Groundwater data management by water service providers in peri-urban areas of Lusaka

Groundwater management by water service providers in Lusaka, Zambia, includes borehole siting, drilling and on-going monitoring. Semi-structured interviews were conducted with Lusaka Water and Sewerage Company (LWSC) and devolved Water Trust managers, in order to assess their needs and collect their suggestions to improve data management. The research found that both the Water Trusts and LWSC lacked the capacity to fully utilize hydrogeological information. Prior to the research, none of the ten Water Trusts collected water level data. Four have started to collect data recently and another four have plans to, and they would like to share this data more widely

Day School on The Novels of George Eliot at Birkbeck College

A seminar on George Eliot\u27s novels was held at Birkbeck College, London, on Saturday, November 13th, 1993. It was chaired by Laurel Brake, of the Extra-Mural Department of London University, and the speakers were Rosemary Ashton, Professor of English at University College, London, Sally Shuttleworth, Senior Lecturer in English at the University of Leeds, and Gillian Beer, Professor of Literature and Narrative at the University of Cambridge. Rosemary Ashton\u27s paper, was \u27The Mill on the Floss and Natural History\u27, which drew interesting parallels between the novel and Darwin\u27s Origin of Species. Ideas on the science of natural history,- evolution, survival and inheritance from one generation to the next were very much in the air (particularly in the circle of G. H. Lewes), but Ashton\u27s final verdict was that they could not be seen as central to the story. A suggestion in the discussion which followed that Maggie\u27s choice between the crippled Philip and the fit Stephen was symbolically an evolutionary one found no general favour

Review of George Eliot

This is the first of a series which will \u27take full account of contemporary literary theory, providing collections of key modern readings of major authors.... Among the critical positions represented are British poststructuralism, deconstruction, feminism, psychoanalysis, Marxism and new historicism\u27. Do not despair, however. Things are not quite as bad as they sound. Most of the essays here can be understood with a bit of effort and most of those are worth the effort. They have been carefully chosen not only to include the main schools of criticism but also to cover the whole range of George Eliot\u27s work, and even to convey a real whiff of controversy by juxtaposing critics who radically disagree with each other (McCabe v. Lodge, Chase v. Newton). What is chiefly worrying the academic world at the moment seems to be the problem of realism. Put crudely, the idea is that most Victorian novelists thought they could and should represent objective reality, whereas according to modem critical thinking they couldn\u27t and shouldn\u27t. It is not difficult to show that few of them held that belief naively and that they were just as well aware of the nature of fiction, the conventions of narrative and status of the authorial voice as any of their critics. J. Hillis Miller and Colin McCabe both devote keen attention to this point, suggesting that George Eliot was in some way making unjustified claims for what she was doing. Their views are answered by Jonathan Arac and, to my mind finally. by David Lodge in a splendid analysis of certain passages of Middlemarch leading to this conclusion: the authorial commentary, so far from telling the reader what to think or pulling him in a position of dominance in relation to the discourse of the characters, constantly forces him to think for himself and constantly implicates him in the moral judgements being formulated.\u2

Assessing Operational Excellence

PresentationThe discipline of process safety management is mature. For example, the OSHA (the United States Occupational Safety & Health Administration) standard was promulgated in May 1992; the standard is older than people who are now entering the energy and process industries. The elements of process safety management are just one aspect of an effective, overall safety program. Other elements include formal education and practical experience. When combined they create what can be referred to as process safety wisdom as shown in Figure 1. They also move the program beyond just safety into overall Operational Excellence in which issues such as production, productivity and efficiency are considered. Of the three elements shown in Figure 1 the one that is most difficult to systematize is practical experience — the knowledge and insights built up by people who have worked in industry for many years. In order to gather and assess such experience an Operational Excellence Assessment system has been developed. It is buit up of hundreds of questions to which there is no “right answer” — merely an expert response. This response is supported by the guidance and suggestions that an expert might provide

An exploration of the missing data mechanism in an Internet based smoking cessation trial.

