Sequential spectrofluorimetric determination of free and total glycerol in biodiesel in a multicommuted flow system

A new procedure for spectrofluorimetric determination of free and total glycerol in biodiesel samples is presented. It is based on the oxidation of glycerol by periodate, forming formaldehyde, which reacts with acetylacetone, producing the luminescent 3,5-diacetyl-1,4-dihydrolutidine. A flow system with solenoid micro-pumps is proposed for solution handling. Free glycerol was extracted off-line from biodiesel samples with water, and total glycerol was converted to free glycerol by saponification with sodium ethylate under sonication. For free glycerol, a linear response was observed from 5 to 70 mg L(-1) with a detection limit of 0.5 mg L(-1), which corresponds to 2 mg kg(-1) in biodiesel. The coefficient of variation was 0.9% (20 mg L(-1), n = 10). For total glycerol, samples were diluted on-line, and the linear response range was 25 to 300 mg L(-1). The detection limit was 1.4 mg L(-1) (2.8 mg kg(-1) in biodiesel) with a coefficient of variation of 1.4% (200 mg L(-1), n = 10). The sampling rate was ca. 35 samples h(-1) and the procedure was applied to determination of free and total glycerol in biodiesel samples from soybean, cottonseed, and castor beans.

Laser-induced breakdown spectroscopy and chemometrics for classification of toys relying on toxic elements

Quality control of toys for avoiding children exposure to potentially toxic elements is of utmost relevance and it is a common requirement in national and/or international norms for health and safety reasons. Laser-induced breakdown spectroscopy (LIBS) was recently evaluated at authors` laboratory for direct analysis of plastic toys and one of the main difficulties for the determination of Cd. Cr and Pb was the variety of mixtures and types of polymers. As most norms rely on migration (lixiviation) protocols, chemometric classification models from LIBS spectra were tested for sampling toys that present potential risk of Cd, Cr and Pb contamination. The classification models were generated from the emission spectra of 51 polymeric toys and by using Partial Least Squares - Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA) and K-Nearest Neighbor (KNN). The classification models and validations were carried out with 40 and 11 test samples, respectively. Best results were obtained when KNN was used, with corrected predictions varying from 95% for Cd to 100% for Cr and Pb. (C) 2011 Elsevier B.V. All rights reserved.

Sequential injections as an alternative to gradient exploitation for implementing differential kinetic analysis in a flow injection system

A novel flow-based strategy for implementing simultaneous determinations of different chemical species reacting with the same reagent(s) at different rates is proposed and applied to the spectrophotometric catalytic determination of iron and vanadium in Fe-V alloys. The method relies on the influence of Fe(II) and V(IV) on the rate of the iodide oxidation by Cr(VI) under acidic conditions, the Jones reducing agent is then needed Three different plugs of the sample are sequentially inserted into an acidic KI reagent carrier stream, and a confluent Cr(VI) solution is added downstream Overlap between the inserted plugs leads to a complex sample zone with several regions of maximal and minimal absorbance values. Measurements performed on these regions reveal the different degrees of reaction development and tend to be more precise Data are treated by multivariate calibration involving the PLS algorithm The proposed system is very simple and rugged Two latent variables carried out ca 95% of the analytical information and the results are in agreement with ICP-OES. (C) 2010 Elsevier B V. All rights reserved.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

DOES THE CHRONOTYPE CLASSIFICATION NEED TO BE UPDATED? PRELIMINARY FINDINGS

Traditionally, chronotype classification is based on the Morningness-Eveningness Questionnaire (MEQ). It is implicit in the classification that intermediate individuals get intermediate scores to most of the MEQ questions. However, a small group of individuals has a different pattern of answers. In some questions, they answer as ""morning-types"" and in some others they answer as ""evening-types,"" resulting in an intermediate total score. ""Evening-type"" and ""Morning-type"" answers were set as A(1) and A(4), respectively. Intermediate answers were set as A(2) and A(3). The following algorithm was applied: Bimodality Index = (Sigma A(1) x Sigma A(4))(2) - (Sigma A(2) x Sigma A(3))(2). Neither-types that had positive bimodality scores were classified as bimodal. If our hypothesis is validated by objective data, an update of chronotype classification will be required. (Author correspondence: brunojm@ymail.com)

Sequential Injection Analysis (SIA) for Arsenic Speciation by Capillary Electrophoresis Hyphenated to Inductively Coupled Plasma Sector Field Mass Spectrometry (CE-ICP-SFMS)

Sequential injection analysis (SIA) is proposed for managing microvolumes of sample and arsenic species solutions for speciation analysis by capillary electrophoresis focusing on the reduction of hazardous waste residues. An electronically controlled hydrodynamic injector was projected to introduce microvolumes of solutions prepared by SIA into the CE capillary with precision better than 2%. The determination of arsenite, arsenate, monomethylarsonic acid, dimethylarsinic acid, and arsenobetaine was performed from 50 mu L volumes of lyophilized urine and extract of shrimp with the system hyphenated to inductively coupled plasma mass spectrometry (CE-ICP-SFMS).

