Measuring equipment and test bench to control evolution of acoustic-deformation and heat fields induced in solids under failure by fluids

The authors have developed a procedure and a test bench for studying evolution of various nature physical fields in modeling geomedium fracture by fluids. The test bench performs synchronous recording of macro- and micro-deformation, heat and acoustic emission induced in physical models of geomedium under loading to discontinuity. The experimental procedure has been trialed. The analysis of the synchronized test data allows a conclusion on the existence of time–space relationship between different nature physical fields induced during failure of solids.     

Production technology for low-carbon,low-sulfur boron steel

A technology for slag formation in the ladle–furnace unit is considered; the slag is based on the CaO–SiO₂–MgO–Al₂O₃–B₂O₃ system. This technology permits both microalloying of the steel with boron (reduced from the oxide phase) and desulfurization of the steel. The resulting boron content in the steel is 0.001–0.008%; the sulfur content in low-alloy steel and pipe steel is low (0.004–0.010%); and the consumption of manganese ferroalloys is reduced to 0.5 kg/t for 08кп steel and 1.4 kg/t for 09Г2C steel. In addition, the proposed technology increases the strength of the rolled steel, without loss in its plasticity; and reduces the environmental impact thanks to the replacement of fluorspar by colemanite.     

The Spatial LASSO With Applications to Unmixing Hyperspectral Biomedical Images

Hyperspectral imaging (HSI) is a spectroscopic method that uses densely sampled measurements along the electromagnetic spectrum to identify the unique molecular composition of an object. Traditionally HSI has been associated with remote sensing-type applications, but recently has found increased use in biomedicine, from investigations at the cellular to the tissue level. One of the main challenges in the analysis of HSI is estimating the proportions, also called abundance fractions of each of the molecular signatures. While there is great promise for HSI in the area of biomedicine, large variability in the measurements and artifacts related to the instrumentation has slow adoption into more widespread practice. In this article, we propose a novel regularization and variable selection method called the spatial LASSO (SPLASSO). The SPLASSO incorporates spatial information via a graph Laplacian-based penalty to help improve the model estimation process for multivariate response data. We show the strong performance of this approach on a benchmark HSI dataset with considerable improvement in predictive accuracy over the standard LASSO. Supplementary materials for this article are available online.