Characterization of composite materials based on LDPE loaded with agricultural tunisian waste

This study investigated the use of an available agricultural Tunisian vine stem waste as a filler material. Composites of green materials were prepared using vine stems as filler and low density polyethylene (LDPE) as a matrix. A series of composite films was prepared by different loadings of the vine stem waste with 10–50% of the filler in 10% intervals. The ensuing materials were characterized by several techniques. The morphology of the composites was investigated using scanning electron microscopy (SEM). The thermal and mechanical properties were studied using differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA), respectively. The results indicated that vine‐stem based particles enhanced the thermo‐mechanical properties of the thermoplastic matrix and demonstrated that this available lignocellulosic biomass of vine stems can be considered to be a promising filler material. However, the obtained result of water absorption indicated that the maximum limit of the filler content should not exceed 30% of vine stems. POLYM. COMPOS., 36:817–824, 2015. © 2014 Society of Plastics Engineers     

A new frequency domain approach of testing for covariance stationarity and for periodic stationarity in multivariate linear processes

In modelling seasonal time series data, periodically (non‐)stationary processes have become quite popular over the last years and it is well known that these models may be represented as higher‐dimensional stationary models. In this article, it is shown that the spectral density matrix of this higher‐dimensional process exhibits a certain structure if and only if the observed process is covariance stationary. By exploiting this relationship, a new L₂‐type test statistic is proposed for testing whether a multivariate periodically stationary linear process is even covariance stationary. Moreover, it is shown that this test may also be used to test for periodic stationarity. The asymptotic normal distribution of the test statistic under the null is derived and the test is shown to have an omnibus property. The article concludes with a simulation study, where the small sample performance of the test procedure is improved by using a suitable bootstrap scheme.     

A Nonparametric Model for Stationary Time Series

Stationary processes are a natural choice as statistical models for time series data, owing to their good estimating properties. In practice, however, alternative models are often proposed that sacrifice stationarity in favour of the greater modelling flexibility required by many real‐life applications. We present a family of time‐homogeneous Markov processes with nonparametric stationary densities, which retain the desirable statistical properties for inference, while achieving substantial modelling flexibility, matching those achievable with certain non‐stationary models. A latent extension of the model enables exact inference through a trans‐dimensional Markov chain Monte Carlo method. Numerical illustrations are presented.     

Convolution‐closed models for count time series with applications

There has recently been an upsurge of interest in time series models for count data. Many papers focus on the model with first‐order (Markov) dependence and Poisson innovations. Our paper considers practical models that can capture higher‐order dependence based on the work of Joe (1996). In this framework we are able to model both equidispersed and overdispersed marginal distributions of data. The latter is approached using generalized Poisson innovations. Central to the models is the use of the property of closure under convolution of certain families of random variables. The models can be thought of as stationary Markov chains of finite order. Parameter estimation is undertaken by maximum likelihood, inference procedures are considered and means of assessing model adequacy employed. Applications to two new data sets are provided.     

A Robbins–Monro Algorithm for Non‐Parametric Estimation of NAR Process with Markov Switching: Consistency

We approach the problem of non‐parametric estimation for autoregressive Markov switching processes. In this context, the Nadaraya–Watson‐type regression functions estimator is interpreted as a solution of a local weighted least‐square problem, which does not admit a closed‐form solution in the case of hidden Markov switching. We introduce a non‐parametric recursive algorithm to approximate the estimator. Our algorithm restores the missing data by means of a Monte Carlo step and estimates the regression function via a Robbins–Monro step. We prove that non‐parametric autoregressive models with Markov switching are identifiable when the hidden Markov process has a finite state space. Consistency of the estimator is proved using the strong α‐mixing property of the model. Finally, we present some simulations illustrating the performances of our non‐parametric estimation procedure.     

Bayesian analysis of multivariate Gaussian hidden Markov models with an unknown number of regimes

Multivariate Gaussian hidden Markov models with an unknown number of regimes are introduced here in the Bayesian setting and new efficient reversible jump Markov chain Monte Carlo algorithms for estimating both the dimension and the unknown parameters of the model are presented. Hidden Markov models are an extension of mixture models that can be applied to time series so as to classify the observations in a small number of groups, to understand when change points occur in the dynamics of the series and to model data heterogeneity through the switching among subseries with different means and covariance matrices. These aims can be achieved by assuming that the observed phenomenon is driven by a latent, or hidden, Markov chain. The methodology is illustrated through two different examples of multivariate time series.     

