Direct and simultaneous determination of As, Cu, and Pb in Brazilian sugar cane spirits by graphite furnace AAS using tungsten permanent modifier with co-injection of Pd/Mg(N03)2

A method was developed using the multi-element graphite furnace atomic absorption spectrometry technique for the direct and simultaneous determination of As, Cu, and Pb in Brazilian sugar cane spirit (cachaça) samples. Also employed was the end-capped transversely heated graphite atomizer (THGA) with platforms pre-treated with W permanent modifier and co-injection of Pd/Mg(N03)2. Pyrolysis and atomization temperature curves were established in a cachaça medium (1+1; v/v) containing 0.2% (v/v) HN03 and spiked with 20 μg L-1 As and Pb and 200 μg L-1Cu. The effect of the concentration of major elements usually present in cachaça matrices (Ca, Mg, Na, and K) and ethanol on the absorbance of As, Cu, and Pb was investigated. Analytical working solutions of As, Cu, and Pb were prepared in 10% (v/v) ethanol plus 5.0 mg L-1 Ca, Mg, Na, and K. Acidified to 0.2% (v/v) HNO3, these solutions were suitable to build calibration curves by matrix matching. The proposed method was applied to the simultaneous determination of As, Cu, and Pb in commercial sugar cane spirits. The characteristic mass for the simultaneous determination was 16 pg As, 119 pg Cu, and 28 pg Pb. The pretreated tube lifetime was about 450 firings. The limit of detection (LOD) was 0.6 μg L-1As, 9.2 μg L-1 Cu, and 0.3 μg L-1Pb. The found concentrations varied from 0.81 to 4.28 μg L-1As, 0.28 to 382 mg L-1 Cu and 0.82 to 518 μg L-1 Pb. The recoveries of the spiked samples varied from 94-112% (As), 97-111% (Cu), and 95-101% (Pb). The relative standard deviation (n=12) was 6.9%, 7.4%, and 7.7% for As, Cu, and Pb, respectively, present in a sample at 0.87 μgL-1, 0.81 mgL-1, and 38.9 μgL-1concentrations.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

A multisampling time-domain CMOS imager with synchronous readout circuit

A novel multisampling time-domain architecture for CMOS imagers with synchronous readout and wide dynamic range is proposed. The architecture was implemented in a prototype of imager with 32x32 pixel array fabricated in AMS CMOS 0.35νm and was characterized for sensitivity and color response. The pixel is composed of an n+/psub photodiode, a comparator and a D flip-flop having 16% fill-factor and 30νmx26νm dimensions. The multisampling architecture requires only a 1 bit per pixel memory instead of 8 bits which is typical for time-domain active pixel architectures. The advantage is that the number of transistors in the pixel is low, saving area and providing higher fill-factor. The maximum frame rate is analyzed as a function of number of bits and array size. The analysis shows that it is possible to achieve high frame rates and operation in video mode with 10 bits. Also, we present analysis for the impact of comparator offset voltage in the fixed pattern noise. Copyright 2007 ACM.

Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.

Influence of bleaching treatment on flexural resistance of hybrid materials.

The aim of this study is to evaluate the flexural resistance of three types of restorative materials: compomer (Freedom), resin-modified glass-ionomer (Vitremer) and composite resin (Esthet-X), observing whether the application of bleaching agent can cause alterations of their flexural properties. Sixty samples were made using a 10 x 1 x 1 mm brass mold, and divided into three groups: G1- Freedom (SDI); G2- Vitremer (3M ESPE); G3- Esthet-X (Dentsply). On half of the samples of each group (10 samples) the bleaching treatment was applied and the other half used as control, was stored in distilled water at a temperature of 37 degrees C. Whiteness HP Maxx bleaching system was applied on the sample surface following the manufacturer's recommendations, simulating the bleaching treatment at the clinic. After this period, a flexural strength (three-point bending) test was conducted using (EMIC DL 1000) machine until the samples fractured. The data were submitted to ANOVA and Tukey tests. Of the restorative materials studied, G3-(87.24 +/- 31.40 MPa) presented the highest flexural strength, followed by G1-(61.67 +/- 21.32 MPa) and G2-(61.67 +/- 21.32 MPa). There was a statistical difference in flexural strength after the bleaching treatment. It was concluded that the use of a beaching agent can promote significant alteration of the flexural strength of these restorative materials.

