Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments

Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering.The latter is particularly true for slope stability assessment,where the effects of uncertainty are synthesized in the so-called probability of failure.This probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical viewpoint.In view of this issue,this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional moments.The spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève expansion.Then,failure probabilities are estimated employing maximum entropy distribution with fractional moments.The application of the proposed approach is examined with two examples:a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained slope.The results show that the proposed approach has excellent accuracy and high efficiency,and it can be applied straightforwardly to similar geotechnical engineering problems.

Convergence analysis for complementary-label learning with kernel ridge regression

Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Fault Isolation by Partial Dynamic Principal Component Analysis in Dynamic Process

Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.

Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

A multivariate partial least squares approach to joint association analysis for multiple correlated traits

Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion(BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability,polymorphic information content(PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

Metabolomics Analysis of the Responses to Partial Hepatectomy in Hepatocellular Carcinoma Patients

In this study, liquid chromatography/quadrupole time of flight mass spectrometry (LC/QTOFMS) was employed for investigating the metabolome of the sera collected from hepatocellular carcinoma (HCC) patients before and 3 to 5 months after partial hepatectomy. To investigate the changes in metabolic phenotypes after the hepatic resection, principal components analysis (PCA) and support vector machine (SVM) were performed for the data grouping and classification. Based on the obtained SVM model, mass spectrometry spectra, database searching as well as the confirmation from authentic standards, several differentiating metabolites were tentatively identified. To improve visualization, z-score plot and heat map display were performed, which exhibited the changes in concentration of the metabolites. As a result, depletion of circulating carnitine, reduced amino acid biosynthesis and increased rate of lipid peroxidation were observed. Meanwhile, up-regulation of hypoxanthine indicated that purine metabolism might serve as the salvage pathway. Collectively, the results reflected metabolic responses to surgical operation in HCC patients, suggesting perturbation of energy metabolism may occur in 3 to 5 months after the partial hepatectomy.

Comparison of Calibration Curve Method and Partial Least Square Method in the Laser Induced Breakdown Spectroscopy Quantitative Analysis

The Laser Induced Breakdown Spectroscopy (LIBS) is a fast, non-contact, no sample preparation analytic technology;it is very suitable for on-line analysis of alloy composition. In the copper smelting industry, analysis and control of the copper alloy concentration affect the quality of the products greatly, so LIBS is an efficient quantitative analysis tech- nology in the copper smelting industry. But for the lead brass, the components of Pb, Al and Ni elements are very low and the atomic emission lines are easily submerged under copper complex characteristic spectral lines because of the matrix effects. So it is difficult to get the online quantitative result of these important elements. In this paper, both the partial least squares (PLS) method and the calibration curve (CC) method are used to quantitatively analyze the laser induced breakdown spectroscopy data which is obtained from the standard lead brass alloy samples. Both the major and trace elements were quantitatively analyzed. By comparing the two results of the different calibration method, some useful results were obtained: both for major and trace elements, the PLS method was better than the CC method in quantitative analysis. And the regression coefficient of PLS method is compared with the original spectral data with background interference to explain the advantage of the PLS method in the LIBS quantitative analysis. Results proved that the PLS method used in laser induced breakdown spectroscopy was suitable for simultaneous quantitative analysis of different content elements in copper smelting industry.

Importance Analysis of a Multi-state System Based on Direct Partial Logic Derivatives and Multi-valued Decision Diagrams

Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.

Near-Infrared Spectroscopy Combined with Absorbance Upper Optimization Partial Least Squares Applied to Rapid Analysis of Polysaccharide for Proprietary Chinese Medicine Oral Solution

Near-infrared (NIR) spectroscopy was applied to reagent-free quantitative analysis of polysaccharide of a brand product of proprietary Chinese medicine (PCM) oral solution samples. A novel method, called absorbance upper optimization partial least squares (AUO-PLS), was proposed and successfully applied to the wavelength selection. Based on varied partitioning of the calibration and prediction sample sets, the parameter optimization was performed to achieve stability. On the basis of the AUO-PLS method, the selected upper bound of appropriate absorbance was 1.53 and the corresponding wavebands combination was 400 - 1880 & 2088 - 2346 nm. With the use of random validation samples excluded from the modeling process, the root-mean-square error and correlation coefficient of prediction for polysaccharide were 27.09 mg·L-1 and 0.888, respectively. The results indicate that the NIR prediction values are close to those of the measured values. NIR spectroscopy combined with AUO-PLS method provided a promising tool for quantification of the polysaccharide for PCM oral solution and this technique is rapid and simple when compared with conventional methods.

