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This paper explores the intricate issues that prompt water pricing reform in China and India. China adopts a comprehensive pricing framework of cost of resources, treatment and distribution, and environmental requirements, which has been gradually developed part by part since 1980 based on the perception and change of water issues in the country. India follows a simple approach of cost recovery, though its recent policy guidelines talk about more systematic pricing. The results present that both countries fail to realize water pricing policy targets regardless of different pricing structures. But China and India are on the same road and direction of water pricing and China goes a little farther. The treatment of water resources and its services, and property rights have a significant impact on pricing, and costs, including service, resources and environment, are difficult to recover. [ABSTRACT FROM AUTHOR]

This paper explores the dynamic interface of state-water users' relationships in large-scale surface irrigation in India and China, to inquire to what extent the issues encountered in large-scale irrigation management and governance are independent of regime characteristics. Though operating in very different overall political regimes, China and India exhibit strong similarities in the way a central state has attempted to relate with local water users, in the types of policy instruments deployed to shape that relationship, and in the problems encountered. Both China and India have a long history of state involvement in irrigation management. Both saw massive expansion after the late 1940s. The state then tried to extend its control in response to 'underutilisation' of the created infrastructure, revenue shortfalls and perceived inefficiencies and yield gaps. In recent decades the policy approaches have, at least rhetorically, emphasised water users' self-management and governance, including financial self-management/cost recovery. In both countries the instrument for this has been Water User Associations. Results have been mixed, to say the least, on all counts. Larger institutional and policy characteristics adhering to the problematic of the state-user interface subvert stated reform objectives. [ABSTRACT FROM AUTHOR]

Water rights are widely regarded as a crucial component to enhance efficient water use and for meeting a country's water resource challenges. This article presents a framework for analyzing and comparing the similarities as well as differences of the water rights systems between India and China. The article relies on the method of document research and comparative analysis to compare general characteristics of India and China's water rights systems based on six evaluation indicators and evaluation principles. Using this analytical framework, this paper compares the implementation effects of the water rights systems in terms of the degree of meeting water resources demand, conflict-resolution means and the protection of water resources. Our findings provide insights for the reformation of the water rights systems and bring out lessons that other developing countries can learn from India and China's experiences. [ABSTRACT FROM AUTHOR]

The water resources in Rajasthan State are facing a crucial stage even after average/good rainfall. Temporal distributions as well as the spatial variability of rainfall within the state were investigated by applying an analysis of variance (ANOVA) test. The effect of change in catchment characteristics and anthropogenic activities on overland flow are also investigated in this paper by applying a regression technique. Inflow to the surface water resources of the state is regularly decreasing. Time series analysis and sequential cluster analysis reveals that 1994 was the critical year, which divides the two consecutive non-overlapping epochs viz. pre-disturbance and post-disturbance. Due to increasing population and the subsequent increase in agriculture (specifically using groundwater sources) having increased catchment interceptions, there is a regular decreasing trend of surface runoff and surface water availability. The study highlights that, in spite of an increasing trend of rainfall witnessed during the last 100 years, inflow to the surface water resources of the state is decreasing at a fast pace owing to a decrease in the percentage area contributing to surface runoff. [ABSTRACT FROM AUTHOR]

Livelihoods of rural poor in developing countries like India are exposed to risks due to natural disasters like floods. Although these risks are covariate, households are affected in varying degrees due to their location and their relative capacity to cope with them. These in turn are household-specific and are decisive in defining the efficacy of coping mechanisms adopted by households to hedge against the impact of floods. The paper attempts to study the nature of risks faced by households in a major flood-prone region of rural India and examine the effectiveness of coping mechanisms adopted. Findings suggest that (i) floods significantly reduce the consumption of households, (ii) traditional coping mechanisms used to manage the impacts are ineffective in safeguarding the risks to their livelihood and (iii) designing comprehensive coping measures, targeting vulnerable groups and combining informal insurance with safety nets will be useful. [ABSTRACT FROM AUTHOR]

