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With the continuous increase in the operating speed of high-speed trains, the wear and tear of rails on high-speed lines has also gradually deteriorated. At present, the phenomenon of asymmetric wear of rails in high-speed lines is relatively serious. This paper aims to analyze the effect of three typical rail profile wear on vehicle operation performance.

To solve this problem, by analyzing the wheel-rail contact relationship and establishing a vehicle dynamics model, the influence of worn typical rail profiles on the vehicle’s dynamic performance and carbody abnormal vibration is analyzed. Additionally, the effect of worn rail profiles on wheel wear is analyzed using a wear model.

The results showed that, compared to the standard rail profile, the three typical wear rail profiles show an increase in normal contact stress. The ride and safety indexes of the three rail profiles also increased compared with the CHN60 profile. The rail Profile 3 does not affect carbody vibration, while Profile 1 and Profile 2 can cause hunting vibrations of the carbody, with the main vibration frequencies around 7 Hz. The wheel wear depths under three typical rail profiles are 1.185 mm, 1.11 mm and 1.058 mm.

The effect of the measured typical rail profiles on the vehicle’s performance is analyzed, particularly in terms of abnormal vibrations and wheel wear. This analysis can provide guidance for the long-term maintenance of the rail system.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0270/

This paper aims to present an efficient IMPES algorithm based on a global model order reduction method, proper orthogonal decomposition (POD), to achieve the fast solution and prediction of two-phase flows in porous media.

The key point of the proposed algorithm is to establish an accurate POD reduced-order model (ROM) for two-phase porous flows. To this end, two projection methods including projecting the original governing equations (Method I) and projecting the discrete form of original governing equations (Method II) are respectively applied to construct the POD-ROM, and their distinctions are compared and analyzed in detail. It is found the POD-ROM established by Method I is inapplicable to multiphase porous flows due to its failed introduction of fluid saturation and permeability that locate on the edge of grid cell, which would lead to unphysical results.

By using Method II, an efficient IMPES algorithm that can substantially speed up the simulation of two-phase porous flows is developed based on the POD-ROM. The computational efficiency and numerical accuracy of the proposed algorithm are validated through three numerical examples, and simulation results illustrate that the proposed algorithm displays satisfactory computational speed-up (one to two orders of magnitude) without sacrificing numerical accuracy obviously when comparing to the standard IMPES algorithm that without any acceleration technique. In addition, the determination of POD modes number, the relative errors of wetting phase pressure and saturation, and the influence of POD modes number on the overall performances of the proposed algorithm, are investigated.

1. Two projection methods are applied to establish the POD-ROM for two-phase porous flows and their distinctions are analyzed. The reason why POD-ROM is difficult to be applied to multiphase porous flows is clarified firstly in this study. 2. A highly efficient IMPES algorithm based on the POD-ROM is proposed to accelerate the simulation of two-phase porous flows. 3. Satisfactory computational speed-up (one to two orders of magnitude) and prediction accuracy of the proposed algorithm are observed under different conditions.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

As the core technology of blockchain, various consensus mechanisms have emerged to satisfy the demands of different application scenarios. Since determining the security, scalability and other related performance of the blockchain, how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.

The paper opted for a research overview on the blockchain consensus mechanism, including the consensus mechanisms' consensus progress, classification and comparison, which are complemented by documentary analysis.

This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms. First, the authors outline the consensus processes, the advantages and disadvantages of the mainstream consensus mechanisms. Additionally, the consensus mechanisms are subdivided into four types according to their characteristics. Then, the consensus mechanisms are compared and analyzed based on four evaluation criteria. Finally, the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.

This paper summarizes the future research development of the consensus mechanisms.

The purpose of this paper is to explore the impact of multidimensional perceived value and perceived pressure on physicians’ continuous intention to use (CIU) online health communities (OHCs) based on perceived value (PV) theory and conservation of resources (COR) theory.

This study developed a research model to test the proposed hypotheses, and the proposed model was tested using partial least squares structural equation modelling (PLS-SEM) for which data were collected from 481 physicians with OHC experience using an online survey.

The empirical results show the following: (1) Physicians’ CIU is influenced by perceived value and perceived pressure, with attitude towards OHCs using (ATU) playing a crucial role in the pathways. (2) Additional value, work pressure, peer pressure and social pressure have a positive impact on CIU, with consultation value, relationship value, work pressure and peer pressure positively influencing CIU through ATU as a mediator. (3) Reputation value has a positive effect on CIU moderated by seniority (online seniority and offline seniority).

This study emphasises the importance of different dimensions of perceived value and perceived pressure in CIU. Meanwhile, we broaden the research scope of PV theory and COR theory and provide inspiration to OHC managers and healthcare institution managers.

This study aims to investigate the effect of microtextured tools on the geometric morphology of serrated chips, and further improve the cutting performance of polycrystalline cubic boron nitride (PCBN) tool and extend the tool life and the surface quality of the machined surface.

A three-dimensional finite element cutting model of hardened steel AISI D2 with microtextured PCBN tools were established using the finite element software Abaqus, and cutting tests were carried out. Furthermore, the stress distribution in the primary deformation zone was investigated based on the triaxiality of stress, and the influence of microtexture on the geometric morphology of serrated chips and crack development was researched.

The results show that compared with nontexture tools, elliptical pits and wavy grooves microtexture tools have lower serrated degree Gs, higher serrated frequency f per unit length and more miniature serrated step Pc. The serrated phenomenon is intensified because the tensile stress zone of chips generated by nontextured tools is longer than that of elliptic pits and wavy grooves microtexture tools. Simultaneously, the maximum value of triaxiality in the tensile stress zone achieved by nontexture tools is larger than that of the two microtexture tools, and chips obtained by nontextured tools are more susceptible to propagation fractures.

