Potential of series hybrid drive systems to reduce fuel use and emissions in domestic vehicles : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Energy Development at Massey University

For the last 100 years the reciprocating internal combustion engine (ICE) powered vehicle and the fossil oil that it is reliant upon has dominated our transport culture. In terms of land-based domestic and commercial transport, we would be completely lost without it. Most goods and services are transported by it. We use it to get to work, pick up the children, do the shopping and for some of us the domestic vehicle is an extension of our personalities. It has become an indispensable business and recreational tool of modern contemporary society. The conventional ICE powered vehicle initially gave us freedom, the ability to go wherever and whenever we wanted and to do it relatively cheaply. Now, however, our ever-increasing search for more mobility and the transport of goods and services has imprisoned modern society into high levels of emissions, pollution, increasing oil dependence, oil depletion concerns and the creation of resource wars in search of more energy (oil) to pursue our need for travel, transportation and the proper running of a modern society. This is because transport in general registers the most rapid increases in energy consumption and remains almost entirely dependent on oil because of few substitution possibilities to less carbon intensive fuels (IEA, 2000b). In most affluent countries the ICE powered vehicle meets 75 to 80 percent of personal travel (Sperling, 1996a)

Forecasting with measurement errors in dynamic models

This paper explores the effects of measurement error on dynamic forecasting models. The paper sets out to illustrate a trade off that confronts forecasters and policymakers when they use data that are measured with error. On the one hand, observations on recent data give valuable clues as to the shocks that are hitting the system and will be propagated into the variables to be forecast (and which ultimately will inform monetary policy). But on the other, those recent observations are likely to be those least well measured. Two broad classes of results are illustrated. The first relates to cases where it is imagined that the forecaster takes the coefficients in the data generating process as a given, and has to choose how much of the historical time series of data to use to form a forecast. It is shown that if recent data is sufficiently badly measured, relative to older data, that it can be optimal in this case not to use old data at all. The second class of results is more general. Here, it is shown that for a general class of linear autoregressive forecasting models, the optimal weight to place on a data observation of some age, relative to the weight in the true data generating process, will depend on the measurement error in that data. The gains to be had in forecasting are illustrated using a model of UK business investment growth.measurement error, forecasting, signal-extraction

Using time-varying VARs to diagnose the source of ‘Great Moderations’: a Monte Carlo analysis

In this paper, we assess the ability of time-varying VAR models to correctly diagnose the source of ‘Great Moderations’ generated in simulations of a learning model. We find that, in general, they can. For example, in data sets with Great Moderations generated by good policy, the VAR correctly identifies a downward shift in the policy disturbance. And it shows that if the policy behaviour associated with the latter part of the sample (during which policy is conducted well) are applied to the earlier part of the sample, the implied variances of output, inflation and interest rates would have been much lower. An important caveat to our results is that they appear to be sensitive to the method used to identification of monetary policy shocks. When we identify monetary policy shocks using a Cholesky decomposition, the VAR provides quite clear evidence in favour of the correct explanation for our simulated Great Moderations When sign restrictions are used to identify the monetary policy shocks, conclusions from the counterfactual experiments are less precise. The contrast between our results and previous work based on Monte Carlo evidence using RE models suggests that the ability of VARs to correctly diagnose the source of the Great Moderation may be dependent on the nature of the expectations-formation process in the private sector.

The determination of the topological structure of skin friction lines on a rectangular wing-body combination

A short tutorial in the application of topological ideas to the intepretation of oil flow patterns is presented. Topological concepts such as critical points, phase portraits, topological stability, and indexing are discussed. These concepts are used in an ordered procedure to construct phase portraits of skin friction lines with oil flow patterns for a wing-body combination and two angles of attack. The relationship between the skin friction phase portrait and planar cuts of the velocity field is also discussed

A platform for discovering and sharing confidential ballistic crime data.

Criminal investigations generate large volumes of complex data that detectives have to analyse and understand. This data tends to be "siloed" within individual jurisdictions and re-using it in other investigations can be difficult. Investigations into trans-national crimes are hampered by the problem of discovering relevant data held by agencies in other countries and of sharing those data. Gun-crimes are one major type of incident that showcases this: guns are easily moved across borders and used in multiple crimes but finding that a weapon was used elsewhere in Europe is difficult. In this paper we report on the Odyssey Project, an EU-funded initiative to mine, manipulate and share data about weapons and crimes. The project demonstrates the automatic combining of data from disparate repositories for cross-correlation and automated analysis. The data arrive from different cultural/domains with multiple reference models using real-time data feeds and historical databases