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Based on the scenario that the K 0 ∗ (1430) is viewed as the ground state of s q ¯ or q s ¯ , we study the K 0 ∗ (1430) leading-twist distribution amplitude (DA) ϕ 2 ; K 0 ∗ (x , μ) with the QCD sum rules in the framework of background field theory. A more reasonable sum rule formula for ξ -moments ⟨ ξ n ⟩ 2 ; K 0 ∗ is suggested, which eliminates the influence brought by the fact that the sum rule of ⟨ ξ p 0 ⟩ 3 ; K 0 ∗ cannot be normalized in whole Borel region. More accurate values of the first ten ξ -moments, ⟨ ξ n ⟩ 2 ; K 0 ∗ (n = 1 , 2 , ... , 10) , are evaluated. A new light-cone harmonic oscillator (LCHO) model for K 0 ∗ (1430) leading-twist DA is established for the first times. By fitting the resulted values of ⟨ ξ n ⟩ 2 ; K 0 ∗ (n = 1 , 2 , ... , 10) via the least squares method, the behavior of K 0 ∗ (1430) leading-twist DA described with LCHO model is determined. Further, by adopting the light-cone QCD sum rules, we calculate the B s , D s → K 0 ∗ (1430) transition form factors and branching fractions of the semileptonic decays B s , D s → K 0 ∗ (1430) ℓ ν ℓ . The corresponding numerical results can be used to extract the Cabibbo-Kobayashi-Maskawa matrix elements by combining the relative experimental data in the future. [ABSTRACT FROM AUTHOR]

In the paper, we investigate the moments ⟨ ξ 2 ; a 1 ‖ ; n ⟩ of the axial-vector a 1 (1260) -meson distribution amplitude by using the QCD sum rules approach under the background field theory. By considering the vacuum condensates up to dimension-six and the perturbative part up to next-to-leading order QCD corrections, its first five moments at an initial scale μ 0 = 1 GeV are ⟨ ξ 2 ; a 1 ‖ ; 2 ⟩ | μ 0 = 0.223 ± 0.029 , ⟨ ξ 2 ; a 1 ‖ ; 4 ⟩ | μ 0 = 0.098 ± 0.008 , ⟨ ξ 2 ; a 1 ‖ ; 6 ⟩ | μ 0 = 0.056 ± 0.006 , ⟨ ξ 2 ; a 1 ‖ ; 8 ⟩ | μ 0 = 0.039 ± 0.004 and ⟨ ξ 2 ; a 1 ‖ ; 10 ⟩ | μ 0 = 0.028 ± 0.003 , respectively. We then construct a light-cone harmonic oscillator model for a 1 (1260) -meson longitudinal twist-2 distribution amplitude ϕ 2 ; a 1 ‖ (x , μ) , whose model parameters are fitted by using the least squares method. As an application of ϕ 2 ; a 1 ‖ (x , μ) , we calculate the transition form factors (TFFs) of D → a 1 (1260) in large and intermediate momentum transfers by using the QCD light-cone sum rules approach. At the largest recoil point ( q 2 = 0 ), we obtain A (0) = 0. 130 - 0.013 + 0.015 , V 1 (0) = 1. 898 - 0.121 + 0.128 , V 2 (0) = 0. 228 - 0.021 + 0.020 , and V 0 (0) = 0. 217 - 0.025 + 0.023 . By applying the extrapolated TFFs to the semi-leptonic decay D 0 (+) → a 1 - (0) (1260) ℓ + ν ℓ , we obtain B (D 0 → a 1 - (1260) e + ν e) = (5. 261 - 0.639 + 0.745) × 10 - 5 , B (D + → a 1 0 (1260) e + ν e) = (6. 673 - 0.811 + 0.947) × 10 - 5 , B (D 0 → a 1 - (1260) μ + ν μ) = (4. 732 - 0.590 + 0.685) × 10 - 5 , B (D + → a 1 0 (1260) μ + ν μ) = (6. 002 - 0.748 + 0.796) × 10 - 5 . [ABSTRACT FROM AUTHOR]