• A compartmental mathematical model has been proposed in this paper incorporating all possible non-pharmaceutical intervention policies. • Short-term projection results have been illustrated for Bangladesh and India. • All necessary graphical simulations have been performed with the help of Caputo–Fabrizio fractional derivatives. • Optimal control problem for fractional system has been designed and the existence of unique solution has also been showed by using Picard–Lindelof technique. When the entire world is eagerly waiting for a safe, effective and widely available COVID-19 vaccine, unprecedented spikes of new cases are evident in numerous countries. To gain a deeper understanding about the future dynamics of COVID-19, a compartmental mathematical model has been proposed in this paper incorporating all possible non-pharmaceutical intervention strategies. Model parameters have been calibrated using sophisticated trust-region-reflective algorithm and short-term projection results have been illustrated for Bangladesh and India. Control reproduction numbers (R c) have been calculated in order to get insights about the current epidemic scenario in the above-mentioned countries. Forecasting results depict that the aforesaid countries are having downward trends in daily COVID-19 cases. Nevertheless, as the pandemic is not over in any country, it is highly recommended to use efficacious face coverings and maintain strict physical distancing in public gatherings. All necessary graphical simulations have been performed with the help of Caputo–Fabrizio fractional derivatives. In addition, optimal control strategies for fractional system have been designed and the existence of unique solution has also been showed using Picard–Lindelof technique. Finally, unconditional stability of the fractional numerical technique has been proved. [ABSTRACT FROM AUTHOR]

In this paper, a Chikungunya dynamical model with virus mutation and transovarial transmission is developed, which incorporates the effect of seasonal temperature changes on disease transmission through time-dependent parameters. Firstly, the threshold parameter R 0 m that determines the persistence and extinction of mosquito populations is given, and then the disease reproduction number R 0 is defined. Secondly, it is proved that if R 0 m > 1 and R 0 < 1, the disease disappears; if R 0 m > 1 and R 0 > 1, then Chikungunya with mutants and non-mutants will persist simultaneously. Finally, a case study is carried out with the data in Kerala, India, where the virus mutation causes the outbreak of Chikungunya. Data on newly confirmed human cases in the state between 2007 and 2010 is fitted and the theoretical results obtained in the previous section are validated. In addition, the effects of seasonal temperature change, virus mutation and transovarial transmission on the prevalence of the disease are studied by numerical simulations from different aspects. 2020 MSC: 34K13; 37N25; 92D30. • A Chikungunya model with virus mutation and transovarial transmission is developed. • The effect of seasonal temperature changes is incorporated. • If there is no seasonality, R 0 will be overestimated. • If there is no virus mutation, the infected population will be underestimated. • When R 0 is less than and close to 1, increasing of transovarial transmission rate will lead to disease outbreak. [ABSTRACT FROM AUTHOR]

This paper examines the issue of non-revenue water (NRW) in urban India, taking the city of Bangalore as a case study. Using empirical evidence from a survey conducted among 601 low-income households in Bangalore, we investigate the major sources of NRW for water utilities and propose policy recommendations based on the analysis. Our key finding is that public stand posts and public wells, which supply free water, are a non-trivial source of NRW. In addition, we find that revenue generation from metered tap connections is sub-optimal. Further, we observe potential revenue being shifted away from the public water utilities toward private providers, as several households pay for water obtained from neighbours, tankers, or other private sources. Drawing upon our findings, we propose a new tariff structure for urban water utilities to consider. We also review the implementation of prepayment metering for public stand posts in other developing countries and its feasibility in Bangalore. [ABSTRACT FROM AUTHOR]

Artemisia species possess pharmacological properties that are used for medical purposes worldwide. In this paper, the essential oils from the aerial parts of Artemisia nilagirica and Artemisia maritima from the western Indian Himalaya region are described. The main compounds analyzed by simultaneous GC/MS and GC/FID were camphor and 1,8-cineole from A. maritima, and camphor and artemisia ketone from A. nilagirica. Additionally, the oilswere evaluated for their antibacterial, antifungal, mosquito biting deterrent, and larvicidal activities. A. nilagirica essential oil demonstrated nonselective antifungal activity against plant pathogens Colletotrichum acutatum, Colletotrichum fragariae, and Colletotrichum gloeosporioides, whereas A. maritima did not showantifungal activity. Both Artemisia spp. exhibited considerable mosquito biting deterrence, whereas only A. nilagirica showed larvicidal activity against Aedes aegypti. Antibacterial effects assessed by an agar dilution assay demonstrated greater activity of A. maritima essential oil against Staphylococcus aureus and Pseudomonas aeruginosa compared to A. nilagirica. [ABSTRACT FROM AUTHOR]

