A systematic review of success factors in the community management of rural water supplies over the past 30 years

Community management is the accepted management model for rural water supplies in many low and middleincome countries. However, endemic problems in the sustainability and scalability of this model are leading many to conclude we have reached the limits of an approach that is too reliant on voluntarism and informality. Accepting this criticism but recognising that many cases of success have been reported over the past 30 years, this study systematically reviews and analyses the development pattern of 174 successful community management case studies. The synthesis confirms the premise that for community management to be sustained at scale, community institutions need a ‘plus’ that includes long-term external support, with the majority of high performing cases involving financial support, technical advice and managerial advice. Internal community characteristics were also found to be influential in terms of success, including collective initiative, strong leadership and institutional transparency. Through a meta-analysis of success in different regions, the paper also indicates an important finding on the direct relationship between success and the prevailing socio-economic wealth in a society. This holds implications for policy and programme design with a need to consider how broad structural conditions may dictate the relative success of different forms of community management

Antibiotic resistance in the environment, with particular reference to MRSA

The introduction of β-lactam antibiotics (penicillins and cephalosporins) in the 1940s and 1950s probably represents the most dramatic event in the battle against infection in human medicine. Even before widespread global use of penicillin, resistance was already recorded. E. coli producing a penicillinase was reported in Nature in 1940 (Abraham, 1940) and soon after a similar penicillinase was discovered in Staphylococcus aureus (Kirby, 1944). The appearance of these genes, so quickly after the discovery and before the widespread introduction of penicillin, clearly shows that the resistance genes pre-dated clinical use of the antibiotic itself

Exoplanet imaging data challenge: benchmarking the various image processing methods for exoplanet detection

The Exoplanet Imaging Data Challenge is a community-wide effort meant to offer a platform for a fair and common comparison of image processing methods designed for exoplanet direct detection. For this purpose, it gathers on a dedicated repository (Zenodo), data from several high-contrast ground-based instruments worldwide in which we injected synthetic planetary signals. The data challenge is hosted on the CodaLab competition platform, where participants can upload their results. The specifications of the data challenge are published on our website https://exoplanet-imaging-challenge.github.io/. The first phase, launched on the 1st of September 2019 and closed on the 1st of October 2020, consisted in detecting point sources in two types of common data-set in the field of high-contrast imaging: data taken in pupil-tracking mode at one wavelength (subchallenge 1, also referred to as ADI) and multispectral data taken in pupil-tracking mode (subchallenge 2, also referred to as ADI+mSDI). In this paper, we describe the approach, organisational lessons-learnt and current limitations of the data challenge, as well as preliminary results of the participants’ submissions for this first phase. In the future, we plan to provide permanent access to the standard library of data sets and metrics, in order to guide the validation and support the publications of innovative image processing algorithms dedicated to high-contrast imaging of planetary systems

Searching for success in community management for rural water supplies over 30 years

Community management of rural water supplies is an approach that gives control of the water systems to the communities. Over the past three decades, it has become common for rural communities to adopt this approach to manage their water systems. Past experiences have shown that community water supply needs some add-ons or “Plus factors” to ensure sustainability and scalability. The present study contributes to the Community Water Plus project funded by Australian Aid, which aims at determining the extent of “Plus factors” required for success. The aim of this study is to critically review and analyse the development pattern of successful community-managed rural water supplies over the past three decades. Two research questions were addressed: - What Plus Factors are associated with successful community-managed rural water supplies? - Is the socio-economic setting indicative of the likely success of a community-managed rural water supply? The research method consisted of a systematic review of the case studies using a “Success Framework” followed by in-depth evaluation of the case studies and the socio-economic setting. The study has showed that for community management to be successful, a certain level of socioeconomic wealth is necessary, but not sufficient. A combination of different Plus factors, both internal and external, is also needed to make the community management approach sustainable and successful

Exoplanet imaging data challenge: benchmarking the various image processing methods for exoplanet detection

The Exoplanet Imaging Data Challenge is a community-wide effort meant to offer a platform for a fair and common comparison of image processing methods designed for exoplanet direct detection. For this purpose, it gathers on a dedicated repository (Zenodo), data from several high-contrast ground-based instruments worldwide in which we injected synthetic planetary signals. The data challenge is hosted on the CodaLab competition platform, where participants can upload their results. The specifications of the data challenge are published on our website https://exoplanet-imaging-challenge.github.io/. The first phase, launched on the 1st of September 2019 and closed on the 1st of October 2020, consisted in detecting point sources in two types of common data-set in the field of high-contrast imaging: data taken in pupil-tracking mode at one wavelength (subchallenge 1, also referred to as ADI) and multispectral data taken in pupil-tracking mode (subchallenge 2, also referred to as ADI+mSDI). In this paper, we describe the approach, organisational lessons-learnt and current limitations of the data challenge, as well as preliminary results of the participants' submissions for this first phase. In the future, we plan to provide permanent access to the standard library of data sets and metrics, in order to guide the validation and support the publications of innovative image processing algorithms dedicated to high-contrast imaging of planetary systems. © 2020 SPIE.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]