A solid-like FEM for geometrically non-linear 3D frames

This study presents a solid-like finite element formulation to solve geometric non-linear three-dimensional inhomogeneous frames. To achieve the desired representation, unconstrained vectors are used instead of the classic rigid director triad; as a consequence, the resulting formulation does not use finite rotation schemes. High order curved elements with any cross section are developed using a full three-dimensional constitutive elastic relation. Warping and variable thickness strain modes are introduced to avoid locking. The warping mode is solved numerically in FEM pre-processing computational code, which is coupled to the main program. The extra calculations are relatively small when the number of finite elements. with the same cross section, increases. The warping mode is based on a 2D free torsion (Saint-Venant) problem that considers inhomogeneous material. A scheme that automatically generates shape functions and its derivatives allow the use of any degree of approximation for the developed frame element. General examples are solved to check the objectivity, path independence, locking free behavior, generality and accuracy of the proposed formulation. (C) 2009 Elsevier B.V. All rights reserved.

Robust integration of real time optimization with linear model predictive control

Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.

Evaluation of natural and synthetic compounds according to their antioxidant activity using a multivariate approach

The antioxidant activity of natural and synthetic compounds was evaluated using five in vitro methods: ferric reducing/antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydradzyl (DPPH), oxygen radical absorption capacity (ORAL), oxidation of an aqueous dispersion of linoleic acid accelerated by azo-initiators (LAOX), and oxidation of a meat homogenate submitted to a thermal treatment (TBARS). All results were expressed as Trolox equivalents. The application of multivariate statistical techniques suggested that the phenolic compounds (caffeic acid, carnosic acid, genistein and resveratrol), beyond their high antioxidant activity measured by the DPPH, FRAP and TBARS methods, showed the highest ability to react with the radicals in the ORAC methodology, compared to the other compounds evaluated in this study (ascorbic acid, erythorbate, tocopherol, BHT, Trolox, tryptophan, citric acid, EDTA, glutathione, lecithin, methionine and tyrosine). This property was significantly correlated with the number of phenolic rings and catecholic structure present in the molecule. Based on a multivariate analysis, it is possible to select compounds from different clusters and explore their antioxidant activity interactions in food products.

Quantification of chlorpheniramine maleate enantiomers by ultraviolet spectroscopy and chemometric methods

Chlorpheniramine maleate (CLOR) enantiomers were quantified by ultraviolet spectroscopy and partial least squares regression. The CLOR enantiomers were prepared as inclusion complexes with beta-cyclodextrin and 1-butanol with mole fractions in the range from 50 to 100%. For the multivariate calibration the outliers were detected and excluded and variable selection was performed by interval partial least squares and a genetic algorithm. Figures of merit showed results for accuracy of 3.63 and 2.83% (S)-CLOR for root mean square errors of calibration and prediction, respectively. The ellipse confidence region included the point for the intercept and the slope of 1 and 0, respectively. Precision and analytical sensitivity were 0.57 and 0.50% (S)-CLOR, respectively. The sensitivity, selectivity, adjustment, and signal-to-noise ratio were also determined. The model was validated by a paired t test with the results obtained by high-performance liquid chromatography proposed by the European pharmacopoeia and circular dichroism spectroscopy. The results showed there was no significant difference between the methods at the 95% confidence level, indicating that the proposed method can be used as an alternative to standard procedures for chiral analysis.

A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.

A scheme for efficient quantum computation wtih linear optics

Quantum computers promise to increase greatly the efficiency of solving problems such as factoring large integers, combinatorial optimization and quantum physics simulation. One of the greatest challenges now is to implement the basic quantum-computational elements in a physical system and to demonstrate that they can be reliably and scalably controlled. One of the earliest proposals for quantum computation is based on implementing a quantum bit with two optical modes containing one photon. The proposal is appealing because of the ease with which photon interference can be observed. Until now, it suffered from the requirement for non-linear couplings between optical modes containing few photons. Here we show that efficient quantum computation is possible using only beam splitters, phase shifters, single photon sources and photo-detectors. Our methods exploit feedback from photo-detectors and are robust against errors from photon loss and detector inefficiency. The basic elements are accessible to experimental investigation with current technology.

HPLC-FLD methods to quantify chloroaluminum phthalocyanine in nanoparticles, plasma and tissue: application in pharmacokinetic and biodistribution studies

Analytical and bioanalytical methods of high-performance liquid chromatography with fluorescence detection (HPLC-FLD) were developed and validated for the determination of chloroaluminum phthalocyanine in different formulations of polymeric nanocapsules, plasma and livers of mice. Plasma and homogenized liver samples were extracted with ethyl acetate, and zinc phthalocyanine was used as internal standard. The results indicated that the methods were linear and selective for all matrices studied. Analysis of accuracy and precision showed adequate values, with variations lower than 10% in biological samples and lower than 2% in analytical samples. The recoveries were as high as 96% and 99% in the plasma and livers, respectively. The quantification limit of the analytical method was 1.12 ng/ml, and the limits of quantification of the bioanalytical method were 15 ng/ml and 75 ng/g for plasma and liver samples, respectively. The bioanalytical method developed was sensitive in the ranges of 15-100 ng/ml in plasma and 75-500 ng/g in liver samples and was applied to studies of biodistribution and pharmacokinetics of AlClPc. (C) 2011 Elsevier B.V. All rights reserved.

