Análise bioclimática do bairro do Renascença II São Luis-MA: realidade e perspectiva do conforto térmico em espaços externos

The present research concerns about outdoor s thermal comfort conditions in hot-humid climate cities, understanding that life quality is a result of the urban object s type built for the human being in an environment with specific climate and morphological characteristics. It is presented as object of study the correlation between the neighborhood Renascença II s microclimate in São Luis /MA-Brazil, hot-humid climate city, and its urban morphological changes. As well as the thermal comfort s satisfaction level of its outdoor users. The research has as general goal to diagnosis the way these transformations caused by the urbanization influence the Renascença II s microclimate, identifying critical spots of the studied area, in order to contribute with land use recommendations based on bioclimatic architecture concepts and supply bases to urban design decisions adequate to the São Luis climate. It is presented as theoretical bases the urban climate, its concepts and elements. After that, the thermal comfort conditioners and its prediction models of thermal comfort sensation in outdoor are presented. The predictive models are presented along with bioclimatic assessment methods. Finally the use of bioclimatic assessment as an effective tool to identify places that need changes or preservation in order to seek environment quality. The applied methodology was based on the studies of Katzschner (1997), complemented by Oliveira s (1988) and Bustos Romero s (2001) studies that suggest an analysis and evaluation of maps of topography, buildings floors, land use, green areas and land covering, in order to overlap their characteristics and identify climate variable s measurements points; then a quantitative analysis of the climate variables (air temperature and humidity, wind speed and direction) of the chosen points takes place. It was perceived that Renaissance II has no permanence areas as squares or parks, its outdoor has little vegetation and presets high land impermeability and built density levels. The majority of the people interviewed said that was comfortable in a range of air temperature between 27,28ºC and 30,71ºC. The elaboration of a neighborhood master plan is important, which defines strategies for improvement of the life quality of its inhabitants

Tratamento de borra de petróleo com sistemas microemulsionados

During the storage of oil, sludge is formed in the bottoms of tanks, due to decantation, since the sludge is composed of a large quantity of oil (heavy petroleum fractions), water and solids. The oil sludge is a complex viscous mixture which is considered as a hazardous waste. It is then necessary to develop methods and technologies that optimize the cleaning process, oil extraction and applications in industry. Therefore, this study aimed to determine the composition of the oil sludge, to obtain and characterize microemulsion systems (MES), and to study their applications in the treatment of sludge. In this context, the Soxhlet extraction of crude oil sludge and aged sludge was carried out, and allowing to quantify the oil (43.9 % and 84.7 % - 13 ºAPI), water (38.7 % and 9.15 %) and solid (17.3 % and 6.15 %) contents, respectively. The residues were characterized using the techniques of X-ray fluorescence (XRF), Xray diffraction (XRD) and transmission Infrared (FT-IR). The XRF technique determined the presence of iron and sulfur in higher proportions, confirming by XRD the presence of the following minerals: Pyrite (FeS2), Pyrrhotite (FeS) and Magnetite (Fe3O4). The FT-IR showed the presence of heavy oil fractions. In parallel, twelve MES were prepared, combining the following constituents: two nonionic surfactants (Unitol L90 and Renex 110 - S), three cosurfactants (butanol, sec-butanol and isoamyl alcohol - C), three aqueous phase (tap water - ADT, acidic solution 6 % HCl, and saline solution - 3.5 % NaCl - AP) and an oil phase (kerosene - OP). From the obtained systems, a common point was chosen belonging to the microemulsion region (25 % [C+S] 5 % OP and AP 70 %), which was characterized at room temperature (25°C) by viscosity (Haake Rheometer Mars), particle diameter (Zeta Plus) and thermal stability. Mixtures with this composition were applied to oil sludge solubilization under agitation at a ratio of 1:4, by varying time and temperature. The efficiencies of solubilization were obtained excluding the solids, which ranged between 73.5 % and 95 %. Thus, two particular systems were selected for use in storage tanks, with efficiencies of oil sludge solubilization over 90 %, which proved the effectiveness of the MES. The factorial design delimited within the domain showed how the MES constituents affect the solubilization of aged oil sludge, as predictive models. The MES A was chosen as the best system, which solubilized a high amount of aged crude oil sludge (~ 151.7 g / L per MES)

Identificação e comparação entre controle preditivo com modelo não linear e PI sintonizados com PSO em sistema de separação gravitacional de águia-óleo

The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances

Bone metabolism biomarkers, body weight, and bone age in healthy Brazilian male adolescents

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

Rheological properties of frozen concentrated orange juice (FCOJ) as a function of concentration and subzero temperatures

Data on flow properties of Frozen Concentrated Orange Juice (FCOJ) produced from oranges cv. Pera-Rio (65.04 Brix, 8.8% w/w pulp content, 2.5% w/w pectin, 3.84% citric acid, 1.293 g cm(-3)) from -18 to 0 degrees C were fitted with appropriate predictive models. The power law model was found to be the most appropriate to fit the flow curves obtained for FCOJ between 46.56 and 65.04 degrees Brix. In higher concentrations, thixotropy was observed and showed more temperature dependence. A single equation combining Arrhenius and exponential relationships was applied to describe the temperature effect and shear rate on the quantity of breakdown of FCOJ.

