853 resultados para energetic resources


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

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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.

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A Third Report from the Capitation Formula Review Group

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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.

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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.

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Glutamate was previously shown to enhance aerobic glycolysis i.e. increase glucose utilization and lactate production with no change in oxygen levels, in mouse cortical astrocytes by a mechanism involving glutamate uptake. It is reported here that a similar response is produced in both hippocampal and cerebellar astrocytes. Application of the cognitive-enhancing drug CX546 promoted further enhancement of glucose utilization by astrocytes from each brain area following glutamate exposure. alpha-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors represent the purported molecular target of cognitive-enhancing drugs such as CX546, and the presence of AMPA receptor subunits GluR1-4 was evidenced in astrocytes from all three regions by immunocytochemistry. AMPA itself did not stimulate aerobic glycolysis, but in the presence of CX546, a strong enhancement of glucose utilization and lactate production was obtained in cortical, hippocampal and cerebellar astrocytes. The effect of CX546 was concentration-dependent, with an EC(50) of 93.2 microm in cortical astrocytes. AMPA-induced glucose utilization in the presence of CX546 was prevented by the AMPA receptor antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) and the negative modulator GYKI 52466. In addition, the metabolic effect of CX546 in the presence of AMPA was mimicked by the AMPA receptor modulator cyclothiazide. Our data suggest that astrocyte energetics represents a novel target for cognitive-enhancing drugs acting as AMPA receptor modulators.

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This contribution, based on a statistical approach, undertakes to link data on resources (personnel and financial means) and the working of the administration of penal justice (prosecution, sentencing) taking into account the nationality of those prosecuted. In order to be able to distinguish prosecution and sentencing practices of judicial authorities and possible processes of discrimination, diverse sources have been used such as data from court administrations, public finances and police forces, collected by the Swiss Federal Statistical Office and the Swiss Federal administration of finances. The authors discuss discrimination in prosecution and sentencing between Swiss residents and foreigners taking into account localization and resources regarding personnel and public finances.

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OBJECTIVES: To assess whether patients' characteristics and healthcare resources consumption and costs were different between native and migrant populations in Switzerland. METHODS: All adult patients followed-up in the Swiss HIV-cohort study in our institution during 2000-2003 were considered. Patients' characteristics were retrieved from the cohort database. Hospital and outpatient resource use were extracted from individual charts and valued with 2002 tariffs. RESULTS: The 66 migrants were younger (29 +/- 8 years versus 37 +/- 11, p < 0.001), less often of male gender (38 % versus 70 %, p < 0.001), predominantly infected via heterosexual contact (87 % versus 52 %, p < 0.01), with lower mean CD4 level at enrollment (326 +/- 235 versus 437 +/- 305, p = 0.002) than their 200 native counterparts. Migrants had fewer hospitalizations, more frequent outpatient visits, laboratory tests, and lower total cost of care per year of follow-up (<euro> 2'215 +/- 4'206 versus 4'155 +/- 12'304, p = 0.037). Resource use and costs were significantly higher in people with < 200 CD4 cell counts in both groups. CONCLUSIONS: Migrant population had more advanced disease, more outpatient visits but less hospitalizations, resulting in lower costs of care when compared with native population.

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Is part of Health inequalities - Vulnerable groups Has part Vulnerable adults (DH)

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Has part Understanding the health needs of migrants in the South East region Assault Prevention Data Sharing Toolkit

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This is a collection of HPI resources stored on the SEPHO web site