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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.

The Asia‐Pacific region has enjoyed robust economic growth in the last two decades and a substantial manufacturing network has been established in the region. The manufacturers in this region are now able to offer very good quality products cost‐effectively in the low‐end product category. But the recent regional financial turmoil, besides the increasingly integrated global economy and the emergence of regional trade pacts, has had an impact on the whole region and also presented many new challenges for businesses. Under this condition, manufacturers have to consider seriously how to win competition as they move into the new millennium. This paper applies axiomatic design to develop the guidelines for high value‐added activities. Manufacturers may discover their own weak points and develop their own core competencies or competitive priorities to drive change according to these guidelines.

Concurrent engineering can help manufacturing enterprises to achieve shorter time to market, reduced development costs, and high‐quality products. In order to realize the concurrent engineering, a lot of integrations are required according to parallel development technique, such as the integration of the people with different disciplines, the integration of the software of design methods and design data, and so on. This paper discusses the integration of the software of existing design methods for concurrent engineering by using axiomatic design. The results show that a very complicated software system for concurrent engineering becomes simple and consists of 26 modules corresponding to 26 design methods and one main module which contains all the junctional properties at each level. The task of the programmer for the integration becomes clear and is mainly programming for the main module.

The extremely high and ever‐increasing annual disposal rates of solid waste have caused a big problem for environmental protection in the world. Unlike the first environmental revolution in the 1970s, which was aimed at cleaning up hazardous waste from contaminated sites and natural resources, the second revolution is addressing waste reduction at the source. The solution of these problems cannot rely only on legislation and must be supported by effective methods. This goal can be achieved through the design of products that promote disassembly, reusing and recycling. In order to design environmentally friendly products in concurrent engineering, this paper applies axiomatic design to develop the integrated design guidelines with Axiom 1 (independence axiom) for generating acceptable designs and an evaluation score with Axiom 2 (information axiom) for determining better or the best design from the acceptable designs.

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.

In autonomous driving, the inherent sparsity of point clouds often limits the performance of object detection, while existing multimodal architectures struggle to meet the real-time requirements for 3D object detection. Therefore, the main purpose of this paper is to significantly enhance the detection performance of objects, especially the recognition capability for small-sized objects and to address the issue of slow inference speed. This will improve the safety of autonomous driving systems and provide feasibility for devices with limited computing power to achieve autonomous driving.

BRTPillar first adopts an element-based method to fuse image and point cloud features. Secondly, a local-global feature interaction method based on an efficient additive attention mechanism was designed to extract multi-scale contextual information. Finally, an enhanced multi-scale feature fusion method was proposed by introducing adaptive spatial and channel interaction attention mechanisms, thereby improving the learning of fine-grained features.

Extensive experiments were conducted on the KITTI dataset. The results showed that compared with the benchmark model, the accuracy of cars, pedestrians and cyclists on the 3D object box improved by 3.05, 9.01 and 22.65%, respectively; the accuracy in the bird’s-eye view has increased by 2.98, 10.77 and 21.14%, respectively. Meanwhile, the running speed of BRTPillar can reach 40.27 Hz, meeting the real-time detection needs of autonomous driving.

This paper proposes a boosting multimodal real-time 3D object detection method called BRTPillar, which achieves accurate location in many scenarios, especially for complex scenes with many small objects, while also achieving real-time inference speed.

Consistent with the requirements of online auditing performance assessments, the purpose of this paper is to propose an influence factors analysis method using analytic hierarchy process (AHP) and grey incidence analysis (GIA) to analyze the importance degree of influence factors on online auditing performance quantitatively.

A grey incidence model is developed to analyze the influence factors of online auditing performance based on the characteristics of online auditing. Then, the AHP is used to compute the weights of each assessment criterion of online auditing, and the performance of online auditing are computed. Finally, representing the performance assessment results computed by AHP and values of each assessment criterion as two sequences, GIA is used to analyze the importance degree of influence factors of online auditing performance quantitatively.