A quality relevant non‐Gaussian latent subspace projection method for chemical process monitoring and fault detection

Partial least‐squares (PLS) method has been widely used in multivariate statistical process monitoring field. The goal of traditional PLS is to find the multidimensional directions in the measurement‐variable and quality‐variable spaces that have the maximum covariances. Therefore, PLS method relies on the second‐order statistics of covariance only but does not takes into account the higher‐order statistics that may involve certain key features of non‐Gaussian processes. Moreover, the derivations of control limits for T² and squared prediction error (SPE) indices in PLS‐based monitoring method are based on the assumption that the process data follow a multivariate Gaussian distribution approximately. Meanwhile, independent component analysis (ICA) approach has recently been developed for process monitoring, where the goal is to find the independent components (ICs) that are assumed to be non‐Gaussian and mutually independent by means of maximizing the high‐order statistics such as negentropy instead of the second‐order statistics including variance and covariance. Nevertheless, the IC directions do not take into account the contributions from quality variables and, thus, ICA may not work well for process monitoring in the situations when the quality variables have strong influence on process operations. To capture the non‐Gaussian relationships between process measurement and quality variables, a novel projection‐based monitoring method termed as quality relevant non‐Gaussian latent subspace projection (QNGLSP) approach is proposed in this article. This new technique searches for the feature directions within the measurement‐variable and quality‐variable spaces concurrently so that the two sets of feature directions or subspaces have the maximized multidimensional mutual information. Further, the new monitoring indices including I² and SPE statistics are developed for quality relevant fault detection of non‐Gaussian processes. The proposed QNGLSP approach is applied to the Tennessee Eastman Chemical process and the process monitoring results of the present method are demonstrated to be superior to those of the PLS‐based monitoring method. © 2013 American Institute of Chemical Engineers AIChE J 60: 485–499, 2014     

Fault Detection and Diagnosis in a Food Pasteurization Process with Hidden Markov Models

Hidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a high‐temperature‐short‐time pasteurization system showed that HMM can diagnose the faults with certain characteristics such as fault duration and magnitude.     

Efficient estimation and particle filter for max‐stable processes

Extreme values are often correlated over time, for example, in a financial time series, and these values carry various risks. Max‐stable processes such as maxima of moving maxima (M3) processes have been recently considered in the literature to describe time‐dependent dynamics, which have been difficult to estimate. This article first proposes a feasible and efficient Bayesian estimation method for nonlinear and non‐Gaussian state space models based on these processes and describes a Markov chain Monte Carlo algorithm where the sampling efficiency is improved by the normal mixture sampler. Furthermore, a unique particle filter that adapts to extreme observations is proposed and shown to be highly accurate in comparison with other well‐known filters. Our proposed algorithms were applied to daily minima of high‐frequency stock return data, and a model comparison was conducted using marginal likelihoods to investigate the time‐dependent dynamics in extreme stock returns for financial risk management.     

Operating condition diagnosis based on HMM with adaptive transition probabilities in presence of missing observations

A new approach for modeling and monitoring of the multivariate processes in presence of faulty and missing observations is introduced. It is assumed that operating modes of the process can transit to each other following a Markov chain model. Transition probabilities of the Markov chain are time varying as a function of the scheduling variable. Therefore, the transition probabilities will be able to vary adaptively according to different operating modes. In order to handle the problem of missing observations and unknown operating regimes, the expectation maximization algorithm is used to estimate the parameters. The proposed method is tested on two simulations and one industrial case studies. The industrial case study is the abnormal operating condition diagnosis in the primary separation vessel of oil‐sand processes. In comparison to the conventional methods, the proposed method shows superior performance in detection of different operating conditions of the process. © 2014 American Institute of Chemical Engineers AIChE J, 61: 477–493, 2015     

Separation and Preconcentration of Cesium from Acidic Media by Extraction Chromatography

