any given region, this paper proposes a comprehensive performance assessment of the hybrid residential microgrid based on a load model of Kibber village in Himachal Pradesh, India. The proposed approach is divided into three parts for the best planning of microgrids. Firstly, the MATLAB–Simulink software technically analyzes the system performance under perturbations considering the available renewable sources. Secondly, an economic analysis using HOMER Pro software is done to examine the cost-effectiveness of the proposed microgrid model through the simulation of electrical loads for Kibber village, considering the available renewable sources. Lastly, a real-time analysis of the proposed prototype of programmable logic controller-based hardware test bench has been developed, aiming for future regional microgrid deployment. System voltage, frequency, power shared, percentage of load met, energy cost, available renewable energy resource, etc. have been considered for validating the proposed controller. The proposed comprehensive assessment of the microgrid model is reproducible with necessary modifications for any geographical location. It will be helpful for its future deployment aiming at rural and remote electrification. [ABSTRACT FROM AUTHOR]

The human-computer interaction has become inevitable in digital world. HCI helps humans to incorporate technology to resolve even their day-to-day problems. The main objective of the paper is to utilize HCI in Intelligent Transportation Systems. In India, the most common and convenient mode of transportation is the buses. Every state government provides the bus transportation facility to all routes at an affordable cost. The main difficulty faced by the passengers (humans) is lack of information about bus numbers available for the particular route and Estimated Time of Arrival (ETA) of the buses. There may be different reasons for the bus delay. These include heavy traffic, breakdowns, and bad weather conditions. The passengers waiting in the bus stops are neither aware of the delay nor the bus arrival time. These issues can be resolved by providing an HCI-based web/mobile application for the passengers to track their bus locations in real time. They can also check the Estimated Time of Arrival (ETA) of a particular bus, calculated using machine learning techniques by considering the impacts of environmental dynamics, and other factors like traffic density and weather conditions and track their bus locations in real time. This can be achieved by developing a real-time bus management system for the benefit of passengers, bus drivers, and bus managers. This system can effectively address the problems related to bus timing transparency and arrival time forecasting. The buses are equipped with real-time vehicle tracking module containing Raspberry Pi, GPS, and GSM. The traffic density in the current location of the bus and weather data are some of the factors used for the ETA prediction using the Support Vector Regression algorithm. The model showed RMSE of 27 seconds when tested. The model is performing well when compared with other models. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is the main cause of diabetic retinopathy, the most common cause of blindness worldwide. In order to slow down or prevent vision loss and degeneration, early detection and treatment are essential. For the purpose of detecting and classifying diabetic retinopathy on fundus retina images, numerous artificial intelligence-based algorithms have been put forth by the scientific community. Due to its real-time relevance to everyone's lives, smart healthcare is attracting a lot of interest. With the convergence of IoT, this attention has increased. The leading cause of blindness among persons in their working years is diabetic eye disease. Millions of people live in the most populous Asian nations, including China and India, and the number of diabetics among them is on the rise. To provide medical screening and diagnosis for this rising population of diabetes patients, skilled clinicians faced significant challenges. Our objective is to use deep learning techniques to automatically detect blind spots in eyes and determine how serious they may be. We suggest an enhanced convolutional neural network (ECNN) utilizing a genetic algorithm in this paper. The ECNN technique's accuracy results are compared to those of existing approaches like the K-nearest neighbor approach, convolutional neural network, and support vector machine with the genetic algorithm. [ABSTRACT FROM AUTHOR]

One of the main sources of dangerous chemicals that are dumped untreated into land and water bodies and have a negative influence on the ecosystem are industrial effluents. Seaweeds are currently used for treating industrial effluent effectively. The technology is at a maturing stage. This paper reviews the characterization and cultivation of seaweeds for wastewater treatment. In this present study, different extracts of four seaweeds such as Gracilaria edulis, Sargassum wightii, Turbinaria ornata, and Kappaphycus alvarezii, from the Mandapam coastal regions were analyzed. The seaweeds are used to treat the leather industry effluents collected from EKM leather processing company, Erode, Tamil Nadu, India. Among all, extracts of Gracilaria edulis survived at different concentrations of TDS: 15,000, 25000, and 35000 mg/l. Out of these different ranges, TDS of about 25000 mg/l seaweed named Gracilaria edulis reduced more amounts of chemicals present in the effluent like TDS (93.90%), phosphates (72.71%), nitrate (75.08%), nitrite (76.92%), and turbidity (99.01%) content. Additionally, we produce the quality and strength of agar gel from the cultivation of Gracilaria edulis by the Nikansui method. Finally, we got the extraction procedure to obtain a higher yield of about 10.26% and a maximum gel strength of 92.06 g·cm−2 while maintaining the melting point at 78°C. [ABSTRACT FROM AUTHOR]

