Appendix H: The Lifecycle of Materials: An Appendix to the Report, “A Lifecycle Emissions Model (LEM): Lifecycle Emissions From Transportation Fuels, Motor Vehicles, Transportation Modes, Electricity Use, Heating and Cooking Fuels, and Materials”

This report is an appendix to the report titled "'A Lifecycle emissions model (LEM): lifecycle emissions from transportation fuels motor vehicles, transportation modes, electricity use, heating and cooking fuels, and materials". This report presents an analysis of the energy and emissions associated with the lifecycle of materials and automobiles. The materials composition of motor vehicles is discussed including descriptions of manufacturing processes, tabulations of energy and emissions data, and data sources. Energy use in and emissions from the assembly of motor vehicles is discussed as well as the transportation of raw materials, semi-fabricated products, and motor vehicles. The report also discusses energy used to make agricultural chemicals, with focus on materials used in automobiles

Modelling the air-gap field strength of electric machines to improve performance of haptic mechanisms

The air-gap of electro-magnetic (EM) actuators determines key operating parameters such as their ability to generate force. In haptic devices these parameters are not optimised for the conditions typically seen in operation and include the heat produced in the air-gap, the volume of the air-gap, and the intensity and direction of the magnetic field. The relationship between these parameters is complex thus design decisions are difficult to make. This paper considers the role of the radial magnetic field in cylindrical electric motors, a type often used in haptic devices. Two models are derived and compared with experimental measurements. The first model is a closed form solution, the second is a classic Poisson solution to Ampere's equation. These models are shown to be valid for making more general design decisions in relation to haptic actuators, and in particular allow an evaluation of the trade off between the volume of the air-gap, the resulting radial magnetic field and hence heat generated and the resulting forces

Passive ocean acoustic tomography: theory and experiment

In this paper the Passive Ocean Acoustic Tomography (P-OAT) methodology is presented. This technique, avoiding the use of a dedicated active sound source, estimates the sea water temperature spatial distribution from the received noise emitted from ships of opportunity. The feasibility of the proposed methodology has been confirmed both by test-runs on semi-synthetic data and by the use of real acoustic and environmental data collected during INTIMATE00 experiment performed on October 2000 in the Atlantic Ocean off the Portuguese coasts

Preliminary deployment of Grid-assisted oceanographic applications

Abstract. Grid integration of OGS oceanographic remote instruments and coupled physical-biogeochemical model has been explored in the framework of the EC-FP7 DORII project. We discuss here the first preliminary results achieved, describing the different tools developed with the support of the project consortium. A general background of the Grid technology for the e-Science is also provided.</p

Pre-operational short-term forecasts for Mediterranean Sea biogeochemistry

Operational prediction of the marine environment is recognised as a fundamental research issue in Europe. We present a pre-operational implementation of a biogeochem- ical model for the pelagic waters of the Mediterranean Sea, developed within the framework of the MERSEA-IP Euro- pean project. The OPATM-BFM coupled model is the core of a fully automatic system that delivers weekly analyses and forecast maps for the Mediterranean Sea biogeochem- istry. The system has been working in its current configura- tion since April 2007 with successful execution of the fully automatic operational chain in 87% of the cases while in the remaining cases the runs were successfully accomplished af- ter operator intervention. A description of the system devel- oped and also a comparison of the model results with satel- lite data are presented, together with a measure of the model skill evaluated by means of seasonal target diagrams. Future studies will address the implementation of a data assimila- tion scheme for the biogeochemical compartment in order to increase the skill of the model’s performance

Model Order Reduction for Rotating Electrical Machines

The simulation of electric rotating machines is both computationally expensive and memory intensive. To overcome these costs, model order reduction techniques can be applied. The focus of this contribution is especially on machines that contain non-symmetric components. These are usually introduced during the mass production process and are modeled by small perturbations in the geometry (e.g., eccentricity) or the material parameters. While model order reduction for symmetric machines is clear and does not need special treatment, the non-symmetric setting adds additional challenges. An adaptive strategy based on proper orthogonal decomposition is developed to overcome these difficulties. Equipped with an a posteriori error estimator the obtained solution is certified. Numerical examples are presented to demonstrate the effectiveness of the proposed method

Novel complex MAD phasing and RNase H structural insights using selenium oligonucleotides

The crystal structures of protein–nucleic acid complexes are commonly determined using selenium-derivatized proteins via MAD or SAD phasing. Here, the first protein–nucleic acid complex structure determined using selenium-derivatized nucleic acids is reported. The RNase H–RNA/DNA complex is used as an example to demonstrate the proof of principle. The high-resolution crystal structure indicates that this selenium replacement results in a local subtle unwinding of the RNA/DNA substrate duplex, thereby shifting the RNA scissile phosphate closer to the transition state of the enzyme-catalyzed reaction. It was also observed that the scissile phosphate forms a hydrogen bond to the water nucleophile and helps to position the water molecule in the structure. Consistently, it was discovered that the substitution of a single O atom by a Se atom in a guide DNA sequence can largely accelerate RNase H catalysis. These structural and catalytic studies shed new light on the guide-dependent RNA cleavage

Grouped graphical Granger modeling for gene expression regulatory networks discovery

We consider the problem of discovering gene regulatory networks from time-series microarray data. Recently, graphical Granger modeling has gained considerable attention as a promising direction for addressing this problem. These methods apply graphical modeling methods on time-series data and invoke the notion of ‘Granger causality’ to make assertions on causality through inference on time-lagged effects. Existing algorithms, however, have neglected an important aspect of the problem—the group structure among the lagged temporal variables naturally imposed by the time series they belong to. Specifically, existing methods in computational biology share this shortcoming, as well as additional computational limitations, prohibiting their effective applications to the large datasets including a large number of genes and many data points. In the present article, we propose a novel methodology which we term ‘grouped graphical Granger modeling method’, which overcomes the limitations mentioned above by applying a regression method suited for high-dimensional and large data, and by leveraging the group structure among the lagged temporal variables according to the time series they belong to. We demonstrate the effectiveness of the proposed methodology on both simulated and actual gene expression data, specifically the human cancer cell (HeLa S3) cycle data. The simulation results show that the proposed methodology generally exhibits higher accuracy in recovering the underlying causal structure. Those on the gene expression data demonstrate that it leads to improved accuracy with respect to prediction of known links, and also uncovers additional causal relationships uncaptured by earlier works