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This study aims to efficiently estimate the extremely small failure probability with high-dimensional inputs and multiple failure domains.

This paper proposed an adaptive stratified mixture importance sampling method. The proposed method first constructs an explicit and regular mixture importance sampling probability density function (M-IS-PDF) by taking the clustering centroids as the density centers. Then by the constructed M-IS-PDF, the proposed method explores the rare multiple failure domains by adaptively stratifying, thereby addressing the issue of estimating extremely small failure probability robustly and efficiently.

Compared with the existing cross-entropy based IS method, the constructed M-IS-PDF not only covers the domains significantly contributing to the failure probability through clustering centroids to reduce the variance of failure probability estimation, but also has no undetermined parameter set to optimize, enhancing the adaptability in high-dimensional problems. Compared with the subset simulation method, the adaptive stratified M-IS-PDF constructed is explicit, regular and easy sampling. It not only has high sampling efficiency but also avoids estimating conditional failure probabilities layer by layer, improving the algorithmic robustness for estimating extremely small failure probability.

Both numerical and engineering examples indicate that, under the similar failure probability estimation accuracy, the proposed method requires significantly smaller sample size and lower computational cost than subset simulation and cross-entropy based IS methods, demonstrating higher efficiency and robustness in addressing intractable reliability analysis problems with high-dimensional inputs, multiple failure domains and rare failure.

This study aims to propose an efficient method for solving reliability-based design optimization (RBDO) problems.

In the proposed algorithm, genetic algorithm (GA) is employed to search the global optimal solution of design parameters satisfying the reliability and deterministic constraints. The Kriging model based on U learning function is used as a classification tool to accurately and efficiently judge whether an individual solution in GA belongs to feasible region.

Compared with existing methods, the proposed method has two major advantages. The first one is that the GA is employed to construct the optimization framework, which is helpful to search the global optimum solutions of the RBDO problems. The other one is that the use of Kriging model is helpful to improve the computational efficiency in solving the RBDO problems.

Since the boundaries are concerned in two Kriging models, the size of the training set for constructing the convergent Kriging model is small, and the corresponding efficiency is high.

Corporate governance, privatisation.

Masters level programmes, with particular focus on corporate governance, privatisation, and organizational development.

Yutong Bus is a real and highly publicized case in China. It is a listed company carved out from a state-owned enterprise (SOE), Yutong Group. Later the management successfully bought out Yutong Group and thus indirectly controlled the company. The deal transformed Yutong Group from a SOE to a private company. The management was innovative in pushing through the management buy-out (MBO), but politically, it created a public outcry about the loss of state-owned assets. The key issue here is the selection of state owned enterprises suitable for privatization and, more importantly, the determination of selling price. In China “the market for corporate control is still lagging behind” (Shanghai Stock Exchange).

Students would be expected to gain an understanding of recent economic reform in China, Corporate Governance in the Chinese context and wider issues associated with privatization and MBOs.

Teaching note.

Timely intrusion detection in extensive traffic remains a pressing and complex challenge, including for Web services. Current research emphasizes improving detection accuracy through machine learning, with scant attention paid to the dataset’s impact on the capability for fast detection. Many datasets rely on flow-level features, requiring entire flow completion before determining if it constitutes an attack, reducing efficiency. This paper aims to introduce a new feature extraction method and construct a new security dataset that enhances detection efficiency.

This paper proposes a novel partial-flow feature extraction method that extracts packet-level features efficiently to reduce the high latency of flow-level extraction. The method also integrates statistical and temporal features derived from partial flows to improve accuracy. The method was applied to the original packet capture (PCAP) files utilized in creating the CSE-CIC-IDS 2018 dataset, resulting in the development of the WKLIN-WEB-2023 dataset specifically designed for web intrusion detection. The effectiveness of this method was evaluated by training nine classification models on both the WKLIN-WEB-2023 and CSE-CIC-IDS 2018 datasets.

The experimental results show that models trained on the WKLIN-WEB-2023 dataset consistently outperform those on the CSE-CIC-IDS 2018 dataset across precision, recall, f1-score, and detection latency. This demonstrates the superior effectiveness of the new dataset in enhancing both the efficiency and accuracy of intrusion detection.

This study proposes the partial-flow feature extraction method, creating the WKLIN-WEB-2023 dataset. This novel approach significantly enhances detection efficiency while maintaining classification performance, providing a valuable foundation for further research on intrusion detection efficiency.

Compared with the fierce price competition in 1998, the well‐order price competition is witnessed in Chinese iron and steel industry now and the pricing behaviors of steel firms also follow the certain rules. Based on the methods of collecting the secondary data and interviewing, this paper examines the pricing behaviors of firms to explain the how Chinese steel firms make their pricing decisions and maintain the well‐order competitive relationship among them. The authors found out that (1) most Chinese steel companies adopt a kind of strategic perspective in their pricing decision making, in which understanding of the market trend and the close attention to their competitors are both important; (2) there obviously exists price leader and followers in Chinese iron and steel industry, and the relationship between price leader and followers is relatively stable and the factor behind this phenomenon is the existence of a kind of informal platform of communication among competitors, government and trade associations.

Global sensitivity can measure the influence of input variables on model responses and is of positive significance for the improvement design of structural systems. This work aims to study the global sensitivity of structural models by combining the active subspace theory and neural network.

