Improvement of quantitative analysis of molybdenum element using PLS-based approaches for laser-induced breakdown spectroscopy in various pressure environments

An experimental setup has been designed and realized in order to optimize the characteristics of laser-induced breakdown spectroscopy system working in various pressure environments. An approach combined the normalization methods with the partial least squares(PLS) method are developed for quantitative analysis of molybdenum(Mo) element in the multi-component alloy,which is the first wall material in the Experimental Advanced Superconducting Tokamak. In this study, the different spectral normalization methods(total spectral area normalization,background normalization, and reference line normalization) are investigated for reducing the uncertainty and improving the accuracy of spectral measurement. The results indicates that the approach of PLS based on inter-element interference is significantly better than the conventional PLS methods as well as the univariate linear methods in the various pressure for molybdenum element analysis.     

Constrained ALS-based tensor blind receivers for multi-user MIMO systems

Tensor factorizations has shown to be an efficient approach for symbols and/or channel estimation in multi-input multi-output (MIMO) systems, where the factor matrices of tensor that correspond to symbols, channel, code/diversity of signals, are often estimated by using alternating least squares (ALS) algorithm. Although the performance of tensor approaches strongly depend on the initializations of the factor matrices. However, due to the absence of a priori on channels, these initializations are done randomly in traditional ALS algorithm. This generally implies a slow convergence. Further, ALS does not take into account the potential orthogonal structure in the factor matrices, which can be exploited to improve the accuracy of factor matrices recovery. To address these insures, this paper proposes constrained ALS tensor blind receivers for multi-user MIMO systems. We show that the multi-user MIMO signals can be expressed as a third-order tensor model, where the matrices of users symbols, direction-of-arrival (DOA) and delay can be viewed as three factor matrices of the tensor model. Two constrained ALS blind algorithms that take into account the potential orthogonal and Vandermonde structures in the factor matrices, are proposed to learn the tensor model, where the users symbols, DOA and delay are joint estimated as three factor matrices. Besides provide the estimations for the factor matrices, the orthogonal and Vandermonde structures also give a better uniqueness results for the use of tensor model. Interestingly, these structures are the nature properties of the factor matrices in our system. This results in an efficient blind approach that has better performance and lower complexity compare with the traditional ALS.     

Response to Robert G. Easterling

In this article, I derive sufficient conditions for an orthogonal main-effect plan having k factors at S _i levels, i = 1, …, k, to have a minimal number of observations. These sufficient conditions are then used to show that many of the orthogonal main-effect plans given prcviously in the literature have minimal numbers of observations.     

Applying likelihood on Hopfield neural network for radar tracking

Abstract

The multiple‐target tracking (MTT) algorithm plays an important role in radar systems. Data association is the most important technique to solve the tracking problems associating dense measurements with existing tracks. A new approach applying Likelihood to measurements and existing tracks in a radar system based on Neural Network computation is investigated in this paper. The proposed algorithm will solve both the data association and the target tracking problems simultaneously. With this approach, the matching between radar measurements and existing target tracks can achieve global relevance. Computer simulation results indicate the ability of this algorithm to keep track of targets under various conditions.     

Short communication: Identification of variation in the ovine prolactin gene of Chios sheep with a cost-effective sequence-based typing assay

The present study identified single nucleotide polymorphisms (SNP) in the coding and untranslated regions of the ovine prolactin gene of Chios sheep. By developing a cost-effective direct sequence-based typing assay, around 600 bp of reliable sequencing data and clear identification of heterozygous positions was achieved. Five SNP were found, located in exons 2 (KC764410:g.567G>A, g.625C>T, g.683C>A) and 3 (KC764410:g.2015C>A, g.2101G>A), whereas the remaining exons were monomorphic. The identified SNP were synonymous, with the exception of the g.567G>A SNP, which results in an Arg to His amino acid change. As the sequencing cost of the sequence-based typing assay was 20 orders of magnitude lower compared with a standard Sanger method, the assay was also used as a genotyping tool. The identified polymorphism was genotyped for 247 ewes and was subsequently used in mixed model association analyses of milk yield, milk fat content, and litter size at birth. The association analysis revealed a significant dominance effect of 0.17 ± 0.07 of the g.2015C>A SNP on milk fat percentage, whereas a dominance effect of ?21.33 ± 10.51 of the same SNP on total lactation milk yield was also estimated. The g.2015C>A SNP explained 2.47 and 3.68% of the total phenotypic variance of milk yield and milk fat percentage, respectively, whereas the corresponding values for the animal variance were 7.14 and 11.75%. A suggestive association of the nonsynonymous g.567G>A SNP with litter size at birth was also detected.     

