Jitendra Singh

Composition and medicinal properties of camel milk: A Review

Many research findings proved that camel milk is closer to human milk than any other milk. Camel milk is different from other ruminant milk, having low cholesterol, low sugar, high minerals (sodium, potassium, iron, copper, zinc and magnesium), high vitamin C. Camel milk is unique from other ruminants milk in terms of composition as well as claimed health effects. Camel milk has potential therapeutic characteristics, such as anti-hypertensive, anti-diabetic and anti-carcinogenic. It is often easily digested by lactose-intolerant individuals. On the other hand, camel milk also has ability to reduce the elevated level of bilirubin, globulin and granulocytes. Camel milk failed to show any effect towards improving the level of hemoglobin and leukocytes, and decreasing the erythrocyte sedimentation rate. Camel milk proteins contained satisfactory balance of essential amino acids. It contains disease-fighting immunoglobulins which are small in size, allowing penetration of antigens and boosting the effectiveness of the immune system. This review focused on the medicinal properties of camel milk which will be more useful to generate value added product.

Urbanization causes Nonstationarity in Indian Summer Monsoon Rainfall Extremes

Global and local environmental changes are likely to introduce nonstationarity in the characteristics of Indian Summer Monsoon Rainfall (ISMR) extremes. Here, we perform a nonstationary frequency analysis on ISMR extremes in a Generalized Additive Model for Location, Scale and Shape (GAMLSS) framework with a cluster of 74 models, considering nonstationarity in different possible combinations. Interestingly, we observe significant nonstationarity in ISMR extremes in urbanizing/developing-urban areas (transitioning from rural to urban), compare to completely urbanized or rural areas. This presents a postulation that the extent of urbanization plays a significant role in introducing nonstationarity in ISMR extremes. We emphasize the effect of urbanization in changing the character of ISMR extremes, which further needs a scientific re-evaluation by implementing physics-based modeling. The impact of these observational studies will be critical in correcting the bias of model projections of ISMR.

A Framework for Assessing Uncertainty Associated with Human Health Risks from MSW Landfill Leachate Contamination.

Landfilling is a cost-effective method, which makes it a widely used practice around the world, especially in developing countries. However, because of the improper management of landfills, high leachate leakage can have adverse impacts on soils, plants, groundwater, aquatic organisms, and, subsequently, human health. A comprehensive survey of the literature finds that the probabilistic quantification of uncertainty based on estimations of the human health risks due to landfill leachate contamination has rarely been reported. Hence, in the present study, the uncertainty about the human health risks from municipal solid waste landfill leachate contamination to children and adults was quantified to investigate its long-term risks by using a Monte Carlo simulation framework for selected heavy metals. The Turbhe sanitary landfill of Navi Mumbai, India, which was commissioned in the recent past, was selected to understand the fate and transport of heavy metals in leachate. A large residential area is located near the site, which makes the risk assessment problem both crucial and challenging. In this article, an integral approach in the form of a framework has been proposed to quantify the uncertainty that is intrinsic to human health risk estimation. A set of nonparametric cubic splines was fitted to identify the nonlinear seasonal trend in leachate quality parameters. LandSim 2.5, a landfill simulator, was used to simulate the landfill activities for various time slices, and further uncertainty in noncarcinogenic human health risk was estimated using a Monte Carlo simulation followed by univariate and multivariate sensitivity analyses.

A Framework for Investigating the Diagnostic Trend in Stationary and Nonstationary Flood Frequency Analyses Under Changing Climate

The Nonstationary analysis drew formidable attention to the flood frequency analysis (FFA) research community due to analytically perceivable impacts of climate change, urbanisation and concomitant land use pattern on the flood event series. Albeit, the inclusion of nonstationarity in FFA significantly enhanced the accurate estimation of the return period, however, its application is questionable when the flood variables (FV) are not having persisting significant nonstationarity. In such cases, the assumption of stationarity is still valid and will direct to accurate estimation of the flood quantiles. Hence, prior to conducting the comprehensive FFA, it is vital to inspect the existence of stationarity/nonstationarity in the FV. This can be accomplished by a comprehensive trend analysis. The aim of present study is to emphasize the importance of a comprehensive trend analysis during FFA by proposing a framework to conduct the same. Further, the proposed framework has been demonstrated on unregulated daily streamflow series of two gauging stations, at the Kanawha Fall of Kanawha River, West Virginia, USA, and at the Baltara gauging station of Kosi River, Bihar, India. The results show that the annual maxima (AM) delineated flood peak series has a significant trend in both the gauging stations, providing sufficient evidence of nonstationarity, which is modelled by first- and second-order nonstationary analyses. A comparison between first-order and second-order nonstationarity analyses has also been performed, which suggests higher order nonstationary analysis might give more accurate information on the occurrence of flood extremes. Overall, our study highlights that the proposed framework is an important initial step before initiating FFA to avoid the ambiguity between the selection of stationary and nonstationary analysis.

