This paper analyzes the level of scientific collaboration and interaction in different subject fields using a novel social network analysis (SNA) on a data set provided by the Department of Energy (DOE) Office of Scientific and Technologic Information (OSTI) in Oak Ridge, Tennessee. This paper not only determines the level of scientific collaboration between different disciplines, but it also analyzes the trends among the subject fields considered. The results in this paper show clear pattern recognition discovery on the social network model among key subject fields using weighted networks.

We propose an information complexity-based regularization parameter selection method for solution of ill conditioned inverse problems. The regularization parameter is selected to be the minimizer of the Kullback–Leibler (KL) distance between the unknown data-generating distribution and the fitted distribution. The KL distance is approximated by an information complexity criterion developed by Bozdogan. The method is not limited to the white Gaussian noise case. It can be extended to correlated and non-Gaussian noise. It can also account for possible model misspecification. We demonstrate the performance of the proposed method on a test problem from Hansen's regularization tools.

We develop a novel Adaptive Elastic Net (AEN) modelling using a new covariance-regularization approach via the Hybridized Smoothed Covariance Estimators (HSCEs) to identify and select the best subset of predictors in undersized high-dimensional data sets. We introduce and score the Consistent and Misspecification Resistant Information Measure of Complexity $\left(CICOM{P}_{Misspec}\right)$ criterion, and the Extended Consistent Akaike's Information Criterion with Fisher Information $\left(CAIC{F}_E\right)$ in AEN models for the first time. We carry out a large Monte Carlo simulation study using the medianmean-squared-error (MMSE) to demonstrate and compare the performance of the MMSE prediction. This is done using Cross-validated Fit Adaptive Elastic Net (CV-AEN) to avoid double shrinkage by varying both the error variance and the correlation structure of the model. Later, the new proposed AEN model is applied to a real undersized benchmark data set to predict the Riboflavin (Vitamin B2) production to select the best subset of predictors to predict the production rate of vitamin B2 and provide the best predictive model. The proposed new approach enables a simple and reliable identification of the best subset of predictive genes of the production rate of Riboflavin (Vitamin B2) without an exhaustive search of all possible subset selection in undersized high-dimensional data. It is a new and novel approach that has generalizability to other regularized General Linear Regression (GLM) models to determine the best predictor space for undersized data.