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3D steady heat conduction analysis considering heat source is conducted on the fundamental of the fast multipole method (FMM)-accelerated line integration boundary element method (LIBEM).

Due to considering the heat source, domain integral is generated in the traditional heat conduction boundary integral equation (BIE), which will counteract the well-known merit of the BEM, namely, boundary-only discretization. To avoid volume discretization, the enhanced BEM, the LIBEM with dimension reduction property is introduced to transfer the domain integral into line integrals. Besides, owing to the unsatisfactory performance of the LIBEM when it comes to large-scale structures requiring massive computation, the FMM-accelerated LIBEM (FM-LIBEM) is proposed to improve the computation efficiency further.

Assuming N and M are the numbers of nodes and integral lines, respectively, the FM-LIBEM can reduce the time complexity from O(NM) to about O(N+ M), and a full discussion and verification of the advantage are done based on numerical examples under heat conduction.

(1) The LIBEM is applied to 3D heat conduction analysis with heat source. (2) The domain integrals can be transformed into boundary integrals with straight line integrals by the LIM. (3) A FM-LIBEM is proposed and can reduce the time complexity from O(NM) to O(N+ M). (4) The FM-LIBEM with high computational efficiency is exerted to solve 3D heat conduction analysis with heat source in massive computation successfully.

This study aims to study the formation mechanism of micro-arc oxidation (MAO) coating on AZ31 magnesium alloy and how the annealing process affects its corrosion resistance.

This study involved immersion experiments, electrochemical experiments and slow strain rate tensile experiments, along with scanning electron microscopy, optical microscopy observation and X-ray diffraction analysis.

The findings suggest that annealing treatment can refine the grain size of AZ31 magnesium alloy to an average of 6.9 µm at 300°C. The change in grain size leads to a change in conductivity, which affects the performance of MAO coatings. The MAO coating obtained by annealing the substrate at 300°C has smaller pores and porosity, resulting in better adhesion and wear resistance.

The coating acts as a barrier to prevent corrosive substances from entering the substrate. However, the smaller pores and porosity reduce the channels for the corrosive solution to pass through the coating. When the coating cracks or falls off, the corrosive medium and substrate come into direct contact. Smaller and uniform grains have better corrosion resistance.

The paper aims to expand the scope of application of the lattice Boltzmann method (LBM), especially in the field of aircraft engineering. The traditional LBM is usually applied to incompressible flows at a low Reynolds number, which is not sufficient to satisfy the needs of aircraft engineering. Devoted to tackling the defect, the paper proposes a developed LBM combining the subgrid model and the multiple relaxation time (MRT) approach. A multilayer adaptive Cartesian grid method to improve the computing efficiency of the traditional LBM is also employed.

The subgrid model and the multilayer adaptive Cartesian grid are introduced into MRT-LBM for simulations of incompressible flows at a high Reynolds number. Validated by several typical flow simulations, the numerical methods in this paper can efficiently study the flows under high Reynolds numbers.

Some numerical simulations for the lid-driven flow of cavity, flow around iced GLC305, LB606b and ONERA-M6 are completed. The paper presents the investigation results, indicating that the methods are accurate and effective for the separated flow after icing.

LBM is developed with the addition of the subgrid model and the MRT method. A numerical strategy is proposed using a multilayer adaptive Cartesian grid method and its treatment of boundary conditions. The paper refers to innovative algorithm developments and applications to the aircraft engineering, especially for iced wing simulations with flow separations.

In the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks.

This paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient.

The research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly.

The Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

Expanding and developing the theoretical implications of socialist market economy with China’s experience.

In the course of exploring the development of a market economy, China has given full play to the advantages of the socialist system, which has distinguished itself from the Western countries and some of its developing peers. China's market economy is not directly transplanted and copied from the West. China not only insists on growing its market economy but also unswervingly sticks to the socialist orientation of its market economy.

So far, the socialist market economy has proved to be more effective than the conventional one-sided, planned economy and also outperforms the capitalist market economy.

The socialist market economy has surpassed the conventional planned economy and the capitalist market economy – it is a reconstructed and reshaped mechanism and system as well as a successful and new path of market economy explored by China through reform and opening up, rather than a counterpart of Western market economy or the simple combination of socialism and market economy.

The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult.

In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition.

The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition.

A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal.

To address the problem that the current train operation mode that train selects one of several offline pre-generated control schemes before the departure and operates following the scheme after the departure, energy-saving performance of the whole metro system cannot be guaranteed.

