Modeling the effect of pigment morphology on the dynamic compression of coating layers using DEM

In the present paper, discrete element method (DEM) was employed to investigate the effect of pigment morphology on packing dynamics and compressive behavior of paper coating layers in calendering process. Spherical, platy, and needle-like particles, representing GCC, delaminated clay, and aragonite PCC pigments, were considered in this study. For each particle shape, the compression of coating structures formed by mono-sized and poly-dispersed pigments were modeled. Stress–strain behavior of the coating layers and in-plane and out-of-plane movements of the pigment particles during the compression were computed under the same maximum compressive stress. Simulation results revealed that the in-plane movements of the pigment particles during compression in the calender nip were small in magnitude (<0.35 μm). These findings help to better understand the smoothening phenomena of coating structures during the calendering process.     

Multiple robust H∞ controller design using the nonsmooth optimization method

This paper proposes a systematic technique to design multiple robust H_∞ controllers. The proposed technique achieves a desired robust performance objective, which is impossible to achieve with a single robust controller, by dividing the uncertainty set into several subsets and by designing a robust controller to each subset. To achieve this goal with a small number of divisions of the uncertainty set, an optimization problem is formulated. Since the cost function of this optimization problem is not a smooth function, a numerical nonsmooth optimization algorithm is proposed to solve this problem. This method avoids the use of Lyapunov variables, and therefore it leads to a moderate size optimization problem. A numerical example shows that the proposed multiple robust control method can improve the closed‐loop performance when a single robust controller cannot achieve satisfactory performance. Copyright © 2009 John Wiley & Sons, Ltd.     

PBSVM: Partitioning and biased support vector machine for vocal fold pathology assessment using labeled and unlabeled data sets

Most of the existing classification methods, used for voice pathology assessment, are built based on labeled pathological and normal voice signals. This paper studies the problem of building a classifier using labeled and unlabeled data. We propose a novel learning technique, called Partitioning and Biased Support Vector Machine Classification (PBSVM), which tries to utilize all the available data in two steps: (1) a new heuristically partition-based algorithm, which extracts high quality pathological and normal samples from an unlabeled set, and (2) a more principle approach based on biased formulation of support vector machine, which is fairly robust to mislabeling and unbalance data problem. Experiments with wavelet-based energy features extracted from sustained vowels show that the new recognition scheme is highly feasible and significantly outperform the baseline classical SVM classifier, especially in the situation where the labeled training data is small.     

Bitter sweet: How sustainable is bio-ethanol production in Brazil?

While biofuels have currently been regarded as a good alternative for fossil fuels, there remain many debates on their impacts on human and environment. This paper tried to shed light on bio-ethanol in Brazil as one of the main producers and exporters in the world. The main question was to understand “how sustainable is bio-ethanol production in Brazil?” To answer, the political motives of producing bio-ethanol followed by its ecological and socio-economic impacts were discussed. The paper concluded that although bio-ethanol production in Brazil seems environmentally friendly, it might socio-economically be hazardous.     

Effect of parameters on roll dynamic response of an articulated vehicle carrying liquids

Directional response and roll stability characteristics of a partly filled tractor semi-trailer vehicle, with cylindrical tank, under different parameters and conditions are investigated. The effective parameters and conditions in the stability of a tractor semi-trailer carrying liquid can be considered as filled volume, kinematic viscosity of liquid cargo and vehicle steering input. The dynamic interaction of liquid cargo with the tractor semi-trailer vehicle is evaluated by integrating a dynamic slosh model of the partly filled tank with five-degrees-offreedom of a tractor semi-trailer tank model. The dynamic fluid slosh within the tank is modeled using three-dimensional Navier-Stokes equations, coupled with the volume-of-fluid equations and analyzed using FLUENT software. The coupled tank-vehicle model is subsequently analyzed to determine the roll stability characteristics for different conditions and maneuvers. The results show effects of parameter variations on roll stability of the vehicle carrying liquid and also fluid’s behavior in interaction with a vehicle’s dynamics.     

Immobilization of palladium on benzimidazole functionalized mesoporous silica nanoparticles: catalytic efficacy in Suzuki–Miyaura reaction and nitroarenes reduction

Journal of Porous Materials - Benzimidazole functionalized mesoporous silica nanoparticles immobilized Pd(0)/Pd(II) has been proposed as an efficient catalyst for the one-pot preparation of biaryls...     

Pd(II)/Pd(0) Anchored on Magnetic Organic–Inorganic Hybrid Mesoporous Silica Nanoparticles: A Nanocatalyst for Suzuki–Miyaura and Heck–Mizoroki Coupling Reactions

Journal of Inorganic and Organometallic Polymers and Materials - 3-Chloropropyltrimethoxysilane (CPTMS) was grafted on the surface of silica coated Fe3O4 core (Fe3O4@MCM-41) and then condensed with...     

Using backward elimination with a new model order reduction algorithm to select best double mixture model for document clustering

Probabilistic latent semantic analysis (PLSA) is a double structure mixture model which has got a wide application in text and web mining. This method is capable of establishing hidden semantic relations among the observed features, using a number of latent variables. In this approach, the selection of the correct number of latent variables is critical. In the most of the previous researches, the number of latent topics was selected based on the number of invoked classes. This paper presents a method, based on backward elimination approach, which is capable of unsupervised order selection in PLSA. This method starts with a model having a number of components more than the needed value, and then prunes the mixtures to reach their optimum size. During the elimination process, proper selection of some latent variables which must be deleted is the most essential problem, and its relation to the final performance of the pruned model is straightforward. To treat this problem, we introduce a new combined pruning method which selects the best options for removal, while keeping a low computational cost, at all. We conducted some experiments on two datasets from Reuters-21578 corpus. The obtained results show that this algorithm leads to an optimized number of latent variables and in turn achieves better clustering performance compared to the conventional model selection methods. It also shows superiority over the case in which a PLSA model with a fixed number of latent variables, equal to the real number of clusters, is exploited.     

Modeling of crack propagation via an automatic adaptive mesh refinement based on modified superconvergent patch recovery technique

In this paper, an automated adaptive remeshing procedure is presented for simulation of arbitrary shape crack growth in a 2D finite element mesh. The Zienkiewicz-Zhu error estimator is employed in conjunction with a modified SPR technique based on the recovery of gradients using analytical crack-tip fields in order to obtain more accurate estimation of errors. The optimization of crack-tip singular finite element size is achieved through the adaptive mesh strategy. Finally, several numerical examples are illustrated to demonstrate the effectiveness, robustness and accuracy of computational algorithm in calculation of fracture parameters and prediction of crack path pattern.     

Photophoretic Levitation of Macroscopic Nanocardboard Plates

Scaling down miniature rotorcraft and flapping-wing flyers to sub-centimeter dimensions is challenging due to complex electronics requirements, manufacturing limitations, and the increase in viscous damping at low Reynolds numbers. Photophoresis, or light-driven fluid flow, was previously used to levitate solid particles without any moving parts, but only with sizes of 1–20 µm. Here, architected metamaterial plates with 50 nm thickness are leveraged to realize photophoretic levitation at the millimeter to centimeter scales. Instead of creating lift through conventional rotors or wings, the nanocardboard plates levitate due to light-induced thermal transpiration through microchannels within the plates, enabled by their extremely low mass and thermal conductivity. At atmospheric pressure, the plates hover above a solid substrate at heights of ≈0.5 mm by creating an air cushion beneath the plate. Moreover, at reduced pressures (10–200 Pa), the increased speed of thermal transpiration through the plate's channels creates an air jet that enables mid-air levitation and allows the plates to carry small payloads heavier than the plates themselves. The macroscopic metamaterial structures demonstrate the potential of this new mechanism of flight to realize nanotechnology-enabled flying vehicles without any moving parts in the Earth's upper atmosphere and at the surface of other planets.