Abstract

A novel extraction chromatographic resin for the separation and preconcentration of cesium from acidic nitrate media comprising an inert polymeric substrate impregnated with 1,3‐calix[4]bis‐o‐benzo‐crown‐6 (“BC6B”) in a chlorinated diluent is described. Cesium is shown to be both strongly and selectively retained by the resin at low (<1 M) acid concentrations and readily eluted from it using 6 M HNO₃. Only potassium ion (at concentrations exceeding ca. 0.01 M) exerts a significant adverse impact on cesium retention. Unexpectedly, cesium uptake by the resin does not exhibit the acid dependency anticipated from liquid‐liquid extraction data. This is also the case for a resin employing a related macrocyclic extractant, calix[4]arene‐bis‐(t‐octylbenzocrown‐6) (“BobCalix”), prepared and partly characterized in an effort to overcome certain limitations of the BC6B‐based material. Despite this, the resin is shown to be well suited to the isolation of radiocesium from acidic solution for subsequent determination or for the removal of cesium intereference in the quantitation of other radionuclides.     

A family of multivariate non-gaussian time series models

In this article, we propose a class of multivariate non-Gaussian time series models which include dynamic versions of many well-known distributions and consider their Bayesian analysis. A key feature of our proposed model is its ability to account for correlations across time as well as across series (contemporary) via a common random environment. The proposed modeling approach yields analytically tractable dynamic marginal likelihoods, a property not typically found outside of linear Gaussian time series models. These dynamic marginal likelihoods can be tied back to known static multivariate distributions such as the Lomax, generalized Lomax, and the multivariate Burr distributions. The availability of the marginal likelihoods allows us to develop efficient estimation methods for various settings using Markov chain Monte Carlo as well as sequential Monte Carlo methods. Our approach can be considered to be a multivariate generalization of commonly used univariate non-Gaussian class of state space models. To illustrate our methodology, we use simulated data examples and a real application of multivariate time series for modeling the joint dynamics of stochastic volatility in financial indexes, the VIX and VXN.     

绿色微生物源抗菌剂申嗪霉素（M18）

高效、低毒与环境相容性好的微生物源抗菌剂申嗪霉素 (M18)是我国自主开发的原创性农药。该产品已经获得农业部颁发的农药登记证 ,其主要的有效成分是吩嗪 1 羧酸。经多年的定点和推广试验 ,申嗪霉素对西瓜枯萎病、甜椒疫病的防治效果可达到 70 %以上 ,对甜瓜蔓枯病具有良好的的治疗作用 ,目前上海农乐生物制品股份有限公司已经开始批量生产。     

A Bayesian Approach to Modelling Graphical Vector Autoregressions

Abstract. We introduce a Bayesian approach to model assessment in the class of graphical vector autoregressive processes. As a result of the very large number of model structures that may be considered, simulation‐based inference, such as Markov chain Monte Carlo, is not feasible. Therefore, we derive an approximate joint posterior distribution of the number of lags in the autoregression and the causality structure represented by graphs using a fractional Bayes approach. Some properties of the approximation are derived and our approach is illustrated on a four‐dimensional macroeconomic system and five‐dimensional air pollution data.     

Process monitoring method based on correlation variable classification and vine copula

Chemical processes are becoming increasingly complicated, leading to an increase in process variables and more complex relationships among them. The vine copula has a significant advantage in portraying the dependence of high-dimensional variables. However, as the dimensions increase, the vine copula model incurs a high computational load; such pressure greatly reduces model efficiency. Relationships among variables in the industrial process are complex. Different variables may be strongly or weakly associated or even independent. This paper proposes a process monitoring method based on correlation variable classification and vine copula. The weighted correlation measure is first used to divide variables into a correlated subspace and weakly correlated subspace. Then, two vine structures, C-vine and D-vine, are applied to the correlated and weakly correlated subspaces, respectively. This method takes advantage of C-vine for correlated variables and the flexibility of D-vine for weakly correlated variables. Finally, comprehensive statistics are established based on different subspaces. Monitoring results of the numerical system and the Tennessee Eastman process demonstrate the effectiveness and validity of the proposed method.     