This paper mainly studies the effect of microtexture on chip microgeometry, which is relatively little studied at present. At the same time, this paper has a certain engineering significance for PCBN tool turning hardening steel.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0149/

This paper aims to implement quality of service(QoS) dynamic optimization for the integrated satellite-terrestrial network(STN) of the fifth-generation Inmarsat system(Inmarsat-5).

The structure and operational logic of Inmarsat-5 STN are introduced to build the graphic evaluation and review technique(GERT) model. Thus, the equivalent network QoS metrics can be derived from the analytical algorithm of GERT. The center–point mixed possibility functions of average delay and delay variation are constructed considering users' experiences. Then, the grey clustering evaluation of link QoS is obtained combined with the two-stage decision model to give suitable rewards for the agent of GERT-Q-learning, which realizes the intelligent optimization mechanism under real-time monitoring data.

A case study based on five time periods of monitoring data verifies the adaptability of the proposed method. On the one hand, grey clustering based on possibility function enables a more effective measurement of link QoS from the users' perspective. On the other hand, the method comparison intuitively shows that the proposed method performs better.

With the development trend of integrated communication, STN has become an important research object in satellite communications. This paper establishes a modular and extensible optimization framework whose loose coupling structure and flexibility facilitate management and development. The grey-clustering-based GERT-Q-Learning model has the potential to maximize design and application benefits of STN throughout its life cycle.

This paper explores how platforms shape the representation of iconic architecture across visual social media platforms, particularly how their unique features and affordances impact these representations.

This paper develops an integrated framework connecting iconic architecture representation, platform affordance and triadic spatial theory. Taking Rotterdam’s Markthal as a case study and collecting multimodal data from TikTok, Instagram and Google Maps, the paper uses a mixed-methods approach that integrates content analysis with qualitative coding and quantitative image/video label detection and image clustering.

TikTok is more associated with enriching lived space by enabling users to share vibrant, personal experiences and activities. Instagram influences perceived space more by shaping how users view and experience the physical environment through curated visual content. Meanwhile, Google Maps effectively shapes conceived and perceived space by users’ feedback of place ratings and reviews.

Understanding how platform affordances influence the representation of iconic architecture helps designers and researchers generate design strategies and guides marketers in strategically selecting effective social media platforms for architecture promotion.

This research addresses the gap in understanding the impact of platform affordances on social media representations, especially through comparisons across multiple visual social media platforms. Its novel contribution is introducing the concept of affordance from media studies to architecture and urban studies while enhancing traditional text analysis with computational methods for analysing videos and images.

This study aims to examine the impact of the regulatory focus of the top management team (TMT) members on the technological diversification of firms in high-technology industries based on the upper echelons theory and regulatory focus theory and explore the moderating effect of environmental uncertainty.

This paper uses data on the Chinese Growth Enterprises Market Board (GEM)-listed companies from 2012 to 2016. The authors collected data on TMT regulatory focus from firms’ annual reports by Python programming. A fixed-effects model was used to test our hypotheses.

Results indicate that TMTs with a high promotion focus are associated with greater technological diversification, while TMTs with a high prevention focus are linked to lower technological diversification. Moreover, environmental uncertainty amplifies the positive relationship between promotion-focused TMTs and technological diversification, while it diminishes the negative relationship between prevention-focused TMTs and technological diversification.

This study is limited to high-technology firms listed on the Chinese GEM, which may restrict the generalizability of the findings. Future research could validate these results in different countries and industries to enhance their robustness. Additionally, this study focuses on the impact of TMT regulatory focus on technological diversification; future studies could explore its influence on other strategic decisions, such as digital transformation or innovation strategies.

The results suggest that firms should carefully consider the regulatory focus of their TMT when making strategic decisions regarding technological diversification. Boards of directors should ensure that the TMT’s regulatory focus aligns with the firm’s strategic objectives, particularly in high-technology industries. Moreover, firms should adapt their strategies to the level of environmental uncertainty to better navigate the risks and opportunities presented by a dynamic market environment.

Supportive evidence allows authors to discuss how our findings contribute to the upper echelons theory, as well as the emerging stream of firm technological diversification, which provided valuable psychological insights into the factors influencing TMT strategic decision-making. Meanwhile, this paper integrates the factors of the industry macro-environment to explore the changes in the TMT regulatory focus on firm technological diversification under different contexts.

With rising environmental concerns, recent years have witnessed a significant surge of academic and corporate interest in green supply chain coordination (GSCC). This is evident from the rise in channel coordination literature focused toward the elimination of sub-optimal in the green supply chain (GSC). This paper seeks to summarize the model-based research on coordination in GSCs with the help of a framework developed specifically for this paper. The purpose of this paper is to present an in-depth analysis of the widely used models in the area.

A review of literature is presented in this paper to examine the underlying concepts peculiar to GSCC. A classification framework is developed to present an exhaustive survey of commonly used concepts.

Around 90 percent of the papers on GSCC come from game theory (GT) application, which explicitly utilizes coordination through contracts. The review concludes prospective area of research in GSCC. The study posits that there exists a potential of creating a more rational and efficient coordination strategies to improve GSC’s operational performance, with the view of the optimum distribution of resources and better environmental management.

To the best of authors’ knowledge, this is the first state-of-the-art review of GSCC literature focused primarily on mathematical model-based literature. This review identifies various methodological and content-oriented characteristics of GSCC. The paper also opens avenues of future research.