Optimal discrete stopping times for reliability growth tests

Often, the duration of a reliability growth development test is specified in advance and the decision to terminate or continue testing is conducted at discrete time intervals. These features are normally not captured by reliability growth models. This paper adapts a standard reliability growth model to determine the optimal time for which to plan to terminate testing. The underlying stochastic process is developed from an Order Statistic argument with Bayesian inference used to estimate the number of faults within the design and classical inference procedures used to assess the rate of fault detection. Inference procedures within this framework are explored where it is shown the Maximum Likelihood Estimators possess a small bias and converges to the Minimum Variance Unbiased Estimator after few tests for designs with moderate number of faults. It is shown that the Likelihood function can be bimodal when there is conflict between the observed rate of fault detection and the prior distribution describing the number of faults in the design. An illustrative example is provided

Adding flavour to twistor strings

Twistor string theory is known to describe a wide variety of field theories at tree-level and has proved extremely useful in making substantial progress in perturbative gauge theory. We explore the twistor dual description of a class of N=2 UV-finite super-Yang-Mills theories with fundamental flavour by adding 'flavour' branes to the topological B-model on super-twistor space and comment on the appearance of these objects. Evidence for the correspondence is provided by matching amplitudes on both sides.Comment: 6 pages; contribution to the proceedings for the European Physical Society conference on High Energy Physics in Manchester, 19-25 July 2007. v3: Typos correcte

Towards Assurance for Plug & Play Medical Systems

Traditional safety-critical systems are designed and integrated by a systems integrator. The system integrator can asses the safety of the completed system before it is deployed. In medicine, there is a desire to transition from the traditional approach to a new model wherein a user can combine various devices post-hoc to create a new composite system that addresses a specific clinical scenario. Ensuring the safety of these systems is challenging: Safety is a property of systems that arises from the interaction of system components and it’s not possible to asses overall system safety by assessing a single component in isolation. It is unlikely that end-users will have the engineering expertise or resources to perform safety assessments each time they create a new composite system. In this paper we describe a platform-oriented approach to providing assurance for plug & play medical systems as well as an associated assurance argument pattern