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This study aims to provide a new method of multiscale directional Lyapunov exponents (MSDLE) calculated based on the state space reconstruction for the nonstationary time series, which can be applied to detect the small target covered by sea clutter.

Reconstructed state space is divided into non-overlapping submatrices whose columns are equal to a predetermined scale. The authors compute eigenvalues and eigenvectors of the covariance matrix of each submatrix and extract the principal components σ_ip and their corresponding eigenvectors. Then, the angles ψ_ip of eigenvectors between two successive submatrices were calculated. The curves of (σ_ip, ψ_ip) reflect the nonlinear dynamics both in kinetic and directional and form a spectrum with multiscale. The fluctuations of (σ_ip, ψ_ip), which are sensitive to the differences of backscatter between sea wave and target, are taken out as the features for the target detection.

The proposed method can reflect the local dynamics of sea clutter and the small target within sea clutter is easily detected. The test on the ice multiparameter imaging X-ban radar data and the comparison to K distribution based method illustrate the effectiveness of the proposed method.

The detection of a small target in sea clutter is a compelling issue, as the conventional statistical models cannot well describe the sea clutter on a larger timescale, and the methods based on statistics usually require the stationary sea clutter. It has been proven that sea clutter is nonlinear, nonstationary or cyclostationary and chaotic. The new method of MSDLE proposed in the paper can effectively and efficiently detect the small target covered by sea clutter, which can be also introduced and applied to military, aerospace and maritime fields.

Traditional adaptive analysis based on a coarse mesh, using finite element method (FEM) analysis, produces the original solution. Then post-processing the result and figuring out the regions should be refined and these regions refined once. Finally, this new mesh is used to get the solution of first refinement. After several iterations of above procedures, we can achieve the last result that is closer to the true solution, which takes time, making adaptive scheme inpractical to engineering application. The paper aims to discuss these issues.

This paper based on FEM proposes a multi-level refinement strategy with a refinement strategy and an indicator. The proposed indicator uses value of the maximum difference of strain energy density among the elements that associated with one node, and divides all nodes into several categories based on the value. A multi-level refinement strategy is proposed according to which category the node belongs to refine different elements to different times rather than whether refine or not.

Multi-level refinement strategy takes full use of the numerical calculation, resulting in the whole adaptive analysis that only need to iterate twice while other schemes must iterate more times. Using much less times of numerical calculation and approaches, more accurate solution, making adaptive analysis more practical to engineering.

Multi-level refinement strategy takes full use of the numerical calculation, resulting in the whole adaptive analysis only need iterate twice while other schemes must iterate more times. using much less times of numerical calculation and approaches more accurate solution, making adaptive analysis more practical to engineering.

The paper aims to provide an integrated system for collaborative maintenance training of complex equipment based on virtual maintenance and immersive virtual reality environment.

An integrated platform for collaborative virtual maintenance operation and training of complex equipment is developed. The simulation supporting platform for collaborative virtual maintenance is designed with the combined hierarchical structure and modularized members to support the interactive communication of heterogeneous data and information. By analyzing the collaboration mode of multi operators, the maintenance task allocation model, maintenance operation model and Extensible Markup Language‐based object information template are presented.

The research finds that the proposed system is an efficient platform for collaborative maintenance training of complex equipment.

Appropriate and efficient maintenance task allocation and collaborative maintenance operation of multi operators can significantly improve the maintenance efficiency of complex equipment.

A collaborative virtual maintenance training system of complex equipment based on immersive virtual reality environment is presented.

Large supply and computer networks contain heterogeneous information and correlation among their components, and are distributed across a large geographical region. This paper aims to investigate and develop a generic knowledge integration framework that can handle the challenges posed in complex network management. It also seeks to examine this framework in various applications of essential management tasks in different infrastructures.

Efficient information and knowledge integration technologies are key to capably handling complex networks. An adaptive fusion framework is proposed that takes advantage of dependency modelling, active configuration planning and scheduling, and quality assurance of knowledge integration. The paper uses cases of supply network risk management and computer network attack correlation (NAC) to elaborate the problem and describe various applications of this generic framework.

Information and knowledge integration becomes increasingly important, enabled by technologies to collect and process data dynamically, and faces enormous challenges in handling escalating complexity. Representing these systems into an appropriate network model and integrating the knowledge in the model for decision making, directed by information and complexity measures, provide a promising approach. The preliminary results based on a Bayesian network model support the proposed framework.

First, the paper discussed and defined the challenges and requirements faced by knowledge integration in complex networks. Second, it proposed a knowledge integration framework that systematically models various network structures and adaptively integrates knowledge, based on dependency modelling and information theory. Finally, it used a conceptual Bayesian model to elaborate the application to supply chain risk management and computer NAC of this promising framework.

This paper examines the influence Japan’s participation in the TPP negotiations will have on the course of subsequent East Asian economic integration, and mainly from the perspective of the three Northeast Asian countries of Japan, China and the ROK. In the first half, as a premise thereto, we first bring together the development of the FTA policies of China, the ROK and Japan, and then the connections with East Asian economic integration.