Improving access to water and sanitation for vulnerable groups has been a significant development priority in recent decades and this has been coupled with calls for water and sanitation to be recognised as fundamental human rights. However, to date there has been very limited attention on the right to water and sanitation for homeless people, despite their high vulnerability to a range of water and sanitation insecurities. Drawing on empirical data from the Indian cities of Delhi and Bangalore, this paper examines homelessness and the right to water and sanitation. It highlights the everyday practices and experiences of homeless people in their efforts to access water and sanitation, and sheds light on some of the factors that contribute to their water and sanitation insecurity. It concludes that addressing the human right to water and sanitation for homeless people will require going beyond a technical and sector approach, to the more challenging task of tackling the complex factors that create and sustain their vulnerability and marginality in urban spaces. [ABSTRACT FROM AUTHOR]

• A dynamic model of infected population to estimate the propagation pattern of COVID -19 pandemics. • Provision of incorporating several influencing factors in proposed model such as inter and intra zone mobilization, detection rate etc. • Different remedial steps are applied as operating procedures to the model in both standalone and hybridized mode. • Flexibility of obtaining probable infected pattern of a particular region or country by proper tuning of the different coefficient of proposed model. • Validation of the model for three geographical locations within India with real world data. Most of the widely populated countries across the globe have been observing vicious spread and detrimental effects of pandemic COVID-19 since its inception on December 19. Therefore to restrict the spreading of pandemic COVID-19, various researches are going on in both medical and administrative sectors. The focus has been given in this research keeping an administrative point of view in mind. In this paper a dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection has been proposed. Few factors related to intra zone mobilization; inter zone mobilization and rate of detection are the key points in the proposed model. Various remedial steps are taken into consideration in the form of operating procedures. Further such operating procedures are applied over the model in standalone or hybridized mode and responses are reported in this paper in a case-studies manner. Further zone-wise increase in infected population due to the spreading of pandemic COVID-19 has been studied and reported in this paper. Also the proposed model has been applied over the real world data considering three states of India and the predicted responses are compared with real data and reported with bar chart representation in this paper. [ABSTRACT FROM AUTHOR]

The Severe Acute Respiratory Syndrome Coronavirus 2 (SAR-CoV-2) is the strain of coronavirus that causes coronavirus disease (COVID-19), the respiratory illness that resulted in COVID-19 pandemic in early December 2019. Due to lack of knowledge of the epidemiological cycle and absence of any type of vaccine or medications, the Government issued various non-pharmacological measures to end the COVID-19 pandemic. Several researchers applied the Susceptible-Infected-Recovered-Deceased (SIRD) compartmental epidemiology process model to identifying the effect of different governments intervention methods enforced to mollify the spread of COVID-19 epidemic. In this paper, we aim to provide a modified SIRD model for COVID-19 spread prediction. We have analyzed the data of the Northern and Southern states of India from January 30, 2020, to October 24, 2020 using the proposed SIRD model and existing SIRD model. We have made the predictions with reasonable assumptions based on real data, considering that the precise course of an epidemic is highly dependent on how and when quarantine, isolation, and precautionary measures were imposed. The proposed method gives better approximation values of new cases, R0 (Reproductive Number), daily deaths, daily infectious, transmission rate, and recovered individuals.Through the analysis of the reported results, the proposed SIRD model can be an effective method for investigating the effect of government interventions on COVID-19 associated transmission and mortality rate at the time of epidemic. [ABSTRACT FROM AUTHOR]

• Compartment model susceptible-exposed-home quarantine-infected-hospital quarantine-recovered is developed to understand the transmitting behaviour of pandemic COVID-19 in human population. • Local and global asymptotic stability established for the conditions of reproduction number. • Statistical Analysis has been performed to analyse the impact of lockdown in different part of the world badly infected by COVID-19 with India. • Also time series forecasting model for the case fatality rate in India and time series forecasting of actual cases versus predicted cases in India is performed. Pandemic COVID-19 which has infected more than 35,027,546 people and death more than 1,034,837 people in 235 countries as on October 05, 2020 has created a chaos across the globe. In this paper, we develop a compartmental epidemic model to understand the spreading behaviour of the disease in human population with a special case of Bhilwara, a desert town in India where successful control measures TTT (tracking, testing and treatment) was adopted to curb the disease in the very early phase of the spread of the disease in India. Local and global asymptotic stability is established for endemic equilibrium. Extensive numerical simulations with real parametric values are performed to validate the analytical results. Trend analysis of fatality rate, infection rate, and impact of lockdown is performed for USA, European countries, Russia, Iran, China, Japan, S. Korea with a comparative assessment by India. Kruskal - Wallis test is performed to test the null hypothesis for infected cases during the four lockdown phases in India. It has been observed that there is a significant difference at both 95% and 99% confidence interval in the infected cases, recovered cases and the case fatality rate during all the four phases of the lockdown. [ABSTRACT FROM AUTHOR]

