Beyond deforestation monitoring in conservation hotspots: Analysing landscape mosaic dynamics in north-eastern Madagascar

Due to its extraordinary biodiversity and rapid deforestation, north-eastern Madagascar is a conservation hotspot of global importance. Reducing shifting cultivation is a high priority for policy-makers and conservationists; however, spatially explicit evidence of shifting cultivation is lacking due to the difficulty of mapping it with common remote sensing methods. To overcome this challenge, we adopted a landscape mosaic approach to assess the changes between natural forests, shifting cultivation and permanent cultivation systems at the regional level from 1995 to 2011. Our study confirmed that shifting cultivation is still being used to produce subsistence rice throughout the region, but there is a trend of intensification away from shifting cultivation towards permanent rice production, especially near protected areas. While large continuous forest exists today only in the core zones of protected areas, the agricultural matrix is still dominated by a dense cover of tree crops and smaller forest fragments. We believe that this evidence makes a crucial contribution to the development of interventions to prevent further conversion of forest to agricultural land while improving local land users' well-being.

A simulation study on the statistical monitoring of condemnation rates from slaughterhouses for syndromic surveillance: an evaluation based on Swiss data

Syndromic surveillance (SyS) systems currently exploit various sources of health-related data, most of which are collected for purposes other than surveillance (e.g. economic). Several European SyS systems use data collected during meat inspection for syndromic surveillance of animal health, as some diseases may be more easily detected post-mortem than at their point of origin or during the ante-mortem inspection upon arrival at the slaughterhouse. In this paper we use simulation to evaluate the performance of a quasi-Poisson regression (also known as an improved Farrington) algorithm for the detection of disease outbreaks during post-mortem inspection of slaughtered animals. When parameterizing the algorithm based on the retrospective analyses of 6 years of historic data, the probability of detection was satisfactory for large (range 83-445 cases) outbreaks but poor for small (range 20-177 cases) outbreaks. Varying the amount of historical data used to fit the algorithm can help increasing the probability of detection for small outbreaks. However, while the use of a 0·975 quantile generated a low false-positive rate, in most cases, more than 50% of outbreak cases had already occurred at the time of detection. High variance observed in the whole carcass condemnations time-series, and lack of flexibility in terms of the temporal distribution of simulated outbreaks resulting from low reporting frequency (monthly), constitute major challenges for early detection of outbreaks in the livestock population based on meat inspection data. Reporting frequency should be increased in the future to improve timeliness of the SyS system while increased sensitivity may be achieved by integrating meat inspection data into a multivariate system simultaneously evaluating multiple sources of data on livestock health.

Segmented ionization chambers for beam monitoring in hadrontherapy.

Segmented ionization chambers represent a good solution to monitor the position, the intensity and the shape of ion beams in hadrontherapy. Pixel and strip chambers have been developed for both passive scattering and active scanning dose delivery systems. In particular, strip chambers are optimal for pencil beam scanning, allowing for spatial and time resolutions below 0.1 mm and 1 ms, respectively. The MATRIX pixel and the Strip Accurate Monitor for Beam Applications (SAMBA) detectors are described in this paper together with the results of several beam tests and industrial developments based on these prototypes.

A detector based on silica fibers for ion beam monitoring in a wide current range

A detector based on doped silica and optical fibers was developed to monitor the profile of particle accelerator beams of intensity ranging from 1 pA to tens of µA. Scintillation light produced in a fiber moving across the beam is measured, giving information on its position, shape and intensity. The detector was tested with a continuous proton beam at the 18 MeV Bern medical cyclotron used for radioisotope production and multi-disciplinary research. For currents from 1 pA to 20 µA, Ce3+ and Sb3+ doped silica fibers were used as sensors. Read out systems based on photodiodes, photomultipliers and solid state photomultipliers were employed. Profiles down to the pA range were measured with this method for the first time. For currents ranging from 1 pA to 3 µA, the integral of the profile was found to be linear with respect to the beam current, which can be measured by this detector with an accuracy of ∼1%. The profile was determined with a spatial resolution of 0.25 mm. For currents ranging from 5 µA to 20 µA, thermal effects affect light yield and transmission, causing distortions of the profile and limitations in monitoring capabilities. For currents higher than ∼1 µA, non doped optical fibers for both producing and transporting scintillation light were also successfully employed.