Fluid flow in a dual permeable medium (DPM) is essential solute transport mining and aquifer studies. In this paper, water flushing into contaminated DPM containing fine-grained lenses with different geometries was investigated the Lattice Boltzmann Method (LBM). The LBM model used study D2Q9 relaxation time of 1, cohesion value 3 for fluid density 1 (mu.Lu-3). saturated contaminant that usuall...

In this paper, a method for the improvement of calculation accuracy distributed parameter model (DPM) electromagnetic devices is proposed based on kriging basis function predictive identification program (PIP). Kriging mainly an optimal interpolation which uses spatial self-covariance, and takes polynomial as function. The kriging-based surrogate can be improved by adjusting related functions h...

We study the problem of designing prediction markets for random variables with continuous or countably infinite outcomes on the real line. Our interval betting languages allow traders to bet on any interval of their choice. Both the call market mechanism and two automated market maker mechanisms, logarithmic market scoring rule (LMSR) and dynamic parimutuel markets (DPM), are generalized to han...

We study the strategic behavior of risk-neutral non-myopic agents in Dynamic Parimutuel Markets (DPM). In a DPM, agents buy or sell shares of contracts, whose future payoff in a particular state depends on aggregated trades of all agents. A forward-looking agent hence takes into consideration of possible future trades of other agents when making its trading decision. In this paper, we analyze n...

We present a method for comparing semiparametric Bayesian models, constructed under the Dirichlet process mixture (DPM) framework, with alternative semiparameteric or parameteric Bayesian models. A distinctive feature of the method is that it can be applied to semiparametric models containing covariates and hierarchical prior structures, and is apparently the Ž rst method of its kind. Formally,...

The Dirichlet process mixture (DPM) is a widely used model for clustering and for general nonparametric Bayesian density estimation. Unfortunately, like in many statistical models, exact inference in a DPM is intractable, and approximate methods are needed to perform efficient inference. While most attention in the literature has been placed on Markov chain Monte Carlo (MCMC) [1, 2, 3], variati...

In 1999, Stroebe and Schut published their seminal article on the Dual Process Model (DPM), a conceptual model which changed the direction of bereavement research. While earlier models of grief focused primarily on psychological adjustment in the wake of a severed emotional attachment, the DPM model places equal emphasis on practical—even mundane—daily life strains that follow from bereavement,...

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dense medium cyclone is a high capacity device that is widely used in coal preparation. it is simple in design but the swirling turbulent flow, the presence of medium and coal with different density and size fraction and the presence of the air-core make the flow pattern in dmcs complex. in this article the flow pattern simulation of dmc is performed with computational fluid dynamics and fluent...

Despite consistent advancement in powerful deep learning techniques recent years, large amounts of training data are still necessary for the models to avoid overfitting. Synthetic datasets using generative adversarial networks (GAN) have recently been generated overcome this problem. Nevertheless, despite advancements, GAN-based methods usually hard train or fail generate high-quality samples. ...