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The paper aims to focus on: implementation of the fast‐multipole method (FMM) to open perfect electric conductors (PEC) problems involving triangular type wire‐to‐surface junctions; investigation and analysis of the effect of wire‐to‐surface junction configuration on the conditioning of the linear systems; application of the preconditioning technique to improve the efficiency of the FMM scheme on such problems.

A complete set of formulations is proposed to evaluate the far‐field terms of the impedance matrix that represent the couplings between the wire‐to‐surface junction and standard wire and PEC surfaces. The formulations are derived in a convenient form suitable for the application of the FMM. An iterative scheme is adopted to estimate the condition number of the linear systems arising from open‐PEC problems with wire‐to‐surface junctions and to investigate the effect of wire‐to‐surface junction configuration on the conditioning of the linear systems. The Crout version of ILU (ILUC) preconditioning strategy is applied to improve the convergence rate of the iterative solver on such problems.

The solutions show that the proposed formulations have accurately evaluated the far‐field terms that represent the couplings between the wire‐to‐surface junction and standard wire and PEC surfaces. The investigation of the conditioning of open‐PEC problems with junctions shows that the effect of the wire‐to‐surface junction configuration induced to the conditioning of the linear systems is negligible. The convergence records of several open‐PEC problems involving wire‐to‐surface junctions show that the ILUC preconditioning strategy is suitable to apply to such problems, as it significantly improves the performance of the iterative solver.

The proposed FMM strategy can be applied to many practical large‐scale open‐PEC problems that involve wire‐to‐surface junctions, such as antenna arrays and electromagnetic compatibility problems, to effectively speed up the overall electromagnetic simulation progress and overcome the bottleneck associated with the dense impedance matrix of the method‐of‐moments.

The application of the FMM to open‐PEC problems that involve wire‐to‐surface junctions has yet to be reported, which has been addressed in this work. This work also investigates the conditioning of such problems and analyzes the effect of wire‐to‐surface junction configuration on the conditioning of the linear systems. In addition, the performance of the ILUC preconditioner on such problems has not been reported, which has also been included in this report.

The purpose of this paper is to investigate and analyze the efficiency and stability of the implementation of the Crout version of ILU (ILUC) preconditioning on fast‐multipole method (FMM) for solving large‐scale dense complex linear systems arising from electromagnetic open perfect electrical conductor (PEC).

The FMM is employed to reduce the computational complexity of the matrix‐vector product and the memory requirement of the impedance matrix. The numerical examples are initially solved by the quasi‐minimal residual (QMR) method with ILUC preconditioning. In order to fully investigate the performance of ILUC in connection with other iterative solvers, a case is also solved by bi‐conjugate gradient solver and conjugate gradient squared solver with ILUC preconditioning.

The solutions show that the ILUC preconditioner is stable and significantly improves the performance of the QMR solver on large ill‐conditioned open PEC problems compared to using ILU(0) and threshold‐based ILU (ILUT) preconditioners. It dramatically decreases the number of iterations required for convergence and consequently reduces the total CPU solving time with a reasonable overhead in memory.

The preconditioning scheme can be applied to large ill‐conditioned open PEC problems to effectively speed up the overall electromagnetic simulation progress while maintaining the computational complexity of FMM. More complex structures including wire‐PEC junctions and microstrip arrays may be addressed in future work.

The performance of ILUC has been previously reported only on preconditioning sparse linear systems, in which the ILU preconditioner is constructed by the ILUC of the coefficient matrix (e.g. matrix arised from two‐dimensional finite element convection‐diffusion problem) and subsequently applied to the same sparse linear systems; so it is important to report its performance on the dense complex linear systems that arised from open PEC electromagnetic problems. In contrast, the preconditioner is constructed upon the near‐field matrix of the FMM and subsequently applied to the whole dense linear system. The comparison of its performance against the diagonal, ILU(0) and ILUT precoditioners is also presented.

