Post-mortem toxicology: A pilot study to evaluate the use of a Bayesian network to assess the likelihood of fatality

The challenge of interpreting post-mortem drug concentrations is well documented and relies on appropriate sample collection, knowledge of case circumstances as well as reference to published tables of data, whilst taking into account the known issues of post-mortem drug redistribution and tolerance. Existing published data has evolved from simple data tables to those now including sample origin and single to poly drug use, but additional information tends to be specific to those reported in individual case studies. We have developed a Bayesian network framework to assign a likelihood of fatality based on the contribution of drug concentrations whilst taking into account the pathological findings. This expert system has been tested against casework within the coronial jurisdiction of Sunderland, UK. We demonstrate in this pilot study that the Bayesian network can be used to proffer a degree of confidence in how deaths may be reported in cases when drugs are implicated. It has also highlighted the potential for deaths to be reported according to the pathological states at post-mortem when drugs have a significant contribution that may have an impact on mortality statistics. The Bayesian network could be used as complementary approach to assist in the interpretation of post-mortem drug concentrations

Decision-Theoretic Meta-reasoning in Partially Observable and Decentralized Settings

This thesis examines decentralized meta-reasoning. For a single agent or multiple agents, it may not be enough for agents to compute correct decisions if they do not do so in a timely or resource efficient fashion. The utility of agent decisions typically increases with decision quality, but decreases with computation time. The reasoning about one\u27s computation process is referred to as meta-reasoning. Aspects of meta-reasoning considered in this thesis include the reasoning about how to allocate computational resources, including when to stop one type of computation and begin another, and when to stop all computation and report an answer. Given a computational model, this translates into computing how to schedule the basic computations that solve a problem. This thesis constructs meta-reasoning strategies for the purposes of monitoring and control in multi-agent settings, specifically settings that can be modeled by the Decentralized Partially Observable Markov Decision Process (Dec-POMDP). It uses decision theory to optimize computation for efficiency in time and space in communicative and non-communicative decentralized settings. Whereas base-level reasoning describes the optimization of actual agent behaviors, the meta-reasoning strategies produced by this thesis dynamically optimize the computational resources which lead to the selection of base-level behaviors

Modeling Pilot State in Next Generation Aircraft Alert Systems

The Next Generation Air Transportation System will introduce new, advanced sensor technologies into the cockpit that must convey a large number of potentially complex alerts. Our work focuses on the challenges associated with prioritizing aircraft sensor alerts in a quick and efficient manner, essentially determining when and how to alert the pilot This "alert decision" becomes very difficult in NextGen due to the following challenges: 1) the increasing number of potential hazards, 2) the uncertainty associated with the state of potential hazards as well as pilot slate , and 3) the limited time to make safely-critical decisions. In this paper, we focus on pilot state and present a model for anticipating duration and quality of pilot behavior, for use in a larger system which issues aircraft alerts. We estimate pilot workload, which we model as being dependent on factors including mental effort, task demands. and task performance. We perform a mathematically rigorous analysis of the model and resulting alerting plans. We simulate the model in software and present simulated results with respect to manipulation of the pilot measures

Target: Wellbeing Evaluation - Annual Report (2010)

Target: Wellbeing (TWB) aims to help people across the North West live healthier and happier lives. TWB is delivered through a portfolio of community based programmes and projects, and has been funded by the National Lottery for the period October 2007 to March 2012 through the Big Lottery Fund, with funding linked to health outcomes. This evaluation update of the TWB portfolio provides reach analysis and evidence of behaviour change from the ten local programmes between January 2009 and August 2010, and provides an update to last year’s Target: Wellbeing Evaluation – Annual Report February 2010. The analysis relates to the area based community projects within the TWB portfolio and covers the TWB participants and their evidence of behaviour change. Reach analysis is produced via the registration database with behaviour change evidence from analysis of welcome and exit questionnaires. Forty-four projects have contributed responses to this evaluation, about half of the total number, and sixty-six have used the participant database, contributing to the reach analysis