There is a global recognition that the homeless in different clusters are isolated social groups. They are highly susceptible to loneliness and related psycho‐physical health outcomes owing to their less supportive and fragmented social networks. The conditions are worse in the case of chronic or lifetime homelessness. Beyond this popular notion, other critical perspectives are also there. This paper attempts to analyse the differences in inter‐personal social networks between the chronic and the non‐chronic homeless groups at a railway junction in India. The data have been collected through structured and semi‐structured questionnaires from 28 homeless people (15 chronically and 13 non‐chronically homeless) of Burdwan Railway Junction – a vital railway junction of West Bengal, India. The chronically homeless group displays more supportive and compact inter‐personal networks in this study than the non‐chronic homeless group. Living in the station area for a long time, they have formed a society of their own. Barring some psychological stress and social abuse, the livelihood scenario of these people is relatively stress‐free and social networks relations are more intensive and supportive. Considering the outcomes, instead of the housing‐first approach of rehabilitation, we favour the development of community shelters with minimum basic amenities close to their present location. [ABSTRACT FROM AUTHOR]

The novel coronavirus 2019 (COVID-19) disease is a pandemic which affects thousands of people throughout the world. It has rapidly spread throughout India since the first case in India was reported on 30 January 2020. The official report says that totally 4, 11,773 cases are positive, 2, 28,307 recovered, and the country reported 12,948 deaths as of 21 June 2020. Vaccination is the only way to prevent the spreading of COVID-19 disease. Due to various reasons, there is vaccine hesitancy across many people. Hence, the Indian government has the solution to avoid the spread of the disease by instructing their citizens to maintain social distancing, wearing masks, avoiding crowds, and cleaning your hands. Moreover, lots of poverty cases are reported due to social distancing, and hence, both the center government and the respective state governments decide to issue relief funds to all its citizens. The government is unable to maintain social distancing during the relief schemes as the population is huge and available support staffs are less. In this paper, the proposed algorithm makes use of graph theory to schedule the timing of the relief funds so that with the available support staff, the government would able to implement its relief scheme while maintaining social distancing. Furthermore, we have used LSTM deep learning model to predict the spread rate and analyze the daily positive COVID cases. [ABSTRACT FROM AUTHOR]

Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient's internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance. [ABSTRACT FROM AUTHOR]

The rising price and demand for energy are significant issues for the food sector, which consumes a substantial amount of energy throughout the supply chain. Hence, improving energy efficiency has become an essential priority for the food sector. However, most food businesses have limited awareness of the recent technological advancements in real‐time energy monitoring. Thus, the concept of "Internet of Things" (IoT) has been investigated to increase the visibility, transparency, and awareness of various energy usage levels. This paper presents a case study of a beverage factory where the implementation of an IoT‐enabled sensing technology based on the embodied product energy (EPE) model helped to reduce the energy consumption. This arrangement made provision for the collection of real‐time energy data within a food production system to support informed and energy‐aware operational decisions, which lead to optimized energy consumption and significant savings of approximately 163,000 kWh in the year 2017. Practical applications: Given the importance of energy efficiency and Internet of Things (IoT), especially in the food manufacturing industry, this research reports a baseline application at a beverage company in India. The results allowed the company to use energy more efficiently to have an advantage over its competitors and better market positioning. More data could be incorporated into the energy management system with the use of IoT. The availability and accuracy of such valuable data would help managers to make better energy‐efficient decisions. [ABSTRACT FROM AUTHOR]