Effects of lubricant-mixing time on prolongation of dissolution time and its prediction by measuring near infrared spectra from tablets

The relationship between lubricant-mixing time and dissolution time was investigated, and we established a calibration model to predict dissolution time by near infrared (NIR) spectroscopy and the rationale of the prediction. The bulk powder consisted of theophylline, lactose, and potato starch were pre-mixed. After magnesium stearate (Mg-St) was added, the material was mixed for up to 180?min. The mixed powders were compressed to tablets and dissolution tests were performed. From each dissolution curve, 50% dissolution time (T50) was calculated. The NIR spectra of each tablet’s upper surface was measured and a chemometric analysis was conducted. With the extension of mixing time, T50 was prolonged. The Mg-St widely covered the surface of each particle of the bulk powder after material mixing. This coating effect may decrease the wettability of the particles and cause the prolongation of dissolution time. The T50 was predicted by NIR spectroscopy with chemometrics and a calibration model was established. The regression vector showed typical peaks derived from ?CH group of Mg-St, and it is suggested that those peaks, which were caused by the thin layer extension of Mg-St particles over the particle surfaces of other materials, contributed to the prediction of T50 prolongation. These studies show the usefulness of NIR measurements to control the effect of a lubricant in the process of raw powder material mixing.     

Discussion: Integrating SPC and APC

The problem of partitioning the blends (runs) of four mixture components into two orthogonal blocks when a quadratic model is fitted is considered. This is motivated by an industrial investigation of bread-making flours carried out at Spillers Milling Limited, a member of the Dalgety group of companies in the United Kingdom. The design solution proposed by John and described by Cornell is discussed and extended. Study of the characteristics of Latin squares of side 4 leads to reliable rules for quickly obtaining designs of specified kinds. One such design was selected for the experiment at Spillers Milling. Mixture-component values that cause singularity in the new designs are identified, and values that provide designs with highest D-criterion values are obtained for the class of designs discussed. Conveniently rounded, near-optimal mixture component values were chosen for the Spillers Milling experiment, and the analysis led to the prediction of an optimal flour mixture.     

Integrative Analysis of Metabolomic,Proteomic and Genomic Data to Reveal Functional Pathways and Candidate Genes for Drip Loss in Pigs

The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in Musculus longissimus dorsi samples of 97 Duroc × Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein “phosphoglycerate mutase 2” and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes.     

A Genomics-Based Model for Prediction of Severe Bioprosthetic Mitral Valve Calcification

Severe bioprosthetic mitral valve calcification is a significant problem in cardiovascular surgery. Unfortunately, clinical markers did not demonstrate efficacy in prediction of severe bioprosthetic mitral valve calcification. Here, we examined whether a genomics-based approach is efficient in predicting the risk of severe bioprosthetic mitral valve calcification. A total of 124 consecutive Russian patients who underwent mitral valve replacement surgery were recruited. We investigated the associations of the inherited variation in innate immunity, lipid metabolism and calcium metabolism genes with severe bioprosthetic mitral valve calcification. Genotyping was conducted utilizing the TaqMan assay. Eight gene polymorphisms were significantly associated with severe bioprosthetic mitral valve calcification and were therefore included into stepwise logistic regression which identified male gender, the T/T genotype of the rs3775073 polymorphism within the TLR6 gene, the C/T genotype of the rs2229238 polymorphism within the IL6R gene, and the A/A genotype of the rs10455872 polymorphism within the LPA gene as independent predictors of severe bioprosthetic mitral valve calcification. The developed genomics-based model had fair predictive value with area under the receiver operating characteristic (ROC) curve of 0.73. In conclusion, our genomics-based approach is efficient for the prediction of severe bioprosthetic mitral valve calcification.