Border parasites: schistosomiasis control among Uganda's fisherfolk

Copyright @ 2012 Taylor & Francis. This article has been made publically available through the Brunel Open Access Publishing Fund.It is recognized that the control of schistosomisais in Uganda requires a focus on fisherfolk. Large numbers suffer from this water-borne parasitic disease; notably along the shores of lakes Albert and Victoria and along the River Nile. Since 2004, a policy has been adopted of providing drugs, free of charge, to all those at risk. The strategy has been reported to be successful, but closer investigation reveals serious problems. This paper draws upon long-term research undertaken at three locations in northwestern and southeastern Uganda. It highlights consequences of not engaging with the day to day realities of fisherfolk livelihoods; attributable, in part, to the fact that so many fisherfolk live and work in places located at the country’s international borders, and to a related tendency to treat them as "feckless" and "ungovernable". Endeavours to roll out treatment end up being haphazard, erratic and location-specific. In some places, concerted efforts have been made to treat fisherfolk; but there is no effective monitoring, and it is difficult to gauge what proportion have actually swallowed the tablets. In other places, fisherfolk are, in practice, largely ignored, or are actively harassed in ways that make treatment almost impossible. At all sites, the current reliance upon resident "community" drug distributors or staff based at static clinics and schools was found to be flawed.The Schistosomiasis Control Initiative, Imperial College, under the auspices of the Bill and Melinda Gates Foundation

Ultracold collisions in tight harmonic traps: Quantum defect model and application to metastable helium atoms

We analyze a system of two colliding ultracold atoms under strong harmonic confinement from the viewpoint of quantum defect theory and formulate a generalized self-consistent method for determining the allowed energies. We also present two highly efficient computational methods for determining the bound state energies and eigenfunctions of such systems. The perturbed harmonic oscillator problem is characterized by a long asymptotic region beyond the effective range of the interatomic potential. The first method, which is based on quantum defect theory and is an adaptation of a technique developed by one of the authors (GP) for highly excited states in a modified Coulomb potential, is very efficient for integrating through this outer region. The second method is a direct numerical solution of the radial Schr\"{o}dinger equation using a discrete variable representation of the kinetic energy operator and a scaled radial coordinate grid. The methods are applied to the case of trapped spin-polarized metastable helium atoms. The calculated eigenvalues agree very closely for the two methods, and with those computed self-consistently using the generalized self-consistent method.Comment: 11 pages,REVTEX, text substantially revised, title modifie

Relative, local and global dimension in complex networks

Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical spaces, which can be constrained by boundaries and distorted by inhomogeneities, or to intrinsically discrete systems such as networks. To take into account locality, finiteness and discreteness, dynamical processes can be used to probe the space geometry and define its dimension. Here we show that each point in space can be assigned a relative dimension with respect to the source of a diffusive process, a concept that provides a scale-dependent definition for local and global dimension also applicable to networks. To showcase its application to physical systems, we demonstrate that the local dimension of structural protein graphs correlates with structural flexibility, and the relative dimension with respect to the active site uncovers regions involved in allosteric communication. In simple models of epidemics on networks, the relative dimension is predictive of the spreading capability of nodes, and identifies scales at which the graph structure is predictive of infectivity. We further apply our dimension measures to neuronal networks, economic trade, social networks, ocean flows, and to the comparison of random graphs

An FFAG Transport Line for the PAMELA Project

The PAMELA project to design an accelerator for hadron therapy using non-scaling Fixed Field Alternating Gradient (NS-FFAG) magnets requires a transport line and gantry to take the beam to the patient. The NS-FFAG principle offers the possibility of a gantry much smaller, lighter and cheaper than conventional designs, with the added ability to accept a wide range of fast changing energies. This paper will build on previous work to investigate a transport line which could be used for the PAMELA project. The design is presented along with a study and optimisation of its acceptance

Data-driven unsupervised clustering of online learner behaviour

The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here we introduce a mathematical framework for the analysis of time series of online learner engagement, which allows the identification of clusters of learners with similar online temporal behaviour directly from the raw data without prescribing a priori subjective reference behaviours. The method uses a dynamic time warping kernel to create a pairwise similarity between time series of learner actions, and combines it with an unsupervised multiscale graph clustering algorithm to identify groups of learners with similar temporal behaviour. To showcase our approach, we analyse task completion data from a cohort of learners taking an online post-graduate degree at Imperial Business School. Our analysis reveals clusters of learners with statistically distinct patterns of engagement, from distributed to massed learning, with different levels of regularity, adherence to pre-planned course structure and task completion. The approach also reveals outlier learners with highly sporadic behaviour. A posteriori comparison against student performance shows that, whereas high performing learners are spread across clusters with diverse temporal engagement, low performers are located significantly in the massed learning cluster, and our unsupervised clustering identifies low performers more accurately than common machine learning classification methods trained on temporal statistics of the data. Finally, we test the applicability of the method by analysing two additional datasets: a different cohort of the same course, and time series of different format from another university

