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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.

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 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 introduce modeling, numerical simulation and convergence analysis of the problem of nanoparticles’ transport carried by a two-phase flow in a porous medium. The model consists of equations of pressure, saturation, nanoparticles’ concentration, deposited nanoparticles’ concentration on the pore-walls and entrapped nanoparticles concentration in pore-throats.

A nonlinear iterative IMPES-IMC (IMplicit Pressure Explicit Saturation–IMplicit Concentration) scheme is used to solve the problem under consideration. The governing equations are discretized using the cell-centered finite difference (CCFD) method. The pressure and saturation equations are coupled to calculate the pressure, and then the saturation is updated explicitly. Therefore, the equations of nanoparticles concentration, the deposited nanoparticles concentration on the pore walls and the entrapped nanoparticles concentration in pore throats are computed implicitly. Then, the porosity and the permeability variations are updated.

Three lemmas and one theorem for the convergence of the iterative method under the natural conditions and some continuity and boundedness assumptions were stated and proved. The theorem is proved by induction states that after a number of iterations, the sequences of the dependent variables such as saturation and concentrations approach solutions on the next time step. Moreover, two numerical examples are introduced with convergence test in terms of Courant–Friedrichs–Lewy (CFL) condition and a relaxation factor. Dependent variables such as pressure, saturation, concentration, deposited concentrations, porosity and permeability are plotted as contours in graphs, whereas the error estimations are presented in a table for different values of the number of time steps, number of iterations and mesh size.

The domain of the computations is relatively small; however, it is straightforward to extend this method to the oil reservoir (large) domain by keeping similar definitions of CFL number and other physical parameters.

The model of the problem under consideration has not been studied before. Also, both solution technique and convergence analysis have not been used before with this model.

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.

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.

As China’s industrial structure continues to upgrade and optimize, the consumption capacity of rural residents gradually improves and the role of consumption in economic growth is increasingly prominent. Against the background of weak external demand, the untapped potential of rural consumption has become a key force in expanding domestic demand. As one of the important means that the government has long relied on, fiscal support for agriculture has played a crucial role in activating the rural consumer market. This manuscript aims to explore the impact of local fiscal support for agricultural expenditure (FSAE) on rural consumption in China, as well as to examine the mediating role of the level of rural financial development.

In this manuscript, the authors use the provincial panel data of 31 provinces in China from 2000 to 2020. The data of all variables mainly come from China Statistical Yearbook and China Rural Statistical Yearbook. According to the variable selection above, 651 sample data of 31 provinces and cities across China from 2000 to 2020 are organized. In terms of methodology, multiple fixed-effects panel model is applied to regression.

Firstly, FSAE varies significantly, while rural consumption slowly but steadily rises, with a relatively stable consumption structure. Secondly, FSAE has a significant positive effect on rural consumption. Thirdly, mediation testing indicates that mechanisms such as income, uncertainty and financial development have significant positive mediating effects on rural consumption. Thirdly, there is evident regional heterogeneity in FSAE’s impact on rural consumption. The Western regions, under government fiscal support, show a more significant effect on the elevation of rural consumption levels, while the role of FSAE in optimizing the consumption structure of rural residents in eastern and central regions is more pronounced.

Firstly, a systematic examination of local FSAE and rural consumption has been conducted, enriching relevant theories. Secondly, utilizing econometric empirical methods to research the relationship between local FSAE and rural consumption provides an exploratory extension to empirical studies on rural consumption in China. This offers empirical evidence for local fiscal support in agricultural development and the promotion of rural consumption.

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.

The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc.

Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree.

The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved.

The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model.

In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.