In this paper, moments of a 0 (980) -meson twist-2 light-cone distribution amplitudes were deeply researched by using QCD sum rules approach within background field theory. Up to 9th-order accuracy, we present 〈 ξ 2 ; a 0 n 〉 | μ 0 at the initial scale μ 0 = 1 GeV , i.e. 〈 ξ 2 ; a 0 1 〉 | μ 0 = − 0.307 (43) , 〈 ξ 2 ; a 0 3 〉 | μ 0 = − 0.181 (34) , 〈 ξ 2 ; a 0 5 〉 | μ 0 = − 0.078 (28) , 〈 ξ 2 ; a 0 7 〉 | μ 0 = − 0.049 (26) , 〈 ξ 2 ; a 0 9 〉 | μ 0 = − 0.036 (24) , respectively. An improved light-cone harmonic oscillator model for a 0 (980) -meson twist-2 light-cone distribution amplitudes is adopted, where its parameters are fixed by using the least squares method based on the 〈 ξ 2 ; a 0 n 〉 | μ 0 , and their goodness of fit reach to 95.4%. Then, we calculate the D → a 0 (980) transition form factors within the light-cone sum rules approach, and at largest recoil point, we obtain f + D → a 0 (0) = 1.058 − 0.035 + 0.068 and f − D → a 0 (0) = 0.764 − 0.036 + 0.044. As a further application, the branching fractions of the D → a 0 (980) ℓ ν ¯ ℓ semileptonic decays are given. Taking the decay a 0 (980) → η π into consideration, we obtain B (D 0 → a 0 (980) − (→ η π −) e + ν e) = (1.330 − 0.134 + 0.216) × 10 − 4 , B (D + → a 0 (980) 0 (→ η π 0) e + ν e) = (1.675 − 0.169 + 0.272) × 10 − 4 , which are consistent with the BESIII collaboration and PDG data within errors. Finally, we present the angle observables of forward-backward asymmetries, q 2 -differential flat terms and lepton polarization asymmetry of the semileptonic decay D → a 0 (980) ℓ ν ¯ ℓ. [ABSTRACT FROM AUTHOR]

Large-class-number classification (LCNC) would bring new challenges to pattern recognition due to increased data complexity and class overlapping. In this study, a novel ensemble strategy (ES) was proposed to tackle LCNC problems. By combining the One-Versus-Rest (OVR) and One-Versus-One (OVO) strategies to design a set of classifiers with reduced class numbers, ES assigns a new object to the class receiving the most votes. When two or more classes obtain the most votes, an additional OVR model is developed to discriminate them. ES, OVR, OVO and the softmax function were investigated to discriminate the geographical origins of 25 green tea samples using near-infrared (NIR) spectroscopy and Partial Least Squares Discriminant Analysis (PLSDA). Using the Standard Normal Variate (SNV) as a spectral scatter correction technique, the total accuracy was 0.6468 for OVR-PLSDA, 0.8494 for OVO-PLSDA, 0.9299 for PLSDA-softmax, and 0.9377 for ES-PLSDA, respectively. Using other preprocessing methods and multiple random splitting of the data sets obtained the similar results. The poor performance of OVR can be attributed to the increased possibility of class overlapping and high sub-model complexity. OVO was less influenced by LCNC because it is based on a set of relatively simpler two-class classifiers. PLSDA-softmax could overcome the class overlapping by nonlinear transformations. ES was demonstrated to be capable of extracting more useful information from sub-models and achieved improved performance in LCNC. [ABSTRACT FROM AUTHOR]

Dampening during processing or storage can largely influence the quality of white lotus seeds (WLS). This paper investigated the feasibility of using near-infrared (NIR) spectroscopy and chemometrics for rapid and nondestructive discrimination of the dampened WLS. Regular (n=167) and dampened (n=118) WLS objects were collected from five main producing areas and NIR reflectance spectra (4000–12000 cm−1) were measured for bare kernels. The influence of spectral preprocessing methods, including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV), on partial least squares discrimination analysis (PLSDA) was compared to select the optimal data preprocessing method. A moving-window strategy was combined with PLSDA (MWPLSDA) to select the most informative wavelength intervals for classification. Based on the selected spectral ranges, the sensitivity, specificity, and accuracy were 0.927, 0.950, and 0.937 for SNV-MWPLSDA, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method. [Copyright &y& Elsevier]