This study aims to improve the efficiency of global sensitivity analysis for high-dimensional structural systems, a novel method based on active subspace and surrogate model is proposed. Active subspace can reduce the dimension of input variables, and an adaptive scaling strategy is proposed to improve the accuracy in finding the active subspace. The uncertainty propagation of active variables and model response is performed through the artificial neural network. Then the global sensitivity analysis is carried out.

Several examples are studied by using the Monte Carlo simulation method and the proposed method. Comparison of the results shows that the proposed method has preferable accuracy and low computational cost.

The proposed method provides a practicable tool for the variance-based sensitivity analysis of structural systems. Apart from sensitivity analysis, the method can be also extended for use in other fields relating to uncertainty propagation.

This paper aims to propose a systematic approach to facilitate passenger flow in high speed rail (HSR) stations. A case study is conducted to validate the approach.

This paper argues that HSR station management consists of train management, station capacity management, waiting room management and passenger flow management, and that two-way and dynamic information exchange exists among the components. Accordingly, the proposed approach integrates the components in HSR station management based on information exchange and heuristic algorithms. A HSR station in China with four regular waiting rooms, one mother-and-infant waiting room and one VIP waiting room located on two floors are chosen for a case study. An arrangement plan for waiting zones and waiting rooms is generated by following the proposed approach.

The result of the case study indicates that the proposed approach is capable of arranging waiting zones and waiting rooms quickly and effectively to maximize utilization of waiting room capacity and to minimize passenger walking distance from waiting rooms to platforms.

The management problem of railway station waiting room also relates to some other factors such as platforms, tracks adjustment and delay of the trains. As an emerging technology, the Internet of Things (IoT) is expected to offer promising solutions to transform the operation and role of many existing industrial systems such as transportation systems including railway systems. The future study of the authors will focus on IoT intelligent railway management system.

The result indicates that the proposed method is an effective solution to the management of railway station waiting room, which can be applied to railway station management system.

China Railway System provides service to billions of passengers. This research has significant social-economic impacts.

This paper fulfills an information integrated approach for improving waiting room management in high speed railway stations and a case study testing the approach.

The purpose of this paper is to compare the curriculum developments of civic education in three emerging Chinese societies: China and two Special Administrative Regions of Hong Kong and Macao, which are increasingly under the impacts of globalisation in this information world.

The analytical method is used and the following are identified: active and global civic education-related learning units and key themes and main contents in official curriculum guidelines and updated textbooks related to civic education.

A major finding is that elements of both active and global citizenship, such as participation in the community and understanding about the world and thus forming multiple identities, can be found alongside their emphasis on enhancing national citizenship. Thus, ideas of global citizenship and multiple levels of citizenship from local, national to global start to develop in these three Chinese societies.

The implications of such findings of both active and global citizenship, as well as multiple identities, found in these three Chinese societies could be huge for informing civic literature and sociological point of views, in particular, pointing to the next generations receiving a broadened and transcended notion of multiple levels of citizenship, apart from local and national citizenship.

The significance of this paper is that it argues that ideas of active citizenship in terms of community participation and global citizenship have been found in China, Hong Kong and Macao civic education curriculum and textbooks because of the expectations placed on students to compete in a globalized world, though national citizenship and patriotic concerns have been primary concerns. Globalisation makes the world society by impacting on these three Chinese societies for active and global citizenship, though they still retain their particular curricular focusses.

The purpose of this paper is to mesoscopically analyze the impact of parameter variations in the random pore structure on the stress distribution of layered-porous sintered silver used in high-power electronics, and to conduct a variable importance analysis of the parameter variations in the random pore structure.

Sintered silver, featuring a porous structure, improves thermal and mechanical performance by effectively absorbing stress and facilitating heat dissipation. To ensure the performance and scalability of layered-porous sintered silver, this paper uses Gaussian random fields to model the random pore structure and performs a sensitivity analysis on pore characteristic length and porosity, both of which significantly impact the stress distribution within the sintered silver layer. First, multiple sets of random pore models with varying characteristic lengths and porosities were generated using Gaussian random fields. Then, the maximum stress of the sintered silver layer containing random pores under power cycling conditions was extracted. Finally, the Morris screening method was used to perform a sensitivity analysis on the variables of the random pore structure that affect the maximum stress in the sintered silver layer. The systematic evaluation of the parameter variations in the random pore structure was conducted to assess their impacts on the maximum stress in the sintered silver layer.

Due to the high randomness of the pore structure generated by the Gaussian random field function, the maximum stress in the sintered layer fluctuates with different mesoscopic models. After systematic evaluation using the Morris screening method, it was found that the maximum stress in the sintered silver layer is most sensitive to the variation in the pore characteristic length in the x-direction. Reducing the length of pores in the x-direction can significantly decrease the stress concentration between pores in the sintered silver layer after power cycling.

This paper innovatively uses a Gaussian random field to model the mesoscopic structure of layered-porous sintered silver for high-power electronics, and applies the Morris screening method to perform variable importance analysis on the stress distribution results within the sintered silver layers. The mesoscopic study demonstrates that the maximum stress in the sintered silver layer is most sensitive to changes in the pore characteristic length in the x-direction.