高浓介质法强化处理一水硬铝石型铝土矿

采用高浓碱性介质强化浸出一水硬铝石型铝土矿 ,研究反应温度、矿粉粒度、添加剂等因素对氧化铝溶出率的影响。高浓碱性介质中Al2 O3溶出率较传统拜耳法提高约 10 % ,在 2 80℃ ,NaOH∶ore =4∶1,反应时间 90min ,矿粉粒度 -76μm ,CaO添加量 8%的条件下 ,Al2 O3的溶出率最高 ,达到 98 7%。高浓介质法所得赤泥中Na2 O含量仅为拜耳法赤泥的约 1/4 ,碱耗低 。

Investigation on nonlinear rolling dynamics of amphibious vehicle under wind and wave load

Nonlinear amphibious vehicle rolling under regular waves and wind load is analyzed by a single degree of freedom system.Considering nonlinear damping and restoring moments,a nonlinear rolling dynamical equation of amphibious vehicle is established.The Hamiltonian function of the nonlinear rolling dynamical equation of amphibious vehicle indicate when subjected to joint action of periodic wave excitation and crosswind,the nonlinear rolling system degenerates into being asymmetric.The threshold value of excited moment of wave and wind is analyzed by the Melnikov method.Finally,the nonlinear rolling motion response and phase portrait were simulated by four order Runge-Kutta method at different excited moment parameters.

An Optimized Vertical Handoff Algorithm Based on Markov Process in Vehicle Heterogeneous Network

In order to solve the problem the existing vertical handoff algorithms of vehicle heterogeneous wireless network do not consider the diversification of network's status, an optimized vertical handoff algorithm based on markov process is proposed and discussed in this paper. This algorithm takes into account that the status transformation of available network will affect the quality of service(Qo S) of vehicle terminal's communication service. Firstly, Markov process is used to predict the transformation of wireless network's status after the decision via transition probability. Then the weights of evaluating parameters will be determined by fuzzy logic method. Finally, by comparing the total incomes of each wireless network, including handoff decision incomes, handoff execution incomes and communication service incomes after handoff, the optimal network to handoff will be selected. Simulation results show that: the algorithm proposed, compared to the existing algorithm, is able to receive a higher level of load balancing and effectively improves the average blocking rate, packet loss rate and ping-pang effect.

Finite Difference Approach for Estimating the Thermal Conductivity by 6-point Crank-Nicolson Scheme

Based on inverse heat conduction theory, a theoretical model using 6-point Crank-Nicolson finite difference scheme was used to calculate the thermal conductivity from temperature distribution, which can be measured experimentally. The method is a direct approach of second-order and the key advantage of the present method is that it is not required a priori knowledge of the functional form of the unknown thermal conductivity in the calculation and the thermal parameters are estimated only according to the known temperature distribution. Two cases were numerically calculated and the influence of experimental deviation on the precision of this method was discussed. The comparison of numerical and analytical results showed good agreement.

STUDYING THE IDENTIFIABILITY OF EPIDEMIOLOGICAL MODELS USING MCMC

Studying different theoretical properties of epidemiological models has been widely addressed, while numerical studies and especially the calibration of models, which are often complicated and loaded with a high number of unknown parameters, against mea- sured data have received less attention. In this paper, we describe how a combination of simulated data and Markov Chain Monte Carlo （MCMC） methods can be used to study the identifiability of model parameters with different type of measurements. Three known models are used as case studies to illustrate the importance of parameter identi- fiability： a basic SIR model, an influenza model with vaccination and treatment and a HIV-Malaria co-infection model. The analysis reveals that calibration of complex models commonly studied in mathematical epidemiology, such as the HIV Malaria co-dynamics model, can be difficult or impossible, even if the system would be fully observed. The pre- sented approach provides a tool for design and optimization of real-life field campaigns of collecting data, as well as for model selection.

Bayesian model predicts the aboveground biomass of Caragana microphylla in sandy lands better than OLS regression models

Aims In forest ecosystems,different types of regression models have been frequently used for the estimation of aboveground biomass,where Ordinary Least Squares(OLS)regression models are the most common prediction models.Yet,the relative performance of Bayesian and OLS models in predicting aboveground biomass of shrubs,especially multi-stem shrubs,has relatively been less studied in forests.Methods In this study,we developed the biomass prediction models for Caragana microphylla Lam.which is a widely distributed multi-stems shrub,and contributes to the decrease of wind erosion and the fixation of sand dunes in the Horqin Sand Land,one of the largest sand lands in China.We developed six types of formulations under the framework of the regression models,and then,selected the best model based on specific criteria.Consequently,we estimated the parameters of the best model with OLS and Bayesian methods with training and test data under different sample sizes with the bootstrap method.Lastly,we compared the performance of the OLS and Bayesian models in predicting the aboveground biomass of C.microphylla.Important Findings The performance of the allometric equation(power=1)was best among six types of equations,even though all of those models were significant.The results showed that mean squared error of test data with non-informative prior Bayesian method and the informative prior Bayesian method was lower than with the OLS method.Among the tested predictors(i.e.plant height and basal diameter),we found that basal diameter was not a significant predictor either in OLS or Bayesian methods,indicating that suitable predictors and well-fitted models should be seriously considered.This study highlights that Bayesian methods,the bootstrap method and the type of allometric equation could help to improve the model accuracy in predicting shrub biomass in sandy lands.

Optimal Reinsurance and Investment Strategies for Insurers with Regime-Switching and State-Dependent Utility Function

This paper considers a proportional reinsurance-investment problem and an excess-of-loss reinsurance-investment problem for an insurer,where price processes of the risky assets and wealth process of the insurer are both described by Markovian regime switching.The target of the insurer is assumed to maximize the expected exponential utility from her terminal wealth with a state-dependent utility function.By employing the dynamic programming approach,the optimal value functions and the optimal reinsurance-investment strategies are derived.In addition,the impact of some parameters on the optimal strategies and the optimal value functions is analyzed,and lots of interesting results are discovered,such as the conclusion that excess-of-loss reinsurance is better than proportional reinsurance is not held in the regime-switching jump-diffusion model.