The influence of oscillations on global existence for a class of semi-linear wave equations

The goal of this paper is to study the global existence of small data solutions to the Cauchy problem for the nonlinear wave equation u(tt) - a(t)(2) Delta u = u(t)(2) - a(t)(2)vertical bar del u vertical bar(2). In particular we are interested in statements for the 1D case. We will explain how the interplay between the increasing and oscillating behavior of the coefficient will influence global existence of small data solutions. Copyright c 2011 John Wiley & Sons, Ltd.

Assessment of the differential linear coherent scattering coefficient of biological samples

New differential linear coherent scattering coefficient, mu(CS), data for four biological tissue types (fat pork, tendon chicken, adipose and fibroglandular human breast tissues) covering a large momentum transfer interval (0.07 <= q <= 70.5 nm(-1)), resulted from combining WAXS and SAXS data, are presented in order to emphasize the need to update the default data-base by including the molecular interference and the large-scale arrangements effect. The results showed that the differential linear coherent scattering coefficient demonstrates influence of the large-scale arrangement, mainly due to collagen fibrils for tendon chicken and fibroglandular breast samples, and triacylglycerides for fat pork and adipose breast samples at low momentum transfer region. While, at high momentum transfer, the mu(CS) reflects effects of molecular interference related to water for tendon chicken and fibroglandular samples and, fatty acids for fat pork and adipose samples. (C) 2009 Elsevier B.V. All rights reserved.

Validated spectrophotometric and spectrofluorimetric methods for determination of chloroaluminum phthalocyanine in nanocarriers

UV-VIS-Spectrophotometric and spectrofluorimetric methods have been developed and validated allowing the quantification of chloroaluminum phthalocyanine (CIAIPc) in nanocarriers. In order to validate the methods, the linearity, limit of detection (LOD), limit of quantification (LOQ), precision, accuracy, and selectivity were examined according to USP 30 and ICH guidelines. Linearities range were found between 0.50-3.00 mu g.mL(-1) (Y=0.3829 X [CIAIPc, mu g.mL(-1)] + 0.0126; r=0.9992) for spectrophotometry, and 0.05-1.00 mu g.mL(-1) (Y=2.24 x 10(6) X [CIAIPc, mu g.L(-1)] + 9.74 x 10(4); r=0.9978) for spectrofluorimetry. In addition, ANOVA and Lack-of-fit tests demonstrated that the regression equations were statistically significant (p<0.05), and the resulting linear model is fully adequate for both analytical methods. The LOD values were 0.09 and 0.01 mu g.mL(-1), while the LOCI were 0.27 and 0.04 mu g.mL(-1) for spectrophotometric and spectrofluorimetric methods, respectively. Repeatability and intermediate precision for proposed methods showed relative standard deviation (RSD) between 0.58% to 4.80%. The percent recovery ranged from 98.9% to 102.7% for spectrophotometric analyses and from 94.2% to 101.2% for spectrofluorimetry. No interferences from common excipients were detected and both methods were considered specific. Therefore, the methods are accurate, precise, specific, and reproducible and hence can be applied for quantification of CIAIPc in nanoemulsions (NE) and nanocapsules (NC).

A family of perfect hashing methods

Minimal perfect hash functions are used for memory efficient storage and fast retrieval of items from static sets. We present an infinite family of efficient and practical algorithms for generating order preserving minimal perfect hash functions. We show that almost all members of the family construct space and time optimal order preserving minimal perfect hash functions, and we identify the one with minimum constants. Members of the family generate a hash function in two steps. First a special kind of function into an r-graph is computed probabilistically. Then this function is refined deterministically to a minimal perfect hash function. We give strong theoretical evidence that the first step uses linear random time. The second step runs in linear deterministic time. The family not only has theoretical importance, but also offers the fastest known method for generating perfect hash functions.

A comparison of computer-based methods for the determination of onset of muscle contraction using electromyography

Little consensus exists in the literature regarding methods for determination of the onset of electromyographic (EMG) activity. The aim of this study was to compare the relative accuracy of a range of computer-based techniques with respect to EMG onset determined visually by an experienced examiner. Twenty-seven methods were compared which varied in terms of EMG processing (low pass filtering at 10, 50 and 500 Hz), threshold value (1, 2 and 3 SD beyond mean of baseline activity) and the number of samples for which the mean must exceed the defined threshold (20, 50 and 100 ms). Three hundred randomly selected trials of a postural task were evaluated using each technique. The visual determination of EMG onset was found to be highly repeatable between days. Linear regression equations were calculated for the values selected by each computer method which indicated that the onset values selected by the majority of the parameter combinations deviated significantly from the visually derived onset values. Several methods accurately selected the time of onset of EMG activity and are recommended for future use. Copyright (C) 1996 Elsevier Science Ireland Ltd.