Complete or partial split in associating liver partition and portal vein ligation for staged hepatectomy: A systematic review and meta-analysis

BACKGROUND Associating liver partition and portal vein ligation for staged hepatectomy(ALPPS)has been adopted by liver surgeons in recent years.However,high morbidity and mortality rates have limited the promotion of this technique.Some recent studies have suggested that ALPPS with a partial split can effectively induce the growth of future liver remnant(FLR)similar to a complete split with better postoperative safety profiles.However,some others have suggested that ALPPS can induce more rapid and adequate FLR growth,but with the same postoperative morbidity and mortality rates as in partial split of the liver parenchyma in ALPPS(p-ALPPS).AIM To perform a systematic review and meta-analysis on ALPPS and p-ALPPS.METHODS A systematic literature search of PubMed,Embase,the Cochrane Library,and ClinicalTrials.gov was performed for articles published until June 2019.Studies comparing the outcomes of p-ALPPS and ALPPS for a small FLR in consecutive patients were included.Our main endpoints were the morbidity,mortality,and FLR hypertrophy rates.We performed a subgroup analysis to evaluate patients with and without liver cirrhosis.We assessed pooled data using a random-effects model.RESULTS Four studies met the inclusion criteria.Four studies reported data on morbidity and mortality,and two studies reported the FLR hypertrophy rate and one study involved patients with cirrhosis.In the non-cirrhotic group,p-ALPPS-treated patients had significantly lower morbidity and mortality rates than ALPPStreated patients[odds ratio(OR)=0.2;95%confidence interval(CI):0.07–0.57;P=0.003 and OR=0.16;95%CI:0.03-0.9;P=0.04].No significant difference in the FLR hypertrophy rate was observed between the two groups(P>0.05).The total effects indicated no difference in the FLR hypertrophy rate or perioperative morbidity and mortality rates between the ALPPS and p-ALPPS groups.In contrast,ALPPS seemed to have a better outcome in the cirrhotic group.CONCLUSION The findings of our study suggest that p-ALPPS is safer than ALPPS in patients without cirrhosis and exhibits the same rate of FLR hypertrophy.

Quantitative Analysis of Groundwater Flow near a Partially Penetrating River under Riverside Pumping

According to practical geological and hydrogeological conditions of riverside water-supply well fields in northwestern China, an ideal hydrogeological model has been generalized and a three-dimensional mathematical model has been set up. A finite difference method was applied to simulating groundwater flow near a partially penetrating river under riverside pumping, and to analyzing the effects of river width, partial penetration and permeability of riverbed sediments on groundwater recharges. Results show that riverside pumping may cause groundwater to flow beneath the partially penetrating river, and that river width, penetration and riverbed permeability obviously influence flows from the partially penetrating river and constant-head boundaries. However, the pumping output is mainly from the partially penetrating river.

Molecular Genetic Analysis of Partial 9p Trisomy in Two Chinese Families with Mental Retardation and Facial Anomaly

Mental retardation is defined by significant limitations in intellectual function and adaptive behavior that occur before 18 years of age.Many chromosomal diseases come with mental retardation.We reported two Chinese families with partial trisomy 9p and other chromosome partial monosomy,clinical features of mental retardation and mild facial and pinkie anomalies.In the family 1,we showed that the proband carried a trisomy 9p21.3→pter and monosomy 21q22.3→qter by using fluorescence in situ hybridization analysis.Molecular genetic analysis defined the precise breakpoint on chromosome 9p between markers D9S1846 and D9S171,an interval of about 2.9 Mb on 9p21.3,and the breakpoint on chromosome 21q between markers D21S1897 and D21S1446,a region of about 1.5 Mb on 21q22.3.In the family 2,a patient with trisomy 9p21.3→pter and monosomy 5p15.33→pter,and a de novo maternal balanced translocation between chromosomes 5 and 9 was identified in his mother.Cytogenetic and molecular genetic analysis defined the precise breakpoints on chromosome 9p21.3 and chromosome 5p15.33.Further clinical investigation found that any individual had no refractoriness eczema disease except the proband in this family.These results further implicate that trisomy 9p is associated with mental retardation,and that there may be key gene duplication on chromosome 9p21.3→9pter responsible for mental retardation and mild facial anomaly.This result has been applied successfully in prenatal diagnosis of the second family.

