Relationships of tensile strength with crosslink density for the high-carbon black-filled EPDM compounds with various softeners

Equilibrium swelling and rheological tests were adopted to systematically investigate the effects of softener type and dosage on the crosslink densities. The results turned out that the chemical crosslink density could be distinguished from the physical crosslink density by comparing the results of equilibrium swelling and rheological tests. The liquid butadiene (LB) as a softener leads to the greatest reduction in crosslink density, followed by polyethylene wax (PW) and paraffinic oil (PO). The tensile strength decreases with increasing PO content while shows peak values with increase of LB and PW contents. The dependencies of chemical crosslink density on the aging time under 150°C are quite different for the three softeners, which can be expected from the double crosslinking networks consisting of small softener and large main crosslinking networks. Further investigation has been performed to correlate the tensile strength with chemical crosslink density of ethylene propylene diene monomer elastomer vulcanizates. Three different linear relationships can be obtained for the softeners independent of the aging time. It can now be expected from this study that the role of some new softeners in rubber compounds is not only confined to plasticization but also forms crosslinking networks in the peroxide-cured rubbers.     

Optimized Biocatalytic Synthesis of 2-Selenopyrimidine Nucleosides by Transglycosylation**

Selenium-modified nucleosides are powerful tools to study the structure and function of nucleic acids and their protein interactions. The widespread application of 2-selenopyrimidine nucleosides is currently limited by low yields in established synthetic routes. Herein, we describe the optimization of the synthesis of 2-Se-uridine and 2-Se-thymidine derivatives by thermostable nucleoside phosphorylases in transglycosylation reactions using natural uridine or thymidine as sugar donors. Reactions were performed at 60 or 80 °C and at pH 9 under hypoxic conditions to improve the solubility and stability of the 2-Se-nucleobases in aqueous media. To optimize the conversion, the reaction equilibria in analytical transglycosylation reactions were studied. The equilibrium constants of phosphorolysis of the 2-Se-pyrimidines were between 5 and 10, and therefore differ by an order of magnitude from the equilibrium constants of any other known case. Hence, the thermodynamic properties of the target nucleosides are inherently unfavorable, and this complicates their synthesis significantly. A tenfold excess of sugar donor was needed to achieve 40−48 % conversion to the target nucleoside. Scale-up of the optimized conditions provided four Se-containing nucleosides in 6–40 % isolated yield, which compares favorably to established chemical routes.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Bayesian Inference for ARFIMA Models

This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations. These methods utilize sequentially adaptive Bayesian learning, a sequential Monte Carlo algorithm that can exploit massively parallel desktop computing with graphics processing units (GPUs). The article identifies and solves several problems in the computation of the likelihood function that apparently have not been addressed in the literature. Four applications illustrate the utility of the approach. The most ambitious is an ARFIMA(2,d,2) model for the Campito tree ring time series (length 5405), for which the methods developed in the article provide an essentially uncorrelated sample of size 16,384 from the exact posterior distribution in under four hours. Less ambitious applications take as little as 4 minutes without exploiting GPUs.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

Nonuniform linear array DOA estimation using EM criterion

In this letter, we address the problem of Direction of Arrival (DOA) estimation with nonuniform linear array in the context of sparse Bayesian learning (SBL) framework. The nonuniform array output is deemed as an incomplete-data observation, and a hypothetical uniform linear array output is treated as an unavailable complete-data observation. Then the Expectation-Maximization (EM) criterion is directly utilized to iteratively maximize the expected value of the complete-data log likelihood under the posterior distribution of the latent variable. The novelties of the proposed method lie in its capability of interpolating the actual received data to a virtual uniform linear array, therefore extending the achievable array aperture. Simulation results manifests the superiority of the proposed method over off-the-shelf algorithms, specially on circumstances such as low SNR, insufficient snapshots, and spatially adjacent sources.