Evolution of thermograph parameters during the oxidation of extra virgin olive oil

The modulated differential scanning calorimetry (M‐DSC) was used as a rapid and effective method to characterize the olive oil at different levels of oxidation. Thermograph parameters have been related to oxidative degradation of the triglycerides. In this study, their relation to the characteristic off‐flavor compounds, correlated to the oxidative degradation of the oil, was also investigated. Extra virgin olive oil samples were subjected to the following oxidation treatments: a) purged with air using glass washing bottles at two flow rate values, b) heated in a conventional oven at two area/oil mass ratios, and c) heated in a microwave oven also at two area/oil mass ratios. Samples were withdrawn and analyzed at predetermined intervals. Flavor and off‐flavor compounds were isolated using a dynamic thermal stripping apparatus and transferred into a gas chromatograph by using a thermal desorption unit. All oil samples were analyzed by M‐DSC during cooling from 25 °C to ?60 °C at 7 °C/min, and heating back to 40 °C at 10 °C/min. High correlation values were obtained between various M‐DSC thermograph parameters and certain volatile compounds. Results showed that M‐DSC could be used as a simple method to indicate compositional changes in olive oil during oxidation.     

RDX-CMDB推进剂燃速温度敏感系数的实验研究

为了揭示RDX-CMDB推进剂中各常见组分对其燃速温度敏感系数的影响规律,制备了一系列含RDX、铝粉及燃烧催化剂的CMDB推进剂样品。采用氮气靶线法测得其在2~14MPa下的燃速温度敏感系数(σp)。讨论了RDX含量、铝粉、燃烧催化剂对RDX-CMDB推进剂燃速温度敏感系数的影响。结果表明,提高工作压强、增加RDX含量、添加燃烧催化剂均有助于降低RDX-CMDB推进剂在一定初始条件下的燃速温度敏感系数。配方中引入铝粉后可降低中低压下RDX-CMDB推进剂的燃速温度敏感系数,且燃速温度敏感系数几乎不随压强变化而变化。选用含邻苯二甲酸铅和没食子酸铋锆作燃烧催化剂,均可在2~10MPa下降低RDX-CMDB推进剂的燃速压强指数,同时降低燃速温度敏感系数。     

A note on the mixture transition distribution and hidden Markov models

We discuss an interpretation of the mixture transition distribution (MTD) for discrete‐valued time series which is based on a sequence of independent latent variables which are occasion‐specific. We show that, by assuming that this latent process follows a first order Markov Chain, MTD can be generalized in a sensible way. A class of models results which also includes the hidden Markov model (HMM). For these models we outline an EM algorithm for the maximum likelihood estimation which exploits recursions developed within the HMM literature. As an illustration, we provide an example based on the analysis of stock market data referred to different American countries.     

DETERMINING THE NUMBER OF REGIMES IN MARKOV SWITCHING VAR AND VMA MODELS

We give stable finite‐order vector autoregressive moving average (p ^* ,q ^* ) representations for M‐state Markov switching second‐order stationary time series whose autocovariances satisfy a certain matrix relation. The upper bounds for p ^* and q ^* are elementary functions of the dimension K of the process, the number M of regimes, the autoregressive and moving‐average orders of the initial model. If there is no cancellation, the bounds become equalities, and this solves the identification problem. Our classes of time series include every M‐state Markov switching multi‐variate moving‐average models and autoregressive models in which the regime variable is uncorrelated with the observable. Our results include, as particular cases, those obtained by Krolzig (1997) and improve the bounds given by Zhang and Stine (2001) and Francq and Zakoïan (2001) for our classes of dynamic models. A Monte Carlo experiment and an application on foreign exchange rates complete the article. Copyright © 2013 John Wiley & Sons, Ltd.     

Emerging Enzymes for ATP Regeneration in Biocatalytic Processes

Adenosine‐5′‐triphosphate‐dependent enzyme catalysed reactions are widespread in nature. Consequently, the enzymes involved have an intrinsic potential for use in syntheses of high value products. Although regeneration systems for ATP starting from adenosine‐5′‐diphosphate are available, certain limitations exist for both in vitro and in vivo applications requiring ATP regeneration from adenosine‐5′‐monophosphate, or adenosine. Following a short overview of the chemical and thermodynamic background, this Minireview focuses on emerging enzymes and methodologies for ATP regeneration. A large range of as yet unexploited reactions will be accessible with new, powerful, multistep ATP regeneration systems that use cheap phosphate donors and provide high longevity, compatibility, and robustness under process conditions. Their potential might go far beyond the direct use of ATP in enzymatic reactions; enzyme discovery, and engineering, as well as immobilisation strategies, will help to realise such systems.