A utilização de uma rede neural artificial para previsão da incidência da malária no município de Cantá, estado de Roraima

INTRODUÇÃO: A malária é uma doença endêmica na Amazônia Legal Brasileira, apresentando riscos diferentes para cada região. O Município de Cantá, no Estado de Roraima, apresentou para todo o período estudado, um dos maiores índices parasitários anuais do Brasil, com valor sempre maior que 50. O presente estudo visa à utilização de uma rede neural artificial para previsão da incidência da malária nesse município, a fim de auxiliar os coordenadores de saúde no planejamento e gestão dos recursos. MÉTODOS: Os dados foram coletados no site do Ministério da Saúde, SIVEP - Malária entre 2003 e 2009. Estruturou-se uma rede neural artificial com três neurônios na camada de entrada, duas camadas intermediárias e uma camada de saída com um neurônio. A função de ativação foi à sigmoide. No treinamento, utilizou-se o método backpropagation, com taxa de aprendizado de 0,05 e momentum 0,01. O critério de parada foi atingir 20.000 ciclos ou uma meta de 0,001. Os dados de 2003 a 2008 foram utilizados para treinamento e validação. Comparam-se os resultados com os de um modelo de regressão logística. RESULTADOS: Os resultados para todos os períodos previstos mostraram-se que as redes neurais artificiais obtiveram um menor erro quadrático médio e erro absoluto quando comparado com o modelo de regressão para o ano de 2009. CONCLUSÕES: A rede neural artificial se mostrou adequada para um sistema de previsão de malária no município estudado, determinando com pequenos erros absolutos os valores preditivos, quando comparados ao modelo de regressão logística e aos valores reais.

Variation in Aedes aegypti (Linnaeus) (Diptera, Culicidae) infestation in artificial containers in Caxias, state of Maranhão, Brazil

INTRODUCTION: Dengue is a serious public health problem worldwide, with cases reported annually in tropical and subtropical regions. Aedes aegypti (Linnaeus, 1762), the main vector of dengue, is a domiciliary species with high dispersal and survival capacities and can use various artificial containers as breeding sites. We assessed potential container breeding sites of A. aegypti in the municipality of Caxias, Maranhão, Brazil. METHODS: In the initial phase, we analyzed 900 properties in 3 neighborhoods during the dry and rainy seasons (August-October 2005 and February-April 2006, respectively). During the second sampling period, September 2006-August 2007, we used 5 assessment cycles for 300 properties in a single neighborhood. RESULTS: During the dry and rainy seasons, water-storage containers comprised 55.7% (n = 1,970) and 48.5% (n = 1,836) of the total containers inspected, and showed the highest productivity of immature A. aegypti; we found 23.7 and 106.1 individuals/container, respectively, in peridomicile sites. In intradomicile sites, water-storage containers were also the most important breeding sites with 86.4% (n = 973) and 85.6% (n = 900) of all containers and a mean of 7.9 and 108.3 individuals/container in the dry and rainy seaso-October 2006 (1,342). The highest number of positives (70) was recorded in May, mostly (94%) in storage containers. CONCLUSIONS: Storage containers are the principal and most productive A. aegypti breeding sites and are a major contributing factor to the maintenance of this vector in Caxias.

Machine diagnosis based on artificial immune systems

Nowadays, many of the manufactory and industrial system has a diagnosis system on top of it, responsible for ensuring the lifetime of the system itself. It achieves this by performing both diagnosis and error recovery procedures in real production time, on each of the individual parts of the system. There are many paradigms currently being used for diagnosis. However, they still fail to answer all the requirements imposed by the enterprises making it necessary for a different approach to take place. This happens mostly on the error recovery paradigms since the great diversity that is nowadays present in the industrial environment makes it highly unlikely for every single error to be fixed under a real time, no production stop, perspective. This work proposes a still relatively unknown paradigm to manufactory. The Artificial Immune Systems (AIS), which relies on bio-inspired algorithms, comes as a valid alternative to the ones currently being used. The proposed work is a multi-agent architecture that establishes the Artificial Immune Systems, based on bio-inspired algorithms. The main goal of this architecture is to solve for a resolution to the error currently detected by the system. The proposed architecture was tested using two different simulation environment, each meant to prove different points of views, using different tests. These tests will determine if, as the research suggests, this paradigm is a promising alternative for the industrial environment. It will also define what should be done to improve the current architecture and if it should be applied in a decentralised system.

