Abstract: This disclosure discusses systems, methods, and techniques for charging a plurality of electric vehicles (EVs). In one aspect, a system may include a plurality of electric vehicle supply equipment (EVSE), and the EVSEs are coupled between a power grid and the EVs. The system may also include a plurality of power meters, and a respective power meter may measure an amount of power received from a respective EV. The system may determine an instance of communication connectivity between the system and the EVs. After establishing communication, the system may communicate with the EVs using any communication protocol and/or standard. The communication may then enable the system to receive EV characteristics, for example, a state of charge of each EV. Based on the EV characteristics, the system may then determine power conversion efficiencies of each EVSE. By so doing, the system can determine power losses associated with each EVSE.

Abstract: A method for organizing genomic and proteomic information in a database having a plurality of data nodes and a plurality of links capable of binding data nodes two by two, genomic and proteomic information being stored in a plurality of independent databases and an access method to access by query the contents of a database organized by the preceding organization method for a defined query. The method uses the steps of: a) organizing of the query in the form of a graph pattern having a plurality of nodes and a plurality of links binding the nodes two by two, the nodes and the links being taken in the set of data node types and links types respectively of the organized database: b) seeking the database of a set of nodes and links whose type corresponding to the query thus organized, the set of nodes and links forming a set of occurrences of the graph pattern; c) provisioning the terminal with the nodes and links.

Abstract: Disclosed is a technique to predict protein-protein interaction maps across organisms called an “interacting-domain profile pair” method. The method uses a high-quality protein interaction map with interaction domain information as input to predict an interaction map in another organism. It combines sequence similarity searches with clustering based on interaction patterns and interaction domain information. Results can be compared with predictions from a naive inference method based only on full-length protein sequence similarity. This domain-based method can eliminate a significant amount of false-positives compared to methods that are the consequences of multi-domain proteins; as well as increase the sensitivity by identifying new potential interactions.