Estudo estatístico sobre eventos de precipitação intensa no nordeste do Brasil

The Northeast of Brazil (NEB) shows high climate variability, ranging from semiarid regions to a rainy regions. According to the latest report of the Intergovernmental Panel on Climate Change, the NEB is highly susceptible to climate change, and also heavy rainfall events (HRE). However, few climatology studies about these episodes were performed, thus the objective main research is to compute the climatology and trend of the episodes number and the daily rainfall rate associated with HRE in the NEB and its climatologically homogeneous sub regions; relate them to the weak rainfall events and normal rainfall events. The daily rainfall data of the hydrometeorological network managed by the Agência Nacional de Águas, from 1972 to 2002. For selection of rainfall events used the technique of quantiles and the trend was identified using the Mann-Kendall test. The sub regions were obtained by cluster analysis, using as similarity measure the Euclidean distance and Ward agglomerative hierarchical method. The results show that the seasonality of the NEB is being intensified, i.e., the dry season is becoming drier and wet season getting wet. The El Niño and La Niña influence more on the amount of events regarding the intensity, but the sub-regions this influence is less noticeable. Using daily data reanalysis ERAInterim fields of anomalies of the composites of meteorological variables were calculated for the coast of the NEB, to characterize the synoptic environment. The Upper-level cyclonic vortex and the South atlantic convergene zone were identified as the main weather systems responsible for training of EPI on the coastland

Classificação Automática de Modulação Digital com uso de Correntropia para Ambientes de Rádio Cognitivo

Modern wireless systems employ adaptive techniques to provide high throughput while observing desired coverage, Quality of Service (QoS) and capacity. An alternative to further enhance data rate is to apply cognitive radio concepts, where a system is able to exploit unused spectrum on existing licensed bands by sensing the spectrum and opportunistically access unused portions. Techniques like Automatic Modulation Classification (AMC) could help or be vital for such scenarios. Usually, AMC implementations rely on some form of signal pre-processing, which may introduce a high computational cost or make assumptions about the received signal which may not hold (e.g. Gaussianity of noise). This work proposes a new method to perform AMC which uses a similarity measure from the Information Theoretic Learning (ITL) framework, known as correntropy coefficient. It is capable of extracting similarity measurements over a pair of random processes using higher order statistics, yielding in better similarity estimations than by using e.g. correlation coefficient. Experiments carried out by means of computer simulation show that the technique proposed in this paper presents a high rate success in classification of digital modulation, even in the presence of additive white gaussian noise (AWGN)

Extensões multidimensionais para correntropia e suas aplicações em estimativas robustas

This present work uses a generalized similarity measure called correntropy to develop a new method to estimate a linear relation between variables given their samples. Towards this goal, the concept of correntropy is extended from two variables to any two vectors (even with different dimensions) using a statistical framework. With this multidimensionals extensions of Correntropy the regression problem can be formulated in a different manner by seeking the hyperplane that has maximum probability density with the target data. Experiments show that the new algorithm has a nice fixed point update for the parameters and robust performs in the presence of outlier noise.

Critério de correntropia no treinamento de redes fuzzy wavelet neural networks para identificação de sistemas dinâmicos não lineares

The great interest in nonlinear system identification is mainly due to the fact that a large amount of real systems are complex and need to have their nonlinearities considered so that their models can be successfully used in applications of control, prediction, inference, among others. This work evaluates the application of Fuzzy Wavelet Neural Networks (FWNN) to identify nonlinear dynamical systems subjected to noise and outliers. Generally, these elements cause negative effects on the identification procedure, resulting in erroneous interpretations regarding the dynamical behavior of the system. The FWNN combines in a single structure the ability to deal with uncertainties of fuzzy logic, the multiresolution characteristics of wavelet theory and learning and generalization abilities of the artificial neural networks. Usually, the learning procedure of these neural networks is realized by a gradient based method, which uses the mean squared error as its cost function. This work proposes the replacement of this traditional function by an Information Theoretic Learning similarity measure, called correntropy. With the use of this similarity measure, higher order statistics can be considered during the FWNN training process. For this reason, this measure is more suitable for non-Gaussian error distributions and makes the training less sensitive to the presence of outliers. In order to evaluate this replacement, FWNN models are obtained in two identification case studies: a real nonlinear system, consisting of a multisection tank, and a simulated system based on a model of the human knee joint. The results demonstrate that the application of correntropy as the error backpropagation algorithm cost function makes the identification procedure using FWNN models more robust to outliers. However, this is only achieved if the gaussian kernel width of correntropy is properly adjusted.

