African mineral production 1999-2003 : a product of the World Mineral Statistics database

The statistics in this publication are from a more comprehensive database that is published as World Mineral Production 1999- 2003. Coverage African Mineral Production covers the majority of economically important mineral commodities. For each commodity constant efforts are made to ensure that as many producing countries as possible are reported. For some commodities, where statistics on production are not publicly available, estimates are made. Users of this compilation are advised that more statistical information than can be included in a publication of this nature is held in the BGS files and is available for consultation. Production Metals Mine production of many metals is expressed in terms of metal content. This is clearly indicated adjacent to the commodity description. Unless otherwise specified, metal production statistics relate to metal recovered from both domestic or imported materials, whether primary or secondary, but exclude remelted material

Dissolution rates of uranium dioxide sintered pellets in nitric acid systems

"October 16, 1963.""Reprinted from Journal of Applied Chemistry Published by the Society of Chemical Industry Volume 13, No. I, January 1963, Pages 32-40.

Under which conditions is quantum brownian motion observable in a microscope?

We investigate under which conditions we can expect to observe quantum brownian motion in a microscope. Using the fluctuation-dissipation theorem, we investigate quantum brownian motion in an ohmic bath, and estimate temporal and spatial accuracy required to observe a crossover from classical to quantum behavior

An exploration strategy for non-stationary opponents

The success or failure of any learning algorithm is partially due to the exploration strategy it exerts. However, most exploration strategies assume that the environment is stationary and non-strategic. In this work we shed light on how to design exploration strategies in non-stationary and adversarial environments. Our proposed adversarial drift exploration (DE) is able to efficiently explore the state space while keeping track of regions of the environment that have changed. This proposed exploration is general enough to be applied in single agent non-stationary environments as well as in multiagent settings where the opponent changes its strategy in time. We use a two agent strategic interaction setting to test this new type of exploration, where the opponent switches between different behavioral patterns to emulate a non-deterministic, stochastic and adversarial environment. The agent’s objective is to learn a model of the opponent’s strategy to act optimally. Our contribution is twofold. First, we present DE as a strategy for switch detection. Second, we propose a new algorithm called R-max# for learning and planning against non-stationary opponent. To handle such opponents, R-max# reasons and acts in terms of two objectives: (1) to maximize utilities in the short term while learning and (2) eventually explore opponent behavioral changes. We provide theoretical results showing that R-max# is guaranteed to detect the opponent’s switch and learn a new model in terms of finite sample complexity. R-max# makes efficient use of exploration experiences, which results in rapid adaptation and efficient DE, to deal with the non-stationary nature of the opponent. We show experimentally how using DE outperforms the state of the art algorithms that were explicitly designed for modeling opponents (in terms average rewards) in two complimentary domains

Efficiently detecting switches against non-stationary opponents

Interactions in multiagent systems are generally more complicated than single agent ones. Game theory provides solutions on how to act in multiagent scenarios; however, it assumes that all agents will act rationally. Moreover, some works also assume the opponent will use a stationary strategy. These assumptions usually do not hold in real world scenarios where agents have limited capacities and may deviate from a perfect rational response. Our goal is still to act optimally in these cases by learning the appropriate response and without any prior policies on how to act. Thus, we focus on the problem when another agent in the environment uses different stationary strategies over time. This will turn the problem into learning in a non-stationary environment, posing a problem for most learning algorithms. This paper introduces DriftER, an algorithm that (1) learns a model of the opponent, (2) uses that to obtain an optimal policy and then (3) determines when it must re-learn due to an opponent strategy change. We provide theoretical results showing that DriftER guarantees to detect switches with high probability. Also, we provide empirical results showing that our approach outperforms state of the art algorithms, in normal form games such as prisoner’s dilemma and then in a more realistic scenario, the Power TAC simulator

‘Green’ on the ground but not in the air: Pro-environmental attitudes are related to household behaviours but not discretionary air travel

The rise in greenhouse gas emissions from air travel could be reduced by individuals voluntarily abstaining from, or reducing, flights for leisure and recreational purposes. In theory, we might expect that people with pro-environmental value orientations and concerns about the risks of climate change, and those who engage in more pro-environmental household behaviours, would also be more likely to abstain from such voluntary air travel, or at least to fly less far. Analysis of two large datasets from the United Kingdom, weighted to be representative of the whole population, tested these associations. Using zero-inflated Poisson regression models, we found that, after accounting for potential confounders, there was no association between individuals’ environmental attitudes, concern over climate change, or their routine pro-environmental household behaviours, and either their propensity to take non-work related flights, or the distances flown by those who do so. These findings contrasted with those for pro-environmental household behaviours, where associations with environmental attitudes and concern were observed. Our results offer little encouragement for policies aiming to reduce discretionary air travel through pro-environmental advocacy, or through ‘spill-over’ from interventions to improve environmental impacts of household routines

Comparing multiscale, presence-only habitat suitability models created with structured survey data and community science data for a rare warbler species at the southern range margin

Golden-winged Warblers (Vermivora chrysoptera, Parulidae) are declining migrant songbirds that breed in the Great Lakes and Appalachian regions of North America. Within their breeding range, Golden-winged Warblers are found in early successional habitats adjacent to mature hardwood forest, and previous work has found that Golden-winged Warbler habitat preferences are scale-dependent. Golden-winged Warbler Working Group management recommendations were written to apply to large regions of the breeding range, but there may be localized differences in both habitat availability and preferences. Rapid declines at the southernmost extent of their breeding range in Western North Carolina necessitate investigation into landscape characteristics governing distribution in this subregion. Furthermore, with the increase in availability of community science data from platforms such as eBird, it would be valuable to know if community science data produces similar distribution models as systemic sampling data. In this study, we described patterns of Golden-winged Warbler presence in Western North Carolina by examining habitat variables at multiple spatial scales using data from standardized Audubon North Carolina (NC) playback surveys and community science data from eBird. We compared model performance and predictions between Audubon NC and eBird models and found that Golden-winged Warbler presence is associated with sites which, at a local scale (150m), have less mature forest, more young forest, more herb/shrub cover, and more road cover, and at a landscape scale (2500m), have less herb/shrub cover. Golden-winged Warbler presence is also associated with higher elevations and smaller slopes. eBird and Audubon models had similar variable importance values, response curves, and overall performance. Based on variable importance values, elevation, mature forest at the local scale, and road cover at the local scale are the primary variables driving the difference between Golden-winged Warbler breeding sites and random background sites in Western North Carolina. Additionally, our results validate the use of eBird data, since they produce species distribution modeling results that are similar to results obtained from more standardized survey methods

Threshold Corrections and Gauge Symmetry in Twisted Superstring Models

Threshold corrections to the running of gauge couplings are calculated for superstring models with free complex world sheet fermions. For two N=1 $SU(2)\times U(1)^5$ models, the threshold corrections lead to a small increase in the unification scale. Examples are given to illustrate how a given particle spectrum can be described by models with different boundary conditions on the internal fermions. We also discuss how complex twisted fermions can enhance the symmetry group of an N=4 $SU(3)\times U(1)\times U(1)$ model to the gauge group $SU(3)\times SU(2)\times U(1)$. It is then shown how a mixing angle analogous to the Weinberg angle depends on the boundary conditions of the internal fermions.Comment: easier to Tex version, figures to be sent separatel