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Six generations namely, P 1 , P 2 , F 2 , F 3 , BC 1s and BC 2s and P 1 , P 2 , F 3 , F 4 , BC 1ss and BC 2ss developed from four parental genotypes viz. DBW 14 (heat tolerant), NP 846 (heat and drought tolerant), WH 147 and Raj 4014 (heat susceptible for late sown). All the six generations from four crosses were evaluated during Rabi 2006-07 and Rabi 2007-08 in a compact family block design with three replications on two sowing dates. Heat susceptibility index values revealed reduction in grain yield in both the years for all the generations of the four crosses. Significant estimates of correlation of grain yield with days to heading, days to anthesis and days to maturity were recorded in late sown condition during first year. While under timely sown condition spike length has high estimate correlation with grain yield in first year itself. Significant estimated were recorded for tillers per plant in both the environments in second year. Lowest yield loss was reported in backcross populations of Cross I in both years and among segregating populations of Cross IV observed to be least affected and therefore suggested to be forwarded to further generations and further selection of heat tolerant genotypes.

The experiment was planned to investigate the tractor mounted N-sensor (Make Yara International) to predict nitrogen (N) for wheat crop under different nitrogen levels. It was observed that, for tractor mounted N-sensor, spectrometers can scan about 32% of total area of crop under consideration. An algorithm was developed using a linear relationship between sensor sufficiency index (SIsensor) and SISPAD to calculate the Napp as a function of SISPAD. There was a strong correlation among sensor attributes (sensor value, sensor biomass, and sensor NDVI) and different N-levels. It was concluded that tillering stage is most prominent stage to predict crop yield as compared to the other stages by using sensor attributes. The algorithms developed for tillering and booting stages are useful for the prediction of N-application rates for wheat crop. N-application rates predicted by algorithm developed and sensor value were almost the same for plots with different levels of N applied.

Small Group evolution has been of central importance in social sciences and also in the industry for understanding dynamics of team formation. While most of research works studying groups deal at a macro level with evolution of arbitrary size communities, in this paper we restrict ourselves to studying evolution of small group (size ≤ 20) which is governed by contrasting sociological phenomenon. Given a previous history of group collaboration between a set of actors, we address the problem of predicting likely future group collaborations. Unfortunately, predicting groups requires choosing from n r possibilities (where r is group size and n is total number of actors), which becomes computationally intractable as group size increases. However, our statistical analysis of a real world dataset has shown that two processes: an external actor joining an existing group (incremental accretion (IA)) or collaborating with a subset of actors of an exiting group (subgroup accretion (SA)), are largely responsible for future group formation. This helps to drastically reduce the n r possibilities. We therefore, model the attachment of a group for different actors outside this group. In this paper, we have built three topology based prediction models to study these phenomena. The performance of these models is evaluated using extensive experiments over DBLP dataset. Our prediction results shows that the proposed models are significantly useful for future group predictions both for IA and SA.