'A load too heavy': Critical reflections on the child labor problem in Africa's small-scale mining sector

This paper provides an extended analysis of the child labor problem in the artisanal and small-scale mining (ASM) sector, focusing specifically on the situation in sub-Saharan Africa. In recent years, the issue of child labor in ASM has garnered significant attention from the International Labor Organization (ILO), which has been particularly active in raising public awareness of the problem; and, has proceeded to implement policies and collaborative project work aimed at Curtailing children's participation in ASM activities in a number of African countries. The analysis concludes with a critical appraisal of an ILO project recently launched in the Talensi-Nabdam District in the Upper East Region of Ghana, which sheds light on how the child labor problem is being tackled in practice in ASM communities in sub-Saharan Africa. (c) 2008 Elsevier Ltd. All rights reserved.

An old problem revisited - and solved: Upton and Dalton's linear production response: A note

This note presents a robust method for estimating response surfaces that consist of linear response regimes and a linear plateau. The linear response-and-plateau model has fascinated production scientists since von Liebig (1855) and, as Upton and Dalton indicated, some years ago in this Journal, the response-and-plateau model seems to fit the data in many empirical studies. The estimation algorithm evolves from Bayesian implementation of a switching-regression (finite mixtures) model and demonstrates routine application of Gibbs sampling and data augmentation-techniques that are now in widespread application in other disciplines.

The extrapolation problem and how population modeling can help

We argue that population modeling can add value to ecological risk assessment by reducing uncertainty when extrapolating from ecotoxicological observations to relevant ecological effects. We review other methods of extrapolation, ranging from application factors to species sensitivity distributions to suborganismal (biomarker and "-omics'') responses to quantitative structure activity relationships and model ecosystems, drawing attention to the limitations of each. We suggest a simple classification of population models and critically examine each model in an extrapolation context. We conclude that population models have the potential for adding value to ecological risk assessment by incorporating better understanding of the links between individual responses and population size and structure and by incorporating greater levels of ecological complexity. A number of issues, however, need to be addressed before such models are likely to become more widely used. In a science context, these involve challenges in parameterization, questions about appropriate levels of complexity, issues concerning how specific or general the models need to be, and the extent to which interactions through competition and trophic relationships can be easily incorporated.

A Bayesian approach for dose-escalation in a phase I clinical trial incorporating pharmacodynamic endpoints

Bayesian decision procedures have already been proposed for and implemented in Phase I dose-escalation studies in healthy volunteers. The procedures have been based on pharmacokinetic responses reflecting the concentration of the drug in blood plasma and are conducted to learn about the dose-response relationship while avoiding excessive concentrations. However, in many dose-escalation studies, pharmacodynamic endpoints such as heart rate or blood pressure are observed, and it is these that should be used to control dose-escalation. These endpoints introduce additional complexity into the modeling of the problem relative to pharmacokinetic responses. Firstly, there are responses available following placebo administrations. Secondly, the pharmacodynamic responses are related directly to measurable plasma concentrations, which in turn are related to dose. Motivated by experience of data from a real study conducted in a conventional manner, this paper presents and evaluates a Bayesian procedure devised for the simultaneous monitoring of pharmacodynamic and pharmacokinetic responses. Account is also taken of the incidence of adverse events. Following logarithmic transformations, a linear model is used to relate dose to the pharmacokinetic endpoint and a quadratic model to relate the latter to the pharmacodynamic endpoint. A logistic model is used to relate the pharmacokinetic endpoint to the risk of an adverse event.

The role of schizotypy and creativity in a group problem-solving task

The well-studied link between psychotic traits and creativity is a subject of much debate. The present study investigated the extent to which schizotypic personality traits - as measured by O-LIFE (Oxford-Liverpool Inventory of Feelings and Experiences) - equip healthy individuals to engage as groups in everyday tasks. From a sample of 69 students, eight groups of four participants - comprised of high, medium, or low-schizotypy individuals - were assembled to work as a team to complete a creative problem-solving task. Predictably, high scorers on the O-LIFE formulated a greater number of strategies to solve the task, indicative of creative divergent thinking. However, for task success (as measured by time taken to complete the problem) an inverted U shaped pattern emerged, whereby high and low-schizotypy groups were consistently faster than medium schizotypy groups. Intriguing data emerged concerning leadership within the groups, and other tangential findings relating to anxiety, competition and motivation were explored. These findings challenge the traditional cliche that psychotic personality traits are linearly related to creative performance, and suggest that the nature of the problem determines which thinking styles are optimally equipped to solve it. (C) 2009 Elsevier Ltd. All rights reserved.

Training conditional and cumulative risk judgements: the role of frequencies, problem-structure and Einstellung

Two experiments implement and evaluate a training scheme for learning to apply frequency formats to probability judgements couched in terms of percentages. Results indicate that both conditional and cumulative probability judgements can be improved in this manner, however the scheme is insufficient to promote any deeper understanding of the problem structure. In both experiments, training on one problem type only (either conditional or cumulative risk judgements) resulted in an inappropriate transfer of a learned method at test. The obstacles facing a frequency-based training programme for teaching appropriate use of probability data are discussed. Copyright (c) 2006 John Wiley & Sons, Ltd.

A knowledge-based evolution strategy for the multi-objective minimum spanning tree problem

A fast Knowledge-based Evolution Strategy, KES, for the multi-objective minimum spanning tree, is presented. The proposed algorithm is validated, for the bi-objective case, with an exhaustive search for small problems (4-10 nodes), and compared with a deterministic algorithm, EPDA and NSGA-II for larger problems (up to 100 nodes) using benchmark hard instances. Experimental results show that KES finds the true Pareto fronts for small instances of the problem and calculates good approximation Pareto sets for larger instances tested. It is shown that the fronts calculated by YES are superior to NSGA-II fronts and almost as good as those established by EPDA. KES is designed to be scalable to multi-objective problems and fast due to its small complexity.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

An asynchronous distributed dynamic channel assignment scheme for dense WLANs

Wireless local area networks (WLANs) have changed the way many of us communicate, work, play and live. Due to its popularity, dense deployments are becoming a norm in many cities around the world. However, increased interference and traffic demands can severely limit the aggregate throughput achievable if an effective channel assignment scheme is not used. In this paper, we propose an enhanced asynchronous distributed and dynamic channel assignment scheme that is simple to implement, does not require any knowledge of the throughput function, allows asynchronous channel switching by each access point (AP) and is superior in performance. Simulation results show that our proposed scheme converges much faster than previously reported synchronous schemes, with a reduction in convergence time and channel switches by tip to 73.8% and 30.0% respectively.