Categories of manual asymmetry and their variation with advancing age

Manual asymmetries were analyzed in 18- to 63-year-old right-handers in different motor tasks. This analysis aimed at describing the asymmetry profile for each task and assessing their stability across ages. For this purpose, performance of the right and left hands were analyzed in the following aspects: simple reaction time, rate of sequential finger movements, maximum grip force, accuracy in anticipatory timing, rate of repetitive tapping, and rate of drawing movements. In addition, stability of manual preference across ages was assessed through the Edinburgh inventory (Oldfield, 1971). The results indicated different profiles of manual asymmetry, with identification of three categories across tasks: symmetric performance (asymmetry indices close to zero), inconsistent asymmetry (asymmetry indices variable in magnitude and direction), and consistent asymmetry (asymmetry indices favoring a single hand). The different profiles observed in the young adults were stable across ages with two exceptions: decreased lateral asymmetry for maximum grip force and increased asymmetry for sequential drawing in older individuals. These results indicate that manual asymmetries are task specific. Such task specificity is interpreted to be the result of different sensorimotor requirements imposed by each motor task in association with motor experiences accumulated over the lifetime. Analysis of manual preference showed that strength of preference for the right hand was greater in older individuals. (C) 2008 Elsevier Masson Srl. All rights reserved.

Sequential production of amylolytic and lipolytic enzymes by bacterium strain isolated from petroleum contaminated soil

Amylases and lipases are highly demanded industrial enzymes in various sectors such as food, pharmaceuticals, textiles, and detergents. Amylases are of ubiquitous occurrence and hold the maximum market share of enzyme sales. Lipases are the most versatile biocatalyst and bring about a range of bioconversion reactions such as hydrolysis, inter-esterification, esterification, alcoholysis, acidolysis, and aminolysis. The objective of this work was to study the feasibility for amylolitic and lipolytic production using a bacterium strain isolated from petroleum contaminated soil in the same submerged fermentation. This was a sequential process based on starch and vegetable oils feedstocks. Run were performed in batchwise using 2% starch supplemented with suitable nutrients and different vegetable oils as a lipase inducers. Fermentation conditions were pH 5.0; 30 degrees C, and stirred speed (200 rpm). Maxima activities for amyloglucosidase and lipase were, respectively, 0.18 and 1,150 U/ml. These results showed a promising methodology to obtain both enzymes using industrial waste resources containing vegetable oils.

Classification of normal swallowing and oropharyngeal dysphagia using wavelet

Oropharyngeal dysphagia is characterized by any alteration in swallowing dynamics which may lead to malnutrition and aspiration pneumonia. Early diagnosis is crucial for the prognosis of patients with dysphagia, and the best method for swallowing dynamics assessment is swallowing videofluoroscopy, an exam performed with X-rays. Because it exposes patients to radiation, videofluoroscopy should not be performed frequently nor should it be prolonged. This study presents a non-invasive method for the pre-diagnosis of dysphagia based on the analysis of the swallowing acoustics, where the discrete wavelet transform plays an important role to increase sensitivity and specificity in the identification of dysphagic patients. (C) 2008 Elsevier Inc. All rights reserved.

A classification methodology for the risk of weed infestation using fuzzy logic

Despite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model.

Anaerobic treatment of sulfate-rich wastewater in an anaerobic sequential batch reactor (AnSBR) using butanol as the carbon source

Biological sulfate reduction was studied in a laboratory-scale anaerobic sequential batch reactor (14 L) containing mineral coal for biomass attachment. The reactor was fed industrial wastewater with increasingly high sulfate concentrations to establish its application limits. Special attention was paid to the use of butanol in the sulfate reduction that originated from melamine resin production. This product was used as the main organic amendment to support the biological process. The reactor was operated for 65 cycles (48 h each) at sulfate loading rates ranging from 2.2 to 23.8 g SO(4)(2-)/cycle, which corresponds to sulfate concentrations of 0.25, 0.5,1.0, 2.0 and 3.0 g SW(4)(2-)L(-1). The sulfate removal efficiency reached 99% at concentrations of 0.25, 0.5 and 1.0 g SO(4)(2-)L(-1). At higher sulfate concentrations (2.0 and 3.0 g SO(4)(2-)L(-1)), the sulfate conversion remained in the range of 71-95%. The results demonstrate the potential applicability of butanol as the carbon source for the biological treatment of sulfate in an anaerobic batch reactor. (C) 2011 Elsevier Ltd. All rights reserved.