Similarity between the in vitro activity and toxicity of two different fungizone™ / lipofundin™ admixtures

Amphotericin B (AmB), an antifungal agent that presents a broad spectrum of activity, remains the gold standard in the antifungal therapy. However, sometimes the high level of toxicity forbids its clinical use. The aim of this work was to evaluate and compare the efficacy and toxicity in vitro of Fungizon™ (AmB-D) and two new different AmB formulations. Methods: three products were studied: Fungizon™, and two Fungizon™ /Lipofundin™ admixtures, which were diluted through two methods: in the first one, Fungizon™ was previously diluted with water for injection and then, in Lipofundin™ (AmB-DAL); the second method consisted of a primary dilution of AmB-D as a powder in the referred emulsion (AmB-DL). For the in vitro assay, two cell models were used: Red Blood Cells (RBC) from human donors and Candida tropicallis (Ct). The in vitro evaluation (K+ leakage, hemoglobin leakage and cell survival rate-CSR) was performed at four AmB concentrations (from 50 to 0.05mg.L-1). Results: The results showed that the action of AmB was not only concentration dependent, but also cellular type and vehicle kind dependent. At AmB concentrations of 50 mg.L-1, although the hemoglobin leakage for AmB-D was almost complete (99.51), for AmB-DAL and AmB-DL this value tended to zero. The p = 0.000 showed that AmB-D was significantly more hemolytic. Conclusion: The Fungizon™- Lipofundin™ admixtures seem to be the more valuable AmB carrier systems due to their best therapeutic index presented

Efeitos do treino em esteira com suporte parcial de peso e do método de facilitação neuromuscular proprioceptiva na marcha hemiparética: estudo comparativo

Objective: To compare the effects of the treadmill training with partial body-weight support (TPBWS) and Proprioceptive Neuromuscular Facilitation (PNF) method on gait of subjects with chronic stroke. Design: Quasi-experimental study. Setting: Laboratorial research. Participants: Twenty-three subjects (13 men and 10 women), with a mean age of 56,7 ± 8,0 years and a mean time since the onset of the stroke of 27,7 ± 20,3 months, and able to walk with personal assistance or assistive devices. Interventions: Two experimental groups underwent gait training based on PNF method (PNF group, n=11) or using the TPBWS - Gait Trainer System 2, Biodex, USA (TPBWS group, n=12), for three weekly sessions, during four weeks. Measures: Evaluation of motor function - using the Stroke Rehabilitation Assessment of Movement (STREAM) and the motor subscale of the Functional Independence Measure (motor FIM) -, and kinematic gait analyze with the Qualisys System (Qualisys Medical AB, Gothenburg, Sweden) were carried out before and after the interventions. Results: Increases in the STREAM scores (F=49.189, P<0.001) and in motor FIM scores (F=7.093, P=0.016), as well as improvement in symmetry ratio (F=7.729, P=0.012) were observed for both groups. Speed, stride length and double-support time showed no change after training. Differences between groups were observed only for the maximum ankle dorsiflexion over the swing phase (F=6.046, P=0.024), which showed an increase for the PNF group. Other angular parameters remain unchanged. Conclusion: Improvement in motor function and in gait symmetry was observed for both groups, suggesting similarity of interventions. The cost-effectiveness of each treatment should be considered for your choice

Novos métodos determinísticos para gerar centros iniciais dos grupos no algoritmo fuzzy C-Means e variantes

Data clustering is applied to various fields such as data mining, image processing and pattern recognition technique. Clustering algorithms splits a data set into clusters such that elements within the same cluster have a high degree of similarity, while elements belonging to different clusters have a high degree of dissimilarity. The Fuzzy C-Means Algorithm (FCM) is a fuzzy clustering algorithm most used and discussed in the literature. The performance of the FCM is strongly affected by the selection of the initial centers of the clusters. Therefore, the choice of a good set of initial cluster centers is very important for the performance of the algorithm. However, in FCM, the choice of initial centers is made randomly, making it difficult to find a good set. This paper proposes three new methods to obtain initial cluster centers, deterministically, the FCM algorithm, and can also be used in variants of the FCM. In this work these initialization methods were applied in variant ckMeans.With the proposed methods, we intend to obtain a set of initial centers which are close to the real cluster centers. With these new approaches startup if you want to reduce the number of iterations to converge these algorithms and processing time without affecting the quality of the cluster or even improve the quality in some cases. Accordingly, cluster validation indices were used to measure the quality of the clusters obtained by the modified FCM and ckMeans algorithms with the proposed initialization methods when applied to various data sets