Report of the meeting of government-designated experts of Latin America and the Caribbean preparatory to Rio+20: a green economy in the context of sustainable development and poverty eradication and the institutional framework for sustainable development

Incluye Bibliografía

Biomobile: sistema de identificação de usuários em dispositivos móveis na plataforma Android utilizando reconhecimento de faces a partir de vídeo

Pós-graduação em Ciência da Computação - IBILCE

Framework for implementation of the outcomes of the World Summit on Sustainable Development (WSSD) in the small island developing states of the Caribbean subregion

Includes bibliography

Desenvolvimento de um sistema informatizado para coleta, armazenamento e processamento dos dados referentes à cesta básica do Município de Botucatu -SP

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Types of discrepancy in Millennium Development Goal indicator values and measures for statistical reconciliation: Overall framework and implementation in selected thematic areas and indicators

Includes bibliography.

Development of a modelling framework in support of an assessment of the economic and social impacts of climate change in the Caribbean: the ECLAC – CIAM Model

The Caribbean region remains highly vulnerable to the impacts of climate change. In order to assess the social and economic consequences of climate change for the region, the Economic Commission for Latin America and the Caribbean( ECLAC) has developed a model for this purpose. The model is referred to as the Climate Impact Assessment Model (ECLAC-CIAM) and is a tool that can simultaneously assess multiple sectoral climate impacts specific to the Caribbean as a whole and for individual countries. To achieve this goal, an Integrated Assessment Model (IAM) with a Computable General Equilibrium Core was developed comprising of three modules to be executed sequentially. The first of these modules defines the type and magnitude of economic shocks on the basis of a climate change scenario, the second module is a global Computable General Equilibrium model with a special regional and industrial classification and the third module processes the output of the CGE model to get more disaggregated results. The model has the potential to produce several economic estimates but the current default results include percentage change in real national income for individual Caribbean states which provides a simple measure of welfare impacts. With some modifications, the model can also be used to consider the effects of single sectoral shocks such as (Land, Labour, Capital and Tourism) on the percentage change in real national income. Ultimately, the model is envisioned as an evolving tool for assessing the impact of climate change in the Caribbean and as a guide to policy responses with respect to adaptation strategies.

Logistics and mobility policies for sustainable development and regional integration: conceptual framework and regional experiences

This FAL Bulletin summarizes the main findings and proposals contained in the document “Políticas de logística y movilidad para el desarrollo sostenible y la integración regional”, recently published by the Natural Resources and Infrastructure Division (NRID), ECLAC. It contains a proposal for a paradigm shift in the formulation of national logistics and mobility policies, with common guidelines for Latin American and Caribbean countries.

Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science