The main, secondary and minor influence factors of performance assessment of the online auditing project are identified. For online auditing projects, costs incurred are not the main influence factors of performance. Online auditing projects with higher benefits, higher quality and better design are the really effective ones. Besides, there is no direct relationship between the value of the weight of each criterion and the value of the degree of grey incidence.

The results of this study provide useful decision information to implement online auditing projects.

An effective method for analyzing the importance degree of influence factors of online auditing performance quantitatively is provided in this study.

The purpose of this paper is to attempt to propose a method based on evidential reasoning for hybrid grey attribute decision‐making problems where the attribute weights are partially known, in which the attribute values are interval grey numbers and linguistic grades.

The method is that the decision‐maker gives whitenization of each interval grey number based on their preference and belief structure of the form of qualitative attribute values, whitenization of quantitative attributes can be equivalently expressed in the form of belief structure with the principle of utility value equivalence, and then the grade belief structure decision matrix can be determined. By using the analytical evidential reasoning algorithm, belief degrees of each alternative belonging to each linguistic grade are obtained. Two pairs of nonlinear optimization models which are solved by genetic algorithms (GA) are constructed to compute the maximum and the minimum expected utilities of each alternative, respectively.

The results show that decision‐maker based on his/her risk preference gives whitenization of each interval grey number and selects corresponding alternative policy.

The method exposed in the paper can be used to deal with problems of grey multiple attribute decision making and hybrid grey multiple attribute decision making.

The paper succeeds in constructing two pairs of nonlinear optimization models based on the analytical evidential reasoning algorithm which are solved by genetic algorithms and the hybrid grey multiple attribute decision‐making approach with partial weight information.

This paper aims to test the effects of social presence (SP) and other related factors, including trust, self-construal and brand familiarity, in affecting consumers’ purchase intention of live-streaming workout courses.

Three between-subjects, Web-based experimental studies were conducted. There were separately 108, 208 and 284 valid questionnaires collected in the three studies. Moderation and mediation analyses were performed to test the research hypotheses.

Users’ sense of SP when watching live-streaming courses positively affects their willingness to purchase courses, and users’ sense of trust plays a mediating role in the influence of users’ SP on their purchase intention. In addition, when users belong to dependent self-construal rather than independent self-construal, their trust in live-streaming courses plays a stronger mediating role in the influence of users’ SP on their purchase intentions. What is more, when users have high brand familiarity rather than low brand familiarity, their trust in online live-streaming plays a stronger mediating role in the influence of users’ SP on their purchase intentions.

This research delineates the effects of SP on the consumption of live-streaming courses, thus further adding to the understanding of the role of real-time interaction in determining consumer behavior. It also highlights the roles of self-construal and brand familiarity as mediating influences on the relationship between SP and consumer trust.

This paper attempts to extend classic absolute degree of grey incidence so that the extended model can be used for grey number sequences.

The classic absolute degree of grey incidence was extended according to basic principles of grey incidence analysis. First, modelling methods and theories of the classic grey incidences were summarized. Then, the zeroing starting operator in grey incidence was extended for grey sequence. Third, the parameters in classic incidence degree were redefined, and an absolute degree of grey incidence for grey number sequences was proposed. The degree can not only be applied to grey number sequence, but also contains the uncertain information of analysis result. Fourth, two non‐linear programming models were constructed to estimate the grey coverage interval of absolute degree of incidences. Finally, the comparison method of grey numbers was introduced for sorting the different absolute degrees of incidences.

A theoretically feasible absolute degree of grey incidence was constructed for grey sequence. A case study showed that the proposed incidence degree was an effective method for grey sequence incidence analysis.

The method exposed in the paper can be used for grey sequences clustering, grey decision making, multi‐attribute decision making theory, uncertain target recognition and other related fields.

The paper succeeded in establishing an incidence analysis model for grey sequences which was still a research gap in grey system theory.