Temperature and concentration dependence of heat capacity of model aqueous solutions

Heat capacities of binary aqueous solutions of different concentrations of sucrose, glucose, fructose, citric acid, malic acid, and inorganic salts were measured with a differential scanning calorimeter in the temperature range from 5degreesC to 65degreesC. Heat capacity increased with increasing water content and increasing temperature. At low concentrations, heat capacity approached that of pure water, with a less pronounced effect of temperature, and similar abnormal behavior of pure water with a minimum around 30degreesC-40degreesC. Literature data, when available agreed relatively well with experimental values. A correction factor, based on the assumption of chemical equilibrium between liquid and gas phase in the Differential Scanning Calorimeter, was proposed to correct for the water evaporation due to temperature rise. Experimental data were fitted to predictive models. Excess molar heat capacity was calculated using the Redlich-Kister equation to represent the deviation from the additive ideal model.

Thermal properties of passion fruit juice as affected by temperature and water content

The specific heat, thermal conductivity and density of passion fruit juice were experimentally determined from 0.506 to 0.902 (wet basis) water content and temperatures from 0.4 to 68.8C. The experimental results were compared with existing and widely used models for the thermal properties. In addition, based on empiric equations from literature, new simple models were parameterized with a subset of the total experimental data. The specific heat and thermal conductivity showed linear dependency on water content and temperature, while the density was nonlinearly related to water content. The generalized predictive models were considerably good for this product but the empiric, product-specific models developed in the present work yield better predictions. Even though the existing models showed a moderate accuracy, the new simple ones would be preferred, because they constitute an easier and direct way of evaluating the thermal properties of passion fruit juice, requiring no information about the chemical composition of the product, and a reduced time of the estimation procedure, as the new empiric models are described in terms of only two physical parameters, the water content and the temperature. © Copyright 2005, Blackwell Publishing All Rights Reserved.

Modelagem das exigências de lisina para aves de postura em crescimento

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimativa da produtividade de trigo em função da adubação nitrogenada utilizando modelagem neuro fuzzy

Currently new techniques for data processing, such as neural networks, fuzzy logic and hybrid systems are used to develop predictive models of complex systems and to estimate the desired parameters. In this article the use of an adaptive neuro fuzzy inference system was investigated to estimate the productivity of wheat, using a database of combination of the following treatments: five N doses (0, 50, 100, 150 and 200 kg ha(-1)), three sources (Entec, ammonium sulfate and urea), two application times of N (at sowing or at side-dressing) and two wheat cultivars (IAC 370 and E21), that were evaluated during two years in Selviria, Mato Grosso do Sul, Brazil. Through the input and output data, the system of adaptive neuro fuzzy inference learns, and then can estimate a new value of wheat yield with different N doses. The productivity prediciton error of wheat in function of five N doses, using a neuro fuzzy system, was smaller than that one obtained with a quadratic approximation. The results show that the neuro fuzzy system is a viable prediction model for estimating the wheat yield in function of N doses.

Patch size, functional isolation, visibility and matrix permeability influences neotropical primate occurrence within highly fragmented landscapes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Avaliação dos modelos de predição da erosão hídrica MEUPS e WEPP: contribuição em bacias hidrográficas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Predicting Accurate and Actionable Static Analysis Warnings: An Experimental Approach

Static analysis tools report software defects that may or may not be detected by other verification methods. Two challenges complicating the adoption of these tools are spurious false positive warnings and legitimate warnings that are not acted on. This paper reports automated support to help address these challenges using logistic regression models that predict the foregoing types of warnings from signals in the warnings and implicated code. Because examining many potential signaling factors in large software development settings can be expensive, we use a screening methodology to quickly discard factors with low predictive power and cost-effectively build predictive models. Our empirical evaluation indicates that these models can achieve high accuracy in predicting accurate and actionable static analysis warnings, and suggests that the models are competitive with alternative models built without screening.

