Deriving meteorological variables across Africa for the study and control of vector-borne disease:a comparison of remote sensing and spatial interpolation of climate

This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) onboard the European Meteorological Satellite programme's (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study anti control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy.

Farmer perception of suitable conditions for slurry application compared with decision support system recommendations

The application of slurry nutrients to land can be associated with unintended losses to the environment depending on soil and weather conditions. Correct timing of slurry application, however, can increase plant nutrient uptake and reduce losses. A decision support system (DSS), which predicts optimum conditions for slurry spreading based on the Hybrid Soil Moisture Deficit (HSMD) model, was investigated for use as a policy tool. The DSS recommendations were compared to farmer perception of suitable conditions for slurry spreading for three soil drainage classes (well, moderate and poorly drained) to better understand on farm slurry management practices and to identify potential conflict with farmer opinion. Six farmers participated in a survey over two and a half years, during which they completed a daily diary, and their responses were compared to Soil Moisture Deficit (SMD) calculations and weather data recorded by on farm meteorological stations. The perception of land drainage quality differed between farmers and was related to their local knowledge and experience. It was found that the allocation of grass fields to HSMD drainage classes using a visual assessment method aligned farmer perception of drainage at the national scale. Farmer opinion corresponded to the theoretical understanding that slurry should not be applied when the soil is wetter than field capacity, i.e. when drainage can occur. While weather and soil conditions (especially trafficability) were the principal reasons given by farmers not to spread slurry, farm management practices (grazing and silage) and current Nitrates Directive policies (closed winter period for spreading) combined with limited storage capacities were obstacles to utilisation of slurry nutrients. Despite the slightly more restrictive advice of the DSS regarding the number of suitable spreading opportunities, the system has potential to address an information deficit that would help farmers to reduce nutrient losses and optimise plant nutrient uptake by improved slurry management. The DSS advice was in general agreement with the farmers and, therefore, they should not be resistant to adopting the tool for day to day management.

Offshore wind resource estimation using wave buoy data

In the coming decade installed offshore wind capacity is expected to expand rapidly. This will be both technically and economically challenging. Precise wind resource assessment is one of the more imminent challenges. It is more difficult to assess wind power offshore than onshore due to the paucity of representative wind speed data. Offshore site-specific data is less accessible and is far more costly to collect. However, offshore wind speed data collected from sources such as wave buoys, remote sensing from satellites, national weather ships, and coastal meteorological stations and met masts on barges and platforms may be extrapolated to assess offshore wind power. This study attempts to determine the usefulness of pre-existing offshore wind speed measurements in resource assessment, and presents the results of wind resource estimation in the Atlantic Ocean and in the Irish Sea using data from two offshore meteorological buoys. © 2012 IEEE.

Characteristics of Offshore Wind Speeds using Buoy Measurements

In the coming decade installed offshore wind capacity is expected to expand rapidly. This will be both technically and economically challenging. Precise wind resource assessment is one of the more imminent challenges. It is more difficult to assess wind power offshore than onshore due to the paucity of representative wind speed data. Offshore site-specific data is less accessible and is far more costly to collect. However, offshore wind speed data collected from sources such as wave buoys, remote sensing from satellites, national weather ships, and coastal meteorological stations and met masts on barges and platforms may be extrapolated to assess offshore wind power. This study attempts to determine the usefulness of pre-existing offshore wind speed measurements in resource assessment, and presents the results of wind resource estimation in the Atlantic Ocean and in the Irish Sea using data from two offshore meteorological buoys

Scheduling and Platforming Trains at Busy Complex Stations

We consider the problem of train planning or scheduling for large, busy, complex train stations, which are common in Europe and elsewhere, though not in North America. We develop the constraints and objectives for this problem, but these are too computationally complex to solve by standard combinatorial search or integer programming methods. Also, the problem is somewhat political in nature, that is, it does not have a clear objective function because it involves multiple train operators with conflicting interests. We therefore develop scheduling heuristics analogous to those successfully adopted by train planners using ''manual'' methods. We tested the model and algorithms by applying to a typical large station that exhibits most of the complexities found in practice. The results compare well with those found by traditional methods, and take account of cost and preference trade-offs not handled by those methods. With successive refinements, the algorithm eventually took only a few seconds to run, the time depending on the version of the algorithm and the scheduling problem. The scheduling models and algorithms developed and tested here can be used on their own, or as key components for a more general system for train scheduling for a rail line or network.Train scheduling for a busy station includes ensuring that there are no conflicts between several hundred trains per day going in and out of the station on intersecting paths from multiple in-lines and out-lines to multiple platforms, while ensuring that each train is allowed at least its minimum required headways, dwell time, turnaround time and trip time. This has to be done while minimizing (costs of) deviations from desired times, platforms or lines, allowing for conflicts due to through-platforms, dead-end platforms, multiple sub-platforms, and possible constraints due to infrastructure, safety or business policy.

