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Article
Publication date: 1 November 2003

Ke‐Zhang Chen and Xin‐An Feng

In order to represent, analyze, optimize, and manufacture a component made of multi‐heterogeneous materials for high‐tech applications, a computer model of the heterogeneous…

Abstract

In order to represent, analyze, optimize, and manufacture a component made of multi‐heterogeneous materials for high‐tech applications, a computer model of the heterogeneous component needs to be built first. Heterogeneous materials include composite, functionally graded materials, and heterogeneous materials with a periodic microstructure. Current modeling techniques focus only on capturing the geometric information and cannot satisfy the requirements from modeling the components made of multi‐heterogeneous materials. This paper develops a modeling method, which can be implemented by employing the functions of current CAD graphic software and can obtain the model including both the material information (about its microstructures and constituent composition) and the geometry information without the problems arising from too many data.

Details

Integrated Manufacturing Systems, vol. 14 no. 7
Type: Research Article
ISSN: 0957-6061

Keywords

Article
Publication date: 1 March 2006

Shutian Liu and Yongcun Zhang

In this paper, a homogenization‐based multi‐scale method for predicting the effective thermal conductivity of porous materials with radiation is presented, which considers the…

Abstract

In this paper, a homogenization‐based multi‐scale method for predicting the effective thermal conductivity of porous materials with radiation is presented, which considers the effect of geometry and distribution of pores. Using homogenization method to solve the pure conductive problem of porous materials with periodic structure, the effective thermal conductivity without considering radiation is predicted, and a temperature field in a local domain of a unit cell is obtained. This temperature field is taken as the good approximation of the real temperature distribution, and the radiative thermal conductivity is obtained. The effect of the microstructure, the distribution and geometry of pores on heat transfer of porous materials is discussed. It is concluded that the dimension of the pores is an important influence factor on the thermal transfer property of porous materials if radiation is considered. Increasing the pore’s dimension enhances the contribution of radiation to the heat transfer property of porous materials. For porous materials with cylindrical and spherical pores, the radiative thermal conductivity is proportional to pore’s diameter.

Details

Multidiscipline Modeling in Materials and Structures, vol. 2 no. 3
Type: Research Article
ISSN: 1573-6105

Keywords

Article
Publication date: 12 June 2019

Xin-an Zhang and Wangshuai Wang

Luxury consumption in China is featured by clear conspicuous purposes. The purpose of this paper is to investigate this phenomenon from the indigenous perspective of face…

1816

Abstract

Purpose

Luxury consumption in China is featured by clear conspicuous purposes. The purpose of this paper is to investigate this phenomenon from the indigenous perspective of face consciousness.

Design/methodology/approach

Drawing on Ho’s (1976) framework of gaining vs losing face process, the authors decomposed the construct of face consciousness into two dimensions, namely, desire to gain face and fear of losing face, and developed a multi-dimensional scale for face consciousness. Then, a survey that consisted of 338 participants was conducted to test the relationship between face consciousness and luxury consumption.

Findings

The face consciousness scale was shown to be reliable and valid. Furthermore, the authors found both desire to gain face and fear of losing face had a unique contribution in explaining why Chinese consumers purchase luxury products.

Originality/value

This paper fills the gap in the extant literature by developing a multi-dimensional face consciousness scale, providing convenience for empirical research in future. Moreover, this research shows that Chinese consumers’ luxury consumption behavior contains both promotion and prevention motivation.

Details

Journal of Contemporary Marketing Science, vol. 2 no. 1
Type: Research Article
ISSN: 2516-7480

Keywords

Article
Publication date: 12 August 2020

Fangfang Liu, Yousong Wang, Hongyang Li and Xiaowei Zhou

The purpose of the study is to numerically investigate the relationship between the increase in transaction cost and prolongation of cooperative period acting as a nonmonetary…

Abstract

Purpose

The purpose of the study is to numerically investigate the relationship between the increase in transaction cost and prolongation of cooperative period acting as a nonmonetary incentive for municipal PPP projects.

Design/methodology/approach

A model that combines real option theory and the concept of prospect theory is proposed in the study. Three municipal road PPP projects published by China Public Private Partnerships Center are selected as cases. The data of these cases are analyzed based on the model established.

Findings

The prolongation of the cooperative period affects the increase in transaction cost, which gradually decreases when the prolonged cooperative period increases. Furthermore, the large-investment PPP projects own more transaction cost compared with less-investment projects. The decrease in transaction cost in the former is less than that in the latter. The increase in transaction cost is evidently alleviated in a project with less investment when the cooperative period is prolonged further.

Originality/value

The study systematically analyzes the relationship among transaction cost economics, real option theory and prospect theory and proposes a theoretical flowchart of the effect of nonmonetary incentive on the transaction cost. A model to quantify the effect of nonmonetary incentive (i.e. prolongation of cooperative period) on the transaction cost is proposed for the first time. The results of the study verify that the nonmonetary incentive affects transaction cost.

Details

Engineering, Construction and Architectural Management, vol. 28 no. 4
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 21 December 2021

Laouni Djafri

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P…

375

Abstract

Purpose

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

Design/methodology/approach

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

Findings

The authors got very satisfactory classification results.

Originality/value

DDPML system is specially designed to smoothly handle big data mining classification.

Details

Data Technologies and Applications, vol. 56 no. 4
Type: Research Article
ISSN: 2514-9288

Keywords

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