Sistemas computacionais espaço-temporais para toma de decisão em questões ambientais relacionadas à indústria de petróleo e gás

The area between São Bento do Norte and Macau cities, located in the northern coast of the Rio Grande do Norte State is submitted to intense and constant processes of littoral and aeolian transport, causing erosion, alterations in the sediments balance and modifications in the shoreline. Beyond these natural factors, the human interference is huge in the surroundings, composed by sensitive places, due to the existence of the Guamaré Petroliferous Pole, RN, the greater terrestrial oil producing in Brazil, besides the activities of the salt companies and shrimp farms. This socioeconomic-environmental context justifies the elaboration of strategies of environmental monitoring of that coastal area. In the environmental monitoring of coastal strips, submitted to human impacts, the use of multi-sources and multitemporal data integrated through a Spatio- Temporal Database that allows the multiuser friendly access. The objective was to use the potential of the computational systems as important tools the managers of environmental monitoring. The stored data in the form of a virtual library aid in making decisions from the related results and presented in different formats. This procedure enlarges the use of the data in the preventive attendance, in the planning of future actions and in the definition of new lines of researches on the area, in a multiscale approach. Another activity of this Thesis consisted on the development of a computational system to automate the process to elaborate Oil-Spill Environmental Sensitivity Maps, based on the temporal variations that some coastal ecosystems present in the sensibility to the oil. The maps generated in this way, based on the methodology proposed by the Ministério do Meio Ambiente, supply more updated information about the behavior of the ecosystem, as a support to the operations in case of oil spill. Some parameters, such as the hydrodynamic data, the declivity of the beach face, types of resources in risk (environmental, economical, human or cultural) and use and occupation of the area are some of the essential basic information in the elaboration of the sensitivity maps, which suffer temporal alterations.In this way, the two computational systems developed are considered support systems to the decision, because they provide operational subsidies to the environmental monitoring of the coastal areas, considering the transformations in the behavior of coastal elements resulting from temporal changes related the human and/or natural interference of the environment

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Modelagem estocástica do índice de radiação ultravioleta na costa leste do nordeste do Brasil

The increase in ultraviolet radiation (UV) at surface, the high incidence of non-melanoma skin cancer (NMSC) in coast of Northeast of Brazil (NEB) and reduction of total ozone were the motivation for the present study. The overall objective was to identify and understand the variability of UV or Index Ultraviolet Radiation (UV Index) in the capitals of the east coast of the NEB and adjust stochastic models to time series of UV index aiming make predictions (interpolations) and forecasts / projections (extrapolations) followed by trend analysis. The methodology consisted of applying multivariate analysis (principal component analysis and cluster analysis), Predictive Mean Matching method for filling gaps in the data, autoregressive distributed lag (ADL) and Mann-Kendal. The modeling via the ADL consisted of parameter estimation, diagnostics, residuals analysis and evaluation of the quality of the predictions and forecasts via mean squared error and Pearson correlation coefficient. The research results indicated that the annual variability of UV in the capital of Rio Grande do Norte (Natal) has a feature in the months of September and October that consisting of a stabilization / reduction of UV index because of the greater annual concentration total ozone. The increased amount of aerosol during this period contributes in lesser intensity for this event. The increased amount of aerosol during this period contributes in lesser intensity for this event. The application of cluster analysis on the east coast of the NEB showed that this event also occurs in the capitals of Paraiba (João Pessoa) and Pernambuco (Recife). Extreme events of UV in NEB were analyzed from the city of Natal and were associated with absence of cloud cover and levels below the annual average of total ozone and did not occurring in the entire region because of the uneven spatial distribution of these variables. The ADL (4, 1) model, adjusted with data of the UV index and total ozone to period 2001-2012 made a the projection / extrapolation for the next 30 years (2013-2043) indicating in end of that period an increase to the UV index of one unit (approximately), case total ozone maintain the downward trend observed in study period