Launch of electric vehicles have seen a substantial rise for the past few years in emerging economies like India. In countries like India, the growth and penetration of the electric vehicles in the Indian automotive industry specifically for the two-wheeler segments are driven by the demand surge where cost and motor metrics have a substantial deciding factor. The in-wheel hub-motor, which is the prime mover for the two wheelers, decides the comfort zone of the customer in various metrics such as efficiency, torque, speed range, charging, and hence the distance covered. This paper addresses the design formulation of achieving a high torque Permanent Magnet Synchronous Motor (PMSM) conventionally known as the hub-motor, explicitly for electric two-wheeler application. The hub-motor is aimed for the defined D and L (280 × 30 mm) of volumetric constraints to deliver the rated torque of 50 Nm at the spinning speed of 400 rpm. The hub-motor design is aimed for distance range of 108 km/charge, at the vehicle speed of 54 km/hr for the designed diametric and volumetric constraints. This will lead to a typical cost-effective e-vehicle system since the required distance range of 108 km is achievable at the defined rim size and geometry with an enhanced efficiency greater than 90%. The design is carried out by Finite Element Analysis (FEA) using the electromagnetic software MotorSolve. The results computed are analyzed and validated for the optimal loading conditions for the ambient temperature of 50°C. The results confirm the effectiveness of the proposed design formulation and methodology for achieving the high power density hub-motors for satisfying the customer's comfort zone in establishing the performance metrics of the electromagnetic system. [ABSTRACT FROM AUTHOR]

Plant diseases pose a major challenge in the agricultural sector, which affects plant development and crop productivity. Sugarcane farming is a highly organized part of farming. Owing to the desirable condition for sugarcane cultivation, India stands among the second largest producers of sugarcane over the globe. At the same time, sugarcane gets easily affected by multifarious diseases which significantly influence crop productivity. The recently developed computer vision (CV) and deep learning (DL) models with an effective design can be employed for the detection and classification of diseases in sugarcane plant. The disease detection in sugarcane plant is not accurate in the existing techniques. This paper presents a quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy. The proposed QBPSO-DTL method is designed and trained for the prediction of diseased leaf images. The proposed QBPSO-DTL technique encompasses the design of optimal region growing segmentation to determine the affected regions in the leaf image. In addition, the SqueezeNet model is employed as a feature extractor and the deep stacked autoencoder (DSAE) model is applied as a classification model. Finally, the hyperparameter tuning of the DSAE model is carried out by using the QBPSO algorithm. For demonstrating the enhanced outcomes of the QBPSO-DTL approach, a wide range of experiments were implemented and the results ensured the improvements of the QBPSO-DTL model. [ABSTRACT FROM AUTHOR]

The present paper aims to propose a new hybrid multi-criteria decision-making (MCDM) framework with spherical fuzzy numbers (SFNs). We extend two recently developed algorithms such as level-based weight assessment (LBWA) and MULTIMOOSRAL in spherical fuzzy (SF) domain. We present a case study on six MSME units belonging to engineering cluster for examining their leanness. MSMEs form the backbone of the socioeconomic growth and therefore garner attention of the policy makers. Lean manufacturing (LM) has been a key enabler for the last three decades which help the organizations to achieve business growth. We consider the criteria like leadership, supplier focus, customer focus, process management, waste, culture, human resource focus, technology use and communication, and awareness to compare leanness of the MSMEs using expert opinions. We find that committed leadership, waste reduction, and customer value are given more weightage by the experts for achieving leanness in SMEs. Furthermore, the results show that medium and small units with focused product line score high in terms of leanness. We validate the results obtained by our proposed method by comparing with the same derived by using another widely used approach such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). We carry out sensitivity analysis for examining the stability in the solution with the changes in the given condition such as variations in the criteria weights. Our results using SF-LBWA-MULTIMOOSRAL show reasonable accuracy and stability. [ABSTRACT FROM AUTHOR]

