To integrate wind and solar energy in electric systems, new technologies, such as energy storage and demand response, have been proposed to increase system flexibility. Control approaches and market rules are being developed accordingly to better manage these resources in multiple time scales. Therefore, models and software tools capable of performing hourly scheduling, intra-hour dispatch, and automatic generation control simulations are needed for testing these control approaches and for evaluating new market rules. At Pacific Northwest National Laboratory, we have developed an Electric System Intra-Hour Operation Simulator (ESIOS). Expanding this simulator as a service platform can benefit a larger community involved in exploring new models and controls and reducing the burden of maintaining a computing platform. Moreover the feedback and contribution from community users can help further improve the features of this simulation ecosystem. In this paper, we describe the function of this simulator. Based on our experience, we discuss the architecture design perspectives for transforming this simulator to an integrated collaborative service platform.

Group recommendation plays a significant role in Social TV systems, where online friends form into temporary groups to enjoy watching video together and interact with each other. Online microblogging systems introduce the "following" relationship that reflects the common interests between users in a group and external representative followees outside the group. Traditional group recommendation only considers internal group members' preferences and their relationship. In our study, we measure the external followees' impact on group interest and establish group preference model based on external experts' guidance for group recommendation. In addition, we take advantage of the current watching video to improve context-aware recommendations. Experimental results show that our solution works much better in situations of high group dynamic and inactive group members than traditional approaches.

BACKGROUND: Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. RESULTS: The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. CONCLUSION: In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for analysis of subsequent experimental data. Additionally, PLASMID can be used to construct virtual microarrays with genomes from public databases, which can then be used to identify an optimal set of probes.

This paper considers the problem of learning the parameters of a Bayesian network, assuming the structure of the network is given, from a privacy-sensitive dataset that is distributed between multiple parties. For a binary-valued dataset, we show that the count information required to estimate the conditional probabilities in a Bayesian network can be obtained as a solution to a set of linear equations involving some inner product between the relevant different feature vectors. We consider a random projection-based method that was proposed elsewhere to securely compute the inner product (with a modified implementation of that method).