The epimorphic hull of C(X)

AbstractThe epimorphic hull H(A) of a commutative semiprime ring A is defined to be the smallest von Neumann regular ring of quotients of A.Let X denote a Tychonoff space. In this paper the structure of H(C(X)) is investigated, where C(X) denotes the ring of continuous real-valued functions with domain X. Spaces X that have a regular ring of quotients of the form C(Y) are characterized, and a “minimum” such Y is found. Necessary conditions for H(C(X)) to equal C(Y) for some Y are obtained

Organisational Knowledge Management for Defect Reduction and Sustainable Development in Foundries

Despite many advances in the field of casting technologies the foundry industry still incurs significant lossesdue to the cost of scrap and rework with adverse effects on profitability and the environment. Approachessuch as Six Sigma, DoE, FMEA are used by foundries to address quality issues. However these approacheslack support to manage the heterogeneous knowledge created during process improvement activities. Theproposed revision of ISO9001:2015 quality standard puts emphasis on retaining organisational knowledgeand its continual use in process improvement (ISO, 2014). In this paper a novel framework for creation,storage and reuse of product specific process knowledge is presented. The framework is reviewed taking intoconsideration theoretical perspectives of organisational knowledge management as well as addressing thechallenges concerning its practical implementation. A knowledge repository concept is introduced to demonstratehow organisational knowledge can be effectively stored and reused for achieving continual processimprovement and sustainable development

On the use of socio-demographic indicators in local health planning: A Canadian non-metropolitan perspective

This paper argues that the use of socio-demographic indicators to represent actual or potential demand for mental health services needs to be informed by knowledge of local settlement conditions and trends. Following a selective review of the literature on the use of socio-demographic indicators in mental health care planning and on the spatial ecology of mental illness, a case study of a non-metropolitan jurisdiction in southern Ontario, Canada is presented. The results of a regression-based analysis reveal a strong association between the socio-demographic composition of populations and hospitalization rates for mental illness. The pattern of correlates appears to be underlain by residential location processes that 'filter' populations on the basis of age and socio-economic status. It is concluded that, in the absence of this knowledge of local social geography, applications of indicators methodologies run the risk of being 'black boxes'.socio-demographic indicators mental health health planning

Brain MRI T₁-Map and T₁-weighted Image Segmentation in a Variational Framework

© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Presented at the 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009), 19-24 April 2009, Taipei, Taiwan.DOI: 101109/ICASSP.2009.4959609In this paper we propose a constrained version of Mumford- Shah’s[1] segmentationwith an information-theoretic point of view[2] in order to devise a systematic procedure to segment brain MRI data for two modalities of parametric T₁-Map and T₁-weighted images in both 2-D and 3-D settings. The incorporation of a tuning weight in particular adds a probabilistic flavor to our segmentation method, and makes the three-tissue segmentation possible. Our method uses region based active contours which have proven to be robust. The method is validated by two real objects which were used to generate T₁- Maps and also by two simulated brains of T₁-weighted data from the BrainWeb[3] public database