Characterization of a lectin-like protein isolated from Lachesis muta snake venom

A lectin-like protein was isolated from L. muta venom by gel filtration on BIO Gel P-100 followed by column Chromatography on DEAE-sephades A-50. The protein eluted at 0.4 M Nacl in 0.01 Tris pH 7.3 and exhibited agglutinin activity toward 0+ human erythrocytes. The protein is a dimer with Mr 28 kDa. Amino acid analysis revealed high content of tryptophan and acid recidues and low content of cysteine and methionine residues. No neutral carbohydrates and sialic acid were detected. Circular dichroic spectrum shows 78% of B structure and 1% of alpha structure. In vitro experiments with erythrocytes from rat, rabbit and dog revealed strong agglutination while red blood cells from mice, sheep and goat were not agglutinated. In vivo experiments using anesthetized rats, a sharp and prolonged fall in the blood pressure was observed at protein dose of 1.5 mg/kg. Double dose of protein caused the death of the animal.     

石家庄市室内氡及其子体浓度

本文报道了用双滤膜法对石家庄市室内空气中氡及其子体浓度的调查结果。47个房间室内氡年平均浓度为17.0Bq·m~(-3),氡子体平均浓度是7.14Bq·m~(-3)。氡及其子体浓度的日变化是清晨4—8时出现峰值。季节变化是夏秋季低,冬季高,全年的最低值出现在8月,最高值出现在11月。     

A wavelet-based procedure for process fault detection

To detect faults in a time-dependent process, we apply a discrete wavelet transform (DWT) to several independently replicated data sets generated by that process. The DWT can capture irregular data patterns such as sharp "jumps" better than the Fourier transform and standard statistical procedures without adding much computational complexity. Our wavelet coefficient selection method effectively balances model parsimony against data reconstruction error. The few selected wavelet coefficients serve as the "reduced-size" data set to facilitate an efficient decision-making method in situations with potentially large-volume data sets. We develop a general procedure to detect process faults based on differences between the reduced-size data sets obtained from the nominal (in-control) process and from a new instance of the target process that must be tested for an out-of-control condition. The distribution of the test statistic is constructed first using normal distribution theory and then with a new resampling procedure called "reversed jackknifing" that does not require any restrictive distributional assumptions. A Monte Carlo study demonstrates the effectiveness of these procedures. Our methods successfully detect process faults for quadrupole mass spectrometry samples collected from a rapid thermal chemical vapor deposition process     

Application of Romero García method to the degradation process of DGEBA/ETDA filled with ultra fine metallic copper

Summary The thermal properties of an epoxy resin (diglycidyl ether Bisphenol A, DGEBA) cured with a primary amine (ethylenediamine, ETDA) and filled with 10, 20 and 30 wt. % of ultra fine copper particles were analyzed. The thermal results were evaluated by means of the Romero-Garcia method, which allowed to obtain the resin degradation kinetic parameters, as well as the possible decomposition mechanism. From the obtained results is possible to infer that higger copper content strongly affects the degradation process of the epoxy resin, which causes a drop off on the composites thermal stability. This behavior could be attributed to water presence, being this effect less pronounced for the resin with 10 wt. % copper and unfilled resin. Regarding to decomposition mechanism, three way transport showed the best correlation for all samples.     

Simulation of programmable logic controller

The development and test of a PLC control program takes time, increases equipment down-time, and might damage hardware due to program errors. All of these problems can be eliminated if there is a computer simulation system for testing control programs off line. This paper presented a new method, called Direct Sequential Method, for simulating PLCs. This method resolves a ladder logic sequentially by applying a series of logic deductions. This simulation system has been implemented in C and tested successfully.     

Improved Poisson intensity estimation: denoising application using Poisson data

Recently, Timmermann and Nowak developed algorithms for estimating the means of independent Poisson random variables. The algorithms are based on a multiscale model where certain random variables are assumed to obey a beta-mixture density function. Timmermann and Nowak simplify the density estimation problem by assuming the beta parameters are known and only one mixture parameter is unknown. They use the observed data and the method of moments to estimate the unknown mixture parameter. Taking a different approach, we generate training data from the observed data and compute maximum likelihood estimates of all of the beta-mixture parameters. To assess the improved performance obtained by the proposed modification, we consider a denoising application using Poisson data.