According to the Tamil Nadu Energy Development Agency (TEDA) in the 2019-20 academic year, the wind power plant produces 23% of the biomass power supply in the Indian electrical commodities. To maintain the power withstanding capability needed for future electrical commodities, a yearly power shutdown program is implemented. An additional wind power plant unit will be erected and create more electricity, thereby balancing India's commercial electricity needs. Even in a nonstationary working environment, continuous monitoring and analyzing the efficiency of wind turbines is a more difficult task. Consequently, in this paper, a health index calculation for wind power plants is proposed utilizing neurofuzzy (NF) modeling. Wind generator efficiency can be measured mathematically by recording three crucial primitivistic such as observed rotation speed, generation wound temperature, and gearbox heat. Fuzzy rules are used to design the parameters of the neural network (NN), and the accumulated signal is compared using the nonlinear extrapolation approach to determine the wind generator's behavior and evaluate the hazards. During the experimental study, two windows of 24 hours and 60 hours are used, where the deviation signal required for the hazard induction is investigated. The proposed approach can accurately calculate the wind generator's health state. As a result of an improved health operating and management (HOM) system, the amount of power generated by industrials and domestic appliances has increased dramatically. [ABSTRACT FROM AUTHOR]

Yoga is a 5000-year-old practice developed in ancient India by the Indus-Sarasvati civilization. The word yoga means deep association and union of mind with the body. It is used to keep both mind and body in equilibration in all flip-flops of life by means of asana, meditation, and several other techniques. Nowadays, yoga has gained worldwide attention due to increased stress levels in the modern lifestyle, and there are numerous methods or resources for learning yoga. Yoga can be practiced in yoga centers, through personal tutors, and can also be learned on one's own with the help of the Internet, books, recorded clips, etc. In fast-paced lifestyles, many people prefer self-learning because the abovementioned resources might not be available all the time. But in self-learning, one may not find an incorrect pose. Incorrect posture can be harmful to one's health, resulting in acute pain and long-term chronic concerns. In this paper, deep learning-based techniques are developed to detect incorrect yoga posture. With this method, the users can select the desired pose for practice and can upload recorded videos of their yoga practice pose. The user pose is sent to train models that output the abnormal angles detected between the actual pose and the user pose. With these outputs, the system advises the user to improve the pose by specifying where the yoga pose is going wrong. The proposed method was compared to several state-of-the-art methods, and it achieved outstanding accuracy of 0.9958 while requiring less computational complexity. [ABSTRACT FROM AUTHOR]

Global energy needs have risen in recent years, and traditional energy sources such as fossil fuels are no longer viable. To meet the growing electricity demand, attention has moved to renewable energy sources such as solar and wind energy. Furthermore, the development of clean energy is vital for combating climate change. Various studies have shown the effectiveness of using hybrid systems (combination of solar photovoltaic and wind energy systems) for generating power. However, a significant amount of energy gets wasted. To prevent the wastage of energy, a dual-energy generation system for integrated grids has been suggested in this paper. The load data have been collected from various regions in Rajasthan, India. An optimal grid system configuration is designed using net present cost and cost per unit of energy. Other factors such as the tilt angle of PV array optimization, wind energy, and inverter optimization have also been used for increasing the reliability and stability of the system. Sensitivity analysis has been performed to analyze the effective variations of the capital costs on the developed system economy. The results obtained from the simulations show that the overall costs of generating electricity from diesel systems and nongrid-based systems have been reduced to 20% at 10% annual capacity shortage allowance. Upon cost analysis, we found that the total cost for installing the suggested system is 49,500 USD, whereas for other systems, the costs came out as 66,000 USD, 56,500 USD, and 56,300 USD, respectively. [ABSTRACT FROM AUTHOR]

