An assessment of the economic impact of climate change on the water sector in the Turks and Caicos Islands

The best description of water resources for Grand Turk was offered by Pérez Monteagudo (2000) who suggested that rain water was insufficient to ensure a regular water supply although water catchment was being practised and water catchment possibilities had been analysed. Limestone islands, mostly flat and low lying, have few possibilities for large scale surface storage, and groundwater lenses exist in very delicate equilibrium with saline seawater, and are highly likely to collapse due to sea level rise, improper extraction, drought, tidal waves or other extreme event. A study on the impact of climate change on water resources in the Turks and Caicos Islands is a challenging task, due to the fact that the territory of the Islands covers different environmental resources and conditions, and accurate data are lacking. The present report is based on collected data wherever possible, including grey data from several sources such as the Intergovernmental Panel on Climate Change (IPCC) and Cuban meteorological service data sets. Other data were also used, including the author’s own estimates and modelling results. Although challenging, this was perhaps the best approach towards analysing the situation. Furthermore, IPCC A2 and B2 scenarios were used in the present study in an effort to reduce uncertainty. The main conclusion from the scenario approach is that the trend observed in precipitation during the period 1961 - 1990 is decreasing. Similar behaviour was observed in the Caribbean region. This trend is associated with meteorological causes, particularly with the influence of the North Atlantic Anticyclone. The annual decrease in precipitation is estimated to be between 30-40% with uncertain impacts on marine resources. After an assessment of fresh water resources in Turks and Caicos Islands, the next step was to estimate residential water demand based on a high fertility rate scenario for the Islands (one selected from four scenarios and compared to countries having similar characteristics). The selected scenario presents higher projections on consumption growth, enabling better preparation for growing water demand. Water demand by tourists (stopover and excursionists, mainly cruise passengers) was also obtained, based on international daily consumption estimates. Tourism demand forecasts for Turks and Caicos Islands encompass the forty years between 2011 and 2050 and were obtained by means of an Artificial Neural Networks approach. for the A2 and B2 scenarios, resulting in the relation BAU>B2>A2 in terms of tourist arrivals and water demand levels from tourism. Adaptation options and policies were analysed. Resolving the issue of the best technology to be used for Turks and Caicos Islands is not directly related to climate change. Total estimated water storage capacity is about 1, 270, 800 m3/ year with 80% capacity load for three plants. However, almost 11 desalination plants have been detected on Turks and Caicos Islands. Without more data, it is not possible to estimate long term investment to match possible water demand and more complex adaptation options. One climate change adaptation option would be the construction of elevated (30 metres or higher) storm resistant water reservoirs. The unit cost of the storage capacity is the sum of capital costs and operational and maintenance costs. Electricity costs to pump water are optional as water should, and could, be stored for several months. The costs arising for water storage are in the range of US$ 0.22 cents/m3 without electricity costs. Pérez Monteagudo (2000) estimated water prices at around US$ 2.64/m3 in stand points, US$ 7.92 /m3 for government offices, and US$ 13.2 /m3for cistern truck vehicles. These data need to be updated. As Turks and Caicos Islands continues to depend on tourism and Reverse Osmosis (RO) for obtaining fresh water, an unavoidable condition to maintaining and increasing gross domestic product(GDP) and population welfare, dependence on fossil fuels and vulnerability to increasingly volatile prices will constitute an important restriction. In this sense, mitigation supposes a synergy with adaptation. Energy demand and emissions of carbon dioxide (CO2) were also estimated using an emissions factor of 2. 6 tCO2/ tonne of oil equivalent (toe). Assuming a population of 33,000 inhabitants, primary energy demand was estimated for Turks and Caicos Islands at 110,000 toe with electricity demand of around 110 GWh. The business as usual (BAU), as well as the mitigation scenarios were estimated. The BAU scenario suggests that energy use should be supported by imported fossil fuels with important improvements in energy efficiency. The mitigation scenario explores the use of photovoltaic and concentrating solar power, and wind energy. As this is a preliminary study, the local potential and locations need to be identified to provide more relevant estimates. Macroeconomic assumptions are the same for both scenarios. By 2050, Turks and Caicos Islands could demand 60 m toe less than for the BAU scenario.

Aplicação da rede neural MLP (Multilayer Perceptron) em indústria de pisos e revestimentos do Pólo Cerâmico de Santa Gertrudes - SP

Pós-graduação em Geociências e Meio Ambiente - IGCE

Detecção de refletores sísmicos por rede neural discreta

As redes neurais artificiais têm provado serem uma poderosa técnica na resolução de uma grande variedade de problemas de otimização. Nesta dissertação é desenvolvida uma nova rede neural, tipo recorrente, sem realimentação (self-feedback loops) e sem neurônios ocultos, para o processamento do sinal sísmico, para fornecer a posição temporal, a polaridade e as amplitudes estimadas dos refletores sísmicos, representadas pelos seus coeficientes de reflexão. A principal característica dessa nova rede neural consiste no tipo de função de ativação utilizada, a qual permite três possíveis estados para o neurônio. Busca-se estimar a posição dos refletores sísmicos e reproduzir as verdadeiras polaridades desses refletores. A idéia básica desse novo tipo de rede, aqui denominada rede neural discreta (RND), é relacionar uma função objeto, que descreve o problema geofísico, com a função de Liapunov, que descreve a dinâmica da rede neural. Deste modo, a dinâmica da rede leva a uma minimização local da sua função de Liapunov e consequentemente leva a uma minimização da função objeto. Assim, com uma codificação conveniente do sinal de saída da rede tem-se uma solução do problema geofísico. A avaliação operacional da arquitetura desta rede neural artificial é realizada em dados sintéticos gerados através do modelo convolucional simples e da teoria do raio. A razão é para explicar o comportamento da rede com dados contaminados por ruído, e diante de pulsos fonte de fases mínima, máxima e misturada.