Modelo de covariância bayesiana para seleção de protfólios de investimentos

The portfolio theory is a field of study devoted to investigate the decision-making by investors of resources. The purpose of this process is to reduce risk through diversification and thus guarantee a return. Nevertheless, the classical Mean-Variance has been criticized regarding its parameters and it is observed that the use of variance and covariance has sensitivity to the market and parameter estimation. In order to reduce the estimation errors, the Bayesian models have more flexibility in modeling, capable of insert quantitative and qualitative parameters about the behavior of the market as a way of reducing errors. Observing this, the present study aimed to formulate a new matrix model using Bayesian inference as a way to replace the covariance in the MV model, called MCB - Covariance Bayesian model. To evaluate the model, some hypotheses were analyzed using the method ex post facto and sensitivity analysis. The benchmarks used as reference were: (1) the classical Mean Variance, (2) the Bovespa index's market, and (3) in addition 94 investment funds. The returns earned during the period May 2002 to December 2009 demonstrated the superiority of MCB in relation to the classical model MV and the Bovespa Index, but taking a little more diversifiable risk that the MV. The robust analysis of the model, considering the time horizon, found returns near the Bovespa index, taking less risk than the market. Finally, in relation to the index of Mao, the model showed satisfactory, return and risk, especially in longer maturities. Some considerations were made, as well as suggestions for further work

Estudo comparativo da marcha de indivíduos hemiparéticos no solo e na esteira

The purpose of the study was to compare hemiparetic gait overground and on the treadmill. Seventeen chronic stroke patients were included in the study. They walked overground and on a treadmill level at the same speed. The Qualisys Medical AB motion analysis system was used to quantify the joint kinematic of the paretic lower limb and the spatio-temporal parameters on the two conditions: overground walking and treadmill walking on three samples of 5-minutes. During the first sample, the subjects walked on the treadmill with greater cadence, shorter stride length, shorter step time on the lower paretic limb, greater range of motion in the hip and knee, greater knee flexion at the initial contact, more extension of the knee and lower dorsiflexion of the ankle at the stance phase. It is important to emphasize that the maximal knee flexion and ankle dorsiflexion just occurred later on the treadmill. Comparisons between each walking sample on the treadmill hadn t revealed any changes on the gait parameters over time. Nonetheless, when analyzing the third walking sample on the treadmill and overground, some variables showed equivalence as such as the total range of motion of the hip, the knee angle at the initial contact and its maximal extension at the stance phase. In summary, walking on a treadmill, even thought having some influence on the familiarization process, haven t demonstrated a complete change in its characteristics of hemiparetic chronic patients

Efeitos da inclinação da esteira na marcha de crianças com síndrome de down

Background: Down syndrome (DS) is a genetic alteration characterized by being a nonprogressive congenital encephalopathy. Children with DS have hypotonia and developmental delays that interfere in the movement`s acquisition for these children. Objective: Analyze the effects of treadmill inclination on angle and spatiotemporal gait characteristics of these individuals. Methodology: We studied 23 subjects of both sexes, with ages ranged between 05 and 11 years, they presented ability to walk on level 5 classified according to the Functional Ambulation Category (FAC). Initially held a subjective evaluation of balance through a questionnaire (Berg Balance Scale-BBS) then the kinematic gait analysis was realized on a treadmill first, without inclination and then, with inclination of 10%, using the motion system analysis Qualisys System. Data analysis was done using BioStat 5.0 attributing significance level of 5%. Normality of data was verified using D'Agostino test and later was applied paired t-test to compare data in two experimental conditions. Results: There was a statistically significant difference in the spatiotemporal variables: reduction in the cadence (from 108.92 ± 39.07 to 99.11 ± 27.51, p <0.04), increase in cycle time (from 1.24 ± 0.27 to 1.36 ± 0.34, p = 0.03 ) and increase in time to take stock (from 0.77 ± 0.15 to 0.82 ± 0.18, p <0.001). Angular variables that showed statistically significant increasing were: the hip in the initial contact (12.23 ± 4.63 to 18.49 ± 5.17, p <0.0001) and max. flexion in balance (12.96±4:32 to 19.50 ± 4.51, p <0.0001 ), knee in the initial contact (15.59 to ± 6.71 to 21.63 ± 6.48, p <0.0001), the ankle in the initial contact (-2.79 ± 9.8 to 2.25 ± 8.79, p <0.0001), max dorsiflexion in stance (4.41 ± 10.07 to 7.13 ± 11.58, p <0.0009), maximum plantar flexion in the pre-assessment of the ankle joint (increase of -6.33 ± 8.77 to -2.69 ± 8.62, p <0.0004).Conclusions: The inclination acts in a positive way for angular and spatiotemporal features gait of children with Down syndrome, demonstrating possible benefit of using this surface in the gait rehabilitation of children with Down Syndrome