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

Soil physical and Chemical properties of cultivated pasture on forest land, Roraima, Brazil .

Soil conditions under pasture were examined in a range of sites representing the sequence of conversion of forest to pasture at two locations in the vicinity of Ilha de Maracã, Roraima. Comparisons were made with adjacent savana. Soil bulk densities shown to increase after forest clearance and soil chemical data indicate that the initial beneficial effects on nutrient supply of burning forest debris are rather short-lived. Very low levels of available phosphorus prevail in areas of savanna and cultivated pasture of all ages. Variations in the status of older cultivated pastures are mainly attributable to different grazing levelt.

Observations on dynamics of foraging hole construction of two leaf-feeding, soil-inhabiting Syntermes species (insecta: Isoptera) in an Amazonian Rainforest, Brazil

The intensity of construction of foraging access holes by two leaf-litter feeding, soil- inhabiting termite species, Syntermes molestus and Syntermes spinosus, in a Central Amazonian rain forest, was observed on consecutive nights for two weeks. Between 11 and 48 nest entrances per m2 were counted. Interaction between the two species was intense; some entrance holes were overtaken by the larger species during the observations; however, both species coexist in the area. A calculated minimum of 35 entrances/m2 is built every year by both species, emphasizing the importance of soil-burrowing termites for soil structure, aeration and water regime.

Artificial thymus: a tissue engineering strategy

The thymus is the central organ responsible for the generation of T lymphocytes (1). Various diseases cause the thymus to produce in- sufficient T cells, which can lead to immune-suppression (2). Since T cells are essential for the protection against pathogens, it is crucial to promote de novo differentiation of T cells on diseased individuals. The available clinical solutions are: 1) one protocol involving the transplant of thymic stroma from unrelated children only applicable for athymic children (3); 2) for patients with severe peripheral T cell depletion and reduced thymic activity, the administration of stimu- lating molecules stimulating the activity of the endogenous thymus (4). A scaffold (CellFoam) was suggested to support thymus regen- eration in vivo (5), although this research was discontinued. Herein, we propose an innovative strategy to generate a bioartificial thymus. We use a polycaprolactone nanofiber mesh (PCL-NFM) seeded and cultured with human thymic epithelial cells (hTECs). The cells were obtained from infant thymus collected during pediatric cardio-tho- racic surgeries. We report new data on the isolation and characterization of those cells and their interaction with PCL-NFM, by expanding hTECs into relevant numbers and by optimizing cell seeding methods.

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Modern optimization in earthwork construction

Earthworks tasks are often regarded in transportation projects as some of the most demanding processes. In fact, sequential tasks such as excavation, transportation, spreading and compaction are strongly based on heavy mechanical equipment and repetitive processes, thus becoming as economically demanding as they are time-consuming. Moreover, actual construction requirements originate higher demands for productivity and safety in earthwork constructions. Given the percentual weight of costs and duration of earthworks in infrastructure construction, the optimal usage of every resource in these tasks is paramount. Considering the characteristics of an earthwork construction, it can be looked at as a production line based on resources (mechanical equipment) and dependency relations between sequential tasks, hence being susceptible to optimization. Up to the present, the steady development of Information Technology areas, such as databases, artificial intelligence and operations research, has resulted in the emergence of several technologies with potential application bearing that purpose in mind. Among these, modern optimization methods (also known as metaheuristics), such as evolutionary computation, have the potential to find high quality optimal solutions with a reasonable use of computational resources. In this context, this work describes an optimization algorithm for earthworks equipment allocation based on a modern optimization approach, which takes advantage of the concept that an earthwork construction can be regarded as a production line.

