The development of a regional energy assessment model

Shropshire Energy Team initiated this study to examine consumption and associated emissions in the predominantly rural county of Shropshire. Current use of energy is not sustainable in the long term and there are various approaches to dealing with the environmental problems it creates. Energy planning by a local authority for a sustainable future requires detailed energy consumption and environmental information. This information would enable target setting and the implementation of policies designed to encourage energy efficiency improvements and exploitation of renewable energy resources. This could aid regeneration strategies by providing new employment opportunities. Associated reductions in carbon dioxide and other emissions would help to meet national and international environmental targets. In the absence of this detailed information, the objective was to develop a methodology to assess energy consumption and emissions on a regional basis from 1990 onwards for all local planning authorities. This would enable a more accurate assessment of the relevant issues, such that plans are more appropriate and longer lasting. A first comprehensive set of data has been gathered from a wide range of sources and a strong correlation was found between population and energy consumption for a variety of regions across the UK. In this case the methodology was applied to the county of Shropshire to give, for the first time, estimates of primary fuel consumption, electricity consumption and associated emissions in Shropshire for 1990 to 2025. The estimates provide a suitable baseline for assessing the potential contribution renewable energy could play in meeting electricity demand in the country and in reducing emissions. The assessment indicated that in 1990 total primary fuel consumption was 63,518,018 GJ/y increasing to 119,956,465 GJ/y by 2025. This is associated with emissions of 1,129,626 t/y of carbon in 1990 rising to 1,303,282 t/y by 2025. In 1990, 22,565,713 GJ/y of the primary fuel consumption was used for generating electricity rising to 23,478,050 GJ/y in 2025. If targets to reduce primary fuel consumption are reached, then emissions of carbon would fall to 1,042,626 by 2025, if renewable energy targets were also reached then emissions of carbon would fall to 988,638 t/y by 2025.

Manufacturing technology sourcing practices in the USA

Some companies are heavily reliant on the capabilities of their manufacturing technology for product competitiveness. Likewise, the capabilities of a manufacturing technology are dependent on the sourcing policy that the host company practices. This paper describes research that has explored a wide variety of US companies to understand manufacturing technology sourcing policies and how they have been formed. This research finds that there is a preference amongst the US organizations studied not to become involved with equipment manufacture, though some examples of full integration do occur. These policies are not determined by formalized decision processes, rather they are formed implicitly during technology choice. In this research, factors that influence a technology source have been identified. These drivers are then used to establish a methodology that will help practising managers to form a technology sourcing decision. This methodology takes into account the business demands placed on a technology, along with the characteristics of the host company's supplier base.

Sub-50 fs Yb-doped laser with anomalous-dispersion photonic crystal fiber

We report on the generation of 42 fs pulses at 1 µm in a completely fiber-integrated format, which are, to the best of our knowledge, the shortest from all-fiber-integrated Yb-doped fiber lasers to date. The ring fiber cavity incorporates anomalous-dispersion, solid-core photonic crystal fiber with low birefringence, which acts as a broadband, in-fiber Lyot filter to facilitate mode locking. The oscillator operates in the stretched-pulse regime under slight normal net cavity dispersion. The cavity generates 4.7 ps long pulses with a spectral bandwidth of 58.2 nm, which are dechirped to 42 fs via a grating pair compressor outside of the cavity. Relative intensity noise (RIN) of the laser is characterized, with the integrated RIN found to be 0.026% in the 3 Hz-250 kHz frequency range.

Modification of the B2-type matrix of aluminide diffusion coatings on nickel-base superalloys-bulk aluminide analogues

Pack aluminide coating is a useful method for conferring oxidation resistance on nickel-base superalloys. Nominally, these coatings have a matrix composed of a Ni-Al based B2-type phase (commonly denoted as Β). However, following high-temperature exposure in oxidative envi-ronments, aluminum is depleted from the coating. Aluminum depletion in turn, leads to de-stabilization of the Β phase, resulting in the formation of a characteristic lathlike Β-derivative microstructure. This article presents a transmission electron microscopy study of the formation of the lathlike Β-derivative microstructure using bulk nickel aluminides as model alloys. In the bulk nickel aluminides, the lathlike microstructure has been found to correspond to two distinct components: L10-type martensite and a new Β derivative. The new Β derivative is characterized and the conditions associated with the presence of this feature are identified and compared with those leading to the formation of the L10 martensitic phase. © 1995 The Minerals, Metals & Material Society.

Generalized KPCA by adaptive rules in feature space

Principal component analysis (PCA) is well recognized in dimensionality reduction, and kernel PCA (KPCA) has also been proposed in statistical data analysis. However, KPCA fails to detect the nonlinear structure of data well when outliers exist. To reduce this problem, this paper presents a novel algorithm, named iterative robust KPCA (IRKPCA). IRKPCA works well in dealing with outliers, and can be carried out in an iterative manner, which makes it suitable to process incremental input data. As in the traditional robust PCA (RPCA), a binary field is employed for characterizing the outlier process, and the optimization problem is formulated as maximizing marginal distribution of a Gibbs distribution. In this paper, this optimization problem is solved by stochastic gradient descent techniques. In IRKPCA, the outlier process is in a high-dimensional feature space, and therefore kernel trick is used. IRKPCA can be regarded as a kernelized version of RPCA and a robust form of kernel Hebbian algorithm. Experimental results on synthetic data demonstrate the effectiveness of IRKPCA. © 2010 Taylor & Francis.