Demographic and Socio-economic Determinants of Birth Interval Dynamics in Manipur: A Survival Analysis

The birth interval is a major determinant of levels of fertility in high fertility populations. A house-to-house survey of 1225 women in Manipur, a tiny state in North Eastern India was carried out to investigate birth interval patterns and its determinants. Using survival analysis, among the nine explanatory variables of interest, only three factors – infant mortality, Lactation and use of contraceptive devices have highly significant effect (P<0.01) on the duration of birth interval and only three factors – age at marriage of wife, parity and sex of child are found to be significant (P<0.05) on the duration variable

Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions

Machine learning-based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested. The models are based on function-, kernel- and grid-based representations of the reactant and product state distributions. While all three methods predict final state distributions from explicit quasi-classical trajectory simulations with R$^2$ > 0.998, the grid-based approach performs best. Although a function-based approach is found to be more than two times better in computational performance, the kernel- and grid-based approaches are preferred in terms of prediction accuracy, practicability and generality. The function-based approach also suffers from lacking a general set of model functions. Applications of the grid-based approach to nonequilibrium, multi-temperature initial state distributions are presented, a situation common to energy distributions in hypersonic flows. The role of such models in Direct Simulation Monte Carlo and computational fluid dynamics simulations is also discussed

Integrated assessment of climate change adaptation options for water resources management using participatory and hydrological modelling approaches

Climate change adaptation (CCA) is a vital strategy for river basin water management which binds together environmental, agricultural and human water requirements in an uncertain future climate. Policy makers face a difficult task balancing demand and supply for conflicting water requirements, especially to justify present day economic costs for future benefits, like in CCA. No-regret adaptation options, applicable in both, current and future uncertain conditions, provide a way of dealing with these issues. However, determination of such options needs to be based on an integrated assessment of hydrologic, environmental, social, economic and institutional characteristics to be suitable in the future. Here, a three step process for determining no- regret options is presented, having been applied to the Kangsabati River basin in India. Firstly a participatory approach is used to identify potential CCA options, followed by a Multi Criteria Analysis (MCA) to determine the no- regret and suitability characteristics for the region. This approach was replicated at three levels; community, district and state (sub-national), targeting different stakeholders. Finally, hydrological modeling using Water Evaluation And Planning (WEAP) model, of the high ranking adaptation options show the expected efficacy in hydrologic terms. MCA generated no-regret options show importance of currently promoted soil and water conservation measures, like afforestation and check dams and the need for future focus on cropping pattern change. Evaluation criteria important to different stakeholders were also determined in the process, a valuable by-product useful for future water management. Present and future scenario based modelling of CCA options provides comparability in terms of suitability, scale of impacts and costs. Such assessments can be valuable tool-set for policymakers to make evidence based decisions on choice of adaptation measures and their spatio- temporal applications to improve water availability in an uncertain climate

Modified Ratio and Product Estimators for Population Mean in Systematic Sampling

The estimation of population mean in systematic sampling is explored. Properties of a ratio and product estimator that have been suggested in systematic sampling are investigated, along with the properties of double sampling. Following Swain (1964), the cost aspect is also discussed

Isolation, characterization and morphological study of Azotobacter isolates

Among the diazotrops, great attention has been paid to the genus Azotobacter and its role in increasing the growth and health of plants. In the present study, forty two strains of Azotobacter were isolated from soil. These strains were purified and characterized through microscopical and biochemical test for cell shape, pigmentation, colony size, Gram reaction and catalase activity were identified as Azotobacter sp These strains showed wide variability to these characters. Among 42 isolates, 7 were single cocci, 7 coccidal chain and 4 were cocci in clumps. Majority of isolates i.e. 24, were small, medium and large rod shaped. Thirty two isolates were Gram –ve, catalase positive and 10 were Gram +ve, catalase negative. Finally from these isolates, twenty two were confirmed as Azotobacter strains on cyst formation. The carbon-source utilization pattern revealed that out of 22 strains that 16 strains resembled the characters of A. chroococcum, 3 matched with A. vinelandii and 3 with A. beijerinckii. All 22 isolates were analyzed for its nitrogen fixing ability by using Microkjeldhal method. The highest amount of N2 (18.88 mg g-1 sucrose) was fixed by Azo-SBT 72 while lowest (6.04 mg g-1 sucrose) by Azo-SUR 25 strain. However, injudicious and hazardous use of chemical fertilizers have degraded the soil health and there is need of ecofriendly management of soil by screening and hunting of potential nitrogen fixing strains to protect the soil environment and health. In this context, biofertilizers hunting natural environment is the need of soil to ensure better health of future generations

Class(es) of Factor-Type Estimator(s) in Presence of Measurement Error

When data is collected via sample survey it is assumed whatever is reported by a respondent is correct. However, given the issues of prestige bias, personal respect and honor, respondents’ self-reported data often produces over- or under- estimated values as opposed to true values regarding the variables under question. This causes measurement error to be present in sample values. This article considers the factortype estimator as an estimation tool and examines its performance under a measurement error model. Expressions of optimization are derived and theoretical results are supported by numerical examples