Intruder detection by visual cryptography in wireless sensor networks

In this modern era of technology wireless sensor networks have broad area of applications. Various applications of sensor network needs the communication to be authenticated and secured. Researchers have developed many schemes for intruder detection in WSNs, but those schemes are very complicated and require complex encryption and decryption process for intruder detection. In this paper we proposed a intruder detection scheme based on (1, n) visual cryptography in which we use secure image which is partitioned into master share and ownership shares for verifying the authentication of sensor nodes. We believe that proposed technique will take fewer amounts of time and energy for intruder detection and possibly will detect all intruders in wireless sensor network. Main purpose of this paper is to introduce the use of visual cryptography in WSNs.

Spatio-temporal analysis of sub-hourly rainfall over Mumbai, India: Is statistical forecasting futile?

Mumbai, the commercial and financial capital of India, experiences incessant annual rain episodes, mainly attributable to erratic rainfall pattern during monsoons and urban heat-island effect due to escalating urbanization, leading to increasing vulnerability to frequent flooding. After the infamous episode of 2005 Mumbai torrential rains when only two rain gauging stations existed, the governing civic body, the Municipal Corporation of Greater Mumbai (MCGM) came forward with an initiative to install 26 automatic weather stations (AWS) in June 2006 (MCGM 2007), which later increased to 60 AWS. A comprehensive statistical analysis to understand the spatio-temporal pattern of rainfall over Mumbai or any other coastal city in India has never been attempted earlier. In the current study, a thorough analysis of available rainfall data for 2006–2014 from these stations was performed; the 2013–2014 sub-hourly data from 26 AWS was found useful for further analyses due to their consistency and continuity. Correlogram cloud indicated no pattern of significant correlation when we considered the closest to the farthest gauging station from the base station; this impression was also supported by the semivariogram plots. Gini index values, a statistical measure of temporal non-uniformity, were found above 0.8 in visible majority showing an increasing trend in most gauging stations; this sufficiently led us to conclude that inconsistency in daily rainfall was gradually increasing with progress in monsoon. Interestingly, night rainfall was lesser compared to daytime rainfall. The pattern-less high spatio-temporal variation observed in Mumbai rainfall data signifies the futility of independently applying advanced statistical techniques, and thus calls for simultaneous inclusion of physics-centred models such as different meso-scale numerical weather prediction systems, particularly the Weather Research and Forecasting (WRF) model.

A review on fuzzy logic based clustering algorithms for wireless sensor networks

Today wireless sensor Networks (WSNs) has important role in various fields. Sensor networks contain many sensor nodes for the purpose of sensing the data from the environment of a particular region. The major design issue in this area is to optimize energy efficiency of a sensor node as the power source of sensor node is limited and also it is not feasible to replace or recharge the power source of sensor nodes. To increase the lifetime of a sensor network, the energy of sensor node must be utilized in optimized way. Sensor nodes are organized in a number of groups called cluster. Clustering is a technique to use the energy of network efficiently. In this paper, we present some fuzzy logic based clustering protocols researched by the active authors. In fuzzy logic based clustering protocols parameters used to create cluster head are residual energy, rechability from its nearest node, base station distance.

Presence of phytoplasma infections in papaya (Carica papaya L.) Plants in Uttar Pradesh, India

During survey of papaya fields in Meerut, severe symptoms of leaf yellowing, intervenial chlorosis, and curl apical necrosis were observed in approximately 25 per cent of plants in each of the papaya fields. DNA extracted from leaf, midrib and bark of symptomatic and healthy plants of papaya were preceded with universal primer pairs. Expected ~1600 bp fragments were amplified with primer pair P1/Tint in diseased plants. Infection of phytoplasma disease in papaya plants was confirmed by the PCR.