Prediction of apparent protein digestibility of ingredients and diets by in vitro pH-stat degree of protein hydrolysis with species-specific enzymes for juvenile Pacific white shrimp Litopenaeus vannamei

Aquafeed production faces global issues related to availability of feed ingredients. Feed manufacturers require greater flexibility in order to develop nutritional and cost-effective formulations that take into account nutrient content and availability of ingredients. The search for appropriate ingredients requires detailed screening of their potential nutritional value and variability at the industrial level. In vitro digestion of feedstuffs by enzymes extracted from the target species has been correlated with apparent protein digestibility (APD) in fish and shrimp species. The present study verified the relationship between APD and in vitro degree of protein hydrolysis (DH) with Litopenaeus vannamei hepatopancreas enzymes in several different ingredients (n = 26): blood meals, casein, corn gluten meal, crab meal, distiller`s dried grains with solubles, feather meal, fish meals, gelatin, krill meals, poultry by-product meal, soybean meals, squid meals and wheat gluten. The relationship between APD and DH was further verified in diets formulated with these ingredients at 30% inclusion into a reference diet. APD was determined in vivo (30.1 +/- 0.5 degrees C, 32.2 +/- 0.4%.) with juvenile L vannamei (9 to 12 g) after placement of test ingredients into a reference diet (35 g kg(-1) CP: 8.03 g kg(-1) lipid; 2.01 kcal g(-1)) with chromic oxide as the inert marker. In vitro DH was assessed in ingredients and diets with standardized hepatopancreas enzymes extracted from pond-reared shrimp. The DH of ingredients was determined under different assay conditions to check for the most suitable in vitro protocol for APD prediction: different batches of enzyme extracts (HPf5 or HPf6), temperatures (25 or 30 degrees C) and enzyme activity (azocasein): crude protein ratios (4 U: 80 mg CP or 4 U: 40 mg CP). DH was not affected by ingredient proximate composition. APD was significantly correlated to DH in regressions considering either ingredients or diets. The relationships between APD and DH of the ingredients could be suitably adjusted to a Rational Function (y = (a + bx)/(1 + cx + dx2), n = 26. Best in vitro APD predictions were obtained at 25 degrees C, 4 U: 80 mg CP both for ingredients (R(2) = 0.86: P = 0.001) and test diets (R(2) = 0.96; P = 0.007). The regression model including all 26 ingredients generated higher prediction residuals (i.e., predicted APD - determined APD) for corn gluten meal, feather meal. poultry by-product meal and krill flour. The remaining test ingredients presented mean prediction residuals of 3.5 points. A model including only ingredients with APD>80% showed higher prediction precision (R(2) = 0.98: P = 0.000004; n = 20) with average residual of 1.8 points. Predictive models including only ingredients from the same origin (e.g., marine-based, R(2) = 0.98; P = 0.033) also displayed low residuals. Since in vitro techniques have been usually validated through regressions against in vivo APD, the DH predictive capacity may depend on the consistency of the in vivo methodology. Regressions between APD and DH suggested a close relationship between peptide bond breakage by hepatopancreas digestive proteases and the apparent nitrogen assimilation in shrimp, and this may be a useful tool to provide rapid nutritional information. (C) 2009 Elsevier B.V. All rights reserved.

Environmental suitability of a highly fragmented and heterogeneous landscape for forest bird species in south-eastern Brazil

Assessment of the suitability of anthropogenic landscapes for wildlife species is crucial for setting priorities for biodiversity conservation. This study aimed to analyse the environmental suitability of a highly fragmented region of the Brazilian Atlantic Forest, one of the world's 25 recognized biodiversity hotspots, for forest bird species. Eight forest bird species were selected for the analyses, based on point counts (n = 122) conducted in April-September 2006 and January-March 2009. Six additional variables (landscape diversity, distance from forest and streams, aspect, elevation and slope) were modelled in Maxent for (1) actual and (2) simulated land cover, based on the forest expansion required by existing Brazilian forest legislation. Models were evaluated by bootstrap or jackknife methods and their performance was assessed by AUC, omission error, binomial probability or p value. All predictive models were statistically significant, with high AUC values and low omission errors. A small proportion of the actual landscape (24.41 +/- 6.31%) was suitable for forest bird species. The simulated landscapes lead to an increase of c. 30% in total suitable areas. In average, models predicted a small increase (23.69 +/- 6.95%) in the area of suitable native forest for bird species. Being close to forest increased the environmental suitability of landscapes for all bird species; landscape diversity was also a significant factor for some species. In conclusion, this study demonstrates that species distribution modelling (SDM) successfully predicted bird distribution across a heterogeneous landscape at fine spatial resolution, as all models were biologically relevant and statistically significant. The use of landscape variables as predictors contributed significantly to the results, particularly for species distributions over small extents and at fine scales. This is the first study to evaluate the environmental suitability of the remaining Brazilian Atlantic Forest for bird species in an agricultural landscape, and provides important additional data for regional environmental planning.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.