On the classification of mixed construction and demolition waste aggregate by porosity and its impact on the mechanical performance of concrete

The properties of recycled aggregate produced from mixed (masonry and concrete) construction and demolition (C&D) waste are highly variable, and this restricts the use of such aggregate in structural concrete production. The development of classification techniques capable of reducing this variability is instrumental for quality control purposes and the production of high quality C&D aggregate. This paper investigates how the classification of C&D mixed coarse aggregate according to porosity influences the mechanical performance of concrete. Concretes using a variety of C&D aggregate porosity classes and different water/cement ratios were produced and the mechanical properties measured. For concretes produced with constant volume fractions of water, cement, natural sand and coarse aggregate from recycled mixed C&D waste, the compressive strength and Young modulus are direct exponential functions of the aggregate porosity. Sink and float technique is a simple laboratory density separation tool that facilitates the separation of cement particles with lower porosity, a difficult task when done only by visual sorting. For this experiment, separation using a 2.2 kg/dmA(3) suspension produced recycled aggregate (porosity less than 17%) which yielded good performance in concrete production. Industrial gravity separators may lead to the production of high quality recycled aggregate from mixed C&D waste for structural concrete applications.

Sequential decision making with partially ordered preferences

This paper presents new insights and novel algorithms for strategy selection in sequential decision making with partially ordered preferences; that is, where some strategies may be incomparable with respect to expected utility. We assume that incomparability amongst strategies is caused by indeterminacy/imprecision in probability values. We investigate six criteria for consequentialist strategy selection: Gamma-Maximin, Gamma-Maximax, Gamma-Maximix, Interval Dominance, Maximality and E-admissibility. We focus on the popular decision tree and influence diagram representations. Algorithms resort to linear/multilinear programming; we describe implementation and experiments. (C) 2010 Elsevier B.V. All rights reserved.

Animated solid model of the lung constructed from unsynchronized MR sequential images

This work discusses a 4D lung reconstruction method from unsynchronized MR sequential images. The lung, differently from the heart, does not have its own muscles, turning impossible to see its real movements. The visualization of the lung in motion is an actual topic of research in medicine. CT (Computerized Tomography) can obtain spatio-temporal images of the heart by synchronizing with electrocardiographic waves. The FOV of the heart is small when compared to the lung`s FOV. The lung`s movement is not periodic and is susceptible to variations in the degree of respiration. Compared to CT, MR (Magnetic Resonance) imaging involves longer acquisition times and it is not possible to obtain instantaneous 3D images of the lung. For each slice, only one temporal sequence of 2D images can be obtained. However, methods using MR are preferable because they do not involve radiation. In this paper, based on unsynchronized MR images of the lung an animated B-Repsolid model of the lung is created. The 3D animation represents the lung`s motion associated to one selected sequence of MR images. The proposed method can be divided in two parts. First, the lung`s silhouettes moving in time are extracted by detecting the presence of a respiratory pattern on 2D spatio-temporal MR images. This approach enables us to determine the lung`s silhouette for every frame, even on frames with obscure edges. The sequence of extracted lung`s silhouettes are unsynchronized sagittal and coronal silhouettes. Using our algorithm it is possible to reconstruct a 3D lung starting from a silhouette of any type (coronal or sagittal) selected from any instant in time. A wire-frame model of the lung is created by composing coronal and sagittal planar silhouettes representing cross-sections. The silhouette composition is severely underconstrained. Many wire-frame models can be created from the observed sequences of silhouettes in time. Finally, a B-Rep solid model is created using a meshing algorithm. Using the B-Rep solid model the volume in time for the right and left lungs were calculated. It was possible to recognize several characteristics of the 3D real right and left lungs in the shaded model. (C) 2007 Elsevier Ltd. All rights reserved.

A Brazilian registry of juvenile dermatomyositis: onset features and classification of 189 cases

Objective To describe onset features, classification and treatment of juvenile dermatomyositis (JDM) and juvenile polymyositis (JPM) from a multicentre registry. Methods Inclusion criteria were onset age lower than 18 years and a diagnosis of any idiopathic inflammatory myopathy (IIM) by attending physician. Bohan & Peter (1975) criteria categorisation was established by a scoring algorithm to define JDM and JPM based oil clinical protocol data. Results Of the 189 cases included, 178 were classified as JDM, 9 as JPM (19.8: 1) and 2 did not fit the criteria; 6.9% had features of chronic arthritis and connective tissue disease overlap. Diagnosis classification agreement occurred in 66.1%. Medial? onset age was 7 years, median follow-up duration was 3.6 years. Malignancy was described in 2 (1.1%) cases. Muscle weakness occurred in 95.8%; heliotrope rash 83.5%; Gottron plaques 83.1%; 92% had at least one abnormal muscle enzyme result. Muscle biopsy performed in 74.6% was abnormal in 91.5% and electromyogram performed in 39.2% resulted abnormal in 93.2%. Logistic regression analysis was done in 66 cases with all parameters assessed and only aldolase resulted significant, as independent variable for definite JDM (OR=5.4, 95%CI 1.2-24.4, p=0.03). Regarding treatment, 97.9% received steroids; 72% had in addition at least one: methotrexate (75.7%), hydroxychloroquine (64.7%), cyclosporine A (20.6%), IV immunoglobulin (20.6%), azathioprine (10.3%) or cyclophosphamide (9.6%). In this series 24.3% developed calcinosis and mortality rate was 4.2%. Conclusion Evaluation of predefined criteria set for a valid diagnosis indicated aldolase as the most important parameter associated with de, methotrexate combination, was the most indicated treatment.