Integral completeness of locally convex spaces

A locally convex space X is said to be integrally complete if each continuous mapping f: [0, 1] --> X is Riemann integrable. A criterion for integral completeness is established. Readily verifiable sufficient conditions of integral completeness are proved.

‘Glimpses into the unmentionable: Montserrat, tourism and anthropological readings of “subordinate exotic” and “comic exotic” travel writing’

This article explores the relationship between anthropology and identity through the process of travel writing and reading. Specifically, the article examines a range of travel writing about the British Caribbean colony Montserrat to read into the culture of the writer. These deconstructive ‘glimpses into the unmentionable’ reveal an implicit racism. The travel writing texts are also found to divide into two types of representation – the ‘subordinate exotic’ and the ‘comic exotic’.

Optimal proportional-integral-derivative control of shape memory alloy actuators using genetic algorithm and the Preisach model

Shape memory alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics, and so on. Although the number of applications is increasing, there has been limited success in precise motion control owing to the hysteresis effect of these smart actuators. The present paper proposes an optimization of the proportional-integral-derivative (PID) control method for SMA actuators by using genetic algorithm and the Preisach hysteresis model.

A comparison of stress intensity factors through crack closure integral and other approaches using eXtended element-free Galerkin method

In this paper, a new approach for extracting stress intensity factors (SIFs) by the extended element-free Galerkin method, through a crack closure integral (CCI) scheme, is proposed. The CCI calculation is used in conjunction with a local smoothing technique to improve the accuracy of the computed SIFs in a number of case studies of linear elastic fracture mechanics. The cases involve problems of mixed-mode, curved crack and thermo-mechanical loading. The SIFs by CCI, displacement and stress methods are compared with those based on the M-integral technique reported in the literature. The proposed CCI method involves very simple relations, and still gives good accuracy. The convergence of the results is also examined.

Modified crack closure integral technique for extraction of SIFs in meshfee methods

A new approach for extracting stress intensity factors (SIFs) by the element-free Galerkin (EFG) class of methods through a modified crack closure integral (MCCI) scheme is proposed. Its primary feature is that it allows accurate calculation of mode I and mode II SIFs with a relatively simple and straightforward analysis even when a coarser nodal density is employed. The details of the adoption of the MCCI technique in the EFG method are described. Its performance is demonstrated through a number of case studies including mixed-mode and thermal problems in linear elastic fracture mechanics (LEFM). The results are compared with published theoretical solutions and those based on the displacement method, stress method, crack closure integral in conjunction with local smoothing (CCI–LS) technique, as well as the M-integral method. Its advantages are discussed.

Arcellacea (Testate Amoebae) as Bio-indicators of Road Salt Contamination in Lakes

Winter deicing operations occur extensively in mid- to high-latitude metropolitan regions around the world and result in a significant reduction in road accidents. Deicing salts can, however, pose a major threat to water quality and aquatic organisms. In this paper we examine the utility of Arcellacea (testate amoebae) for monitoring lakes that have become contaminated by winter deicing salts, particularly sodium chloride. We analysed 50 sediment samples and salt-related water-property variables (chloride concentrations; conductivity) from 15 lakes in the Greater Toronto Area and adjacent areas of southern Ontario, Canada. The sampled lakes included lakes in proximity to major highways and suburban roads, and control lakes in forested settings away from road influences. Samples from the most contaminated lakes, with chloride concentrations in excess of 400 mg/l and conductivities of >800 μS/cm, were dominated by species typically found in brackish and/or inhospitable lake environments and by lower faunal diversities (lowest Shannon Diversity Index values) than samples with lower readings. Q-R-mode cluster analysis and Detrended Correspondence Analysis (DCA) resulted in the recognition of four assemblage groupings. These reflect varying levels of salt contamination in the study lakes, along with other local influences, including nutrient loading. The response to nutrients can, however, be isolated if the planktic eutrophic indicator species Cucurbitella tricuspis is removed from the counts. The findings show that the group have considerable potential for biomonitoring in salt-contaminated lakes, and through application to lake sediment cores, may provide significant insights into long-term benthic community health, which is integral for remedial efforts.

Characterisation of pavement profile heights using accelerometer readings and a combinatorial optimisation technique

Pavement surface profiles induce dynamic ride responses in vehicles which can potentially be used to classify road surface roughness. A novel method is proposed for the characterisation of pavement roughness through an analysis of vehicle accelerations. A combinatorial optimisation technique is applied to the determination of pavement profile heights based on measured accelerations at and above the vehicle axle. Such an approach, using low-cost inertial sensors, would provide an inexpensive alternative to the costly laser-based profile measurement vehicles. The concept is numerically validated using a half-car roll dynamic model to infer measurements of road profiles in both the left and right wheel paths.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.