Photoplethysmography and electrocardiography for real time evaluation of pulse transit time A diagnostic marker of peripheral vascular diseases

In this work, we report the results of a project, devoted to the development of a real time analysis for non-invasive monitoring of pulse transit time values on people affected by peripheral vascular diseases. Pulse transit time was computed as the time difference between every R-peak of the electrocardiographic signal and the following photopletysmographic peak. Both types of signals were acquired from 12 volunteers and real time signal analysis was performed in LabVIEW. Pathological conditions were simulated on every individual by means of an external cuff pressure applied on their arm. As expected, a PTT increase for increasing values of applied pressure was observed for all the volunteers. The proposed real time analysis could be suggested as a new, non-invasive optical method for real time monitoring of pulse transit time on patients affected by peripheral vascular diseases

Plasma Dynamics

Contains table of contents for Section 2 and reports on two research projects.Princeton University/National Spherical Torus Experiment Grant S04020G PPPLU.S. Department of Energy Grant DE-FGO2-91-ER-54109National Science Foundation Grant ECS 94-24282Los Alamos National Laboratory Grant No. E29060017

The danger of mapping risk from multiple natural hazards

In recent decades, society has been greatly affected by natural disasters (e.g. floods, droughts, earthquakes), losses and effects caused by these disasters have been increasing. Conventionally, risk assessment focuses on individual hazards, but the importance of addressing multiple hazards is now recognised. Two approaches exist to assess risk from multiple-hazards; the risk index (addressing hazards, and the exposure and vulnerability of people or property at risk) and the mathematical statistics method (which integrates observations of past losses attributed to each hazard type). These approaches have not previously been compared. Our application of both to China clearly illustrates their inconsistency. For example, from 31 Chinese provinces assessed for multi-hazard risk, Gansu and Sichuan provinces are at low risk of life loss with the risk index approach, but high risk using the mathematical statistics approach. Similarly, Tibet is identified as being at almost the highest risk of economic loss using the risk index, but lowest risk under the mathematical statistics approach. Such inconsistency should be recognised if risk is to be managed effectively, whilst the practice of multi-hazard risk assessment needs to incorporate the relative advantages of both approaches

Plasma Dynamics

Contains table of contents for Section 2 and reports on three research projects.U.S. Navy - Office of Naval Research Grant N00014-90-J-4130National Science Foundation Contract ATM 94-24282U.S. Department of Energy Contract DE-FG02-91-ER-54109U.S. Department of Energy Tokamak Fusion Test Reactor Contract DE-AC02-78-ET-5101

A quantitative model for estimating risk from multiple interacting natural hazards: an application to northeast Zhejiang, China

Multi-hazard risk assessment is a major concern in risk analysis, but most approaches do not consider all hazard interactions when calculating possible losses. We address this problem by developing an improved quantitative model - Model for multi-hazard Risk assessment with a consideration of Hazard Interaction (MmhRisk-HI). This model calculates the possible loss caused by multiple hazards, with an explicit consideration of interaction between those hazards. There are two main components to the model. In the first, based on the hazard-forming environment, relationships among hazards are classified into four types for calculation of the exceedance probability of multiple hazards occurrence. In the second, a Bayesian network is used to calculate possible loss caused by multiple hazards with different exceedance probabilities. A multi-hazard risk map can then be drawn addressing the probability of multi-hazard occurrence and corresponding loss. This model was applied in northeast Zhejiang, China and validated by comparison against an observed multi-hazard sequence. The validation results show that the model can more effectively represent the real world, and that the modelled outputs, possible loss caused by multiple hazards, are reliable. The outputs can additionally help to identify areas at greatest risk, and allows a determination of the factors that contribute to that risk, and hence the model can provide useful further information for planners and decision-makers concerned with risk mitigation

Plasma Dynamics

Contains table of contents for Section 2 and reports on three research projects.National Science Foundation Grant ECS 89-02990U.S. Air Force - Office of Scientific Research Grant F49620-93-1-0108U.S. Army - Harry Diamond Laboratories Contract DAAL02-92-K-0037U.S. Department of Energy Grant DE-FG02-91-ER-40648U.S. Navy - Office of Naval Research Grant N00014-90-J-4130National Aeronautics and Space Administration Grant NAGW-2048National Science Foundation Grant ECS 88-22475U.S. Department of Energy Grant DE-FG02-91-ER-54109Magnetic Fusion Science Fellowship Progra