Anesthetic Practices For Patients With Preeclampsia Or Hellp Syndrome: A Survey

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On the Optimization of WSN Deployment for Sensing Physical Phenomena: Applications to Urban Air Pollution Monitoring

International audienceWireless sensor networks (WSN) are widely used in environmental applications where the aim is to sense a physical phenomenon. Air pollution is one of the main physical phenomena that still need to be studied and characterized. Using WSN for air pollution monitoring usually targets two main applications: regular mapping and the detection of high pollution concentrations. Both of these applications need a careful deployment of sensors in order to get better knowledge of air pollution while ensuring a minimal deployment cost. In this chapter, we present three formulations of the deployment issue of sensor and sink nodes based on integer linear programming modeling (ILP) while tackling the two main applications of air pollution monitoring. In the first ILP model, we target the regular mapping of air pollution based on predicted pollution maps. In the second and third ILP model, we target the detection of pollution threshold crossings. The second model is based on predicted pollution maps as in the first one, whereas the third one is based on pollution emission inventory which describes pollution sources and their emission rates. In addition to the constraints of pollution coverage, we also ensure that the deployed networks are connected and their financial deployment cost is minimized. We perform extensive simulations in order to analyze the performance of the proposed models in terms of coverage and connectivity results

A spatial optimization approach for solving a multi-facility location problem with continuously distributed demand

Location-related decisions are important considerations in most aspects of human activity. Facility location models are usually employed to assist decision processes concerning the siting of one or more facilities in order to best serve underlying demand that is discretely or continuously distributed across space. Of interest in this chapter, it is the perspective that demand is continuously distributed. Though surfaces defined by mathematical functions or fitted through spatial interpolation can be employed to approximate a continuous demand representation, the results of both of these methods are subject to significant errors and uncertainties. For this reason, we introduce a generic location planning model and the continuous multi-Weber problem, in which facilities may be sited anywhere in space in order to best serve continuously distributed demand. Due to the complexity of the problem, a spatial optimization approach for dealing with continuous demand is proposed through the integration of optimization techniques with geographic information system (GIS) functionality. Results from empirical applications demonstrate the effectiveness of the developed approach and highlight the importance of incorporating GIS functionality into the solution process