Entanglement of mixed macroscopic superpositions: An entangling-power study

We investigate entanglement properties of a recently introduced class of macroscopic quantum superpositions in two-mode mixed states. One of the tools we use in order to infer the entanglement in this non-Gaussian class of states is the power to entangle a qubit system. Our study reveals features which are hidden in a standard approach to entanglement investigation based on the uncertainty principle of the quadrature variables. We briefly describe the experimental setup corresponding to our theoretical scenario and a suitable modification of the protocol which makes our proposal realizable within the current experimental capabilities.

Uncertainty and Sensitivity Analysis in Aircraft Operating Costs in Structural Design Optimization

The paper focuses on the development of an aircraft design optimization methodology that models uncertainty and sensitivity analysis in the tradeoff between manufacturing cost, structural requirements, andaircraft direct operating cost.Specifically,ratherthanonlylooking atmanufacturingcost, direct operatingcost is also consideredintermsof the impact of weight on fuel burn, in addition to the acquisition cost to be borne by the operator. Ultimately, there is a tradeoff between driving design according to minimal weight and driving it according to reduced manufacturing cost. Theanalysis of cost is facilitated withagenetic-causal cost-modeling methodology,andthe structural analysis is driven by numerical expressions of appropriate failure modes that use ESDU International reference data. However, a key contribution of the paper is to investigate the modeling of uncertainty and to perform a sensitivity analysis to investigate the robustness of the optimization methodology. Stochastic distributions are used to characterize manufacturing cost distributions, andMonteCarlo analysis is performed in modeling the impact of uncertainty on the cost modeling. The results are then used in a sensitivity analysis that incorporates the optimization methodology. In addition to investigating manufacturing cost variance, the sensitivity of the optimization to fuel burn cost and structural loading are also investigated. It is found that the consideration of manufacturing cost does make an impact and results in a different optimal design configuration from that delivered by the minimal-weight method. However, it was shown that at lower applied loads there is a threshold fuel burn cost at which the optimization process needs to reduce weight, and this threshold decreases with increasing load. The new optimal solution results in lower direct operating cost with a predicted savings of 640=m2 of fuselage skin over the life, relating to a rough order-of-magnitude direct operating cost savings of $500,000 for the fuselage alone of a small regional jet. Moreover, it was found through the uncertainty analysis that the principle was not sensitive to cost variance, although the margins do change.

‘Harmonising the precaution out of the precautionary principle? The need for maintaining flexibility and an interpretative role’

The precautionary principle has the potential to act as a valuable tool in food law. It operates in areas of scientific uncertainty, calling for protective measures where there are potential threats to human health (or the environment). However, the manner of the principle’s incorporation and implementation within legislation are key to its effectiveness and general legitimacy. Specific considerations include the role and nature of risk assessments, assessors, sources of evidence, divergent opinions, risk communication, other legitimate factors and the weighting of interests. However, more fundamentally, the crystallisation of approaches and removal of all flexibility would undermine the principle’s central tenets. Firstly, principles crucially play a guiding and interpretative role. Secondly, reflexive modernisation and continuing scientific uncertainty call for the precautionary principle’s continued application – precautionary measures do not end the precautionary principle’s relevance. This can be partially achieved through the legislation so as to facilitate later precautionary measures, e.g. through temporary authorisations, derogations and safeguard clauses. However, crucially, it requires that the legislation also be interpreted in light of the precautionary principle. This paper investigates the logic behind the Court of Justice of the EU’s judgments and the circumstances that enable or deter the Court in taking, or permitting, stronger precautionary approaches. Although apparently inconsistent, a number of contextual factors including the legislative provisions and actors involved influence the judgments substantially. The analysis provides insight into improving the principle’s incorporation to facilitate its continued application and maintenance of flexibility, whilst bearing in mind the general desirability of objectivity and legal certainty.

Handling uncertainty in a medical study of dietary intake during pregnancy

This paper is concerned with handling uncertainty as part of the analysis of data from a medical study. The study is investigating connections between the birth weight of babies and the dietary intake of their mothers. Bayesian belief networks were used in the analysis. Their perceived benefits include (i) an ability to represent the evidence emerging from the evolving study, dealing effectively with the inherent uncertainty involved; (ii) providing a way of representing evidence graphically to facilitate analysis and communication with clinicians; (iii) helping in the exploration of the data to reveal undiscovered knowledge; and (iv) providing a means of developing an expert system application.

Knowledge-based risk assessment under uncertainty in engineering projects

In this paper we describe how an evidential-reasoner can be used as a component of risk assessment of engineering projects using a direct way of reasoning. Guan & Bell (1991) introduced this method by using the mass functions to express rule strengths. Mass functions are also used to express data strengths. The data and rule strengths are combined to get a mass distribution for each rule; i.e., the first half of our reasoning process. Then we combine the prior mass and the evidence from the different rules; i.e., the second half of the reasoning process. Finally, belief intervals are calculated to help in identifying the risks. We apply our evidential-reasoner on an engineering project and the results demonstrate the feasibility and applicability of this system in this environment.