Constructing Hierarchical Zeolites with Highly Complete Framework via Controlled Desilication.

Desilication in alkaline medium has been widely used in construction of hierarchical zeolites for industrially relevant catalytic processes. The built of hierarchy in zeolites, especially with low aluminum stability or high Si/Al ratio, often suffers from uncontrolled destruction of zeolitic framework, accompanied by a significant loss of microporous domains and intrinsic acidity after desilication. Here, we report a novel and simple methodology for preparation of hierarchical zeolites with highly complete framework and minimum sacrifice of microporosity and acidity. The pre-impregnated amines in zeolite micropores act as inner pore-directing agents (iPDAs), largely protecting the zeolitic framework and moderating the silicon extraction during the alkaline treatment. The resulting hierarchical zeolites exhibit high crystallinity, tunable hierarchy, stable framework, and well-preserved acidity, endowing them with significantly improved mass transport properties and enhanced activities in catalytic conversion of methanol or furfural.

A Performance Evaluation Scheme for Multiple Object Tracking with HFSWR.

High-frequency surface wave radar (HFSWR) can detect and continuously track ship objects in real time and beyond the horizon. When ships navigate in a sea area, their motions in a time period form a scenario. The diversity and complexity of the motion scenarios make it difficult to accurately track ships, in which failures such as track fragmentation (TF) are frequently observed. However, it is still unclear how and to what degrees the motions of ships affect the tracking performance, especially which motion patterns can cause tracking failures. This paper addresses this problem and attempts to undertake a first step towards providing an intensive quantitative performance assessment and vulnerability detection scheme for ship-tracking algorithms by proposing an evolutionary and data-mining-based approach. Low-dimensional scenarios in terms of multiple maneuvering ship objects are generated using a grammar-based model. Closed-loop feedback is introduced using evolutionary computation to efficiently collect scenarios that cause more and more tracking performance loss, which provides diversified cases for analysing using data-mining technique to discover indicators of tracking vulnerability. Results on different tracking algorithms show that more cluster and convergence patterns and longer duration of our convoy and cluster patterns in the scenarios can cause severer TF to HFSWR ship tracking.

Shrinkage estimation of fixed and random effects in linear quantile mixed models.

This paper presents a Bayesian analysis of linear mixed models for quantile regression using a modified Cholesky decomposition for the covariance matrix of random effects and an asymmetric Laplace distribution for the error distribution. We consider several novel Bayesian shrinkage approaches for both fixed and random effects in a linear mixed quantile model using extended L 1 penalties. To improve mixing of the Markov chains, a simple and efficient partially collapsed Gibbs sampling algorithm is developed for posterior inference. We also extend the framework to a Bayesian mixed expectile model and develop a Metropolis-Hastings acceptance-rejection (MHAR) algorithm using proposal densities based on iteratively weighted least squares estimation. The proposed approach is then illustrated via both simulated and real data examples. Results indicate that the proposed approach performs very well in comparison to the other approaches.

Bayesian variable selection in linear quantile mixed models for longitudinal data with application to macular degeneration.

This paper presents a Bayesian analysis of linear mixed models for quantile regression based on a Cholesky decomposition for the covariance matrix of random effects. We develop a Bayesian shrinkage approach to quantile mixed regression models using a Bayesian adaptive lasso and an extended Bayesian adaptive group lasso. We also consider variable selection procedures for both fixed and random effects in a linear quantile mixed model via the Bayesian adaptive lasso and extended Bayesian adaptive group lasso with spike and slab priors. To improve mixing of the Markov chains, a simple and efficient partially collapsed Gibbs sampling algorithm is developed for posterior inference. Simulation experiments and an application to the Age-Related Macular Degeneration Trial data to demonstrate the proposed methods.