Interpretation of measured profile losses in unsteady wake-turbine blade interaction studies

The interaction of wakes shed by a moving bladerow with a downstream bladerow causes unsteady flow. The meaning of the freestream stagnation pressure and stagnation enthalpy in these circumstances has been examined using simple analyses, measurements and CFD. The unsteady flow in question arises from the behaviour of the wakes as so-called negative-jets. The interactions of the negative-jets with the downstream blades lead to fluctuations in static pressure which in turn generate fluctuations in the stagnation pressure and stagnation enthalpy. It is shown that the fluctuations of the stagnation quantities created by unsteady effects within the bladerow are far greater than those within the incoming wake. The time-mean exit profiles of the stagnation pressure and stagnation enthalpy are affected by these large fluctuations. This phenomenon of energy separation is much more significant than the distortion of the time-mean exit profiles that is caused directly by the cross-passage transport associated with the negative-jet, as described by Kerrebrock and Mikolajczak. Finally, it is shown that if only time-averaged values of loss are required across a bladerow, it is nevertheless sufficient to determine the time-mean exit stagnation pressure.

MicroRNAs and other small RNAs enriched in the Arabidopsis RNA-dependent RNA polymerase-2 mutant.

The Arabidopsis genome contains a highly complex and abundant population of small RNAs, and many of the endogenous siRNAs are dependent on RNA-Dependent RNA Polymerase 2 (RDR2) for their biogenesis. By analyzing an rdr2 loss-of-function mutant using two different parallel sequencing technologies, MPSS and 454, we characterized the complement of miRNAs expressed in Arabidopsis inflorescence to considerable depth. Nearly all known miRNAs were enriched in this mutant and we identified 13 new miRNAs, all of which were relatively low abundance and constitute new families. Trans-acting siRNAs (ta-siRNAs) were even more highly enriched. Computational and gel blot analyses suggested that the minimal number of miRNAs in Arabidopsis is approximately 155. The size profile of small RNAs in rdr2 reflected enrichment of 21-nt miRNAs and other classes of siRNAs like ta-siRNAs, and a significant reduction in 24-nt heterochromatic siRNAs. Other classes of small RNAs were found to be RDR2-independent, particularly those derived from long inverted repeats and a subset of tandem repeats. The small RNA populations in other Arabidopsis small RNA biogenesis mutants were also examined; a dcl2/3/4 triple mutant showed a similar pattern to rdr2, whereas dcl1-7 and rdr6 showed reductions in miRNAs and ta-siRNAs consistent with their activities in the biogenesis of these types of small RNAs. Deep sequencing of mutants provides a genetic approach for the dissection and characterization of diverse small RNA populations and the identification of low abundance miRNAs.

Visual pattern recognition models for remote sensing of civil infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.