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Background: The socio-environmental aspects of southern Assam reflect a general pattern of backwardness. Moreover, child healthcare resources in the region are inadequately used, leading to low vaccination coverage. Given this background, this paper attempted to comprehend wealth-based inequality in full vaccination in rural areas of southern Assam. Methodology: Based on a multistage cluster sampling approach, 360 children of 12–23 months were selected from the study area. To identify the predictors of a child, a non-linear model was estimated by using the generalized linear model (GLM) approach followed by Erreygers decomposition technique to quantify the wealth inequality in the obtained predictors in explaining the disparity in full vaccination. Result: The Bacillus Calmette–Guérin (BCG) vaccination recorded the highest vaccination coverage, at nearly 90% and the lowest was observed for the measles vaccine, around 61 percent. Slightly more than half of the eligible children (54 percent) were vaccinated against all the Universal Immunization Programme (UIP)-recommended vaccines. The decomposition analysis revealed that the occupation of the child's father, maternal age, birth order of the child, and health-seeking behavior such as antenatal care (ANC) were the prime factors related to inequality in full vaccination in the region. Conclusion: Vaccination coverage in the region has improved over time, however, full vaccination is concentrated towards the economically advantaged section of the society in rural southern Assam. Targeted, context-specific, and expanded government initiatives could aid in addressing the overall wealth-related full vaccination inequalities in the valley. [ABSTRACT FROM AUTHOR]

Purpose: Betel quid chewing is a major oral cancer risk factor and the human papillomaviruses (HPV) may play an aetiological role in these cancers. However, little is known about the shape of the dose-response relationship between the betel quid chewing habit and oral cancer risk in populations without HPV. We estimate the shape of this dose-response relationship, and discuss implications for prevention.Methods: Cases with oral squamous cell carcinoma (350) and non-cancer controls (371) were recruited from two major teaching hospitals in South India. Information on socio-demographic and behavioral factors was collected using a questionnaire and the life grid technique. The effect of daily amount of use and duration of the habit were estimated jointly as risk associated with cumulative exposure (chew-years). The shape of the dose-response curve was estimated using restricted cubic spline transformation of chew-years in a conditional logistic regression model. Risk estimates for low dose combinations of daily amount and duration of the habit were computed from flexible regression.Results: Most (72%) oral cancer cases were betel quid chewers in contrast to only 18% of controls. A nonlinear dose-response relationship was observed; the risk increased steeply at low doses and plateaued at high exposures to betel quid (>425 chew-years). A threefold increase in risk (OR=3.92, 95%CI: 1.87-8.21) was observed for the lowest dose; equivalent to the use of one quid per day for one year.Conclusion: Our findings may be used to counsel people to refrain from even low betel quid chewing. [ABSTRACT FROM AUTHOR]

Conventionally the process of rainfall is considered as a stochastic process and the last century witnessed many developments, however recently many rainfall series are proved to be a chaotic series. Out of various chaotic methods available to analyse time series data, the most commonly employed method, correlation dimension method is used in the present study to carry out the behavioral study of catchment rainfall process. The daily average rainfall observed over a period of 49 years (1961-2009) from the catchment area of Koyna reservoir in Maharashtra, India, has been taken up to study its behaviour. Daily rainfall from nine rain gage stations is averaged in order to obtain the above mentioned daily average rainfall. From the detailed non-linear dynamic analysis using correlation dimension method, it is found that the average rainfall of Koyna region is showing a chaotic behaviour. The effect of radius 'r' and its steps in the Heaviside function for plotting log r versus log Cr graph, the effect of scaling region in determining correlation exponent from the above plot and the effect of longer length of zeros in time series are analyzed in the present study. In addition to this, the chaotic behaviour of rainfall for various time steps (daily, weekly, monthly and seasonal) is also analyzed. It is found that the selection of radius 'r' and its steps is important in Heaviside function as there will be initial disturbances for smaller 'r' which is difficult to account while determining the correlation exponent whereas higher 'r' leads to the log Cr values close to zero making it impossible to estimate the slope of log r versus log Cr graph for determining correlation exponent. Nevertheless, the selection of scaling region is very crucial in determining the correlation exponent which subsequently determines the behaviour of the system as chaotic or not. Wrong selection of scaling region results in misinterpretation of results. In the present study, the correlation exponent value is saturating in case of daily full year rainfall, weekly rainfall and 10 daily rainfall indicating its chaotic behaviour whereas the correlation exponent value go on increasing with embedding dimension but not as in case of a stochastic series for daily monsoon rainfall, monthly rainfall and seasonal rainfall. The saturated value of correlation exponent is in between zero and one indicating correlation dimension of the three series (daily full year, weekly and 10 daily) as one. [ABSTRACT FROM AUTHOR]

Severe thunderstorms are a manifestation of deep convection. Conditional instability is known to be the mechanism by which thunderstorms are formed. The energy that drives conditional instability is convective available potential energy (CAPE), which is computed with radio sonde data at each pressure level. The purpose of the present paper is to identify the pattern or shape of CAPE required for the genesis of severe thunderstorms over Kolkata (22°32′N, 88°20′E) confined within the northeastern part (20°N to 24°N latitude, 85°E to 93°E longitude) of India. The method of chaotic graph theory is adopted for this purpose. Chaotic graphs of pressure levels and CAPE are formed for thunderstorm and non-thunderstorm days. Ranks of the adjacency matrices constituted with the union of chaotic graphs of pressure levels and CAPE are computed for thunderstorm and non-thunderstorm days. The results reveal that the rank of the adjacency matrix is maximum for non-thunderstorm days and a column with all zeros occurs very quickly on severe thunderstorms days. This indicates that CAPE loses connectivity with pressure levels very early on severe thunderstorm days, showing that for the genesis of severe thunderstorms over Kolkata short, and therefore broad, CAPE is preferred. [ABSTRACT FROM AUTHOR]

The purpose of the present study is to investigate the existence of deterministic chaos in the time series of occurrence or non-occurrence of severe thunderstorms of the pre-monsoon season over the Northeastern part of India. Results from the current study reveal the existence of chaos in the relevant time series. The corresponding predictabilities are also computed quantitatively. The study recommends that the formulation of numerical weather prediction models for forecasting the occurrence of this high frequency meso-scale convective system must take into account the intrinsic chaos. [ABSTRACT FROM AUTHOR]

Respirable particulate matter (PM10) concentration at one residential site in Delhi, India was predicted using the neural network approach. The concepts of chaotic systems theory were utilized to build the neural network model. The embedding dimension was estimated to provide the inputs to the neural network. The model evaluation results indicated the importance of noise reduction before selecting the embedding dimension of the time series. The selection of a proper embedding dimension is considered to be essential for obtaining reliable predictions. The model’s performance shows the capability of neural networks in modelling the chaotic time series. [ABSTRACT FROM AUTHOR]