To assess and improve the reliability of computerized numerical control (CNC) lathes, it is essential to collect field failure information throughout the products’ life and perform analysis on these data. This paper describes the collection of field failure data, codification of data and establishment of the field failure database for CNC lathes and gives some examples of the kind of analysis possible when sufficient data have been collected and the database has been accrued.

This study aims to examine declines in audit quality after the COVID-19 travel restrictions/stay-at-home orders were issued in the USA in early 2020.

Taking advantage of variation in the dates of stay-at-home orders issued by different US states, this study identifies engagements that were significantly affected by the lock down orders.

The results suggest that engagements affected by the restrictions produced lower audit quality, as measured through restatements and discretionary accruals, relative to those completed before COVID-19 travel restrictions/stay-at-home orders. Further analysis reveals that this decrease in audit quality was attributable to firms with high inventory relative to assets, high R&D expenses relative to assets and non-Big 4 auditors.

This study finds that the restrictions on physical and on-site interaction caused auditors to universally struggle with resource/judgment-intensive accounts such as inventory and R&D expenditures. The results suggest that while Big 4 auditors managed to maintain their status quo level of audit quality following COVID-19 restrictions, non-Big 4 auditors were unable to overcome the challenges of an online work environment and their audit quality declined.

To the best of the authors’ knowledge, this paper is the first to empirically examine changes in audit quality as a response to a substantial change in auditors’ working environment due to the global health crisis. As work-from-home becomes more prevalent in audit firms, the results suggest that, on average, this move does diminish audit quality.

The purpose of this paper is to reveal the solute segregation behavior in the molten and solidified regions during direct chill (DC) casting of a large-size magnesium alloy slab under no magnetic field (NMF), harmonic magnetic field (HMF), pulsed magnetic field (PMF) and two types of out-of-phase pulsed magnetic field (OPMF).

A 3-D multiphysical coupling mathematical model is used to evaluate the corresponding physical fields. The coupling issue is addressed using the method of separating step and result inheritance.

The results suggest that the solute deficiency tends to occur in the central part, while the solute-enriched area appears near the fillet in the molten and solidified regions. Applying magnetic field could greatly homogenize the solute field in the two-phase region. The variance of relative segregation level in the solidified cross-section under NMF is 38.1%, while it is 21.9%, 18.6%, 16.4% and 12.4% under OPMF2 (the current phase in the upper coil is ahead of the lower coil), HMF, PMF and OPMF1 (the current phase in the upper coil lags behind the lower coil), respectively, indicating that OPMF1 is more effective to reduce the macrosegregation level.

There are few reports on the solute segregation degree in rectangle slab under magnetic field, especially for magnesium alloy slab. This paper can act a reference to make clear the solute transport behavior and help reduce the macrosegregation level during DC casting.

The paper aims to explore how the undue state control leads to the weak stock market in China. It analyzes how the undue state control is exerted in some key areas in the Chinese stock market. This paper intends to expand the existing literature in the relationships among law, politics, and economy.

This paper mainly adopts the exploratory method to analyze the undue state influences. Under some circumstances, comparative study and historical explanation are also adopted.

The paper suggests that to create a strong stock market and facilitate the development of the listed companies and the whole economy, the state should first release its control on the stock market.

Various fields are contained in a stock market, in most of which the undue state control can be observed. In this paper, only some key ones are explored. Further research on other fields and if possible more first‐hand data are necessary.

This paper not only offers an answer to concerns on the various misconducts in the inefficient Chinese stock market and helps to realize the possible ways out of such dilemma, but also it offers implications for other emerging economies.

The on‐going debate on the role of common‐law versus civil‐law system in the capital market may have ignored the state involvement. This paper indicates that it is the undue state control rather than the legal system that leads to the weak stock market in China.

This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λ_max method for weight initialization, as a data normalization technique, to achieve high accuracy of classification.

The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λ_max method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λ_max method.

The effectiveness of the proposed FDCNN model with λ_max method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795.

The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λ_max method.

Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition.

A novel FDCNN architecture is implemented with appropriate FAL kernel structures.