998 resultados para Neighbourhood Policy


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Different urban structures might affect the life history parameters of Aedes aegypti and, consequently, dengue transmission. Container productivity, probability of daily survival (PDS) and dispersal rates were estimated for mosquito populations in a high income neighbourhood of Rio de Janeiro. Results were contrasted with those previously found in a suburban district, as well as those recorded in a slum. After inspecting 1,041 premises, domestic drains and discarded plastic pots were identified as the most productive containers, collectively holding up to 80% of the total pupae. In addition, three cohorts of dust-marked Ae. aegypti females were released and recaptured daily using BGS-Traps, sticky ovitraps and backpack aspirators in 50 randomly selected houses; recapture rate ranged from 5-12.2% within cohorts. PDS was determined by two models and ranged from 0.607-0.704 (exponential model) and 0.659-0.721 (non-linear model), respectively. Mean distance travelled varied from 57-122 m, with a maximum dispersal of 263 m. Overall, lower infestation indexes and adult female survival were observed in the high income neighbourhood, suggesting a lower dengue transmission risk in comparison to the suburban area and the slum. Since results show that urban structure can influence mosquito biology, specific control strategies might be used in order to achieve cost-effective Ae. aegypti control.

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Chagas disease, named after Carlos Chagas, who first described it in 1909, exists only on the American Continent. It is caused by a parasite, Trypanosoma cruzi, which is transmitted to humans by blood-sucking triatomine bugs and via blood transfusion. Chagas disease has two successive phases: acute and chronic. The acute phase lasts six-eight weeks. Several years after entering the chronic phase, 20-35% of infected individuals, depending on the geographical area, will develop irreversible lesions of the autonomous nervous system in the heart, oesophagus and colon, and of the peripheral nervous system. Data on the prevalence and distribution of Chagas disease improved in quality during the 1980s as a result of the demographically representative cross-sectional studies in countries where accurate information was not previously available. A group of experts met in Brasilia in 1979 and devised standard protocols to carry out countrywide prevalence studies on human T. cruzi infection and triatomine house infestation. Thanks to a coordinated multi-country programme in the Southern Cone countries, the transmission of Chagas disease by vectors and via blood transfusion was interrupted in Uruguay in 1997, in Chile in 1999 and in Brazil in 2006; thus, the incidence of new infections by T. cruzi across the South American continent has decreased by 70%. Similar multi-country initiatives have been launched in the Andean countries and in Central America and rapid progress has been reported towards the goal of interrupting the transmission of Chagas disease, as requested by a 1998 Resolution of the World Health Assembly. The cost-benefit analysis of investment in the vector control programme in Brazil indicates that there are savings of US$17 in medical care and disabilities for each dollar spent on prevention, showing that the programme is a health investment with very high return. Many well-known research institutions in Latin America were key elements of a worldwide network of laboratories that carried out basic and applied research supporting the planning and evaluation of national Chagas disease control programmes. The present article reviews the current epidemiological trends for Chagas disease in Latin America and the future challenges in terms of epidemiology, surveillance and health policy.

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This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

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Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task