Influência do biofeedback no treino de marcha de sujeitos hemiparéticos: ensaio clínico randomizado

BACKGROUND: Treadmill training with partial body weight support (BWS) has shown many benefits for patients after a stroke. But their findings are not well known when combined with biofeedback. OBJETIVE: The purpose of this study was to evaluate the immediate effects of biofeedback, visual and auditory, combined with treadmill training with BWS on on walking functions of hemiplegic subjects. METHODS: We conducted a clinical trial, randomized controlled trial with 30 subjects in the chronic stage of stroke, underwent treadmill training with BWS (control), combined with visual biofeedback, given by the monitor of the treadmill through the symbolic appearance of feet as the subject gave the step; or auditory biofeedback, using a metronome with a frequency of 115% of the cadence of the individual. The subjects were evaluated by kinematics, and the data obtained by the Motion Analysis System Qualisys. To assess differences between groups and within each group after training was applied to ANOVA 3 x 2 repeated measures. RESULTS: There were no statistical differences between groups in any variable spatio-temporal and angular motion, but within each group there was an increase in walking speed and stride length after the training. The group of visual biofeedback increased the stance period and reduced the swing period and reason of symmetry, and the group auditory biofeedback reduced the double stance period. The range of motion of the knee and ankle and the plantar flexion increased in the visual biofeedback group. CONCLUSION: There are no differences between the immediate effects of gait training on a treadmill with BWS performed with and without visual or auditory biofeedback. However, the visual biofeedback can promote changes in a larger number of variables spatiotemporal and angular gait

Efeitos da adição de carga no treino de marcha na esteira em indivíduos com Doença de Parkinson: ensaio clínico controlado randomizado

Introduction: The intrinsic gait disorders in individuals with Parkinson's disease (PD) are one of the most disabling motor symptoms. Among the therapeutic approaches used in attempts to improve the motor function, especially the gait pattern of individuals, stands out the treadmill gait training associated with the addition of load. However, there are few findings that elucidate the benefits arising from such practice. Objective: To assess the effects of adding load on the treadmill gait training in individuals with PD. Material and Methods: A controlled, randomized and blinded clinical trial, was performed with a sample of 27 individuals (18 men and 9 women) with PD, randomly assigned to three experimental conditions, namely: treadmill gait training (n=9), treadmill gait training associated with addition of 5% load (n=9) and treadmill gait training associated with addition of 10% load (n=9). All volunteers were assessed, during phase on of Parkinson's medication, regarding to demographic, clinical and anthropometric (identification form) data, level of disability (Hoehn and Yahr Modified Scale), cognitive function (Mini Mental State Examination), clinical functional - in those areas activity of daily living and motor examination (Unified Parkinson's Disease Rating Scale - UPDRS) and gait cinematic analysis was performed through Qualisys Motion Capture System®. The intervention protocol consisted of gait training in a period of 4 consecutive weeks, with three weekly sessions, lasting 30 minutes each. The post-intervention assessment occurred the next day after the last training session, which was performed cinematic analysis of gait and the UPDRS. Data analysis was performed using the software Statistical Package for Social Sciences® (SPSS) 17.0. Results: The age of volunteers ranged from 41 to 75 years old (62,26 ± 9,07) and the time of clinical diagnosis of PD between 2 to 9 years (4,56 ± 2,42). There was a reduction regarding the score from motor exam domain (p=0,005), only when training with the addition of a 5% load. As for the space-time variables there was no significant difference between groups (p>0,120); however, the training with addition of 5% load presented the following changes: increase in stride length (p=0,028), in step length (p=0,006), in time balance of the most affected member (p=0,006) and reduction in support time of the referred member (p=0,007). Regarding angular variables significant differences between groups submitted to treadmill gait training without addition load and with 5% of load were observed in angle of the ankle at initial contact (p=0,019), in plantar flexion at toe-off (p=0,003) and in the maximum dorsiflexion in swing (p=0,005). While within groups, there was a reduction in amplitude of motion of the ankle (p=0,048), the only workout on the treadmill. Conclusion: The treadmill gait training with addition of 5% load proved to be a better experimental condition than the others because it provided greater gains in a number of variables (space-time and angular gait) and in the motion function, becoming a therapy capable of effectively improving the progress of individuals with PD

