Mercury in the environment

Problems in assessing mercury concentrations in environmental materials are discussed. Data for situations involving air, water, rocks, soils, sediments, sludges, fossil fuels, plants, animals, foods, and man are drawn together and briefly evaluated. Details are provided regarding the toxicity of mercury along with tentative standards and guidelines for mercury in air, drinking water, and food

Study made of interaction between sound fields and structural vibrations

Study analyzes structural vibrations and the interactions between them and sound fields. It outlines a conceptual framework to analyze the vibrations of systems and their interactions, incorporating the results of earlier studies and establishing a unified basis for continuing research

Corporate Social Responsibility and the Environment: A Theoretical Perspective

We survey the growing theoretical literature on the motives for and welfare effects of corporate greening. We show how both market and political forces are making environmental CSR profitable, and we also discuss morally-motivated or altruistic CSR. Welfare effects of CSR are subtle and situation-contingent, and there is no guarantee that CSR enhances social welfare. We identify numerous areas in which additional theoretical work is needed.corporate social responsibility, environment, self-regulation, preemption, private politics

Astroturf: Interest Group Lobbying and Corporate Strategy

We study three corporate nonmarket strategies designed to influence the lobbying behavior of other special interest groups: (1) astroturf, in which the firm covertly subsidizes a group with similiar views to lobby when it normally would not; (2) the bear hug, in which the firm overtly pays a group to alter its lobbying activitives; and (3) self-regulation, in which the firm voluntarily limits the potential social harm from its activities. All three strategies reduce the informativeness of lobbying, and all reduce the payoff of the public decision-maker. We show that the decision-maker would benefit by requiring the public disclosure of funds but that the availability of alternative strategies limits the impact of such a policy.

Exchange coupling between silicon donors: the crucial role of the central cell and mass anisotropy

Donors in silicon are now demonstrated as one of the leading candidates for implementing qubits and quantum information processing. Single qubit operations, measurements and long coherence times are firmly established, but progress on controlling two qubit interactions has been slower. One reason for this is that the inter donor exchange coupling has been predicted to oscillate with separation, making it hard to estimate in device designs. We present a multivalley effective mass theory of a donor pair in silicon, including both a central cell potential and the effective mass anisotropy intrinsic in the Si conduction band. We are able to accurately describe the single donor properties of valley-orbit coupling and the spatial extent of donor wave functions, highlighting the importance of fitting measured values of hyperfine coupling and the orbital energy of the $1s$ levels. Ours is a simple framework that can be applied flexibly to a range of experimental scenarios, but it is nonetheless able to provide fast and reliable predictions. We use it to estimate the exchange coupling between two donor electrons and we find a smoothing of its expected oscillations, and predict a monotonic dependence on separation if two donors are spaced precisely along the [100] direction.Comment: Published version. Corrected b and B values from previous versio

Hellinger Distance Trees for Imbalanced Streams

Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the learning objective is rare class identification. As accuracy may provide a misleading impression of performance on imbalanced data, existing stream classifiers based on accuracy can suffer poor minority class performance on imbalanced streams, with the result being low minority class recall rates. In this paper we address this deficiency by proposing the use of the Hellinger distance measure, as a very fast decision tree split criterion. We demonstrate that by using Hellinger a statistically significant improvement in recall rates on imbalanced data streams can be achieved, with an acceptable increase in the false positive rate.Comment: 6 Pages, 2 figures, to be published in Proceedings 22nd International Conference on Pattern Recognition (ICPR) 201

Flow field prediction and analysis study for project RAM B3 Final report

Flow field properties in shock layer surrounding Ram B3 vehicl