Variable selection via RIVAL and its application in nuclear material detection

In many situations, the number of data points is fixed, and the asymptotic convergence results of popular model selection tools may not be useful. A new algorithm for model selection, RIVAL (removing irrelevant variables amidst Lasso iterations), is presented and shown to be particularly effective for a large but fixed number of data points. The algorithm is motivated by an application of nuclear material detection where all unknown parameters are to be non-negative. Thus, positive Lasso and its variants are analyzed. Then, RIVAL is proposed and is shown to have some desirable properties, namely the number of data points needed to have convergence is smaller than existing methods.

Photosensitized destruction of Chlorella vulgaris by methylene blue or nuclear fast red combined with hydrogen peroxide under visible light irradiation

A considerable number of investigations have started to elucidate the essential roles biological agents play in the biodeterioration of stone. Chemical biocides are becoming increasingly banned because of the environmental and health hazards associated with these toxic substances. The present study reports the photodynamic effect of Methylene Blue (MB) and Nuclear Fast Red (NFR) in the presence of hydrogen peroxide (H₂O₂) on the destruction of the algae Chlorella vulgaris (C. vulgaris) under irradiation with visible light. Illumination of C. vulgaris in the presence of MB or NFR combined with H₂O₂ results in the decomposition of both the algal species and the photosensitizer. The photodynamic effect was investigated under aerobic and anaerobic conditions. Differences in mechanism type are reported and are dependent on both the presence and the absence of oxygen. The behavior of each photosensitizer leads to a Type II mechanism and a Type I/Type II combination for MB and NFR, respectively, being concluded. This novel combination could be effective for the remediation of biofilm-colonized stone surfaces.