Fertilization and cover crop effects on soil nitrogen and plant nutrition in a young guarana plantation

Fruit tree production is gaining an increasing importance in the central Amazon and elsewhere in the humid tropics, but very little is known about the nutrient dynamics in the soil-plant system. The present study quantified the effects of fertilization and cover cropping with a legume (Pueraria phaseoloides (Roxb.) Benth.) on soil nitrogen (N) dynamics and plant nutrition in a young guarana plantation (Paullinia cupana Kunth. (H.B. and K.) var. sorbilis (Mart.) Ducke) on a highly weathered Xanthic Ferralsol. Large subsoil nitrate (NO3-) accumulation at 0.3-3 m below the guarana plantation indicated N leaching from the topsoil. The NO3- contents to a depth of 2 m were 2.4 times greater between the trees than underneath unfertilized trees (P<0.05). The legume cover crop between the trees increased soil N availability as shown by elevated aerobic N mineralization and lower N immobilization in microbial biomass. The guarana N nutrition and yield did not benefit from the N input by biological fixation of atmospheric N2 by the legume cover (P>0.05). Even without a legume intercrop, large amounts of NO3- were found in the subsoil between unfertilized trees. Subsoil NO3- between the trees could be utilized, however, by fertilized guarana. This can be explained by a more vigorous growth of fertilized trees which had a larger nutrient demand and exploited a larger soil volume. With a legume cover crop, however, more mineral N was available at the topsoil which was leached into the subsoil and consequently accumulated at 0.3-3 m depth. Fertilizer additions of P and K were needed to increase subsoil NO3- use between trees.

As possíveis alterações microclimáticas devido a formação do lago artificial da hidrelétrica de Tucuruí -PA

Existem muitas preocupações ecológicas do impacto que a construção de grandes lagos na Amazônia podem provocar, principalmente relacionadas ao microclima. Este estudo visa aumentar o conhecimento científico sobre a distribuição de chuvas antes e depois da formação do lago artificial da UHE Tucuruí-PA. Foram utilizados dados diários de precipitação dos períodos de 1972 a 1983 (pré-enchimento) e de 1984 a 1996 (pós-enchimento) para as cidades de Tucuruí e Marabá-PA. Comparando-se os totais mensais (pré e pós-enchimento), não se observam diferenças estatisticamente significantes (foram aplicados os testes de Fisher e Man-Whitney). Analisando-se a ocorrência de dias com precipitação superior a 5 e 25 mm.dia-1, também não se observam diferenças estatisticamente significativas. Há um leve aumento do número de dias com chuvas leves no final período sêco após a formação do lago, talvez devido a alta evaporação do lago artificial. Também não se observou modificações do início ou final da estação chuvosa.

Soil mineral N dynamics beneath mixtures of leaves from legume and fruit trees in Central Amazonian multi-strata agroforests

Long term applications of leguminous green mulch could increase mineralizable nitrogen (N) beneath cupuaçu trees produced on the infertile acidic Ultisols and Oxisols of the Amazon Basin. However, low quality standing cupuaçu litter could interfere with green mulch N release and soil N mineralization. This study compared mineral N, total N, and microbial biomass N beneath cupuaçu trees grown in two different agroforestry systems, north of Manaus, Brazil, following seven years of different green mulch application rates. To test for net interactions between green mulch and cupuaçu litter, dried gliricidia and inga leaves were mixed with senescent cupuaçu leaves, surface applied to an Oxisol soil, and incubated in a greenhouse for 162 days. Leaf decomposition, N release and soil N mineralization were periodically measured in the mixed species litter treatments and compared to single species applications. The effect of legume biomass and cupuaçu litter on soil mineral N was additive implying that recommendations for green mulch applications to cupuaçu trees can be based on N dynamics of individual green mulch species. Results demonstrated that residue quality, not quantity, was the dominant factor affecting the rate of N release from leaves and soil N mineralization in a controlled environment. In the field, complex N cycling and other factors, including soil fauna, roots, and microclimatic effects, had a stronger influence on available soil N than residue quality.