Flexural and eigen-buckling analysis of steel-concrete partially composite plates using weak form quadrature element method

Flexural and eigen-buckling analyses for rectangular steel-concrete partially composite plates(PCPs)with interlayer slip under simply supported and clamped boundary conditions are conducted using the weak form quadrature element method(QEM).Both of the derivatives and integrals in the variational description of a problem to be solved are directly evaluated by the aid of identical numerical interpolation points in the weak form QEM.The effectiveness of the presented numerical model is validated by comparing numerical results of the weak form QEM with those from FEM or analytic solution.It can be observed that only one quadrature element is fully competent for flexural and eigen-buckling analysis of a rectangular partially composite plate with shear connection stiffness commonly used.The numerical integration order of quadrature element can be adjusted neatly to meet the convergence requirement.The quadrature element model presented here is an effective and promising tool for further analysis of steel-concrete PCPs under more general circumstances.Parametric studies on the shear connection stiffness and length-width ratio of the plate are also presented.It is shown that the flexural deflections and the critical buckling loads of PCPs are significantly affected by the shear connection stiffness when its value is within a certain range.

Value of partial hepatectomy for the treatment of hilar cholangiocarcinoma: a Meta-analysis study

Objective To discuss the value of partial hepatectomy in patients with hilar cholangiocarcinoma.Methods English articles related to hilar cholangiocarcinoma were screened from January 1,1990 to May 12,2019 in the Pub Med,MEDLINE,EMBASE,and Cochrane Library databases.Information on postoperative radical cure,survival,morbidity,and mortality after surgery were extracted from articles that met the inclusion criteria for the meta-analysis.Results Twenty-two articles that met the inclusion criteria were classified into 4 study groups: the hepatectomy radical cure group(19 articles),the hepatectomy survival group(16 articles),the hepatectomy morbidity group(9 articles),and the hepatectomy mortality group(17 articles).We found that the rate of radical cure after partial hepatectomy(odds ratio [OR] 0.32,95% confidence interval [CI] 0.20-0.51) and the survival rate(hazard ratio [HR] 0.67,95% CI 0.58-0.79) were significantly higher than after simple bile duct resection,but that morbidity(OR 1.99,95% CI 1.37-2.90) and mortality(OR 2.71,95% CI 1.47-4.98) in patients within the partial hepatectomy group were also higher than in the simple bile duct resection group,taking into account the significant heterogeneity in the articles pertaining to the hepatectomy radical cure group(I^2=68.3%,P=0.000),a sub-group analysis was subsequently conducted.Its results showed that when the branches of the secondary bile ducts were not involved during hilar cholangiocarcinoma,then a bile duct resection had a similar radical cure outcome as combined partial hepatectomy(OR 0.94,95% CI 0.54-1.65).Conclusion Partial hepatectomy can increase the proportion of radical cure in patients with hilar cholangiocarcinoma and extend the survival time after surgery.However,the morbidity and mortality after surgery are higher than those in simple bile duct resections.Therefore,simple bile duct resection is still a relevant and efficient tool in the treatment of Bismuth-Corlette Type Ⅰ and Ⅱ hilar cholangiocarcinomas.

Near-Infrared Spectroscopy Coupled with Kernel Partial Least Squares-Discriminant Analysis for Rapid Screening Water Containing Malathion

Near-infrared spectroscopy coupled with kernel partial least squares-discriminant analysis was used to rapidly screen water containing malathion. In the wavenumber of 4348 cm-1 to 9091 cm-1, the overall correct classification rate of kernel partial least squares-discriminant analysis was 100% for training set, and 100% for test set, with the lowest concentration detected malathion residues in water being 1 μg·ml-1. Kernel partial least squares-discriminant analysis was able to have a good performance in classifying data in nonlinear systems. It was inferred that Near-infrared spectroscopy coupled with the kernel partial least squares-discriminant analysis had a potential in rapid screening other pesticide residues in water.

Experimental Study on Shear Lag Effect of Partial Cable-Stayed Bridge and Elasto-Plastic Seismic Analysis

The project of Xiaoxihu Yellow River Bridge in Lanzhou is chosen as partial cable-stayed bridge. To get the shear lag effect and anti-earthquake performance of the actual bridge under various loading conditions, organic glass scaled model was adopted to have an experiment and a theory research at one time. The experiment result is the basically same as the theory calculation which proves the FEA method can well calculate shear lag effect and dynamical performance. As a result, because the bridge is located in a seismic area of 8 degree, an elasto-plastic seismic checking is performed by customized FEA program in this paper.