Synthesis of Depo‐Medrol–chitosan hydrogel as new drug slow‐release appliance and investigation of release kinetics by high‐performance liquid chromatography

The present study deals with preparation and optimization of a novel chitosan hydrogel‐based matrix by suspension cross‐linking method for controlled release of Depo‐Medrol. The controlled release of Depo‐Medrol for effective Rheumatoid arthritis disease has become an imperative field in the drug delivery system. In this context, it was intended to optimize loading circumstances by experimental design and also study the release kinetics of Depo‐Medrol entrapped in the chitosan matrix in order to obtain maximal efficiency for drug loading. The optimum concentrations of chitosan (2.5 g), glutaraldehyde (3.05 μL) and Depo‐Medrol (0.1 mg) were set up to achieve the highest value of drug loaded and the most sustained release from the chitosan matrix. In vitro monitoring of drug release kinetic using high‐performance liquid chromatography showed that 73% of the Depo‐Medrol was released within 120 min, whereas remained drug was released during the next 67 h. High correlation between first‐order and Higuchi's kinetic models indicates a controlled diffusion of Depo‐Medrol through the surrounding media. Moreover, recovery capacity >82% and entrapment efficiency of 58–88% were achieved under optimal conditions. Therefore, the new synthesized Depo Medrol–chitosan is an applicable appliance for arthritis therapy by slow release mechanism. Copyright © 2016 John Wiley & Sons, Ltd.     

Immersed boundary method for unsteady kinetic model equations

Predicting unsteady flows and aerodynamic forces for large displacement motion of microstructures requires transient solution of Boltzmann equation with moving boundaries. For the inclusion of moving complex boundaries for these problems, three immersed boundary method flux formulations (interpolation, relaxation, and interrelaxation) are presented. These formulations are implemented in a 2‐D finite volume method solver for ellipsoidal‐statistical (ES)‐Bhatnagar‐Gross‐Krook (BGK) equations using unstructured meshes. For the verification, a transient analytical solution for free molecular 1‐D flow is derived, and results are compared with the immersed boundary (IB)‐ES‐BGK methods. In 2‐D, methods are verified with the conformal, non‐moving finite volume method, and it is shown that the interrelaxation flux formulation gives an error less than the interpolation and relaxation methods for a given mesh size. Furthermore, formulations applied to a thermally induced flow for a heated beam near a cold substrate show that interrelaxation formulation gives more accurate solution in terms of heat flux. As a 2‐D unsteady application, IB/ES‐BGK methods are used to determine flow properties and damping forces for impulsive motion of microbeam due to high inertial forces. IB/ES‐BGK methods are compared with Navier–Stokes solution at low Knudsen numbers, and it is shown that velocity slip in the transitional rarefied regime reduces the unsteady damping force. Copyright © 2015 John Wiley & Sons, Ltd.     

A multivariate evolutionary credibility model for mortality improvement rates

The present paper proposes an evolutionary credibility model that describes the joint dynamics of mortality through time in several populations. Instead of modeling the mortality rate levels, the time series of population-specific mortality rate changes, or mortality improvement rates are considered and expressed in terms of correlated time factors, up to an error term. Dynamic random effects ensure the necessary smoothing across time, as well as the learning effect. They also serve to stabilize successive mortality projection outputs, avoiding dramatic changes from one year to the next. Statistical inference is based on maximum likelihood, properly recognizing the random, hidden nature of underlying time factors. Empirical illustrations demonstrate the practical interest of the approach proposed in the present paper.     

基于RBM的神经网络时间序列预测

为了克服神经网络依赖初始化结果,泛化能力不强的缺点,提出一种基于受限玻尔兹曼机(RBM)的神经网络模型.利用无监督学习方法优化神经网络的初始权值和阈值,将RBM与神经网络融合起来,模型与时间序列神经网络做实验对比,结果表明,基于受限的玻尔兹曼机的神经网络模型优于神经网络预测模型,模型可以提高预测的精准度,具有一定的应用意义.     

