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Due to the structural layout, mining process, and working environment, curved chains such as horizontal and vertical bends inevitably exist in the armoured face conveyor (AFC). With the increasing power, conveying capacity, and distance of the AFC, the dynamic influence of these curved chains should be highly emphasized. This paper establishes a dynamic model of the AFC by multi-body system theory and finite segment method, in which the curved chains can be fully considered.

The scraper chains are firstly grouped into the straight, horizontal bend, vertical convex and concave bend sections. Each bend section running in a circle is simplified as an ideal arc. Through solving its differential equilibrium equation and using Newton's second law, its running resistance is derived. Then the grouped chains are discretized into finite control elements according to the Kelvin model, and the governing equation of each control element is established. The dynamic model of the AFC is obtained by assembling these equations, and the corresponding simulation model is developed by using MATLAB/Simulink.

Case studies with real scenarios are provided, and simulations are carried out. The results show that the running resistance contributed by the curved chains is larger than the traditional empirical value.

The work in this paper helps the dynamic performance design of AFC, with a deep understanding of the curved chains.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

This study aims to evaluate blast loads on and the response of submerged structures.

An arbitrary Lagrangian–Eulerian method is developed to model fluid–structure interaction (FSI) problems of close-in underwater explosions (UNDEX). The “fluid” part provides the loads for the structure considers air, water and high explosive materials. The spatial discretization for the fluid domain is performed with a second-order vertex-based finite volume scheme with a tangent of hyperbola interface capturing technique. The temporal discretization is based on explicit Runge–Kutta methods. The structure is described by a large-deformation Lagrangian formulation and discretized via finite elements. First, one-dimensional test cases are given to show that the numerical method is free of mesh movement effects. Thereafter, three-dimensional FSI problems of close-in UNDEX are studied. Finally, the computation of UNDEX near a ship compartment is performed.

The difference in the flow mechanisms between rigid targets and deforming targets is quantified and evaluated.

Cavitation is modeled only approximately and may require further refinement/modeling.

The results demonstrate that the proposed numerical method is accurate, robust and versatile for practical use.

Better design of naval infrastructure [such as bridges, ports, etc.].

To the best of the authors’ knowledge, this study has been conducted for the first time.

This study aims to facilitate consistency of information in building information modelling (BIM) and address the current BIM gaps through the perspectives of the productization concept and product structure (PS).

The study follows a conceptual research approach in conjunction with a single case study. First, the previous studies on BIM implementation, productization and PS are reviewed. Further, a case study is used to analyse the current state of productization in the construction sector and develop a functional PS for construction.

A Part-Phase-Elements Matrix is proposed as a construction-specific PS to facilitate consistency in information and to enhance BIM. The proposed matrix provides new avenues to facilitate consistent information exchange through the interconnection between conceptual PS and standard building objects library, and encourage collaborative communication between stakeholders.

This study explores the core of the productization concept and PS as means to facilitate consistency of information and thus address the current gaps in BIM. This as building projects progressively move towards systematic modular and prefabricated construction where the flow of reliable information about product and construction offerings becomes increasingly important.

This study aims to improve the complexity of chaotic systems and the security accuracy of information encrypted transmission. Applying five-dimensional memristive Hopfield neural network (5D-HNN) to secure communication will greatly improve the confidentiality of signal transmission and greatly enhance the anticracking ability of the system.

Chaos masking: Chaos masking is the process of superimposing a message signal directly into a chaotic signal and masking the signal using the randomness of the chaotic output. Synchronous coupling: The coupled synchronization method first replicates the drive system to get the response system, and then adds the appropriate coupling term between the drive The synchronization error and the coupling term of the system will eventually converge to zero with time. The synchronization error and coupling term of the system will eventually converge to zero over time.

A 5D memristive neural network is obtained based on the original four-dimensional memristive neural network through the feedback control method. The system has five equations and contains infinite balance points. Compared with other systems, the 5D-HNN has rich dynamic behaviors, and the most unique feature is that it has multistable characteristics. First, its dissipation property, equilibrium point stability, bifurcation graph and Lyapunov exponent spectrum are analyzed to verify its chaotic state, and the system characteristics are more complex. Different dynamic characteristics can be obtained by adjusting the parameter k.

A new 5D memristive HNN is proposed and used in the secure communication

The purpose of this study is to investigate the association between nonaudit services (NAS) and out-of-period adjustments (OOPAs). Over the years, the number of OOPAs has risen while the number of restatements has decreased. This could indicate an improvement in financial reporting quality. It could also indicate the use of a type of stealth restatement for opportunistic purposes. These less prominent restatements are more likely to go undetected and could perpetuate opportunistic disclosure and mitigate the likelihood of unfavorable market reactions.