This paper aims to adopt the knowledge-based view and social network theory to investigate the relationship between network capability ambidexterity and knowledge creation (KC) in the context of open innovation. It also examines the moderating effects of innovation climate on this relationship.

This paper developed a model including network capability ambidexterity, innovation climate and KC. A total of 463 samples were collected from China to test the model and hypotheses by SEM.

The empirical results indicate that network capability ambidexterity is the crucial antecedent of KC. Specifically, network capability ambidexterity consists of the balanced and combined dimensions that both have significant and positive effects on KC. More importantly, the balanced dimension has a stronger effect on KC than the combined. In addition, an innovation climate positively moderates the effects of network capability ambidexterity and KC.

This study advances a new understanding of how network capability ambidexterity influences KC. Moreover, investigating the relationships should provide fresh insights into network capabilities and KC for practitioners in the open innovation context.

The study adopts congruence theory to explore the structure of inter-organizational compatibility and its structural effects on knowledge transfer in cross-border merger and acquisitions (M&As).

This paper built a moderated-mediation model that presented the relationship between inter-organizational compatibility and knowledge transfer. Regression analysis was conducted with 182 samples from China to examine the model and hypotheses.

The results indicate that inter-organizational compatibility is a four-dimensional construct comprising culture, strategy, routine and knowledge. Additionally, inter-organizational compatibility has structural effects on knowledge transfer. Specifically, routine compatibility mediates the relationships between cultural compatibility and knowledge transfer and between strategic compatibility and knowledge transfer. Moreover, the mediating roles are moderated by knowledge compatibility.

This study updates the construct and provides a comprehensive and fresh understanding of inter-organizational compatibility. Additionally, it presents the structural effects of inter-organizational compatibility on knowledge transfer.

This study aims to investigate the acoustic roughness of rails on China’s high-speed railways, with a focus on short-wavelength irregularities (less than 80 cm), which are known to significantly contribute to noise. The goal is to develop a specific acoustic roughness spectrum tailored for China’s high-speed railway system, as no such spectrum currently exists.

A long-term tracking study was conducted on major railway lines in China, monitoring rail roughness throughout the initial operational period and the rails’ service life. Data preprocessing techniques such as peak removal and curvature correction were applied for acoustic adjustments. A spatial-wavelength domain transformation was performed, providing the distribution patterns and statistical characteristics of acoustic roughness on China’s high-speed rails. Based on these analyses, a model for constructing the acoustic roughness spectrum was developed.

The study found that the acoustic roughness of China’s high-speed railway rails follows a χ² distribution with six degrees of freedom. For wavelengths greater than 8 cm, the acoustic roughness spectrum remains below the ISO specified limits. In the wavelength range of 3.2 cm to 6.3 cm, the roughness is comparable to or within the limits specified by ISO 3095:2005 and ISO 3095:2013. However, for wavelengths shorter than 2.5 cm, the roughness exceeds ISO limits.

This research fills the gap in the lack of a specific acoustic roughness spectrum for China’s high-speed railways. By establishing a tailored spectrum based on long-term data analysis, the findings provide valuable insights for noise control and rail maintenance in the context of China’s high-speed rail system.

The purpose of this paper is to apply the element decomposition method (EDM) in the study of the bending and vibration properties of plate and shell.

In the present method, each quadrilateral element is first divided into four sub-triangular cells, and the local strains are obtained in those sub-triangles based on linear interpolation. The whole strain filed is formulated through a weighted averaging operation of local strains, implying that only one integration point is adopted to construct the stiffness matrix. To reduce the instability of one-point integration and increase the accuracy of the present method, a stabilization item of the stiffness matrix is formulated by variance of the local strains. A mixed interpolated tensorial components (MITC) method is used in eliminating the shear locking phenomenon.

The novel EDM based on linear interpolation is effective in bending and vibration analyses of plate and shell, and the present method used in practical problems is reliable for static and free vibration analysis.

This method eliminated the instability of one-point integration and increased the accuracy by a stabilization item and performed stably in engineering analysis including large-scale problems of vehicle components.

The purpose of this study is to propose a precise and standardized strategy for numerically simulating vehicle aerodynamics.

Error sources in computational fluid dynamics were analyzed. Additionally, controllable experiential and discretization errors, which significantly influence the calculated results, are expounded upon. Considering the airflow mechanism around a vehicle, the computational efficiency and accuracy of each solution strategy were compared and analyzed through numerous computational cases. Finally, the most suitable numerical strategy, including the turbulence model, simplified vehicle model, calculation domain, boundary conditions, grids and discretization scheme, was identified. Two simplified vehicle models were introduced, and relevant wind tunnel tests were performed to validate the selected strategy.

Errors in vehicle computational aerodynamics mainly stem from the unreasonable simplification of the vehicle model, calculation domain, definite solution conditions, grid strategy and discretization schemes. Using the proposed standardized numerical strategy, the simulated steady and transient aerodynamic characteristics agreed well with the experimental results.

Building upon the modified Low-Reynolds Number k-e model and Scale Adaptive Simulation model, to the best of the authors’ knowledge, a precise and standardized numerical simulation strategy for vehicle aerodynamics is proposed for the first time, which can be integrated into vehicle research and design.