Influência da adição de carga na marcha em esteira de crianças com paralisia cerebral hemiparética espástica

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A cafeína exerce efeitos positivos sobre a memória tipo episódica em ratos adultos sem influenciar a sobrevivência neural no giro dentado

The caffeine is a mild psychostimulant that has positive cognitive effects at low doses, while promotes detrimental effects on these processes at higher doses. The episodic-like memory can be evaluated in rodents through hippocampus-dependent tasks. The dentate gyrus is a hippocampal subregion in which neurogenesis occurs in adults, and it is believed that this process is related to the function of patterns separation, such as the identification of spatial and temporal patterns when discriminating events. Furthermore, neurogenesis is influenced spatial and contextual learning tasks. Our goal was to evaluate the performance of male Wistar rats in episodic-like tasks after acute or chronic caffeine treatment (15mg/kg or 30mg/kg). Moreover, we assessed the chronic effect of the caffeine treatment, as well as the influence of the hippocampus-dependent learning tasks, on the survival of new-born neurons at the beginning of treatment. For this purpose, we used BrdU to label the new cells generated in the dentate gyrus. Regarding the acute treatment, we found that the saline group presented a tendency to have better spatial and temporal discrimination than caffeine groups. The chronic caffeine group 15 mg/kg (low dose) showed the best discrimination of the temporal aspect of episodic-like memory, whereas the chronic caffeine group 30mg/kg (high dose) was able to discriminate temporal order, only in a condition of greater difficulty. Assessment of neurogenesis using immunohistochemistry for evaluating survival of new-born neurons generated in the dentate gyrus revealed no difference among groups of chronic treatment. Thus, the positive mnemonic effects of the chronic caffeine treatment were not related to neuronal survival. However, another plastic mechanism could explain the positive mnemonic effect, given that there was no improvement in the acute caffeine groups

O paradoxo da superdifusão de uma caminhada aleatória com memória exponencial

The random walk models with temporal correlation (i.e. memory) are of interest in the study of anomalous diffusion phenomena. The random walk and its generalizations are of prominent place in the characterization of various physical, chemical and biological phenomena. The temporal correlation is an essential feature in anomalous diffusion models. These temporal long-range correlation models can be called non-Markovian models, otherwise, the short-range time correlation counterparts are Markovian ones. Within this context, we reviewed the existing models with temporal correlation, i.e. entire memory, the elephant walk model, or partial memory, alzheimer walk model and walk model with a gaussian memory with profile. It is noticed that these models shows superdiffusion with a Hurst exponent H > 1/2. We study in this work a superdiffusive random walk model with exponentially decaying memory. This seems to be a self-contradictory statement, since it is well known that random walks with exponentially decaying temporal correlations can be approximated arbitrarily well by Markov processes and that central limit theorems prohibit superdiffusion for Markovian walks with finite variance of step sizes. The solution to the apparent paradox is that the model is genuinely non-Markovian, due to a time-dependent decay constant associated with the exponential behavior. In the end, we discuss ideas for future investigations.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Aquisição e construção de modelos estáticos análogos a reservatórios petrolíferos com tecnologia lidar e georadar