BACKGROUND: Missing outcome data are very common in smoking cessation trials. It is often assumed that all such missing data are from participants who have been unsuccessful in giving up smoking ("missing=smoking"). Here we use data from a recent Internet based smoking cessation trial in order to investigate which of a set of a priori chosen baseline variables are predictive of missingness, and the evidence for and against the "missing=smoking" assumption. METHODS: We use a selection model, which models the probability that the outcome is observed given the outcome and other variables. The selection model includes a parameter for which zero indicates that the data are Missing at Random (MAR) and large values indicate "missing=smoking". We examine the evidence for the predictive power of baseline variables in the context of a sensitivity analysis. We use data on the number and type of attempts made to obtain outcome data in order to estimate the association between smoking status and the missing data indicator. RESULTS: We apply our methods to the iQuit smoking cessation trial data. From the sensitivity analysis, we obtain strong evidence that older participants are more likely to provide outcome data. The model for the number and type of attempts to obtain outcome data confirms that age is a good predictor of missing data. There is weak evidence from this model that participants who have successfully given up smoking are more likely to provide outcome data but this evidence does not support the "missing=smoking" assumption. The probability that participants with missing outcome data are not smoking at the end of the trial is estimated to be between 0.14 and 0.19. CONCLUSIONS: Those conducting smoking cessation trials, and wishing to perform an analysis that assumes the data are MAR, should collect and incorporate baseline variables into their models that are thought to be good predictors of missing data in order to make this assumption more plausible. However they should also consider the possibility of Missing Not at Random (MNAR) models that make or allow for less extreme assumptions than "missing=smoking".RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Online Meta-learning by Parallel Algorithm Competition

The efficiency of reinforcement learning algorithms depends critically on a few meta-parameters that modulates the learning updates and the trade-off between exploration and exploitation. The adaptation of the meta-parameters is an open question in reinforcement learning, which arguably has become more of an issue recently with the success of deep reinforcement learning in high-dimensional state spaces. The long learning times in domains such as Atari 2600 video games makes it not feasible to perform comprehensive searches of appropriate meta-parameter values. We propose the Online Meta-learning by Parallel Algorithm Competition (OMPAC) method. In the OMPAC method, several instances of a reinforcement learning algorithm are run in parallel with small differences in the initial values of the meta-parameters. After a fixed number of episodes, the instances are selected based on their performance in the task at hand. Before continuing the learning, Gaussian noise is added to the meta-parameters with a predefined probability. We validate the OMPAC method by improving the state-of-the-art results in stochastic SZ-Tetris and in standard Tetris with a smaller, 10$\times$10, board, by 31% and 84%, respectively, and by improving the results for deep Sarsa($\lambda$) agents in three Atari 2600 games by 62% or more. The experiments also show the ability of the OMPAC method to adapt the meta-parameters according to the learning progress in different tasks.Comment: 15 pages, 10 figures. arXiv admin note: text overlap with arXiv:1702.0311

A general method for handling missing binary outcome data in randomized controlled trials.

AIMS: The analysis of randomized controlled trials with incomplete binary outcome data is challenging. We develop a general method for exploring the impact of missing data in such trials, with a focus on abstinence outcomes. DESIGN: We propose a sensitivity analysis where standard analyses, which could include 'missing = smoking' and 'last observation carried forward', are embedded in a wider class of models. SETTING: We apply our general method to data from two smoking cessation trials. PARTICIPANTS: A total of 489 and 1758 participants from two smoking cessation trials. MEASUREMENTS: The abstinence outcomes were obtained using telephone interviews. FINDINGS: The estimated intervention effects from both trials depend on the sensitivity parameters used. The findings differ considerably in magnitude and statistical significance under quite extreme assumptions about the missing data, but are reasonably consistent under more moderate assumptions. CONCLUSIONS: A new method for undertaking sensitivity analyses when handling missing data in trials with binary outcomes allows a wide range of assumptions about the missing data to be assessed. In two smoking cessation trials the results were insensitive to all but extreme assumptions