Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

Mass Transfer During Osmotic Dehydration of Banana (Dwarf Cavendish) Slices

Abstract: Mass transfer during osmotic dehydration of banana has been studied with respect to the solution concentration (30–70°Brix), temperature (25–65°C) and solution to sample ratio (1–9). The diffusion coefficients have been calculated using the sorption data by a method of successive approximations. The diffusion coefficient has been empirically correlated with the process variables and a high degree of correlation (R2 = 0.91) was observed between predicted and experimental values. The solution of Fick's Law for unstady state mass transfer in a plane sheet has been used to predict the moisture ratios over the entire experimental range. The error between the observed and the predicted values of moisture ratios was less than 10% indicating the adequacy of diffusion coefficient estimation.

Computational analysis of SNPs and INDELs in cluster bean cultivars involved in multiple trait expression

Abstract: SNPs (Single Nucleotide Polymorphisms) are extensively used in plant breeding programs because of their automation and high precision in allele calling. In the present study, transcriptomes of three cultivars of Cyamopsis tetragonoloba, namely, RGC-936, RGC-1066 and M-18 were analysed for the identification of SNPs and Indels using the recently assembled draft genome made available by National Institute of Plant Biotechnology, New Delhi. Besides, a comparison among the identified SNPs and indels of three cultivars was made to mine out the cultivar specific SNPs and indels as well as common markers among the cultivars. In addition, an online database, cbSIR, was developed based on the markers populated from the said cultivars of cluster bean (http://webapp.cabgrid.res.in/clb_ce/index.php). The results reveal that highest number of SNPs (10279) were present in cultivar RGC-1066 followed by RGC-1066 (9714) and M-18 (7933). The detected SNPs were subjected to functional annotation. In a similar way, Indels were also identified and functionally annotated. Predictions were made based on the involvement of SNP/Indel possessing genes in the expression of multiple traits such as gum production, auxin transport, disease resistance in the three cultivars of cluster bean.

Bug prediction using entropy-based measures

Abstract: In the available literature, researchers have proposed and implemented a plethora of bug prediction approaches, which vary in terms of accuracy, complexity and the input data they require, but very few of them has predicted the number of bugs in the software based on the entropy or the complexity of code changes. To use the entropy of code change as a bug predictor, firstly, the history of complexity metric HCM defined with different decay weight and decay models were assigned to it Hassan, 2009. But, they did not propose any method to find out the value of decay rate/factor. In this paper, we proposed a new weight to HCM, a method to find out the value of decay rate/factor and proposed some novel decay-based methods. We have applied simple linear regression SLR and support vector regression SVR to predict the bugs based on existing and proposed methods of HCM. We have also studied the performance of different complexity of code changes entropy-based bug prediction approaches on the basis of various performance measures using four subsystems of Mozilla project. We found that decay models for SVR show better results in comparison with SLR.

Modeling fractionally integrated maximum temperature series in India in presence of structural break

Abstract: In this study, the long memory behaviour of monthly maximum temperature of India for the period 1901 to 2007 is investigated. The correlogram of the series reveals a slow hyperbolic decay, a typical shape for time series having the long memory property. Wavelet transformation is applied to decompose the temperature series into time–frequency domain in order to study the local as well as global variation over different scale and time epochs. Significant increasing trend is found in the maximum temperature series in India. The rate of increase in maximum temperature accelerated after 1960s as compared to the earlier period. Here, an attempt is also made to detect the structural break for seasonally adjusted monthly maximum temperature series. It is found that there is a significant break in maximum temperature during July, 1963. Two-stage forecasting (TSF) approach to deal with the coexistence of long memory and structural change in temperature pattern is discussed thoroughly. The forecast performance of the fitted model is assessed on the basis of relative mean absolute prediction error (RMAPE), sum of squared errors (SSE) and mean squared errors (MSE) for different forecast horizons.