Background. Indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs) are malaria vector control measures used in India, but the development of insecticide resistance poses major impediments for effective vector control strategies. As per the guidelines of the National Vector Borne Disease Control Programme (NVBDCP), the study was conducted in 12 districts of Madhya Pradesh to generate data on insecticide resistance in malaria vectors. Methods. The susceptibility tests were conducted on adult An. culicifacies as per the WHO standard technique with wild-caught mosquitoes. The blood-fed female mosquitoes were exposed in 3 to 4 replicates on each occasion to the impregnated papers with specified discriminating dosages of the insecticides (DDT: 4%, malathion: 5%, deltamethrin: 0.05%, and alphacypermethrin: 0.05%), for one hour, and mortality was recorded after 24-hour holding. Results. An. culicifacies was found resistant to DDT 4% in all the 12 districts and malathion in 11 districts. The resistance to alphacypermethrin was also observed in two districts, and possible resistance was found to alphacypermethrin in seven districts and to deltamethrin in eight districts, while the vector was found susceptible to both deltamethrin and alphacypermethrin in only 3 districts. Conclusion. An. culicifacies is resistant to DDT and malathion and has emerging resistance to pyrethroids, alphacypermethrin, and deltamethrin. Therefore, regular monitoring of insecticide susceptibility in malaria vectors is needed for implementing effective vector management strategies. However, studies to verify the impact of IRS with good coverage on the transmission of disease are required before deciding on the change of insecticide in conjunction with epidemiological data. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a novel extension of a very recently developed multicriteria decision making (MCDM) algorithm known as the preference ranking on the basis of ideal-average distance (PROBID) method in a picture fuzzy (PF) environment. We use the full consistency method (FUCOM) with picture fuzzy numbers (PFNs) for deriving the criteria weights. We attempt to apply our proposed model for addressing a real-life complex decision making problem in social science research that gets influenced by the dynamics of discrete human behaviors. We compare eight popular video conferencing (VC) tools used for teaching-learning and meeting purposes in India using our novel integrated multicriteria decision making (MCDM) framework of FUCOM-PROBID with PF information. The criteria have been derived using the theoretical foundation of usability and user experience (UX). Based on the opinion of the decision makers (DM) or users who took part in the study, we find that ease of operations, compatibility with multiple systems and devices, quality of the voice, and video transmission and features are given more emphasis while Zoom, Microsoft Teams, and Google Meet are found to be preferable options to the users. The result of the proposed model shows stability and robustness as evident from the validation test and sensitivity analysis. [ABSTRACT FROM AUTHOR]

Diabetes is a very fast-growing disease in India, with currently more than 72 million patients. Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels and neurons in the patient eyes, called diabetic retinopathy (DR). This first causes occlusion and then rapid vision loss. The symptoms of the disease are not very conspicuous in its early stage. The disease is featured by the formation of bloated structures in the retinal area called microaneurysms. Because of negligence, the condition of the eye worsens into the generation of more severe blots and damage to retinal vessels causing complete loss of vision. Early screening and monitoring of DR can reduce the risk of vision loss in patients with high possibilities. But the diabetic retinopathy detection and its classification by a human, is a challenging and error-prone task, because of the complexity of the image captured by color fundus photography. Machine learning algorithms armed with some feature extraction techniques have been employed earlier to detect and classify the levels of DR. However, these techniques provide below-par accuracy. Now, with the advent of deep learning (DL) techniques in computer vision, it has become possible to achieve very high levels of accuracy. DL models are an abstraction of the human brain coupled with the eyes. To create a model from scratch and train it is a cumbersome task requiring a huge amount of images. This deficiency of the DL techniques can be patched up by employing another technique to a task called transfer learning. In this, a DL model is trained on image metadata, and to learn features it used hundreds of classes from the DR fundus images. This enables professionals to create models capable of classifying unseen images into a proper grade or level with acceptable accuracy. This paper proposed a DL model coupled with different classifiers to classify the fundus image into its correct class of severity. We have trained the model on IDRD images and it has proven to show very high accuracy. [ABSTRACT FROM AUTHOR]

Summary: This paper mainly deals with the optimum design and performance of the hybrid renewable electrification system for remote villages of southern part of India. Constructed and simulated a hybrid renewable energy (HRE) system, which was consists of fully village owned renewable resources such as solar, wind and biomass energies. Through this case study a typical Indian village load demand wants to meet out by suitable hybrid renewable configuration with optimum performance and least investment. The proposed system's technical performance was ensuring by highest renewable fraction and least annual unmet load percentage. The economical performances of the system were witnessed by the least net present cost and minimum cost of energy. The further sensitivity analysis of the proposed remote village electrification system has been verified by NREL's HOMER software tool. The outcome of the different HRE configuration's competitions were established that the PV/wind/biomass based hybrid renewable power generations at standalone mode was highly commendable electrification system for remote village energy sustainability. [ABSTRACT FROM AUTHOR]

