Modeling uncertain and dynamic casualty health in optimization-based decision support for mass casualty incident response

When designing a decision support program for use in coordinating the response to Mass Casualty Incidents, the modelling of the health of casualties presents a significant challenge. In this paper we propose one such health model, capable of acknowledging both the uncertain and dynamic nature of casualty health. Incorporating this into a larger optimisation model capable of use in real-time and in an online manner, computational experiments examining the effect of errors in health assessment, regular updates of health and delays in communication are reported. Results demonstrate the often significant impact of these factors

Online optimization of casualty processing in major incident response: An experimental analysis

When designing an optimization model for use in mass casualty incident (MCI) response, the dynamic and uncertain nature of the problem environment poses a significant challenge. Many key problem parameters, such as the number of casualties to be processed, will typically change as the response operation progresses. Other parameters, such as the time required to complete key response tasks, must be estimated and are therefore prone to errors. In this work we extend a multi-objective combinatorial optimization model for MCI response to improve performance in dynamic and uncertain environments. The model is developed to allow for use in real time, with continuous communication between the optimization model and problem environment. A simulation of this problem environment is described, allowing for a series of computational experiments evaluating how model utility is influenced by a range of key dynamic or uncertain problem and model characteristics. It is demonstrated that the move to an online system mitigates against poor communication speed, while errors in the estimation of task duration parameters are shown to significantly reduce model utility

Rapid assessment of tissue nitrogen in cultivated Gracilaria gracilis (Rhodophyta) and Ulva lactuca (Chlorophyta)

Tissue nitrogen content and thallus colour were quantified using a rapid assessment method based on the Pantone® matt uncoated formula guide for raft-cultivated Gracilaria gracilis Steentoft Irvine et Farnham at Saldanha Bay and tank-cultivated Ulva lactuca Linnaeus at Jacobsbaai in 2001 – 2002. A relationship between thallus colour and tissue nitrogen, as well as a transition between green-yellows and yellow-browns that occurs between 0.8 – 1.3 mg N per g tissue (Pantone® colours 460U – 455U) for Gracilaria were found, with the green-yellow colour indicating nitrogen-starved material and the yellow-browns indicating nitrogen-replete material. For Ulva a transition between green and yellow-green occurred at a tissue nitrogen content of between 1.5 – 1.7 mg N per g tissue (Pantone® colours 585U and 583U). This relationship can be used by seaweed farmers for cultivation management as a quick guide to determine nutritional status of the seaweeds, and as an indication of protein content when the seaweeds are used as feeds.Web of Scienc

The Possibilist Transactional Interpretation and Relativity

A recent ontological variant of Cramer's Transactional Interpretation, called "Possibilist Transactional Interpretation" or PTI, is extended to the relativistic domain. The present interpretation clarifies the concept of 'absorption,' which plays a crucial role in TI (and in PTI). In particular, in the relativistic domain, coupling amplitudes between fields are interpreted as amplitudes for the generation of confirmation waves (CW) by a potential absorber in response to offer waves (OW), whereas in the nonrelativistic context CW are taken as generated with certainty. It is pointed out that solving the measurement problem requires venturing into the relativistic domain in which emissions and absorptions take place; nonrelativistic quantum mechanics only applies to quanta considered as 'already in existence' (i.e., 'free quanta'), and therefore cannot fully account for the phenomenon of measurement, in which quanta are tied to sources and sinks.Comment: Final version with some minor corrections as published in Foundations of Physics. This paper has significant overlap with Chapter 6 of my book on the Transactional Interpretation, forthcoming from Cambridge University Press: http://www.cambridge.org/us/knowledge/isbn/item6860644/?site_locale=en_US (Additional preview material is available at rekastner.wordpress.com) Comments welcom

Preceding rule induction with instance reduction methods

A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning Repository. Three instance reduction algorithms (Edited Nearest Neighbour, AllKnn and DROP5) are compared. Each one is used to reduce the size of the training set, prior to inducing a set of rules using Clark and Boswell's modification of CN2. A hybrid instance reduction algorithm (comprised of AllKnn and DROP5) is also tested. For most of the datasets, pruning the training set using ENN, AllKnn or the hybrid significantly reduces the number of rules generated by CN2, without adversely affecting the predictive performance. The hybrid achieves the highest average predictive accuracy

Efficient Parallel Statistical Model Checking of Biochemical Networks

We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture

Mechanical characterisation of a fibre reinforced oxide/oxide ceramic matrix composite

Monotonic tension, fatigue and creep experiments were conducted on an oxide/oxide ceramic matrix composite over the range of temperature 20–1200 °C. The role of continuous fibre reinforcement, differential thermal expansion, stress redistribution interactions between fibres and matrix and the influence of inherent processing defects are all considered when describing the deformation and ultimate mechanical failure of these systems

How to dissect viral infections and their interplay with the host-proteome by immunoaffinity and mass spectrometry: A tutorial

The capabilities of bioanalytical mass spectrometry to (i) detect and differentiate viruses at the peptide level whilst maintaining high sample throughput and (ii) to provide diagnosis and prognosis for infected patients are presented as a tutorial in this work to aid analytical chemists and physicians to gain insights into the possibilities offered by current high-resolution mass spectrometry technology and bioinformatics. From (i) sampling to sample treatment; (ii) Matrix-Assisted Laser Desorption Ionization- to Electrospray Ionization -based mass spectrometry; and (iii) from clustering to peptide sequencing; a detailed step-by-step guide is provided and exemplified using SARS-CoV-2 Spike Y839 variant and the variant of concern SARS-CoV-2 Alpha (B.1.1.7 lineage), Influenza B, and Influenza A subtypes AH1N1pdm09 and AH3N2.Highlights: - Immunohistochemistry with magnetic core nanoparticles to isolate viruses. - The use of MALDI-MS for rapid virus detection is explained in detail; - The use of ESI-MS/MS to pinpoint host-patient crosstalk is explained in detail. - The absolute quantitative MS is explained for large-scale protein quantitation.This work received financial support from PT national funds (FCT/MCTES) through the projects UIDB/50006/2020 and UIDP/50006/2020 and from PROTEOMASS Scientific Society through the projects #PM001/2019 and #PM003/2016.info:eu-repo/semantics/publishedVersio