Abstract: This study presented a new version of the nonlinear partial least-squares method on the optimized radial basis function network transformation by particle swarm optimization (PSORBFPLS). This algorithm firstly transformed the training inputs into the hidden outputs using the nonlinear transformation carried by a radial basis function network (RBFN), and then employed linear partial least-squares (PLS) to relate the outputs of the hidden layer to the bioactivities. The widths and centers involved in RBF transformation were optimized by particle swarm optimization (PSO) with the minimized model error via PLS modeling as the criterion. The number of latent variables associated with PLS modeling was automatically identified by F-statistic. Two QSAR data sets were used to evaluate the performance of the newly proposed PSORBFPLS. Results of these two data sets demonstrated that PSORBFPLS offers substantially enhanced capacities in modeling nonlinearity while circumvents overfitting frequently encountered in nonlinear modeling. [Copyright &y& Elsevier]

Abstract: The paper focuses on solving a common and important problem of NIR quantitative analysis in multi-component systems: how to significantly reduce the size of the calibration set while not impairing the predictive precision. To cope with the problem orthogonal discrete wavelet packet transform (WPT), the least correlation design and correlation coefficient test (r-test) have been combined together. As three examples, a two-component carbon tetrachloride system with 21 calibration samples, a two-component aqueous system with 21 calibration samples, and a two-component aqueous system with 41 calibration samples have been treated with the proposed strategy, respectively. In comparison with some previous methods based on much more calibration samples, the results out of the strategy showed that the predictive ability was not obviously decreased for the first system while being clearly strengthened for the second one, and the predictive precision out of the third one was even satisfactory enough for most cases of quantitative analysis. In addition, all important factors and parameters related to our strategy are discussed in detail. [Copyright &y& Elsevier]

• Untargeted analysis of frauds in kiwifruit juice was developed. • Traditional and double-quantum-dots enhanced fluorescent sensor was established. • One-class partial least squares was used to develop class models of pure juice. • The best model sensitivity was 0.929. • It could detect 2.0% or more of sucrose syrup and artificial fruit powder. The feasibility of simultaneous detection of multiple cheap materials in kiwifruit juice (KFJ) was studied by two fluorescent methods and chemometrics. Because the actual frauds usually involve many known and unknown adulterants, the traditional methods to detect one or more known components are insufficient to identify adulterated KFJ. Therefore, class models of pure KFJ were established using one-class partial least squares (OCPLS) to perform untargeted analysis. To enhance the detection specificity, traditional and double-quantum-dots enhanced fluorescent spectroscopic techniques were combined to characterize pure and adulterated KFJ. By data fusion and using standard normal variables (SNV) transformation, OCPLS model with sensitivity of 0.929 was obtained. The proposed method could detect adulterations of 2% (w/w) or more syrup and artificial fruit powder. The fusion of traditional and quantum dots enhanced fluorescence was demonstrated to provide a rapid and highly sensitive method for untargeted analysis of adulterated KFJ. [ABSTRACT FROM AUTHOR]

To enhance the power of untargeted detection, a "turn-off" fluorescent probe with double quantum dots (QDs) was developed and coupled with chemometrics for rapid detection of multiple adulterants in an herbal (Rhus chinensis Mill., RCM) honey. The double water-soluble ZnCdSe-CdTe QDs have two separate and strong fluorescent peaks, which can be quenched by honey and extraneous adulterants with varying degrees. Class models of pure RCM honey samples collected from 6 different producing areas (n = 122) were developed using one-class partial least squares (OCPLS). Four extraneous adulterants, including glucose syrup, sucrose syrup, fructose syrup, and glucose-fructose syrup were added to pure honey samples at the levels of 0.5% to 10% (w/w). As a result, the OCPLS model using the second-order derivative (D2) spectra could detect 1.0% (w/w) of different syrups in RCM honey, with a sensitivity of 0.949. The double water-soluble QDs, which can be adjusted for analysis of other water-soluble food samples, has largely extended the capability of traditional fluorescence and will provide a potentially more sensitive and specific analysis method for food frauds. Unlabelled Image • A fluorescent probe of double quantum dots was used for analysis of honey frauds. • Untargeted detection of multiple sugar syrups was performed using chemometrics. • The method is highly sensitive and the lowest detected doping level was 1.0%. • The double quantum dots are water-soluble and useful for many other food systems. [ABSTRACT FROM AUTHOR]