The present work develops a methodology to establish a 3D digital static models petroleum reservoir analogue using LIDAR and GEORADAR technologies. Therefore, this work introduce The methodolgy as a new paradigm in the outcrop study, to purpose a consistent way to integrate plani-altimetric data, geophysics data, and remote sensing products, allowing 2D interpretation validation in contrast with 3D, complexes depositional geometry visualization, including in environmental immersive virtual reality. For that reason, it exposes the relevant questions of the theory of two technologies, and developed a case study using TerraSIRch SIR System-3000 made for Geophysical Survey Systems, and HDS3000 Leica Geosystems, using the two technologies, integrating them GOCAD software. The studied outcrop is plain to the view, and it s located at southeast Bacia do Parnaíba, in the Parque Nacional da Serra das Confusões. The methodology embraces every steps of the building process shows a 3D digital static models petroleum reservoir analogue, provide depositional geometry data, in several scales for Simulation petroleum reservoir

Avaliação da aplicação de modelos para a secagem de pastas e suspensões em leito de jorro com alimentação contínua

This work studies the development, implementation and improvement of a macroscopic model to describe the behavior of the spouted bed dryer with continuous feeding for pastes and suspensions drying. This model is based on the CST model (Freire et al., 2009) and the model of Fernandes (2005), whose theoretical foundation is based on macroscopic mass and heat balances for the three phases involved in the process: gas, liquid and solid. Because this technique is quite relevant, the studies of modeling and simulation of spouted bed drying are essential in the analysis of the process as a whole, because through them it is possible to predict and understand the behavior of the process, which contributes significantly to more efficient project and operation. The development and understanding of the phenomena involved in the drying process can be obtained by comparing the experimental data with those from computer simulations. Such knowledge is critical for choosing properly the process conditions in order to obtain a good drying efficiency. Over the past few years, researches and development of works in the field of pastes and suspensions drying in spouted bed has been gaining ground in Brazil. The Particulate Systems Laboratory at Universidade Federal do Rio Grande do Norte, has been developing several researches and generating a huge collection of experimental data concerning the drying of fruit pulps, vegetables pastes, goat milk and suspensions of agro-industrial residues. From this collection, some data of goat milk and residue from acerola (Malpighia glabra L.) drying were collected. For the first time, these data were used for the development and validation of a model that can describe the behavior of spouted bed dryer. Thus, it was possible to model the dryer and to evaluate the influence of process variables (paste feeding, temperature and flow rate of the drying air) in the drying dynamics. We also performed water evaporation experiments in order to understand and to study the behavior of the dryer wall temperature and the evaporation rate. All these analysis will contribute to future works involving the implementation of control strategies in the pastes and suspensions drying. The results obtained in transient analysis were compared with experimental data indicating that this model well represents the process

A construção de modelos situacionais no padrão discursivo narrativa em quadrinhos

The object of this study is the construction of situation models in the discourse pattern comic book narrative, and sits in the field of Cognitive Linguistics. Its main foundations are the notions of embodied mind (LAKOFF; JOHNSON, 1999), mental simulation (BARSALOU, 1999), discourse pattern (DUQUE; COSTA, 2012) and situation models (ZWAAN, 1999). I stem from the hypothesis that the process of meaning construction in narratives is attached to the simulation of space, of time and of the characters goals and actions within the story world, dimensions which make up the situation models elaborated by the reader. The simulation of these experiences during discourse processing originates from the fact of it having an embodied and cultural basis, i.e., upon being confronted with the clues found in the narrative, the cognitive structures that make up the reader s personal and social memories are triggered and make it possible to activate information which in turn refer to his/her physical and social experiences, built up in the environment in which he/she lives. As regards comic book narratives, the construction of situation models is closely related to the recurring activation of certain cognitive structures originating from graphic resources that are typical of that discourse pattern. These conclusions were drawn from the data analysis taken from the work Palestina (SACCO, 2003; 2004; 2011)