Maximum likelihood estimation for second level fMRI data analysis with expectation trust region algorithm

The trust region method which originated from the Levenberg–Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed.     

Resonant control of solitons

It is shown that the effect of “scattering on resonance” can be used to control envelope solitons in the driven nonlinear Schrödinger equation. The control occurs by the frequency modulated driving with multiple crossing of the resonant frequency of the soliton.     

Development of Near-Infrared Reflectance Spectroscopy Models for Quantitative Determination of Water-Soluble Carbohydrate Content in Wheat Stem and Glume

Water-soluble carbohydrate (WSC) reserved in stem is an important agronomic trait for crop improvement. The intact samples and pieces of chipped samples were employed to determine WSC content by near-infrared reflectance spectroscopy (NIRS). Three NIRS models were developed to predict WSC content in wheat stem lower internode, upmost internode, and wheat glume, respectively. Moreover, a mixed model was developed for WSC quantitative analysis in the mixed sample of the three wheat organs. Statistics analysis indicated that the four models showed a high determination coefficient (R² ≥ 0.97) and ratio of standard deviation to RMSECV (RPD ≥ 5.99). The NIRS models would allow rapid and high throughout assessments and selections of WSC contents in wheat genetics and breeding programs.     

Hedging mortality/longevity risks of insurance portfolios for life insurer/annuity provider and financial intermediary

In this paper, we propose two risk hedge schemes in which a life insurer (an annuity provider) can transfer mortality (longevity) risk of a portfolio of life (annuity) exposures to a financial intermediary by paying the hedging premium of a mortality-linked security. The optimal units of the mortality-linked security which maximize hedge effectiveness for a life insurer (an annuity provider) can be derived as closed-form formulas under the risk hedge schemes. Numerical illustrations show that the risk hedge schemes can significantly hedge the downside risk of loss due to mortality (longevity) risk for the life insurer (annuity provider) under some stochastic mortality models. Besides, finding an optimal weight of a portfolio of life and annuity business, the financial intermediary can reduce the sensitivity to mortality rates but the model risk; a security loading may be imposed on the hedge premium for a higher probability of gain to compensate the financial intermediary for the inevitable model risk.     

Gauss–Seidel Estimation of Generalized Linear Mixed Models With Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) are often fit by computational procedures such as penalized quasi-likelihood (PQL). Special cases of GLMMs are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints make it difficult to apply these iterative procedures to datasets having a very large number of records.

We propose a computationally efficient strategy based on the Gauss–Seidel algorithm that iteratively fits submodels of the GLMM to collapsed versions of the data. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status, and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. For Poisson and binomial regression models, the Gauss–Seidel approach is found to substantially outperform existing methods in terms of maximum analyzable sample size. Remarkably, for both models, the average time per iteration and the total time until convergence of the Gauss–Seidel procedure are less than 0.3% of the corresponding times for the IWLS algorithm. Platform-independent pseudo-code for fitting GLMS, as well as the source code used to generate and analyze the datasets in the simulation studies, are available online as supplemental materials.     

Verification of the Usefulness of Radiotracing Methods in Hydrodynamic Investigations

In the paper comparison of accuracy of two methods used for investigations of hydrodynamic in continuous flow tanks has been made. Basis for the comparison is the experimental work performed for big industrial wastewater concentration equalization tank. The first method is based on the radiotracer experiment while the second one uses the analysis of response on sudden change of the technological parameters. The radiotracer method has better accuracy connected with the shorter sampling period.

Ks wird die Genauigkeit zweier Methoden zur Untersuchting der Hydrodynamik von kontinuierlich arbeitenden Flüssigkeitsbehältern verglichen. Grundlage dafür sind experimentelle Untersuchungen an groβen Industrieabwasser-Ausgleichsbehältern. Die erste Methode bedient sich der Radiotracertechnik, während die zweite Methode auf einer plötzlichen Veränderung der technologischcn Parameter beruht. Es zeigt sich, daβ die Radiotracermethoden die besseren Ergebnisse liefert, wobei auβerdem noch kürzere Probenaltmeperioden von Vorteil sind.