Ca isotope fingerprints of early crust-mantle evolution

Among the most important factors influencing beer quality is the presence of well-adjusted amounts of higher alcohols and esters; as well as the successful reduction of undesirable by-products such as diacetyl. While higher alcohols and esters contribute rather positively to the beer aroma, diacetyl is mostly unwelcome for beer types with lighter taste. Thus, the complex metabolic pathways in yeast responsible for the synthesis of both pleasant and unpleasant by-products of fermentation were given special attention in this last chapter

Producer Services Linkages in the Hong Kong-Pearl River Delta Region

In the past two decades there has been growing interest in the development of integrated acoustic sensors. Sensors based on bulk (BAW) [1,2] and surface (SAW) [3,4] were reported. Most of these sensors operate in a gaseous medium, although a few are used with liquids or solids, to determine concentrations of chemical and biological substances, as well as viscosity, acceleration, temperature, pressure, etc. Recently, Wenzel and White [5] have found out that flexural plate wave gravimetric sensors can have higher mass sensitivity at low operating frequencies (a few MHz) than the BAW or SAW counterparts. The lowest flexural (antisymmetric) Lamb mode, A0, was used for thin isotropic plates [5]. The thickness of the plates is much smaller than an acoustic wavelength

Sparse-Representation-Based Classification with Structure-Preserving Dimension Reduction

Sparse-representation-based classification (SRC), which classifies data based on the sparse reconstruction error, has been a new technique in pattern recognition. However, the computation cost for sparse coding is heavy in real applications. In this paper, various dimension reduction methods are studied in the context of SRC to improve classification accuracy as well as reduce computational cost. A feature extraction method, i.e., principal component analysis, and feature selection methods, i.e., Laplacian score and Pearson correlation coefficient, are applied to the data preparation step to preserve the structure of data in the lower-dimensional space. Classification performance of SRC with structure-preserving dimension reduction (SRC-SPDR) is compared to classical classifiers such as k-nearest neighbors and support vector machines. Experimental tests with the UCI and face data sets demonstrate that SRC-SPDR is effective with relatively low computation cost © 2014 Springer Science+Business Media New York