High-breakdown linear discriminant analysis

The classification rules of linear discriminant analysis are defined by the true mean vectors and the common covariance matrix of the populations from which the data come. Because these true parameters are generally unknown, they are commonly estimated by the sample mean vector and covariance matrix of the data in a training sample randomly drawn from each population. However, these sample statistics are notoriously susceptible to contamination by outliers, a problem compounded by the fact that the outliers may be invisible to conventional diagnostics. High-breakdown estimation is a procedure designed to remove this cause for concern by producing estimates that are immune to serious distortion by a minority of outliers, regardless of their severity. In this article we motivate and develop a high-breakdown criterion for linear discriminant analysis and give an algorithm for its implementation. The procedure is intended to supplement rather than replace the usual sample-moment methodology of discriminant analysis either by providing indications that the dataset is not seriously affected by outliers (supporting the usual analysis) or by identifying apparently aberrant points and giving resistant estimators that are not affected by them.

Worse renal outcome of lupus nephritis in male patients: a case-control study

Background: Progression and long-term renal outcome of lupus nephritis (LN) in male patients is a controversial subject in the literature. The aim of this study was to evaluate the influence of male gender on the renal outcome of LN. Methods: All male (M) LN patients who fulfilled American College of Rheumatology lupus criteria and who were referred for a kidney biopsy from 1999 to 2009 were enrolled in the study. Subjects with end-stage renal disease at baseline, or follow-up time below 6 months, were excluded. Cases were randomly matched to female (F) patients according to the class of LN, baseline estimated glomerular filtration rate (eGFR, Modification of Diet in Renal Disease simplified formula) and follow-up time. Treatment was decided by the clinical staff based on usual literature protocols. The primary endpoint was doubling of serum creatinine and/or end-stage renal disease. The secondary endpoint was defined as a variation of glomerular filtration rate (GFR) per year (Delta GFR/y index), calculated as the difference between final and initial eGFR adjusted by follow-up time for each patient. Results: We included 93 patients (31 M : 62 F). At baseline, M and F patients were not statistically different regarding WHO LN class (II 9.7%, IV 71%, V 19.3%), eGFR (M 62.4 +/- 36.4 ml/min/1.73 m(2) versus F 59.9 +/- 32.7 ml/min/1.73 m(2)), follow-up time (M 44.2 +/- 27.3 months versus F 39.9 +/- 27.9 months), and 24-hour proteinuria (M 5.3 +/- 4.6 g/day versus F 5.2 +/- 3.0 g/day), as well as age, albumin, C3, antinuclear antibody, anti-DNA antibody and haematuria. There was no difference in the primary outcome (M 19% versus F 13%, log-rank p = 0.62). However, male gender was significantly associated with a worse renal function progression, as measured by Delta GFR/y index (beta coefficient for male gender -12.4, 95% confidence interval -22.8 to -2.1, p = 0.02). The multivariate linear regression model showed that male gender remained statistically associated with a worse renal outcome even after adjustment for eGFR, proteinuria, albumin and C3 complement at baseline. Conclusion: In our study, male gender presented a worse evolution of LN (measured by an under GFR recovering) when compared with female patients with similar baseline features and treatment. Factors that influence the progression of LN in men and sex-specific treatment protocols should be further addressed in new studies. Lupus (2011) 20, 561-567.

The relationship between body mass index and the variation in plasma levels of triglycerides after short-term red wine consumption

BACKGROUND: Alcoholic beverages may have protective cardiovascular effects but are known to increase the plasma levels of triglycerides (TG). Both TG and the ratio of TO to high-density lipoprotein cholesterol (TG/HDL-cholesterol) are associated with increased cardiovascular risk. OBJECTIVES: To determine the predictive factors for variations in plasma levels of TO and the TG/HDL-cholesterol ratio in patients after they had consumed red wine for 14 days. METHODS: Forty-two subjects (64% men, 46 +/- 9 years, baseline body mass index [BMI] 25.13 +/- 2.76 kg/m(2)) were given red wine (12% or 12.2% alc/vol, 250 mL/day with meals). Plasma concentration of lipids and glucose were measured before and after red wine consumption. Blood was collected after 12 hours of fast and alcohol abstention. RESULTS: Red wine increased plasma levels of TO from 105 +/- 42 mg/dL to 120 +/- 56 mg/dL (P = .001) and the TG/HDL-cholesterol ratio from 2.16 +/- 1.10 to 2.50 +/- 1.66 (P = .014). In a multivariate linear regression model that included age, baseline BMI, blood pressure, lipids, and glucose, only BMI was independently predictive of the variation in plasma TO after red wine (beta coefficient 0.592, P < .001). BMI also predicted the variation in TG/HDL-cholesterol ratio (beta coefficient 0.505, P = .001, adjusted model). When individuals were divided into three categories, according to their BMI, the average percentage variation in TG after red wine was -4%, 17%, and 33% in the lower (19.60-24.45 kg/m(2)), intermediate, and greater (26.30-30.44 kg/m(2)) tertiles, respectively (P = .001). CONCLUSIONS: Individuals with higher BMI, although nonobese, might be at greater risk for elevation in plasma TO levels and the TG/HDL-cholesterol ratio after short-term red wine consumption. (C) 2011 National Lipid Association. All rights reserved.