In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.

We used nine complete genome sequences, from grape, poplar, Arabidopsis, soybean, lotus, apple, strawberry, cacao, and papaya, to investigate the paleohistory of rosid crops. We characterized an ancestral rosid karyotype, structured into 7/21 protochomosomes, with a minimal set of 6,250 ordered protogenes and a minimum physical coding gene space of 50 megabases. We also proposed ancestral karyotypes for the Caricaceae, Brassicaceae, Malvaceae, Fabaceae, Rosaceae, Salicaceae, and Vitaceae families with 9, 8, 10, 6, 12, 9, 12, and 19 protochromosomes, respectively. On the basis of these ancestral karyotypes and present-day species comparisons, we proposed a two-step evolutionary scenario based on allohexaploidization involving the newly characterized A, B, and C diploid progenitors leading to dominant (stable) and sensitive (plastic) genomic compartments in any modern rosid crops. Finally, a new user-friendly online tool, “DicotSyntenyViewer” (available from http://urgi.versailles.inra.fr/synteny-dicot), has been made available for accurate translational genomics in rosids.

An out-of-stock (OOS) event is referred as one of the biggest supply-chain management problem concerning retailers, distributors and consumers. We present available PCG data and discuss how to determine the importance of some features (fields), their interconnections and compare them with standard data fields used in other publicly accessible studies and recommendations from Efficient Consumer Response (ECR). We propose several models and algorithms to predict and solve Out of stock problem and at the end the computational results of these models are presented.