Summary: Hybrid renewable energy systems (HRES) are proving a promising solution for rural electrification in India, where grid power availability is limited. This paper aims to identify the optimal design and pre‐feasibility techno‐economic analysis of the hybrid grid‐independent renewable system for rural electrification. The proposed hybrid system integrates the various renewable energy system configurations to provide continuous power for the electric load demands of Kanakadri palle village, Andhra Pradesh, India. HOMER software is utilized to design the proposed hybrid system, perform optimization, techno‐economic analysis, and simulation. Here, six different hybrid system configurations were taken into account to perform a comparative economic analysis. The results obtained shows that one of the hybrid systems, PV/DG/converter/battery system (120 kW‐PV modules, 54 kW‐DG, 200 number of batteries with 260 Ah ratings, and 50 kW‐power converters), is the least economical optimal system based on least net present cost and Levelized cost of energy (COE), highest renewable fraction, and zero unmet electric loads percentage (NPC: $341 280, COE: $0.217, highest renewable fraction: 96.6% and unmet electric loads: 0%). For the site considered, this optimal hybrid system can provide a reliable power supply to the total energy requirements of 332.97 kWh/d and achieve 75.68% carbon emission reduction compared to a diesel generator. Moreover, the comparison of different system configurations was carried out based on economic analysis. This analysis shows that proposed hybrid system can be an appropriate model to provide reliable power at a low generation cost for the village considered in this study and can be recommended for future electrification projects. [ABSTRACT FROM AUTHOR]

In light of the devastation caused by COVID-19, the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) and vaccine research and development (R&D) have been occupying a prominent position in the field of global health diplomacy (GHD). Most countries, international organizations, and charitable organizations have been engaged in the R&D of COVID-19 vaccines to ensure timely affordability and accessibility to all countries. Concomitantly, the World Trade Organization (WTO) provides some provisions and enforcements regarding copyrights, patents, trademarks, geographical indications, and industrial designs. Given these safeguards, it is considered that intellectual property rights (IPRs) have become major barriers to the affordability and accessibility of vaccines/medicines/technology, particularly to the developing/least developed countries. Realizing the gravity of the pandemic impact, as well as its huge population and size, India has elevated this issue in its global health diplomacy by submitting a joint proposal with South Africa to the World Trade Organization (WTO) for a temporary waiver of IPRs to ensure timely affordability and accessibility of COVID-19 medical products to all countries. However, the issue of the temporary waive off had become a geopolitical issue. Countries that used to claim per se as strong advocates of human rights, egalitarianism, and healthy democracy have opposed this proposal. In this contrasting milieu, this paper is aimed at examining how the TRIPS has become a barrier for developing countries' development and distribution of vaccines/technology; secondly, how India strategizes its role in the WTO in pursuant of its global health diplomacy? We conclude that the IPRs regime should not become a barrier to the accessibility/affordability of essential drugs and vaccines. To ensure access, India needs to get more engaged in GHD with all the involved global stakeholders to get strong support for their joint proposal. The developed countries that rejected/resisted the proposal can rethink their full support. [ABSTRACT FROM AUTHOR]

COVID-19 is more virulent and challenging to human life. In India, the Ministry of AYUSH recommended some strategies through Siddha, homeopathy, and other methods to effectively manage COVID-19 (Guidelines for AYUSH Clinical Studies in COVID-19, 2020). Kabasura Kudineer and homeopathy medicines are in use for the prevention and treatment of COVID-19 infection; however, the mechanism of action is less explored. This study aims to understand the antagonist activity of natural compounds found in Kabasura Kudineer and homeopathy medicines against the SARS-CoV-2 using computational methods. Potential compounds were screened against NSP-12, NSP-13, NSP-14, NSP-15, main protease, and spike proteins. Structure-based virtual screening results shows that, out of 14,682 Kabasura Kudineer compounds, the 250395, 129677029, 44259583, 44259584, and 88583189 compounds and, out of 3,112 homeopathy compounds, the 3802778, 320361, 5315832, 14590080, and 74029795 compounds have good scoring function against the SARS-CoV-2 structural and nonstructural proteins. As a result of docking, homeopathy compounds have a docking score ranging from −5.636 to 13.631 kcal/mol, while Kabasura Kudineer compounds have a docking score varying from −8.290 to −13.759 kcal/mol. It has been found that the selected compounds bind well to the active site of SARS-CoV-2 proteins and form hydrogen bonds. The molecular dynamics simulation study shows that the selected compounds have maintained stable conformation in the simulation period and interact with the target. This study supports the antagonist activity of natural compounds from Kabasura Kudineer and homeopathy against SARS-CoV-2's structural and nonstructural proteins. [ABSTRACT FROM AUTHOR]