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.

Modern American lathe practice; a new complete and practical work on the "king of machine shop tools," the American lathe. Giving its origin and development. Its design. Its various types as manufactured by different builders ... etc.

Mode of access: Internet.

Information retrieval and machine translation : based on the International Conference for Standards on a Common Language for Machine Searching and Translation /

Mode of access: Internet.

The Coordinated City’s Mutation Machine: Capitalism, Sympathy, and Urbanization in Seattle’s South Lake Union Neighborhood

Thesis (Ph.D.)--University of Washington, 2016-06

Optimization and Machine Learning Frameworks for Complex Network Analysis

Thesis (Ph.D.)--University of Washington, 2016-06

Depth of cure and surface microhardness of composite resin cured with blue LED curing lights

Objectives. This study examined the depth of cure and surface microhardness of Filtek Z250 composite resin (3M-Espe) (shades B1, A3, and C4) when cured with three commercially available tight emitting diode (LED) curing lights [E-light (GC), Elipar Freelight (3M-ESPE), 475H (RF Lab Systems)], compared with a high intensity quartz tungsten halogen (HQTH) light (Kerr Demetron Optilux 501) and a conventional quartz tungsten halogen (QTH) lamp (Sirona S1 dental unit). Methods. The effects of light source and resin shade were evaluated as independent variables. Depth of cure after 40 s of exposure was determined using the ISO 4049:2000 method, and Vickers hardness determined at 1.0 mm intervals. Results. HQTH and QTH lamps gave the greatest depth of cure. The three LED lights showed similar performances across all parameters, and each unit exceeded the ISO standard for depth of cure except GC ELight for shade B1. In terms of shade, LED lights gave greater curing depths with A3 shade, while QTH and HQTH tights gave greater curing depths with C4 shade. Hardness at the resin surface was not significantly different between LED and conventional curing lights, however, below the surface, hardness reduced more rapidly for the LED lights, especially at depths beyond 3 mm. Significance. Since the performance of the three LED lights meets the ISO standard for depth of cure, these systems appear suitable for routine clinical application for resin curing. (C) 2003 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Human behaviour modelled as a statistico-deterministic turing machine

'Free will' and its corollary, the concept of individual responsibility are keystones of the justice system. This paper shows that if we accept a physics that disallows time reversal, the concept of 'free will' is undermined by an integrated understanding of the influence of genetics and environment on human behavioural responses. Analysis is undertaken by modelling life as a novel statistico-deterministic version of a Turing machine, i.e. as a series of transitions between states at successive instants of time. Using this model it is proven by induction that the entire course of life is independent of the action of free will. Although determined by prior state, the probability of transitions between states in response to a standard environmental stimulus is not equal to 1 and the transitions may differ quantitatively at the molecular level and qualitatively at the level of the whole organism. Transitions between states correspond to behaviours. It is shown that the behaviour of identical twins (or clones), although determined, would be incompletely predictable and non-identical, creating an illusion of the operation of 'free will'. 'Free will' is a convenient construct for current judicial systems and social control because it allows rationalization of punishment for those whose behaviour falls outside socially defined norms. Indeed, it is conceivable that maintenance of ideas of free will has co-evolved with community morality to reinforce its operation. If the concept is free will is to be maintained it would require revision of our current physical theories.

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 super-type molecules with excellent accuracy, even for molecules where no binding data are currently available.

Applying machine learning to catalogue matching in astrophysics

We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study, we take two partially matched catalogues where one of the two catalogues has a large positional uncertainty. The two catalogues we used here were taken from the H I Parkes All Sky Survey (HIPASS) and SuperCOSMOS optical survey. Previous work had matched 44 per cent (1887 objects) of HIPASS to the SuperCOSMOS catalogue. A supervised learning algorithm was then applied to construct a model of the matched portion of our catalogue. Validation of the model shows that we achieved a good classification performance (99.12 per cent correct). Applying this model to the unmatched portion of the catalogue found 1209 new matches. This increases the catalogue size from 1887 matched objects to 3096. The combination of these procedures yields a catalogue that is 72 per cent matched.

Microhardness mapping and the hardness-yield strength relationship in high-pressure diecast magnesium alloy AZ91

Microhardness maps of cross-sections of high-pressure diecast test bars of AZ91 have been determined. Specimens with rectangular cross-sections, 1, 2 and 3 mm thick, or with a circular cross-section 6.4 mm in diameter, have been studied. The hardness is generally higher near the edges in all specimens, and more so near the corners of the rectangular specimens. The hardness at the center of the castings is generally lower, due to a coarser solidification microstructure and the concentration of porosity. The evidence confirms that the surface of the castings is harder than the core, but it does not support the concept of a skin with a sharp. and definable boundary. This harder layer is irregular in hardness and depth and is not equally hard on opposite sides of the casting. The mean hardness obtained by integrating the microhardness maps over the entire cross-section increased with decreasing thickness of the bars, and was found to be in good correlation with each bar's yield strength. (c) 2005 Elsevier B.V. All rights reserved.

Extreme learning machine for predicting HLA-peptide binding

Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and most importantly - prevention from over-fitting for prediction of peptide binding to HLA.

Using machine vision classification to control access of animals to water

Invasive vertebrate pests together with overabundant native species cause significant economic and environmental damage in the Australian rangelands. Access to artificial watering points, created for the pastoral industry, has been a major factor in the spread and survival of these pests. Existing methods of controlling watering points are mechanical and cannot discriminate between target species. This paper describes an intelligent system of controlling watering points based on machine vision technology. Initial test results clearly demonstrate proof of concept for machine vision in this application. These initial experiments were carried out as part of a 3-year project using machine vision software to manage all large vertebrates in the Australian rangelands. Concurrent work is testing the use of automated gates and innovative laneway and enclosure design. The system will have application in any habitat throughout the world where a resource is limited and can be enclosed for the management of livestock or wildlife.

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.