Strategic Resources Framework Report 2007/08 (PDF 455 KB)

Strategic Resources Framework Report for 2007/08

Strategic Resources Framework 2007-08 Appendices (PDF 594 KB)

Appendices to the Strategic Resources Framework Report for 2007/08

Strategic Resources Framework Report for 2006-2007 (PDF 694 KB)

Strategic Resources Framework Report for 2006/07.

Strategic Resources Framework 2006-2007 Appendices (PDF 4056 KB)

Appendices to the Strategic Resources Framework Report for 2006/07.

A Policy to Make Best Use of Resources in Plastic Surgery and Related Specialties (PDF 178 KB)

The purpose of this policy is to introduce a transparent approach to making best use of resources in plastic surgery and related specialties. It was finalised after a formal Public Consultation that included distribution of the Consultation Document to a range of organisations and individuals, meetings with Board representatives as requested and press releases in local and regional media outlets. All responses to the Consultation were considered carefully in developing this final policy. åÊ åÊ

Strategic Resources Framework

Strategic Resources Framework

Allocating Resources to HSS Boards: Proposed Changes to the Weighted Capitation Formula - Final Consultation Summary (PDF 167 KB)

Allocating Resources to HSS Boards: Proposed Changes to the Weighted Capitation Formula - Final Consultation Summary

Information systems for water resources management improvement in developing countries

Water resources management, as also water service provision projects in developing countries have difficulties to take adequate decisions due to scarce reliable information, and a lack of proper information managing. Some appropriate tools need to be developed in order to improve decision making to improve water management and access of the poorest, through the design of Decision Support Systems (DSS). On the one side, a DSS for developing co-operation projects on water access improvement has been developed. Such a tool has specific context constrains (structure of the system, software requirements) and needs (Logical Framework Approach monitoring, organizational-learning, accountability and evaluation) that shall be considered for its design. Key aspects for its successful implementation have appeared to be a participatory design of the system and support of the managerial positions at the inception phase. A case study in Tanzania was conducted, together with the Spanish NGO ONGAWA – Ingeniería para el Desarrollo. On the other side, DSS are required also to improve decision making on water management resources in order to achieve a sustainable development that not only improves the living conditions of the population in developing countries, but that also does not hinder opportunities of the poorest on those context. A DSS made to fulfil these requirements shall be using information from water resources modelling, as also on the environment and the social context. Through the research, a case study has been conducted in the Central Rift Valley of Ethiopia, an endhorreic basin 160 km south of Addis Ababa. There, water has been modelled using ArcSWAT, a physically based model which can assess the impact of land management practices on large complex watersheds with varying soils, land use and management conditions over long periods of time. Moreover, governance on water and environment as also the socioeconomic context have been studied.

Allocating Resources to the HSS Boards: Proposed Changes to the Weighted Capitation Formula (PDF 52 KB)

A Third Report from the Capitation Formula Review Group

Triatoma rubrovaria (Blanchard, 1843) (Hemiptera, Reduviidae, Triatominae) II: trophic resources and ecological observations of five populations collected in the State of Rio Grande do Sul, Brazil

Triatoma rubrovaria has become the most frequently captured triatomine species after the control of T. infestans in the State of Rio Grande do Sul (RS), Brazil. Isoenzymatic and chromatic studies indicate the existence of, at least, two distinct phenotypic patterns of T. rubrovaria in RS. The geographic variation noted through molecular tools may also result in distinct profiles of vectorial potentiality. In order to enhance our understanding of the bionomic knowledge of T. rubrovaria separate batches of the species were collected from different municipalities of RS distant from 72 to 332 km: Santana do Livramento (natural ecotope), Santana do Livramento (artificial ecotope), Santiago (natural ecotope), Canguçu (peridomicile) and Encruzilhada do Sul (natural ecotope). A total of 285 specimens were collected, 85 specimens kept sufficient fecal material in their guts for the precipitin analysis. The results indicated the food eclecticism for this species and the anti-rodent serum showed the highest positivity in most localities. From the total of analyzed samples, only 1.3% of unique positivity for human blood was registered, all of them for Santiago population. This reactivity to human blood may be associated to pastures activities in the field.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.