Validation of three new measure-correlate-predict models for the long-term prospection of the wind resource

The estimation of the long-term wind resource at a prospective site based on a relatively short on-site measurement campaign is an indispensable task in the development of a commercial wind farm. The typical industry approach is based on the measure-correlate-predict �MCP� method where a relational model between the site wind velocity data and the data obtained from a suitable reference site is built from concurrent records. In a subsequent step, a long-term prediction for the prospective site is obtained from a combination of the relational model and the historic reference data. In the present paper, a systematic study is presented where three new MCP models, together with two published reference models �a simple linear regression and the variance ratio method�, have been evaluated based on concurrent synthetic wind speed time series for two sites, simulating the prospective and the reference site. The synthetic method has the advantage of generating time series with the desired statistical properties, including Weibull scale and shape factors, required to evaluate the five methods under all plausible conditions. In this work, first a systematic discussion of the statistical fundamentals behind MCP methods is provided and three new models, one based on a nonlinear regression and two �termed kernel methods� derived from the use of conditional probability density functions, are proposed. All models are evaluated by using five metrics under a wide range of values of the correlation coefficient, the Weibull scale, and the Weibull shape factor. Only one of all models, a kernel method based on bivariate Weibull probability functions, is capable of accurately predicting all performance metrics studied.

Proteomic analyses of a Listeria monocytogenes mutant lacking σB identify new components of the σB regulon and highlight a role for σB in the utilization of glycerol

In Listeria monocytogenes the alternative sigma factor σB plays important roles in both virulence and stress tolerance. In this study a proteomic approach was used to define components of the σB regulon in L. monocytogenes 10403S (serotype 1/2a). Using two-dimensional gel electrophoresis and the recently developed isobaric tags for relative and absolute quantitation technique, the protein expression profiles of the wild type and an isogenic ΔsigB deletion strain were compared. Overall, this study identified 38 proteins whose expression was σB dependent; 17 of these proteins were found to require the presence of σB for full expression, while 21 were expressed at a higher level in the ΔsigB mutant background. The data obtained with the two proteomic approaches showed limited overlap (four proteins were identified by both methods), a finding that highlights the complementarity of the two technologies. Overall, the proteomic data reaffirmed a role for σB in the general stress response and highlighted a probable role for σB in metabolism, especially in the utilization of alternative carbon sources. Proteomic and physiological data revealed the involvement of σB in glycerol metabolism. Five newly identified members of the σB regulon were shown to be under direct regulation of σB using reverse transcription-PCR (RT-PCR), while random amplification of cDNA ends-PCR was used to map four σB-dependent promoters upstream from lmo0796, lmo1830, lmo2391, and lmo2695. Using RT-PCR analysis of known and newly identified σB-dependent genes, as well as proteomic analyses, σB was shown to play a major role in the stationary phase of growth in complex media.