A Bayesian network hybrid model for representing accident and emergency waiting times

The paper introduces a new modeling approach that represents the waiting times in an accident and emergency (A&E) department in a UK based national health service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (emergency rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.

A constraints-based resource discovery model for multi-provider cloud environments

Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user's point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services, such as network configuration or data replication, and operating costs, such as hosting cost and data throughput. Providers' cost models often change and new commodity cost models, such as spot pricing, have been introduced to offer significant savings. In this paper, a software abstraction layer is used to discover infrastructure resources for a particular application, across multiple providers, by using a two-phase constraints-based approach. In the first phase, a set of possible infrastructure resources are identified for a given application. In the second phase, a heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic is most appropriate; for others a performance-based heuristic may be used. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental result shows the proposed model could dynamically select an appropriate set of resouces that match the application's requirements.

The influence of culture on home-based family caregiving at end-of-life: A case study of Dutch reformed family care givers in Ontario, Canada

Families are facing increased pressure to provide care to their terminally-ill or dying kin in the home. It is known that balancing care with other personal and social roles can adversely affect family caregivers' (FCGs) health, yet access to supportive services which can mitigate burden is often inadequate. Cultural factors are known to shape the experience of caregiving; however, most research to date tends to neglect the experiences of FCGs from different cultural groups. This understanding is necessary to ensure that supportive services are both meaningful and culturally-appropriate. Using qualitative methods, we undertook longitudinal research with a sample of Dutch Reformed FCGs (n = 5) to understand their experiences of caregiving and bereavement. The results of the study are suggestive of a cultural specificity with respect to caregiving that impacts both responsibilities and reactions to care. Three themes were salient to this group as a cultural entity: cultural attitudes towards care, religious beliefs and coping, and c. ulturally-informed care-seeking behaviours. These three themes were seen to be a function of their religious and ethnic identities and were reinforced by ties to the communities in which they resided. Cultural identity provided a framework through which to understand and make sense of the experience, while group membership provided access to networks of informal support. This research contributes to the geographical literature on care/caregiving by providing insight into the social, cultural and religious context of informal family caregiving with a population who live in close geographic proximity. On a practical level, this case study indicates the importance of considering how these factors may operate in other settings in order to implement timely and appropriate interventions to better support FCGs who are caring for their terminally-ill loved-ones at home.

Platform Competition as Network Contestability

Recent research in industrial organisation has investigated the essential place that middlemen have in the networks that make up our global economy. In this paper we attempt to understand how such middlemen compete with each other through a game theoretic analysis using novel techniques from decision-making under ambiguity.
We model a purposely abstract and reduced model of one middleman who provides a two-sided platform, mediating surplus-creating interactions between two users. The middleman evaluates uncertain outcomes under positional ambiguity, taking into account the possibility of the emergence of an alternative middleman offering intermediary services to the two users.
Surprisingly, we find many situations in which the middleman will purposely extract maximal gains from her position. Only if there is relatively low probability of devastating loss of business under competition, the middleman will adopt a more competitive attitude and extract less from her position.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Micro-governance and citizen-based security in Brazil

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Micro Welding of Ni-based Alloy Monel 400 Thin Foil by Pulsed Nd:YAG laser

In this study a pulsed Nd:YAG laser was used to join Monel 400 thin foil with 100 mu m thickness. Pulse energy was varied from 1.0 to 2.25J at small increments of 0.25J. The macro and microstructures were analyzed by optical microscopy, tensile shear test and microhardness. Sound laser welds without discontinuities were obtained with 1.5 J pulse energy. Results indicate that using a precise control of the pulse energy, and so a control of the bottom foil dilution rate, it is possible to weld Monel 400 thin foil. The process appeared to be very sensitive to the gap between couples.

Study of charge transport in blends of natural rubber and poly(o-methoxyaniline) based on a resistor network statistical model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)