Identification of poisonous plants in the heart of public squares of ribeirão Preto SP

Toxic plants, many ornamentals, may be present in gardens, backyards, parks, vases, squares and vacant lots around the cities. Some of these plants are well known and exuberant, with strong color and decorative aspects, but when swallowed or handled, can cause severe intoxication specially in children. The main objective was to identify the poisonous plants found in public squares of Ribeirão Preto downtown, among five squares: XV de Novembro Square, Carlos Gomes Square, Bandeiras Square, Luís de Camões Square and Sete de Setembro Square. In this study, a literature review was performed in order to know the species that have been recorded as toxic plant. For the species in question, the common name and therapeutic indication were recorded. Over all evaluated squares toxic species were found. Sete de Setembro Square was the most frequent species in a total of seven toxical species. The most common species in the surveyed places were: Euphorbia pulcherrima, Buxus semprevirens and Dracaena fragrans, popularly known as Poinsettia, Boxwood and Cornstalk Dracaena, respectively. The importance of doing studies in urban squares is to improve care to ensure the afforestation process of the cities.

Comparison between known propagation models using least squares tuning algorithm on 5.8 GHz in Amazon region cities

ABSTRACT: This paper presents a performance comparison between known propagation Models through least squares tuning algorithm for 5.8 GHz frequency band. The studied environment is based on the 12 cities located in Amazon Region. After adjustments and simulations, SUI Model showed the smaller RMS error and standard deviation when compared with COST231-Hata and ECC-33 models.

A brief look at the least-squares approach as a classifier applied to restricted-vocabulary speech recognition

In this letter, a speech recognition algorithm based on the least-squares method is presented. Particularly, the intention is to exemplify how such a traditional numerical technique can be applied to solve a signal processing problem that is usually treated by using more elaborated formulations.

Composition of the Ant Fauna (Hymenoptera: Formicidae) in Public Squares in Southern Brazil

The composition of the ant fauna was examined in public squares of three municipalities that compose the hydrographic basin of the Upper Tiete River: Biritiba Mirim, Salesopolis, and Mogi das Cruzes. Richness, frequency of occurrence, similarity, and influence of seasons on the species composition were examined. The method was standardized as sampling units consisted of a set of three baits arranged in a triangle with vertices two meters apart. Sardines in oil were used as attractant. A total of 86 species was collected. Myrmicinae and Pheidole were the richest subfamily and genus, respectively. Eighty species were collected in Mogi das Cruzes, 49 in Salesopolis, and 45 in Biritiba Mirim, with 34 species common to the three areas. The ordination analysis (NMDS) revealed the presence of two distinct communities: one in Mogi das Cruzes and another in Biritiba Mirim-Salesopolis. These data were supported by the dendogram based on the Bray-Curtis dissimilarity index. This result might be associated with the distinct geographic and demographic characteristics of the areas. Regarding seasonality, the composition of the fauna of Mogi das Cruzes is independent of the season of the year, unlike the observed in Biritiba Mirim and Salesopolis.

Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Regularization methods for the solution of a nonlinear least-squares problem in tomography

In this work we study a polyenergetic and multimaterial model for the breast image reconstruction in Digital Tomosynthesis, taking into consideration the variety of the materials forming the object and the polyenergetic nature of the X-rays beam. The modelling of the problem leads to the resolution of a high-dimensional nonlinear least-squares problem that, due to its nature of inverse ill-posed problem, needs some kind of regularization. We test two main classes of methods: the Levenberg-Marquardt method (together with the Conjugate Gradient method for the computation of the descent direction) and two limited-memory BFGS-like methods (L-BFGS). We perform some experiments for different values of the regularization parameter (constant or varying at each iteration), tolerances and stop conditions. Finally, we analyse the performance of the several methods comparing relative errors, iterations number, times and the qualities of the reconstructed images.

Classification Using Generalized Partial Least Squares

The advances in computational biology have made simultaneous monitoring of thousands of features possible. The high throughput technologies not only bring about a much richer information context in which to study various aspects of gene functions but they also present challenge of analyzing data with large number of covariates and few samples. As an integral part of machine learning, classification of samples into two or more categories is almost always of interest to scientists. In this paper, we address the question of classification in this setting by extending partial least squares (PLS), a popular dimension reduction tool in chemometrics, in the context of generalized linear regression based on a previous approach, Iteratively ReWeighted Partial Least Squares, i.e. IRWPLS (Marx, 1996). We compare our results with two-stage PLS (Nguyen and Rocke, 2002A; Nguyen and Rocke, 2002B) and other classifiers. We show that by phrasing the problem in a generalized linear model setting and by applying bias correction to the likelihood to avoid (quasi)separation, we often get lower classification error rates.

Cadaver dogs--a study on detection of contaminated carpet squares

INTRODUCTION: Cadaver dogs are known as valuable forensic tools in crime scene investigations. Scientific research attempting to verify their value is largely lacking, specifically for scents associated with the early postmortem interval. The aim of our investigation was the comparative evaluation of the reliability, accuracy, and specificity of three cadaver dogs belonging to the Hamburg State Police in the detection of scents during the early postmortem interval. MATERIAL AND METHODS: Carpet squares were used as an odor transporting media after they had been contaminated with the scent of two recently deceased bodies (PMI<3h). The contamination occurred for 2 min as well as 10 min without any direct contact between the carpet and the corpse. Comparative searches by the dogs were performed over a time period of 65 days (10 min contamination) and 35 days (2 min contamination). RESULTS: The results of this study indicate that the well-trained cadaver dog is an outstanding tool for crime scene investigation displaying excellent sensitivity (75-100), specificity (91-100), and having a positive predictive value (90-100), negative predictive value (90-100) as well as accuracy (92-100).

Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).

3D volumetric intensity reconsturction from 2D x-ray images using partial least squares regression

Reconstruction of shape and intensity from 2D x-ray images has drawn more and more attentions. Previously introduced work suffers from the long computing time due to its iterative optimization characteristics and the requirement of generating digitally reconstructed radiographs within each iteration. In this paper, we propose a novel method which uses a patient-specific 3D surface model reconstructed from 2D x-ray images as a surrogate to get a patient-specific volumetric intensity reconstruction via partial least squares regression. No DRR generation is needed. The method was validated on 20 cadaveric proximal femurs by performing a leave-one-out study. Qualitative and quantitative results demonstrated the efficacy of the present method. Compared to the existing work, the present method has the advantage of much shorter computing time and can be applied to both DXA images as well as conventional x-ray images, which may hold the potentials to be applied to clinical routine task such as total hip arthroplasty (THA).