The authors use a two-stage multivariate regression analysis to examine the relationship between NAS and the reporting of an OOPA. The authors use prior research on NAS to guide the model development. The authors perform several robustness checks including different types of NAS and different characteristics of OOPAs.

The results indicate that NAS has a significantly negative association with the existence of OOPAs. The core findings suggest that NAS does not impair auditor independence. Rather, greater amounts of NAS may contribute to knowledge spillover, which leads to higher financial reporting and audit quality. The results are robust to several additional tests.

The results raise interesting implications for regulators, executives, auditors, investors and future research. The authors provide insight into the relationship between NAS and auditor independence.

To the best of the authors’ knowledge, prior research has not considered the effect of NAS on OOPAs. The authors contribute to the literature by providing evidence that OOPAs, a form of stealth restatements, is an important consideration in audit quality research.

This study implements the fourth-order phase field method (PFM) for modeling fracture in brittle materials. The weak form of the fourth-order PFM requires C¹ basis functions for the crack evolution scalar field in a finite element framework. To address this, non-Sibsonian type shape functions that are nonpolynomial types based on distance measures, are used in the context of natural neighbor shape functions. The capability and efficiency of this method are studied for modeling cracks.

The weak form of the fourth-order PFM is derived from two governing equations for finite element modeling. C⁰ non-Sibsonian shape functions are derived using distance measures on a generalized quad element. Then these shape functions are degree elevated with Bernstein-Bezier (BB) patch to get higher-order continuity (C¹) in the shape function. The quad element is divided into several background triangular elements to apply the Gauss-quadrature rule for numerical integration. Both fourth-order and second-order PFMs are implemented in a finite element framework. The efficiency of the interpolation function is studied in terms of convergence and accuracy for capturing crack topology in the fourth-order PFM.

It is observed that fourth-order PFM has higher accuracy and convergence than second-order PFM using non-Sibsonian type interpolants. The former predicts higher failure loads and failure displacements compared to the second-order model due to the addition of higher-order terms in the energy equation. The fracture pattern is realistic when only the tensile part of the strain energy is taken for fracture evolution. The fracture pattern is also observed in the compressive region when both tensile and compressive energy for crack evolution are taken into account, which is unrealistic. Length scale has a certain specific effect on the failure load of the specimen.

Fourth-order PFM is implemented using C¹ non-Sibsonian type of shape functions. The derivation and implementation are carried out for both the second-order and fourth-order PFM. The length scale effect on both models is shown. The better accuracy and convergence rate of the fourth-order PFM over second-order PFM are studied using the current approach. The critical difference between the isotropic phase field and the hybrid phase field approach is also presented to showcase the importance of strain energy decomposition in PFM.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

The purpose of this study is to propose a fast method for predicting flow fields with periodic behavior with verification in the context of a radial turbine to meet the urgent requirement to effectively capture the unsteady flow characteristics in turbomachinery. Aiming at meeting the urgent requirement to effectively capture the unsteady flow characteristics in turbomachinery, a fast method for predicting flow fields with periodic behavior is proposed here, with verification in the context of a radial turbine (RT).

Sparsity-promoting dynamic mode decomposition is used to determine the dominant coherent structures of the unsteady flow for mode selection, and for flow-field prediction, the characteristic parameters including amplitude and frequency are predicted using one-dimensional Gaussian fitting with flow rate and two-dimensional triangulation-based cubic interpolation with both flow rate and rotation speed. The flow field can be rebuilt using the predicted characteristic parameters and the chosen model.

Under single flow-rate variation conditions, the turbine flow field can be recovered using the first seven modes and fitted amplitude modulus and frequency with less than 5% error in the pressure field and less than 9.7% error in the velocity field. For the operating conditions with concurrent flow-rate and rotation-speed fluctuations, the relative error in the anticipated pressure field is likewise within an acceptable range. Compared to traditional numerical simulations, the method requires a lot less time while maintaining the accuracy of the prediction.

It would be challenging and interesting work to extend the current method to nonlinear problems.

The method presented herein provides an effective solution for the fast prediction of unsteady flow fields in the design of turbomachinery.

A flow prediction method based on sparsity-promoting dynamic mode decomposition was proposed and applied into a RT to predict the flow field under various operating conditions (both rotation speed and flow rate change) with reasonable prediction accuracy. Compared with numerical calculations or experiments, the proposed method can greatly reduce time and resource consumption for flow field visualization at design stage. Most of the physics information of the unsteady flow was maintained by reconstructing the flow modes in the prediction method, which may contribute to a deeper understanding of physical mechanisms.