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The purpose of this paper is to enhance the feasibility of the edge-based smoothed triangular (EST) element, some modifications are made in this study.

First, an efficient strategy based on graph theory is proposed to construct the edge system. Second, the stress is smoothed in global coordinate system based on edge instead of strain, which makes the theory of EST more rigorous and can be easily extended to the situation of multi elements sharing the same edge. Third, the singular degree of freedoms (DOFs) of the nodes linked by edges are restrained in edge local coordinate system, which makes the global stiffness matrix non-singular and can be decomposed successfully.

First, an efficient edge constructing strategy can make EST element more standout. Second, some modifications should be made to EST element to extend it to the situation with multi elements sharing the same edge, so that EST element can deal with the geometrical models with this kind of features. Third, the way to restrain the singular DOFs of EST element must be different from normal isoparametric triangle element, because the stiffness matrix of the smoothing domain is not computed in local coordinate system.

The modified EST element performs stably in engineering analysis including large scale problems and the situation with multi elements sharing the same edge, and the efficiency of edge system constructing is no longer the bottleneck.

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

A processing algorithm for freeform fabrication of heterogeneous structures is presented. The algorithm was developed based on the heterogeneous fabrication structural model, which was constructed from the STL based multi‐material volume regions and with material identifications. The reasoning Boolean operation based modelling approach was used to construct the heterogeneous CAD assembly and to output the needed STL format. Procedures for generating the database hierarchy and the storage of the heterogeneous structural model, and derivation for developing the processing algorithm for layered fabrication of heterogeneous structure are presented. The developed algorithm was applied to a heterogeneous structure consisting of two discrete material volumes, and the detailed processing path is described.

Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.

To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.

The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.

The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.

Rapid prototyping (RP) of large‐scale solid models requires the stereolithographic (STL) file to be precisely partitioned. Especially, the selection of cutting positions is critical for the fabrication and assembly of sub‐models. The purpose of this paper is to present an efficient curvature‐based partitioning for selecting the best‐fit loop and decomposing the large complex model into smaller and simpler sub‐models with similar‐shaped joints, which facilitate the final assembly.

The partition algorithm is benefited from curvature analysis of the model surface, including extracting the feature edges and constructing the feature loops. The efficiency enhancement is achieved by selecting the best‐fit loop and constructing the similar‐shape joints. The utility of the algorithm is demonstrated by the fabrication of large‐scale rapid prototypes.

By using the proposed curvature‐based partition algorithm, the reasonability and efficiency of STL model partition can be greatly improved, and the complexity of sub‐models has been reduced. It is found that the large‐scale model is efficiently partitioned and the sub‐models are precisely assembled using the proposed partitioning.

The curvature‐based partition algorithm is used in the RP field for the first time. Based on the curvature‐based partitioning, the reasonability and efficiency of large‐scale RP is addressed in this paper.

This paper aims to develop a parallel fast neighbor search method and communication strategy for particle-based methods with adaptive smoothing-length on distributed-memory computing systems.

With a multi-resolution-based hierarchical data structure, the parallel neighbor search method is developed to detect and construct ghost buffer particles, i.e. neighboring particles on remote processor nodes. To migrate ghost buffer particles among processor nodes, an undirected graph is established to characterize the sparse data communication relation and is dynamically recomposed. By the introduction of an edge coloring algorithm from graph theory, the complex sparse data exchange can be accomplished within optimized frequency. For each communication substep, only efficient nonblocking point-to-point communication is involved.

Two demonstration scenarios are considered: fluid dynamics based on smoothed-particle hydrodynamics with adaptive smoothing-length and a recently proposed physics-motivated partitioning method [Fu et al., JCP 341 (2017): 447-473]. Several new concepts are introduced to recast the partitioning method into a parallel version. A set of numerical experiments is conducted to demonstrate the performance and potential of the proposed parallel algorithms.

The proposed methods are simple to implement in large-scale parallel environment and can handle particle simulations with arbitrarily varying smoothing-lengths. The implemented smoothed-particle hydrodynamics solver has good parallel performance, suggesting the potential for other scientific applications.

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.

A formalism for representing descriptions based upon labelled directed graphs is presented. Node and edge labels are assumed to lie in lattice‐like structures which capture certain semantic relationships between object classes and predicates, respectively. With this as a framework, the problem of inferring descriptions of a concept from examples is defined. In particular, the problem of constructing a more general description can be viewed as a process of iverting transformations used to generate instances of a description. Several examples are given and some problems are cited.

Drawing on a network perspective on enterprise agility, the purpose of this paper is to explore whether firms with superior network structure not only may be better able to generate direct effect on firm performance, but whether a superior network structure may also help firms to create better firm agility and thus enhance their performance.

The study employed a survey method and data were collected from 250 companies in Taiwan's glass industry. Using structural equation modeling (SEM) technology, it specified the measurement properties of survey instrument such as reliabilities and validities and then identified causal relation among latent constructs to examine causal effects of hypotheses testing.

The results show that a firm's agility capability and its network structure are a critical competitive strategy source of firm performance. Moreover, network structure also partially mediates the impact of enterprise agility on firm performance.

Because the data were collected from a single industry and firm performance is evaluated by subjective managerial assessments, further research may be necessary by using the data involving multiple industries with objective performance indices for more meaningful and generalized results.

The findings confirm the importance of enterprise agility for contemporary firms in today's dynamic business environment. By reinforcing enterprise agility, firms could react better to unpredictable changes. In addition, firms also are suggested to put more effort into developing and maintaining their network structures, both as repositories of external resources and as boosters of enterprise agility.

The paper provides evidence regarding the impact of enterprise agility and network structure on firm performance.

This study aims to explore the dependence structure among Chinese firms across the emerging 5G industry at different stages and to provide some strategic insights for market participants.

This study adopt macroeconomic fundamentals and the log-returns of 45 listed firms in the Chinese 5G industry to construct the weighted adjacency matrix by measuring the correlation parameters and then use the triangulated maximally filtered graph (TMFG) algorithm to construct the dependence network. It analyses the topological structure of the constructed networks to obtain the dependence characteristics for each firm in the whole industrial supply chain at different levels.

The empirical results provide a comprehensive and concise snapshot of the industrial structure, across the whole 5G industry at different levels, rather than just a “one-to-one” pattern. Specifically, the dependence characteristics of different firms are heterogeneous, and most firms are highly connected with partners in the whole industrial supply chain, whereas a few firms that are weakly connected. Those closely connected firms are usually in the midstream. In addition, compared with firms at different levels, downstream firms usually have closer dependencies and stronger influence capabilities.

Regulators not only should promote stability development for those firms most intensely connected with whole industry chain but also protect those firms with weak link in the whole industry chain. Investors should better understand the embedded ties among different firms to obtain effective market information and can select multiple firms with fewer connections as backup to conduct joint investment for risk mitigation. Mangers should give priority to the central players/firms in the whole industrial supply chain and establish the alliances with closely connected firms.

This study contributes to both the information system and operation management literature by constructing a new network method, Copula-TMFG, to capture the dependence structure among Chinese firms in 5G industry, empirically providing some strategic insights for 5G industry stakeholders, such as regulators, investors and managers.