Air bubble and oil droplet interactions in centrifugal fields during air-sparged hydrocyclone flotation

Journal ArticleThe interactions of air bubbles and oil droplets in centrifugal flotation have been considered with respect to process conditions present during Air-sparged Hydrocyclone (ASH) flotation. Encounter efficiency of oil droplets with air bubbles has been found to be significantly smaller when compared to encounter efficiency of mineral particles. Collision and sliding contact times have been determined. Collision has been found to be insufficient for successful contact between oil droplets and air bubbles while sliding allows for film rupture depending on specific system conditions. Although the tenacity of oil droplet attachment to an air bubble is believed to be greater than the tenacity of a mineral particle, emulsification makes oil flotation in centrifugal devices with large dissipation of energy inefficient and hence requires the use of high molecular weight polymeric flocculants

Dephasing due to a fluctuating fractional quantum Hall edge current

The dephasing rate of an electron level in a quantum dot, placed next to a fluctuating edge current in the fractional quantum Hall effect, is considered. Using perturbation theory, we first show that this rate has an anomalous dependence on the bias voltage applied to the neighboring quantum point contact, because of the Luttinger liquid physics which describes the fractional Hall fluid. Next, we describe exactly the weak to strong backscattering crossover using the Bethe-Ansatz solution

On the dispersion of solid particles in a liquid agitated by a bubble swarm

This article deals with the dispersion of solid particles in a liquid agitated by a homogeneous swarm of bubbles. The scale of interest lies between the plant scale (of the order of the tank) and the microscale (less than the bubble diameter). The strategy consists in simulating both the twophase flow of deforming bubbles and the motion of solid particles. The evolution of the spatial distribution of particles together with the encounter and entrainment phenomena is studied as a function of the void fraction and the relative size and mass of particles. The influence of the shape of the bubble and of the model of forces that govern the motion of particles is also considered

Singularities of bi-Hamiltonian systems

We study the relationship between singularities of bi-Hamiltonian systems and algebraic properties of compatible Poisson brackets. As the main tool, we introduce the notion of linearization of a Poisson pencil. From the algebraic viewpoint, a linearized Poisson pencil can be understood as a Lie algebra with a fixed 2-cocycle. In terms of such linearizations, we give a criterion for non-degeneracy of singular points of bi-Hamiltonian systems and describe their types

A novel mathematical approach for finite element formulation of flexible robot dynamics

In conventional Finite Element – Lagrangian methods, the dynamics model of a flexible robot is usually formulated based on a critical assumption that the kinetic energy of an element is approximately calculated with an integral of mass point energy. Since the energy integral is implicit, the formulation of the dynamics model is also very complex and implicit. Hence, this paper develops a new mathematical approach for the dynamic modelling of a general flexible/rigid robot. The proposed method is more comprehensive and efficient in comparison with the previous ones because it no longer requires the calculation of the symbolic integrals and the implicit expressions of the elemental and global mass matrices. Besides, the proposed approach is applicable for both the flexible robots and the hybrid flexible/rigid robots. To validate the proposed method, numerical simulations and experimental results are presented

Efficient AutoML via combinational sampling

Automated machine learning (AutoML) aims to automatically produce the best machine learning pipeline, i.e., a sequence of operators and their optimized hyperparameter settings, to maximize the performance of an arbitrary machine learning problem. Typically, AutoML based Bayesian optimization (BO) approaches convert the AutoML optimization problem into a Hyperparameter Optimization (HPO) problem, where the choice of algorithms is modeled as an additional categorical hyperparameter. In this way, algorithms and their local hyper-parameters are referred to as the same level. Consequently, this approach makes the resulting initial sampling less robust. In this study, we describe a first attempt to formulate the AutoML optimization problem as its nature instead of transfer it into a HPO problem. To take advantage of this paradigm, we propose a novel initial sampling approach to maximize the coverage of the AutoML search space to help BO construct a robust surrogate model. We experiment with 2 independent scenarios of AutoML with 2 operators and 6 operators over 117 benchmark datasets. Results of our experiments demonstrate that the performance of BO significantly improved by using our sampling approach.Horizon 2020(H2020)766186Algorithms and the Foundations of Software technolog

An efficient contesting procedure for AutoML optimization

Automated Machine Learning (AutoML) frameworks are designed to select the optimal combination of operators and hyperparameters. Classical AutoML-based Bayesian Optimization (BO) approaches often integrate all operator search spaces into a single search space. However, a disadvantage of this history-based strategy is that it can be less robust when initialized randomly than optimizing each operator algorithm combination independently. To overcome this issue, a novel contesting procedure algorithm, Divide And Conquer Optimization (DACOpt), is proposed to make AutoML more robust. DACOpt partitions the AutoML search space into a reasonable number of sub-spaces based on algorithm similarity and budget constraints. Furthermore, throughout the optimization process, DACOpt allocates resources to each sub-space to ensure that (1) all areas of the search space are covered and (2) more resources are assigned to the most promising sub-space. Two extensive sets of experiments on 117 benchmark datasets demonstrate that DACOpt achieves significantly better results in 36% of AutoML benchmark datasets: 5% when to compared to TPOT, 8% - to AutoSklearn, 15% - to H20 and 18% - to ATM.Horizon 2020(H2020)766186Algorithms and the Foundations of Software technolog

12/13/18/19: the making of Blind Spot

Global Challenges (FSW

Ballistic electron motion in a random magnetic field

Using a new scheme of the derivation of the non-linear $\sigma$-model we consider the electron motion in a random magnetic field (RMF) in two dimensions. The derivation is based on writing quasiclassical equations and representing their solutions in terms of a functional integral over supermatrices $Q$ with the constraint $Q^2=1$. Contrary to the standard scheme, neither singling out slow modes nor saddle-point approximation are used. The $\sigma$-model obtained is applicable at the length scale down to the electron wavelength. We show that this model differs from the model with a random potential (RP).However, after averaging over fluctuations in the Lyapunov region the standard $\sigma$-model is obtained leading to the conventional localization behavior.Comment: 10 pages, no figures, to be submitted in PRB v2: Section IV is remove

On the complexity of strongly connected components in directed hypergraphs

We study the complexity of some algorithmic problems on directed hypergraphs and their strongly connected components (SCCs). The main contribution is an almost linear time algorithm computing the terminal strongly connected components (i.e. SCCs which do not reach any components but themselves). "Almost linear" here means that the complexity of the algorithm is linear in the size of the hypergraph up to a factor alpha(n), where alpha is the inverse of Ackermann function, and n is the number of vertices. Our motivation to study this problem arises from a recent application of directed hypergraphs to computational tropical geometry. We also discuss the problem of computing all SCCs. We establish a superlinear lower bound on the size of the transitive reduction of the reachability relation in directed hypergraphs, showing that it is combinatorially more complex than in directed graphs. Besides, we prove a linear time reduction from the well-studied problem of finding all minimal sets among a given family to the problem of computing the SCCs. Only subquadratic time algorithms are known for the former problem. These results strongly suggest that the problem of computing the SCCs is harder in directed hypergraphs than in directed graphs.Comment: v1: 32 pages, 7 figures; v2: revised version, 34 pages, 7 figure