The mining industry in that part of northern ontario

Mode of access: Internet.

Iron and steel; a treatise on the smelting, refining, and mechanical processes of the iron and steel industry, including the chemical and physical characteristics of wrought iron, carbon, high-speed and alloy steels, cast iron, and steel castings, and the application of these materials in machine and tool construction,

Mode of access: Internet.

Summary of selected injury experience and worktime for the mining industry in the United States, 1931-77 /

Chiefly tables.

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry

International technology transfer and collaborative new product development:evidence and a case from the machine tool industry

In recent years, it has become increasingly common for companies to improve their competitiveness and find new markets by extending their operations through international new product development collaborations involving technology transfer. Technology development, cost reduction and market penetration are seen as the foci in such collaborative operations with the aim being to improve the competitive position of both partners. In this paper, the case of technology transfer through collaborative new product development in the machine tool sector is used to provide a typical example of such partnerships. The paper outlines the links between the operational aspects of collaborations and their strategic objectives. It is based on empirical data collected from the machine tool industries in the UK and China. The evidence includes longitudinal case studies and questionnaire surveys of machine tool manufacturers in both countries. The specific case of BSA Tools Ltd and its Chinese partner the Changcheng Machine Tool Works is used to provide an in-depth example of the operational development of a successful collaboration. The paper concludes that a phased coordination of commercial, technical and strategic interactions between the two partners is essential for such collaborations to work.

Visual data mining: integrating machine learning with information visualization

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. Most existing systems concentrate either on mining algorithms or on visualization techniques. Though visual methods developed in information visualization have been helpful, for improved understanding of a complex large high-dimensional dataset, there is a need for an effective projection of such a dataset onto a lower-dimension (2D or 3D) manifold. This paper introduces a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualization domain. The framework follows Shneiderman’s mantra to provide an effective user interface. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection methods, such as Generative Topographic Mapping (GTM) and Hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, billboarding, and user interaction facilities, to provide an integrated visual data mining framework. Results on a real life high-dimensional dataset from the chemoinformatics domain are also reported and discussed. Projection results of GTM are analytically compared with the projection results from other traditional projection methods, and it is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework.