On the three-phase mass transfer with solid particles adhered to the gas-liquid interface

A heterogeneous, multi-layer mass transfer model is proposed for prediction of the effect of multi-layer packing of catalyst particles adhered to the gas-liquid interface. The behavior of the mass transfer rate with respect to the multi-layer packing, to the particle size and mass transfer coefficient without particles is discussed. It is shown that enhancement can be considerably increased by multi-layer packing compared to that of mono-layer packing, depending on the values of particle size and mass transfer coefficient. The predicted mass transfer rates using the proposed model was verified with experimental data taken from the literature. The model presented should be superior to that of published in the literature.     

含超导材料Thue-Morse序列一维光子晶体传输特性研究

超导光子晶体是一类重要的带隙可调谐的光子晶体.利用传输矩阵法研究了含超导材料的Thue -Morse序列准周期结构—维光子晶体在可见光波段的传输特性.数值结果表明在正入射时,光子带隙宽度和位置基本不随序列项数的增加而改变;准周期超导光子晶体的传输特性可以通过改变传统电介质材料的结构参数和环境温度等来进行调节.     

数字图像法测定养殖水中的孔雀石绿

建立一种简单、可靠的养殖水中孔雀石绿的数字图像检测方法。以1-己基-3-甲基咪唑四氟硼酸盐离子液体富集水样品,再用手机拍照获取样品信息,最后用数字图像结合多层感知器神经网络定量分析孔雀石绿的含量。该法测定结果的相对标准偏差不大于6%(n=5),加标回收率为97.8%~103.0%。该方法可用于养殖水中孔雀石绿的含量测定。     

Chargements axisymétriques d'un bicouche transversalement isotropeAxisymmetric loading on a transversely isotropic two-layered medium

An exact three-dimensional analysis is developped for an axisymmetric loading acting on the surface of a semi-infinite medium composed by two transversely isotropic materials. The loading is assumed to be parallel to the elastic symmetry axis of the upper layer. The solutions of a concentrated force and a uniform loading distributed on a circle are obtained by exact integral expressions. The numerical results are performed to show the anisotropic effect with isovalue curves of stress. To cite this article: C. Ruimy, M. Dahan, C. R. Mecanique 330 (2002) 469–473.     

Support vector regression for warranty claim forecasting

Forecasting the number of warranty claims is vitally important for manufacturers/warranty providers in preparing fiscal plans. In existing literature, a number of techniques such as log-linear Poisson models, Kalman filter, time series models, and artificial neural network models have been developed. Nevertheless, one might find two weaknesses existing in these approaches: (1) they do not consider the fact that warranty claims reported in the recent months might be more important in forecasting future warranty claims than those reported in the earlier months, and (2) they are developed based on repair rates (i.e., the total number of claims divided by the total number of products in service), which can cause information loss through such an arithmetic-mean operation.To overcome the above two weaknesses, this paper introduces two different approaches to forecasting warranty claims: the first is a weighted support vector regression (SVR) model and the second is a weighted SVR-based time series model. These two approaches can be applied to two scenarios: when only claim rate data are available and when original claim data are available. Two case studies are conducted to validate the two modelling approaches. On the basis of model evaluation over six months ahead forecasting, the results show that the proposed models exhibit superior performance compared to that of multilayer perceptrons, radial basis function networks and ordinary support vector regression models.     

Analysis of a Free Boundary Problem Modeling Multi- Layer Tumor Growth in Presence of Inhibitor

In this paper we study well-posedness and asymptotic behavior of solution of a free boundary problem modeling the growth of multi-layer tumors under the action of an external inhibitor. We first prove that this problem is locally well-posed in little Holder spaces. Next we investigate asymptotic behavior of the solution. By making delicate analysis of spectrum of the linearization of the stationary free boundary problem and using the linearized stability theorem, we prove that if the surface tension coefficient γ is larger than γ^＊ 〉 0 the fiat stationary solution is asymptotically stable provided that the constant c representing the ratio between the nutrient diffusion time and the tumor-cell doubling time is sufficient small.     

混凝土复合介质爆炸的不确定优化设计及其高效求解方法

研究不确定因素对炸药在混凝土复合介质中爆炸的影响,并在不确定因素的影响下进行优化设计。用区间数模型来描述系统中的不确定参数,建立相应的区间数优化模型;通过高维代理模型的引入,以及对其中二层嵌套优化问题进行直接解耦,得到了一种高效的求解方法。研究表明：算法经过8个迭代步收敛到最优解,最优解得到的最佳炸药装填深度约为64cm,最佳姿态角约为30°;经过三个迭代步就能迅速定位到最优解附近。算法保证了在最大化毁伤面积的同时,尽量地追求不确定因素影响最小,即综合考虑了毁伤最大和偏差最小。该不确定优化设计方法能有效地推广到相关领域的优化设计,为处理爆炸系统的装药设计和爆炸初始状态参数设计等方面提供了新思路。     

Rounded data analysis based on multi-layer ranked set sampling

Observations of sampling are often subject to rounding, but are modeled as though they were unrounded. This paper examines the impact of rounding errors on parameter estimation with multi-layer ranked set sampling. It shows that the rounding errors seriously distort the behavior of covariance matrix estimate, and lead to inconsistent estimation. Taking this into account, we present a new approach to implement the estimation for this model, and further establish the strong consistency and asymptotic normality of the proposed estimators. Simulation experiments show that our estimates based on rounded multi-layer ranked set sampling are always more efficient than those based on rounded simple random sampling.     

中国城市竞争力评价量化模型研究

本文建立一种将主、客观指标赋权方法优点相结合的城市竞争力综合评价分层构权主成分分析模型,在构建中国城市竞争力综合评价指标体系的基础上,基于该模型方法给出31个中国主要城市的城市竞争力的评价实例。并以并以判别分析方法对相关评价结果进行校验,实证分析的结果表明:这是一种适合于城市竞争力定量评价、稳定性较好且切实有效的方法模型。     

Point Cloud Geometry Compression Based on Multi-Layer Residual Structure

Point cloud data are extensively used in various applications, such as autonomous driving and augmented reality since it can provide both detailed and realistic depictions of 3D scenes or objects. Meanwhile, 3D point clouds generally occupy a large amount of storage space that is a big burden for efficient communication. However, it is difficult to efficiently compress such sparse, disordered, non-uniform and high dimensional data. Therefore, this work proposes a novel deep-learning framework for point cloud geometric compression based on an autoencoder architecture. Specifically, a multi-layer residual module is designed on a sparse convolution-based autoencoders that progressively down-samples the input point clouds and reconstructs the point clouds in a hierarchically way. It effectively constrains the accuracy of the sampling process at the encoder side, which significantly preserves the feature information with a decrease in the data volume. Compared with the state-of-the-art geometry-based point cloud compression (G-PCC) schemes, our approach obtains more than 70–90% BD-Rate gain on an object point cloud dataset and achieves a better point cloud reconstruction quality. Additionally, compared to the state-of-the-art PCGCv2, we achieve an average gain of about 10% in BD-Rate.