A cooperative train control framework is formulated to regulate a novel train operation mode. The classic train four-phase control strategy is improved for generating specific energy-efficient control schemes for each train. An improved brute force (BF) algorithm with a two-layer searching idea is designed to solve the optimisation model of energy-efficient train control schemes.

Case studies on the actual metro line in Guangzhou, China verify the effectiveness of the proposed train control methods compared with four-phase control strategy under different kinds of train operation scenarios and calculation parameters. The verification on the computation efficiency as well as accuracy of the proposed algorithm indicates that it meets the requirement of online optimisation.

Most existing studies optimised energy-efficient train timetable or train control strategies through an offline process, which has a defect in coping with the disturbance or delays effectively and promptly during real-time train operation. This paper studies an online optimisation of cooperative train control based on the rolling optimisation idea, where energy-efficient train operation can be realised once train running time is determined, thus mitigating the impact of unpredictable operation situations on the energy-saving performance of trains.

This study aims to reveal how mobile app stickiness is formed and how the stickiness formation process differs for apps of different social levels.

This study proposed and validated a stickiness formation model following the cognitive–affective–conative framework. Data were collected from surveys of 1,240 mobile app users and analyzed using structural equation modeling. Multigroup analysis was applied to contrast the stickiness formation process among apps of different social levels.

This study revealed a causal link between cognitive, affective and conative factors. It found partial mediation effects of trust in the association between perceptions and satisfaction, and the full mediation role of satisfaction and personal investment (PI) in the effects of subjective norm (SN) on stickiness. The multigroup analysis results suggested that social media affordances benefit stickiness through increased PI and strengthened effects of SN on PI. However, it damages stickiness through increased perceived privacy risk (PPR), decreased trust and strengthened effects of PPR on trust.

This study contributes to both stickiness scholars and practitioners, as it builds a model to understand the stickiness formation process and reveals the effects of the “go social” strategy. The novelty of this study is that it examined social influences, considered privacy issues and revealed two mediation mechanisms. The findings can guide the improvement of mobile app stickiness and the application of the “go social” strategy.

Drawing upon the theory of communication visibility, this research intends to investigate the direct effect of enterprise social media (ESM) usage on team members’ knowledge creation capability (KCC) and the mediating effects of psychological safety and team identification. In addition, it aims to untangle how the efficacy of ESM usage varies between pre- and post-COVID-19 periods.

Using two-wave survey data from 240 members nested within 60 teams, this study utilizes a multilevel approach to test the proposed hypotheses.

We discover that ESM usage enhances team members’ KCC. More importantly, the results show that psychological safety and team identification mediate the ESM–KCC linkage. Interestingly, we further find that the impacts of ESM usage on team members’ KCC, psychological safety, and team identification are stronger in the pre-COVID-19 period than those in the post-COVID-19 period.

This research sheds light on the ESM literature by unraveling the mechanisms of psychological safety and team identification underlying the linkage between ESM usage and team members’ KCC. Moreover, it advances our understanding of the differential efficacy of ESM usage in pre- and post-COVID-19 periods.

This study intends to extend the unified theory of acceptance and use of technology (UTAUT) model by integrating perceived enjoyment as an intrinsic motivation so as to investigate factors influencing Chinese undergraduate English as a Foreign Language (EFL) students' intention to use ChatGPT for English learning.

A cross-sectional quantitative survey method research design was used in this study. Data were collected from 432 undergraduate students at two Chinese universities. The data analysis was carried out using SmartPLS 4, a computer software that employs the partial least squares structural equation modelling (PLS-SEM) technique. The analysis of the model was performed in two stages, including the assessment of reflective measurement model and structural model. The PLS predict was utilized to assess the model’s predicting power.

Findings showed that effort expectancy, performance expectancy, social influence and perceived enjoyment were positively related to Chinese undergraduate EFL learners' intention to use ChatGPT for English learning. Perceived enjoyment mediated the relationships between effort expectancy, performance expectancy, social influence and intention to use ChatGPT for English learning respectively.

Through incorporating the perceived enjoyment as an intrinsic motivation into the UTAUT model to explore factors that impact Chinese undergraduate EFL learners' intention to use ChatGPT for English learning, this study has extended the applicability of the UTAUT model and provide insights into factors affecting students' intention to utilize ChatGPT or other AI-based technologies for English learning.