Overjet and overbite analysis during the eruption of the upper permanent incisors.

ABSTRACT The purpose of this study was to analyze the overjet and overbite behavior during eruption of the upper permanent incisors. Fourth-eight plaster casts of 16 patients from ages 6-13 years were appraised longitudinally. It was found that the overjet remains constant, starting at the eruption of the upper permanent central incisors until eruption of the upper permanent canine teeth, while the overbite increases after eruption of the upper permanent lateral incisors and remains constant with the eruption of the canine teeth.

Developmental Disturbance of an Unerupted permanent incisor due to trauma to its predecessor

Developmental disturbances of permanent teeth can result from trauma to primary teeth because of the proximity of the root of the primary teeth to their permanent successors. We describe the case of a 14-month-old boy who was referred to the baby clinic of the School of Dentistry, Universidad Estadual Paulista, Araçatuba, Brazil, after sustaining a severe trauma that led to intrusion of the right primary central incisor. Radiographic examination 4 years after the trauma showed a developing morphological change in the germ of the permanent successor. On eruption of the permanent central incisor, a crown malformation along with enamel hypoplasia was observed. We concludethat radiographic follow-up is indicated after trauma to monitor possible sequelae in the permanent successors even before their eruption. © 2011 Canadian Dental Association.

Comparison of FACTS STATCOM models for damping oscillations in power system due to small perturbations

This paper presents small-signal stability studies of a multimachine power system, considering Static Synchronous Compensators (STATCOM)and discussed control modes of the STATCOM. The Power Sensitivity Model(PSM)is used to represent the electric power system. The study is based on modal analysis and time domain simulations. The results obtained allow concluding that the STATCOM improves the stabilization in the electric power system. © 2011 IEEE.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Prevalence of chewing side preference in the deciduous, mixed and permanent dentitions

Aim: This study evaluated the prevalence of the chewing side preference (CSP) in the deciduous, mixed and permanent dentitions. Materials and methods: Three-hundred subjects were divided in three groups (n = 100): Group 1 - children with deciduous dentition; group 2-children with mixed dentition; group 3 - subjects with permanent dentition. The CSP was determined using a direct method (visual observation) developed by McDonnell et al. (2004). Descriptive statistic was used to observe the prevalence of CSP. The Pearson's Chi-square was used to investigate signicant associations between gender, presence of CSP and preferred side (right/left). Results and conclusion: Eighty-seven percent of group 1 had a CSP. Eighty-two percent of group 2 had a CSP. Seventy-six percent of group 3 had a CSP. There was no statistically significant association between presence of CSP and gender in all groups. There was no statistically significant association between preferred side (right/left) and gender in all groups. Clinical significance: There is a higher prevalence of subjects in deciduous, mixed and permanent dentition that presented chewing side preference. The early diagnosis of the presence of chewing side preference can prevent the unilateral chewing pattern with prophylactic therapy applied to the first teeth (deciduous).

Prevalence and microscopic features of enamel pearls from permanent human molars

Enamel pearls are ectopic structures observed mainly on the roots of permanent teeth and could be related to periodontal diseases. Aim: To evaluate the prevalence of enamel pearls in extracted human molars and characterize their structures using light and scanning electron microscopy. Methods: The study comprised 2,201 extracted human permanent molars. The teeth were analyzed and classified according to morphological features. The presence, location and shape of enamel pearls were investigated. Fifteen human molars with enamel pearls were embedded in acrylic resin and observed by light microscopy. Results: Seventy-one enamel pearls were identified on third molar root. Microscopically, most pearls were composed of prismatic irregular enamel and normal dentin. The dentinoenamel junction presented an irregular course. The number of dentinal tubules was normal and they presented curvature to continue within the root dentin of the carrier tooth. Dentinal tubules below the enamel pearls were closer to each other. Conclusions: Scanning electron microscopic analysis revealed that the enamel pearls were similar to coronal enamel.

Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.

Development of a support vector machine-based navigation system for driving a mobile robot through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route. © 2012 IEEE.