Vehicle to Grid Demonstration Project

This report summarizes the activities and accomplishments of a two-year DOE-funded project on Grid-Integrated Vehicles (GIV) with vehicle to grid power (V2G). The project included several research and development components: an analysis of US driving patterns; an analysis of the market for EVs and V2G-capable EVs; development and testing of GIV components (in-car and in-EVSE); interconnect law and policy; and development and filing of patents. In addition, development activities included GIV manufacturing and licensing of technologies developed under this grant. Also, five vehicles were built and deployed, four for the fleet of the State of Delaware, plus one for the University of Delaware fleet

MicroRNAs of Gallid and Meleagrid herpesviruses show generally conserved genomic locations and are virus-specific

AbstractMany herpesviruses, including Marek's disease viruses (MDV1 and MDV2), encode microRNAs. In this study, we report microRNAs of two related herpesviruses, infectious laryngotracheitis virus (ILTV) and herpesvirus of turkeys (HVT), as well as additional MDV2 microRNAs. The genome locations, but not microRNA sequences, are conserved among all four of these avian herpesviruses. Most are clustered in the repeats flanking the unique long region (I/TRL), except in ILTV which lacks these repeats. Two abundant ILTV microRNAs are antisense to the immediate early gene ICP4. A homologue of host microRNA, gga-miR-221, was found among the HVT microRNAs. Additionally, a cluster of HVT microRNAs was found in a region containing two locally duplicated segments, resulting in paralogous HVT microRNAs with 96–100% identity. The prevalence of microRNAs in the genomic repeat regions as well as in local repeats suggests the importance of genetic plasticity in herpesviruses for microRNA evolution and preservation of function

The use of organizational self-design to coordinate multiagent systems

Multiagent systems are increasingly being used to solve a wide variety of problems in a range of applications such as distributed sensing, information retrieval, workflow and business process management, air traffic control and spacecraft control, amongst others. Each of these systems has to be designed at two levels: the micro-architecture level, which involves the design of the individual agents and the macro-architecture level which involves the design of the agents' organizational structure. In this research, we are primarily concerned with the agents' macro-architecture. At the macro-architecture level, the multiagent designer is concerned with issues such as the number of agents needed to solve the problem, the assignment of tasks to the agents and the coordination mechanisms being used. The design of the agents' macro-architecture is complicated by the fact that there is no best way to organize and all ways of organizing are not equally effective. Instead the optimal organizational structure depends on the problem at hand and the environmental conditions under which the problem needs to be solved. In some cases, the environmental conditions may not be known a priori, at design time, in which case the multi-agent designer does not know how to develop an optimal organizational structure. In other cases, the environmental conditions may change requiring a re-design of the agents' macro-architecture. These are only a few of the many hurdles confronting the macro-architecture designer. In our research, we simplify the macro-architectural design by passing on some of the macro-architectural design responsibilities to the agents themselves. That is, instead of manually designing the macro-architecture of a multiagent system at design time, we allow the agents to come up with their own organizational structure at run time. This approach is known as Organizational Self Design (OSD) and it allows the agents to adapt their organizational structure to changing environmental conditions and differences in the problems being solved. Most of the current work on OSD has focused on task-oriented domains. In our research, we extend OSD to apply to worth-oriented domains, the hardest class of problems. Our research focuses on developing algorithms and mechanisms that allow (a) the generation of agents as an artifact of the system; and (b) the generation of different organizational structures that make different quality/cost tradeoffs based on the organizational design constraints specified and the performance criteria being optimized. Such tradeoffs are not possible in task-oriented and state-oriented domains

Organizational self-design in semi-dynamic environments

Abstract. Organizations are an important basis for coordination in multiagent systems. However, there is no best way to organize and all ways of organizing are not equally effective. Attempting to optimize an organizational structure depends strongly on environmental features including problem characteristics, available resources, and agent capabilities. If the environment is dynamic, the environmental conditions or the problem task structure may change over time. This precludes the use of static, design-time generated, organizational structures in such systems. On the other hand, for many real environments, the problems are not totally unique either: certain characteristics and conditions change slowly, if at all, and these can have an important effect in creating stable organizational structures. Organizational-Self Design (OSD) has been proposed as an approach for constructing suitable organizational structures at run-time. We extend the existing OSD approach to include worth-oriented domains, model other resources in addition to only processor resources and build in robustness into the organization. We then evaluate our approach against the contract-net approach and show that our OSD agents perform better, are more efficient and flexible to changes in the environmental constraints on the task structures.

An Investigation of Various Information Sources for Classifying Biological Names

The classification task is an integral part of named entity extraction. This task has not received much attention in the biomedical setting, partly due to the fact that protein name recognition has been the focus of the majority of the work in this field

Abstract Using name-internal and contextual features to classify biological terms

There has been considerable work done recently in recognizing named entities in biomedical text. In this paper, we investigate the named entity classification task, an integral part of the named entity extraction task. We focus on the different sources of information that can be utilized for classification, and note the extent to which they are effective in classification. To classify a name, we consider features that appear within the name as well as nearby phrases. We also develop a new strategy based on the context of occurrence and show that they improve the performance of the classification system. We show how our work relates to previous works on named entity classification in the biological domain as well as to those in generic domains. The experiments were conducted on the GENIA corpus Ver. 3.0 developed at University of Tokyo. We achieve f value of 86 in 10-fold cross validation evaluation on this corpus

A comparison of two GIV mechanisms for providing ancillary services at the University of Delaware

At the University of Delaware, we are providing ancillary services by controlling the bidirectional power transfer between 15 EVs and the grid. To control this power transfer, a set of algorithms, models and interactions is used, called a “GIV (Grid Integrated Vehicle) mechanism”. In literature, many GIV mechanisms are proposed. However, because these mechanisms are evaluated independently in specific scenarios, their differences are not always clear. In this paper, we take a first step in tackling this challenge by comparing two different GIV mechanisms in the same scenario at the University of Delaware: a decentralized and a centralized mechanism. In the decentralized mechanism, which is currently operational at our test environment, EVs decide autonomously on the amount of power available for ancillary services. In the centralized mechanism, a central server gathers all EV information and makes a decision for all EVs. In evaluation, both GIV mechanisms are compared with each other. Simulation results show that the centralized mechanism outperforms its decentralized counterpart in terms of available power for ancillary services. On the other hand, the decentralized mechanism enables large-scale integration by distributing computations across all EVs.status: publishe

An Investigation of Various Information Sources for Classifying Biological

The classification task is an integral part of named entity extraction. This task has not received much attention in the biomedical setting, partly due to the fact that protein name recognition has been the focus of the majority of the work in this field