• Effect of lockdown relaxation on COVID-19 spread in India. • Brief literature review of epidemic forecasting models. • Model validation and standardization using actual lockdown time series. • A self-organized data mining technique for forecasting active cases time series. • Forecast of medical infrastructure requirement as per active cases. Ever since the outbreak of novel coronavirus in December 2019, lockdown has been identified as the only effective measure across the world to stop the community spread of this pandemic. India implemented a complete shutdown across the nation from March 25, 2020 as lockdown I and went on to extend it by giving timely partial relaxations in the form of lockdown II, III & IV. This paper statistically analyses the impact of relaxation during Lockdown III and IV on coronavirus disease (COVID) spread in India using the Group Method of Data Handling (GMDH) to forecast the number of active cases using time series analysis and hence the required medical infrastructure for the period of next six months. The Group Method of Data Handling is a novel self organized data mining technique with data driven adaptive learning capability which grasps the auto correlative relations between the samples and gives a high forecasting accuracy irrespective of the length and stochasticity of a time series. The GMDH model has been first validated and standardized by forecasting the number of active and confirmed cases during lockdown III-IV with an accuracy of 2.58% and 2.00% respectively. Thereafter, the number of active cases has been forecasted for the rest of 2020 to predict the impact of lockdown relaxation on spread of COVID-19 and indicate preparatory measures necessary to counter it. [ABSTRACT FROM AUTHOR]

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in ≤ 2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action. [ABSTRACT FROM AUTHOR]

• A novel compartmental epidemic model SIPHERD is employed for COVID-19 prediction for India where it has reached at alarming level. • Impact of lockdown and the number of tests conducted per day on predictions of containment is studied. • Purely Asymptomatic cases and spread from them as well as Exposed in incubation period considered. • Increasing the tests per day by 10k every day, stringent measures on social-distancing and strict lockdown in July have significant impact on the disease spread. Originating from Wuhan, China, in late 2019, and with a gradual spread in the last few months, COVID-19 has become a pandemic crossing 9 million confirmed positive cases and 450 thousand deaths. India is not only an overpopulated country but has a high population density as well, and at present, a high-risk nation where COVID-19 infection can go out of control. In this paper, we employ a compartmental epidemic model SIPHERD for COVID-19 and predict the total number of confirmed, active and death cases, and daily new cases. We analyze the impact of lockdown and the number of tests conducted per day on the prediction and bring out the scenarios in which the infection can be controlled faster. Our findings indicate that increasing the tests per day at a rapid pace (10k per day increase), stringent measures on social-distancing for the coming months and strict lockdown in the month of July all have a significant impact on the disease spread. [ABSTRACT FROM AUTHOR]

In this paper, Deep Learning-based models are used for predicting the number of novel coronavirus (COVID-19) positive reported cases for 32 states and union territories of India. Recurrent neural network (RNN) based long-short term memory (LSTM) variants such as Deep LSTM, Convolutional LSTM and Bi-directional LSTM are applied on Indian dataset to predict the number of positive cases. LSTM model with minimum error is chosen for predicting daily and weekly cases. It is observed that the proposed method yields high accuracy for short term prediction with error less than 3% for daily predictions and less than 8% for weekly predictions. Indian states are categorised into different zones based on the spread of positive cases and daily growth rate for easy identification of novel coronavirus hot-spots. Preventive measures to reduce the spread in respective zones are also suggested. A website is created where the state-wise predictions are updated using the proposed model for authorities,researchers and planners. This study can be applied by other countries for predicting COVID-19 cases at the state or national level. [ABSTRACT FROM AUTHOR]

