Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method

Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile , Annona squamosal , Camellia sinensis (tea), and coriander ( Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS–M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN–M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.     

Estimation des ordres de modèles ARMA faibles multivariés

In this Note, we consider the problem of order selection of vector autoregressive moving-average (VARMA) models under the assumption that the errors are uncorrelated, but not necessarily independent. These models are called weak VARMA by opposition to the standard VARMA models, also called strong VARMA models, in which the error terms are supposed to be iid. This selection is based on minimizing an information criterion, especially that introduced by Akaike. The theoretical foundations of the Akaike information criterion (AIC) are not more established when the iid assumption on the noise is relaxed. We propose a modified AIC criterion, and which may be very different from the standard AIC criterion.     

Low loss negative index metamaterials with one type of meta-atom

Metamaterial building blocks from microwave to optical range are mainly based on metal-dielectric composites. In almost all structures with true negative index (not coming from losses), two kinds of meta-atoms (electric and magnetic) are mixed to drive simultaneously the effective permittivity and permeability to negative values leading in turn to a negative index of refraction. In this paper, we show that two coupled structures with localized plasmons modes (e.g. cut-wires or split ring resonators) can exhibit negative refractive index by their own, by appropriately controlling the hybridization scheme of the plasmons modes. Because of small metal filling factor and reduced optical losses, the resulting structures may pave the way to realistic applications of metamaterials at optical frequencies.     

Modified Schwarz and Hannan–Quinn information criteria for weak VARMA models

Numerous multivariate time series admit weak vector autoregressive moving-average (VARMA) representations, in which the errors are uncorrelated but not necessarily independent nor martingale differences. These models are called weak VARMA by opposition to the standard VARMA models, also called strong VARMA models, in which the error terms are supposed to be independent and identically distributed (iid). This article considers the problem of order selection of the weak VARMA models by using the information criteria. It is shown that the use of the standard information criteria are often not justified when the iid assumption on the noise is relaxed. As a consequence, we propose the modified versions of the Schwarz or Bayesian information criterion and of the Hannan and Quinn criterion for identifying the orders of weak VARMA models. Monte Carlo experiments show that the proposed modified criteria estimate the model orders more accurately than the standard ones. An illustrative application using the squared daily returns of financial series is presented.     

Estimation de la matrice de variance asymptotique des estimateurs du QMV de modèles ARMA faibles multivariés

In this Note, we consider the problems of estimating the asymptotic variance of the quasi-maximum likelihood estimator (QMLE) of vector autoregressive moving-average (VARMA) models under the assumption that the errors are uncorrelated but not necessarily independent (i.e. weak VARMA). We first give expressions for the derivatives of the VARMA residuals in terms of the parameters of the models. Secondly we give an explicit expression of the asymptotic variance of the QMLE, in terms of the VAR and MA polynomials, and of the second- and fourth-order structure of the noise. We deduce a consistent estimator of the asymptotic variance of the QMLE.     

Tests portmanteau multivariés d'adéquation de modèles VARMA faibles

In this Note, we consider portmanteau tests for testing the adequacy of vector autoregressive moving-average (VARMA) models under the assumption that the errors are uncorrelated but not necessarily independent. We relax the standard independence assumption to extend the range of application of the VARMA models, allowing us to treat linear representations of general nonlinear processes. We first study the joint distribution of the quasi-maximum likelihood estimator (QMLE) and the noise empirical autocovariances. We thus obtain the asymptotic distribution of residual empirical autocovariances and autocorrelations under weak assumptions on the noise. We deduce the asymptotic distribution of the Ljung–Box (or Box–Pierce) portmanteau statistics for VARMA models with nonindependent innovations. We propose a method to adjust the critical values of the portmanteau tests.     

Data-to-model: a mixed initiative approach for rapid ethnographic assessment

Rapid ethnographic assessment is used when there is a need to quickly create a socio-cultural profile of a group or region. While there are many forms such an assessment can take, we view it as providing insight into who are the key actors, what are the key issues, sentiments, resources, activities and locations, how have these changed in recent times, and what roles do the various actors play. We propose a mixed initiative rapid ethnographic approach that supports socio-cultural assessment through a network analysis lens. We refer to this as the data-to-model (D2M) process. In D2M, semi-automated computer-based text-mining and machine learning techniques are used to extract networks linking people, groups, issues, sentiments, resources, activities and locations from vast quantities of texts. Human-in-the-loop procedures are then used to tune and correct the extracted data and refine the computational extraction. Computational post-processing is then used to refine the extracted data and augment it with other information, such as the latitude and longitude of particular cities. This methodology is described and key challenges illustrated using three distinct data sets. We find that the data-to-model approach provides a reusable, scalable, rapid approach for generating a rapid ethnographic assessment in which human effort and coding errors are reduced, and the resulting coding can be replicated.     

Estimating structural VARMA models with uncorrelated but non-independent error terms

The asymptotic properties of the quasi-maximum likelihood estimator (QMLE) of vector autoregressive moving-average (VARMA) models are derived under the assumption that the errors are uncorrelated but not necessarily independent nor martingale differences. Relaxing the martingale difference assumption on the errors considerably extends the range of application of the VARMA models, and allows one to cover linear representations of general nonlinear processes. Conditions are given for the asymptotic normality of the QMLE. Particular attention is given to the estimation of the asymptotic variance matrix, which may be very different from that obtained in the standard framework.     

主理想整环上对称矩阵的非结合代数自同构

本文研究了一类非结合代数的自同构.利用分式化方法,得到主理想整环上对称矩阵构成的非结合代数的所有自同构形式,并刻画了相应的Jordan自同构.