SAW based systems for mobile communications satellites

Modern mobile communications satellites, such as INMARSAT 3, EMS, and ARTEMIS, use advanced onboard processing to make efficient use of the available L-band spectrum. In all of these cases, high performance surface acoustic wave (SAW) devices are used. SAW filters can provide high selectivity (100-200 kHz transition widths), combined with flat amplitude and linear phase characteristics; their simple construction and radiation hardness also makes them especially suitable for space applications. An overview of the architectures used in the above systems, describing the technologies employed, and the use of bandwidth switchable SAW filtering (BSSF) is given. The tradeoffs to be considered when specifying a SAW based system are analyzed, using both theoretical and experimental data. Empirical rules for estimating SAW filter performance are given. Achievable performance is illustrated using data from the INMARSAT 3 engineering model (EM) processors

The Contribution of Nursing to the Health of New Zealand

Nursing in New Zealand, has been a recognised profession for one hundred years. Throughout this time the profession has made a significant contribution to the health of communities, nationally and internationally. Despite the obvious effort and achievement, the evidence of this contribution is not well known, is documented in a few literature sources only although it is talked about widely as part of 'myth and legend'. Nurses, now as never before, are challenged to show how they 'add value' and to explain why nursing expertise is essential to safe service delivery. Finding a way to communicate this contribution has been identified as one of the most important issues facing the profession. This thesis explores the concept of contribution and presents a model, the 'Contribution Model', to show how nursing can articulate the action and achievements that show how nursing professionals have and will continue to contribute to health gain in New Zealand. Through the application of the 'Contribution Model' and framework presented in this thesis, nursing is shown to have made a contribution to health gain by using the broad range of knowledge, skills and experiences in a wide range of settings, to provide care wherever and whenever required. Case studies and scenarios from history, observation and prediction are used to show how the actions and achievements of nursing meet the expectations of individuals, the community and society: past, present and future

Drivers of songbird productivity at a restored gravel pit: influence of seasonal flooding and rainfall patterns and implications for habitat management

The restoration of riparian sites following aggregate extraction frequently aims to expand the wetland habitat, and enhance the wetland wildlife community. However, aggregate extraction sites, typically on river flood plains, are subject to unpredictable flooding along with climate variability and other factors beyond the control of local management that may be equally important in determining the success or failure of a restoration project. Here we report on an 18-year study tracking songbird productivity and changes in the avian community following the restoration of a gravel pit on the flood plain of the River Great Ouse, Cambridgeshire. As part of the British Trust for Ornithology's Constant Effort Site ringing scheme, the productivity (ratio of young: adult captured) of 5 migrant and 6 resident species was measured systematically. Capture data along with environmental variables pertinent to the flood plain habitat were analysed using generalised linear models. For some migrant species (e.g. willow warbler Phylloscopus trochilus and reed warbler Acrocephalus scirpaceus) breeding success was predicted by maximum winter flood. The productivity of resident species (e.g. dunnock Prunella modularis) was predicted not only by the overall amount of rain (positively related to production) but also the extent of spring downpours (negatively related to production). We expected that a major influence on the avian community would be the passage of time and associated vegetation succession. However, winter flood was found to be particularly important, as to a lesser extent was spring rain and unseasonal cold snaps. Detrended correspondence analysis of the total numbers of birds captured (adults + young) for 16 species showed that the changing avian community was shaped by winter floods more than by patterns in precipitation. It would appear that the avian community is influenced by patterns of habitat change, shaped as much by climate variability as local land management

Machine Learning in Tremor Analysis: Critique and Directions

Tremor is the most frequent human movement disorder, and its diagnosis is based on clinical assessment. Yet finding the accurate clinical diagnosis is not always straightforward. Fine-tuning of clinical diagnostic criteria over the past few decades, as well as device-based qualitative analysis, has resulted in incremental improvements to diagnostic accuracy. Accelerometric assessments are commonplace, enabling clinicians to capture high-resolution oscillatory properties of tremor, which recently have been the focus of various machine-learning (ML) studies. In this context, the application of ML models to accelerometric recordings provides the potential for less-biased classification and quantification of tremor disorders. However, if implemented incorrectly, ML can result in spurious or nongeneralizable results and misguided conclusions. This work summarizes and highlights recent developments in ML tools for tremor research, with a focus on supervised ML. We aim to highlight the opportunities and limitations of such approaches and provide future directions while simultaneously guiding the reader through the process of applying ML to analyze tremor data. We identify the need for the movement disorder community to take a more proactive role in the application of these novel analytical technologies, which so far have been predominantly pursued by the engineering and data analysis field. Ultimately, big-data approaches offer the possibility to identify generalizable patterns but warrant meaningful translation into clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Charge Exchange Processes between Excited Helium and Fully Stripped Ions

We made a classical trajectory Monte Carlo (CTMC) calculation of state selective cross sections for processes between some light ions and excited helium. The results, useful for analysis of spectroscopic data of fusion devices, are in good agreement with theoretical predictions of scaling laws.Comment: LaTex, 8 pages, 4 figures (available on request to the authors), DFPD/94/TH/57, to be published in Phys. Rev.