• Two contrasting models are presented to understand and predict the spreading of COVID-19. • Estimation of turnaround (peak active cases) day is performed using a mathematical model for India, Italy and USA. • Trend and variability are extracted from the data for daily reported cases using DCT-based Fourier decomposition method. • Bi-modal Gaussian mixture model is fitted to the trend to predict the cases in near future. • End-dates for COVID-19 are predicted with 95% confidence interval in various parts of the world. COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 × 106 and 5.27 × 105, respectively, predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the ongoing dynamics of COVID-19 in India after its emergence in Wuhan, China in December 2019. We discuss the effect of nationwide lockdown implemented in India on March 25, 2020 to prevent the spread of COVID-19. Susceptible-Exposed-Infectious-Recovered (SEIR) model is used to forecast active COVID-19 cases in India considering the effect of nationwide lockdown and possible inflation in the active cases after its removal on May 3, 2020. Our model predicts that with the ongoing lockdown, the peak of active infected cases around 43,000 will occur in the mid of May, 2020. We also predict a 7 to 21% increase in the peak value of active infected cases for a variety of hypothetical scenarios reflecting a relative relaxation in the control strategies implemented by the government in the post-lockdown period. For India, it is an important decision to come up with a non-pharmaceutical control strategy such as nationwide lockdown for 40 days to prolong the higher phases of COVID-19 and to avoid severe load on its public health-care system. As the ongoing COVID-19 outbreak remains a global threat, it is a challenge for all the countries to come up with effective public health and administrative strategies to battle against COVID-19 and sustain their economies. [ABSTRACT FROM AUTHOR]

Detection of primordial gravitational-wave backgrounds generated during the early Universe phase transitions is a key science goal for future ground-based detectors. The rate of compact binary mergers is so large that their cosmological population produces a confusion background that could masquerade the detection of potential primordial stochastic backgrounds. In this paper, we study the ability of current and future detectors to resolve the confusion background to reveal interesting primordial backgrounds. The current detector network of LIGO and Virgo and the upcoming KAGRA and LIGO-India will not be able to resolve the cosmological compact binary source population, and its sensitivity to stochastic background will be limited by the confusion background of these sources. We find that a network of three (and five) third generation (3G) detectors of Cosmic Explorer and Einstein Telescope will resolve the confusion background produced by binary black holes leaving only about 1.3% (respectively, 0.075%) unresolved; in contrast, as many as 25% (respectively, 7.7%) of binary neutron star sources remain unresolved. Consequently, the binary black hole population will likely not limit observation of primordial backgrounds, but the binary neutron star population will limit the sensitivity of 3G detectors to ΩGW∼10-11 at 10 Hz (respectively, ΩGW∼3×10-12). [ABSTRACT FROM AUTHOR]

The article talks about Indian political party Bharatiya Janata Party's (BJP's) stunning win in the 2017 elections in the state of Uttar Pradesh. It discusses how this win revived Indian Prime Minister Narendra Modi's losing momentum due to pressure from rival parties and his demonetization attempt to scrap India's paper currency. It also discusses BJP's win in three other states and the efforts of a member of the Hindu volunteer group Rashtriya Swayamsevak Sangh which led to BJP's success.

The article focuses on the research by G.S. Seethapathy, J.U. Santhosh Kumar and A.S. Hareesha, published in the journal "Current Science," that demands end to research papers submitted from India to pay-to-publish journals, under the pressure to satisfy criteria for appointments and promotions.

The article discusses the notion of an endowment effect, which is the tendency of human beings to hold onto possessions. The paper "Endowment Effects in the Field: Evidence from India's IPO Lotteries" by Santosh Anagol, Vimal Balasubramaniam, and Tarun Ramodorai is addressed. Psychological effects of initial public offering (IPO) prices on investors are noted.

The article discusses allegations that the Telecom Regulatory Authority of India (TRAI) changed a discussion paper it produced in order to better serve the interests of the telecommunications industry in the country with regards to the issue of network neutrality.

The article exhorts people to consume less, reuse and recycle as this reduces waste and emissions. Discarded packaging materials make up about one third of the waste clogging our landfills. Vast amounts of natural resources and fossil fuels are consumed each year to produce the paper, plastic, glass, aluminum and styrofoam that wrap our purchases. According to a report by the Central Pollution Control Board (CPCB), India generates 5.6 million metric tons of plastic waste annually.