Facilitating Knowledge Sharing and Analysis in EnergyInformatics with the Ontology for Energy Investigations (OEI)

Just as the other informatics-related domains (e.g., Bioinformatics) have discovered in recent years, the ever-growing domain of Energy Informatics (EI) can benefit from the use of ontologies, formalized, domain-specific taxonomies or vocabularies that are shared by a community of users. In this paper, an overview of the Ontology for Energy Investigations (OEI), an ontology that extends a subset of the well-conceived and heavily-researched Ontology for Biomedical Investigations (OBI), is provided as well as a motivating example demonstrating how the use of a formal ontology for the EI domain can facilitate correct and consistent knowledge sharing and the multi-level analysis of its data and scientific investigations

Towards a Cloud Infrastructure for Energy Informatics

The development of cloud computing has achieved the goal of computing as a service, abstracting the resource as a cloud. This service has extended to include not only computation but its associated storage and communication components as well. The smart grid hopes to integrate the dynamics of distributed generation and demand. If the computational requirements of these demands are as dynamic as the phenomena they seek to control, then the cloud computing model provides an appropriately flexible platform for smart grid computing. This paper evaluates the Cloud for Energy Informatics (CEI), a computational-control abstraction that provides flexible and efficient computational resources on-demand as defined by the smart grid. We focus on how the CEI addresses performance and efficiency measures of smart grid related computation such as latency, bandwidth, storage and compute cycles. We compare CEI with traditional approaches using simulation to quantify the resource savings, efficiency and reliability gains from switching to a CEI model

An Induced Hypersensitive-Like Response Limits Expression of Foreign Peptides via a Recombinant TMV-Based Vector in a Susceptible Tobacco

BACKGROUND: By using tobacco mosaic virus (TMV)-based vectors, foreign epitopes of the VP1 protein from food-and-month disease virus (FMDV) could be fused near to the C-terminus of the TMV coat protein (CP) and expressed at high levels in susceptible tobacco plants. Previously, we have shown that the recombinant TMV vaccines displaying FMDV VP1 epitopes could generate protection in guinea pigs and swine against the FMDV challenge. Recently, some recombinant TMV, such as TMVFN20 that contains an epitope FN20 from the FMDV VP1, were found to induce local necrotic lesions (LNL) on the inoculated leaves of a susceptible tobacco, Nicotiana tabacum Samsun nn. This hypersensitive-like response (HLR) blocked amplification of recombinant TMVFN20 in tobacco and limited the utility of recombinant TMV vaccines against FMDV. METHODOLOGY/PRINCIPAL FINDINGS: Here we investigate the molecular mechanism of the HLR in the susceptible Samsun nn. Histochemical staining analyses show that these LNL are similar to those induced in a resistant tobacco Samsun NN inoculated with wild type (wt) TMV. The recombinant CP subunits are specifically related to the HLR. Interestingly, this HLR in Samsun nn (lacking the N/N'-gene) was able to be induced by the recombinant TMV at both 25°C and 33°C, whereas the hypersensitive response (HR) in the resistant tobacco plants induced by wt TMV through the N/N'-gene pathways only at a permissive temperature (below 30°C). Furthermore, we reported for the first time that some of defense response (DR)-related genes in tobacco were transcriptionally upregulated during HLR. CONCLUSIONS: Unlike HR, HLR is induced in the susceptible tobacco through N/N'-gene independent pathways. Induction of the HLR is associated with the expression of the recombinant CP subunits and upregulation of the DR-related genes

Scalable path planning and simulation via numerical ring prediction and path optimization through model-centric GPU data analysis

The major research purpose is model-based 3D visualization by path parameter tuning via GPU simulation and model fitting as well as remodeling of system integration for path planning by local scalable solution towards efficiency. Through Hybrid Dynamic Tree(HDT) with branch topology mapping and numerical depth analysis with grid modeling measurement for geometry edges and features, we optimize ring model of 3D CAD design and computer aided manufacturing(CAM) towards controllable path planning with user interactive decision towards efficiency and optimization in embedded system software. This is one of the motivations. The purpose of 3D STL model is for understanding optimal variable selection and limitations of linearization methods for nonlinear path planning. The model also helps path model fitting, which are through simulation parameters and machining. Stochastic models enable evaluation of path validation and data distribution. Path validation and protection help reduce path collision and error as well as avoid noneffective path retraction of travel distance. Accurate simulation saves actual machining time and power towards energy efficiency while reducing material waste and cost. The innovations of our scheme are a scalable path planning solution based on local ring prediction and accessibility map sequence data analysis towards parallel optimization. We manage to extract 2D image features into 3D printing product through subtractive 3D printing on Computer Numerical Control(CNC) machine. The benefit of discussed solution is both flexibility of 3D printing product combined with intelligent control feature of CNC machine with high cutting speed and power. Graphical Processing Unit(GPU) speeds up the interactive simulation of material removal process for path iteration layer by layer in an adaptive way. Smart data modeling is going to drive low-cost and high-quality machining for both time-saving and virtual event planning. Math models enable digital optimization and cloud computing of distributed path as well as understanding the path planning limits. Digital parallel data metrics such as efficiency in Service-Oriented Cloud is discussed. Path planning strategy for path partition and post-processing in G-code optimization as well as scalability study of local and global path are also studied.Ph.D

Power Control By Distribution Tree With Classified Power Capping In Cloud Computing

Power management is becoming very important in data centers. Cloud computing is also one of the newer promising techniques, that are appealing to many big companies. To apply power management in cloud computing has been proposed and considered as green computing. Cloud computing, due to its dynamic structure and property in online services, differs from current data centers in terms of power management. To better manage the power consumption of web services in cloud computing with dynamic user locations and behaviors, we propose a power budgeting design based on the logical level, using a distribution tree. By setting multiple trees, we can differentiate and analyze the effect of workload types and Service Level Agreements (SLAs, e.g. response time) in terms of power characteristics. Base on these, we introduce classified power capping for different services as the control reference to maximize power saving when there are mixed workloads. © 2010 IEEE

Classified Power Capping By Network Distribution Trees For Green Computing

Power management is becoming very important in data centers. To apply power management in cloud computing, Green Computing has been proposed and considered. Cloud computing is one of the new promising techniques, that are appealing to many big companies. In fact, due to its dynamic structure and property in online services, cloud computing differs from current data centers in terms of power management. To better manage the power consumption of web services in cloud computing with dynamic user locations and behaviors, we propose a power budgeting design based on the logical level, using distribution trees. By setting multiple trees or forest, we can differentiate and analyze the effect of workload types and Service Level Agreements (SLAs, e. g. response time) in terms of power characteristics. Based on these, we introduce classified power capping for different services as the control reference to maximize power saving when there are mixed workloads. © 2011 Springer Science+Business Media, LLC

Classified Power Capping With Distribution Trees In Cloud Computing

Power management is becoming very important in data centers. Cloud computing is also one of the newest promising datacenter techniques which is appealing to many big companies. As cloud computing is different from current data centers in terms of power management due to a dynamic structure and property for its online service. Power budgeting, in terms of its important role in power management, provides powerful solutions for cloud computing with dynamic capabilities. To be specific, existing methods for datacenters are based on power distribution units (PD U) divided by fixe dlocations on physical levels. However, it is not suitable for cloud with the dynamic property. We propose a power management design based at the logical level which uses a distribution tree with class ified power capping by different service or workload types. By setting multiple trees, we can differentiate and analyze the effect of workload types and Service Level Agreements (S LAs) in terms of power characteristics. © 2011 IEEE