Keeping it real! Enhancing realism in standardised patient OSCE stations

Background: Objective structured clinical examinations (OSCEs) are a
commonly used method of assessing clinical competency in healthcare education. They can providean opportunity to observe candidates interacting with patients.
There are many challenges in using real patients in OSCEs, and increasingly standardised patients are being used as a preference. However, by using standardised patients there is a risk of making the encounter arti?cial and removed from actual clinical practice.
Context: Efforts made in terms of cognitive, auditory, visual, tactile, psychological and emotional cues can minimise the differences between a simulated
and real clinical scenario. However, a number of factors, including feasibility, cost and usability, need to be considered if such techniques are to be practicable
within an OSCE framework.
Innovation: This article describes a series of techniques that have been used in our institution to enhance the realism of a standardised patient encounter in an
OSCE. Efforts in preparing standardised patient roles, and how they portray these roles, will be considered. A wide variety of equipment can also be used in
combination with a patient and the surrounding environment, which can further enhance the authenticity of the simulated scenario.
Implications: By enhancing the realism in simulated patient OSCE encounters, there is potential to trigger more authentic conscious responses from candidates and implicit reactions that the candidates themselves may be less
aware of. Furthermore, using such techniques may allow faculty members to select scenarios that were previously not thought possible in an OSCE

The effects of climate change on ovine parasitic gastroenteritis determined using veterinary surveillance and meteorological data for Northern Ireland over the period 1999-2009

While the influence of temperature and moisture on the free-living stages of gastrointestinal nematodes have been described in detail, and evidence for global climate change is mounting, there have been only a few attempts to relate altered incidence or seasonal patterns of disease to climate change. Studies of this type have been completed for England Scotland and Wales, but not for Northern Ireland (NI). Here we present an analysis of veterinary diagnostic data that relates three categories of gastrointestinal nematode infection in sheep to historical meteorological data for NI. The infections are: trichostrongylosis/teladorsagiosis (Teladorsagia/Trichostrongylus), strongyloidosis and nematodirosis. This study aims to provide a baseline for future climate change analyses and to provide basic information for the development of nematode control programmes. After identifying and evaluating possible sources of bias, climate change was found to be the most likely explanation for the observed patterns of change in parasite epidemiology, although other hypotheses could not be refuted. Seasonal rates of diagnosis showed a uniform year-round distribution for Teladorsagia and Trichostrongylus infections, suggesting consistent levels of larval survival throughout the year and extension of the traditionally expected seasonal transmission windows. Nematodirosis showed a higher level of autumn than Spring infection, suggesting that suitable conditions for egg and larval development occurred after the Spring infection period. Differences between regions within the Province were shown for strongyloidosis, with peaks of infection falling in the period September-November. For all three-infection categories (trichostrongylosis/teladorsagiosis, strongyloidosis and nematodirosis), significant differences in the rates of diagnosis, and in the seasonality of disease, were identified between regions. (C) 2012 Elsevier B.V. All rights reserved.

Massive MIMO Systems with Hardware-Constrained Base Stations

Massive multiple-input multiple-output (MIMO) systems are cellular networks where the base stations (BSs) are equipped with unconventionally many antennas. Such large antenna arrays offer huge spatial degrees-of-freedom for transmission optimization; in particular, great signal gains, resilience to imperfect channel knowledge, and small inter-user interference are all achievable without extensive inter-cell coordination. The key to cost-efficient deployment of large arrays is the use of hardware-constrained base stations with low-cost antenna elements, as compared to today's expensive and power-hungry BSs. Low-cost transceivers are prone to hardware imperfections, but it has been conjectured that the excessive degrees-of-freedom of massive MIMO would bring robustness to such imperfections. We herein prove this claim for an uplink channel with multiplicative phase-drift, additive distortion noise, and noise amplification. Specifically, we derive a closed-form scaling law that shows how fast the imperfections increase with the number of antennas.