The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

Disturbance detection for optimal database storage in electrical distribution systems using artificial immune systems with negative selection

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Contribuições na modelagem dinâmica e no controle neural de um braço robótico com elos flexíveis

Em geral, estruturas espaciais e manipuladores robóticos leves têm uma característica similar e inerente que é a flexibilidade. Esta característica torna a dinâmica do sistema muito mais complexa e com maiores dificuldades para a análise de estabilidade e controle. Então, braços robóticos bastantes leves, com velocidade elevada e potencia limitada devem considerar o controle de vibração causada pela flexibilidade. Por este motivo, uma estratégia de controle é desejada não somente para o controle do modo rígido mas também que seja capaz de controlar os modos de vibração do braço robótico flexível. Também, redes neurais artificiais (RNA) são identificadas como uma subespecialidade de inteligência artificial. Constituem atualmente uma teoria para o estudo de fenômenos complexos e representam uma nova ferramenta na tecnologia de processamento de informação, por possuírem características como processamento paralelo, capacidade de aprendizagem, mapeamento não-linear e capacidade de generalização. Assim, neste estudo utilizam-se RNA na identificação e controle do braço robótico com elos flexíveis. Esta tese apresenta a modelagem dinâmica de braços robóticos com elos flexíveis, 1D no plano horizontal e 2D no plano vertical com ação da gravidade, respectivamente. Modelos dinâmicos reduzidos são obtidos pelo formalismo de Newton-Euler, e utiliza-se o método dos elementos finitos (MEF) na discretização dos deslocamentos elásticos baseado na teoria elementar da viga. Além disso, duas estratégias de controle têm sido desenvolvidas com a finalidade de eliminar as vibrações devido à flexibilidade do braço robótico com elos flexíveis. Primeiro, utilizase um controlador neural feedforward (NFF) na obtenção da dinâmica inversa do braço robótico flexível e o calculo do torque da junta. E segundo, para obter precisão no posicionamento... (Resumo completo, clicar acesso eletrônico abaixo)

Development of a CAD system for automatic classification of microcalcifications based on FPGA

This paper presents a Computer Aided Diagnosis (CAD) system that automatically classifies microcalcifications detected on digital mammograms into one of the five types proposed by Michele Le Gal, a classification scheme that allows radiologists to determine whether a breast tumor is malignant or not without the need for surgeries. The developed system uses a combination of wavelets and Artificial Neural Networks (ANN) and is executed on an Altera DE2-115 Development Kit, a kit containing a Field-Programmable Gate Array (FPGA) that allows the system to be smaller, cheaper and more energy efficient. Results have shown that the system was able to correctly classify 96.67% of test samples, which can be used as a second opinion by radiologists in breast cancer early diagnosis. (C) 2013 The Authors. Published by Elsevier B.V.

Pruning methods to MLP neural networks considering proportional apparent error rate for classification problems with unbalanced data

This article deals with classification problems involving unequal probabilities in each class and discusses metrics to systems that use multilayer perceptrons neural networks (MLP) for the task of classifying new patterns. In addition we propose three new pruning methods that were compared to other seven existing methods in the literature for MLP networks. All pruning algorithms presented in this paper have been modified by the authors to do pruning of neurons, in order to produce fully connected MLP networks but being small in its intermediary layer. Experiments were carried out involving the E. coli unbalanced classification problem and ten pruning methods. The proposed methods had obtained good results, actually, better results than another pruning methods previously defined at the MLP neural network area. (C) 2014 Elsevier Ltd. All rights reserved.

Preliminary diagnosis of ophtalmological diseases through machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Feasibility Study of a Solar Reactor for Phenol Treatment by the Photo-Fenton process in Aqueous Solution

Solar reactors can be attractive in photodegradation processes due to lower electrical energy demand. The performance of a solar reactor for two flow configurations, i.e., plug flow and mixed flow, is compared based on experimental results with a pilot-scale solar reactor. Aqueous solutions of phenol were used as a model for industrial wastewater containing organic contaminants. Batch experiments were carried out under clear sky, resulting in removal rates in the range of 96100?%. The dissolved organic carbon removal rate was simulated by an empirical model based on neural networks, which was adjusted to the experimental data, resulting in a correlation coefficient of 0.9856. This approach enabled to estimate effects of process variables which could not be evaluated from the experiments. Simulations with different reactor configurations indicated relevant aspects for the design of solar reactors.

A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion

DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.