Search results

This paper aims to build an improved grey panel clustering evaluation model and evaluate the comprehensive development potential of industrial economy, society and ecological environment in the Yangtze River Economic Belt of China. The purpose of this study is to provide some theoretical basis and tool support for management departments and relevant researchers engaged in industrial sustainable development.

This study uses the driving force pressure state impact response analysis framework to build a comprehensive evaluation index system. Based on the center point triangle whitening weight function, it classifies the panel grey clustering of improvement time and index weight.

The results show that there are great differences in the level of industrial ecological development in different regions of the Yangtze River Economic Belt, which further illustrates the scientificity and rationality of the evaluation method proposed in this paper.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. The improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

Due to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. In order to improve the effectiveness of industrial ecological evaluation, the improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.

the new model proposed in this paper complements and improves the grey clustering analysis theory of panel data, that is, aiming at the subjective limitation of using time degree to determine time weight in panel grey clustering, a comprehensive theoretical method for determining time weight is creatively proposed. Combining the DPSIR (Driving force-Pressure-State-Influence-Response) model model with ecological development, a comprehensive evaluation model is constructed to make the evaluation results more authentic and comprehensive.

The purpose of this paper is to analyze the measurement error of a three-dimensional coordinate measurement system based on dual-position-sensitive detector (PSD) under different background light.

The mind evolutionary algorithm (MEA)-back propagation (BP) neural network is used to predict the three-dimensional coordinates of the points, and the influence of the background light on the measurement accuracy of the three-dimensional coordinates based on PSD is obtained.

The influence of the background light on the measurement accuracy of the system is quantitatively calculated. The background light has a significant influence on the prediction accuracy of the three-dimensional coordinate measurement system. The optical method, electrical method and photoelectric compensation method are proposed to improve the measurement accuracy.

BP neural network based on MEA is applied to the coordinate prediction of the three-dimensional coordinate measurement system based on dual-PSD, and the influence of background light on the measurement accuracy is quantitatively analyzed.

The purpose of this paper is to present a new methodology, used for dynamic reliability analysis of a gear transmission system (GTS) of wind turbine (WT), which could be used for assembly decision-making of the parts with errors to improve the GTS’s performance.

This paper involves the dynamic and dynamic reliability analysis of a GTS. The history curves of dynamic responses of the parts are obtained with the developed gear-bearing coupling dynamic model considering the random errors, failure dependency and random load. Then, the surrogate models of the mean and standard deviation of responses are presented by statistics, rain flow counting method and corrected-partial least squares regression response surface method. Further, a novel dynamic reliability model based on the maximum extreme theory, a theory of sequential statistics, equivalent principles and the inverse transform theory of random variable sampling, is developed to overcome the limitations of traditional methods.

The dynamic reliability of GTS considering the different impact factors are evaluated. The proposed reliability methodology not only overcomes the limitations associated with traditional approaches but also provides good guidance to assembly the parts in a GTS to its best performance.

Instead of constant errors, this paper considers the randomness of the impact factors to develop the dynamic reliability model. Further, instead of the limitation of the normal distribution of the random parameters in the traditional method, the proposed methodology can deal with the problems with non-normal distribution parameters, which is more suitable for the real engineering problems.

We investigate the effect of the PCAOB’s Part II report on annually inspected firms’ audit fees and audit quality. The PCAOB replaced the peer review auditor program with an independent inspection of audit firms. Upon completion of each inspection, the PCAOB issued inspection reports that include a public portion (Part I) of identified audit deficiencies, and (in most cases) a nonpublic portion (Part II) of identified quality control weaknesses. The Part II report is only made public when the PCAOB deems that remediation was insuffcient after at least 12 months have passed. Starting around the time of the 2007 Deloitte censure (Boone et al., 2015), the PCAOB shifted from a soft synergistic approach to an antagonistic approach, such that Part II reports were imminent, despite delays that ultimately led to their release one to four years later than expected. Our study spans the period from 2007 to 2015, and examines the effect on audit fees and audit quality at the earliest date that the Part II report could have been released – 12 months after the Part I report was issued. We find that following the 12 month period, that annually inspected audit firms eventually lost reputation by lower audit fees, while they concurrently made remedial efforts to increase the quality of their client’s financial reporting quality (abnormal accruals magnitude and restatements). However, three years after the Part II report was actually released, audit fees increased.

Two types of institutional constraints, namely, institutional voids and policy uncertainty, have been recognised and elaborated, including their influence on new venture performance. However, not enough attention has been paid to the multidimensional attribute of institutional transformation. By hinting at a relatively underexplored third type of institutional constraints, i.e. institutional fragility, this paper aims to build a comprehensive framework of institutional constraints to analyse how innovative start-up performance is influenced by institutional constraints.

Using Chinese manufacturing firms as the empirical sample, the authors use an econometric method to test the relationship between institutional constraints and the performance of innovative start-ups.

On the basis of a uniquely constructed database from 2005 to 2007, the authors find that institutional constraints are negatively associated with innovative start-up performance proxied by return on assets, sales growth and new product sales.

The study offers researchers and practitioners a detailed view of institutional constraints and innovative start-up performance.

The purpose of this paper is to investigate whether Japanese Sarbanes – Oxley Act (J-SOX) impacted earnings management and earnings quality of public firms in Japan.

This archival study compares earnings management and earnings quality of firms that disclose at least one material weakness with a sample matched on size and industry without a material weakness.

The authors investigate whether the differences in regulations, corporate governance and regulatory environment acceptance influence earnings management and earnings management of Japanese listed firms, relative to findings in the USA. They found the Japanese results to be slightly different from the results found in previous USA studies. First, the time-series observations suggest that while accruals management and real earnings management remained unchanged for control firms, accruals management and real earnings management increased for material weaknesses disclosing firms following J-SOX. The regression analyses suggest that accruals management for both the groups is significant in the pre-and post-J-SOX periods, but that real earnings management declined for both the groups post-J-SOX. Second, while, both accruals quality and accuracy of cash flow predictions improved in the post-J-SOX period.

The sample of Japanese firms disclosing a material weakness is small because the number of firms that disclose internal control deficiencies is decreasing in Japan. The authors have no evidence that their results are not generalizable to a larger sample and leave this for future research.

The authors provide evidence that J-SOX, which does not have a direct reporting system, does not constrain earnings management. Their results drive the regulator to reconsider whether the reporting system works in the Japanese business environment. Additionally, their results show that J-SOX has no effect on earnings management; thus, regulators need to reconsider the governance function of directors and internal auditors. This paper communicates to the world how J-SOX works in Japan through changes in earnings quality and management post J-SOX and the root problems.

This paper is the first (of which the authors are aware) to examine whether J-SOX impacted both earnings management and earnings quality in Japan. This paper discusses how the differences in regulations and corporate governance as well as the differences between USA-SOX and J-SOX may explain the results observed in Japan. This paper provides results regarding whether J-SOX improved earnings quality.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

Vertical electrical sounding (VES) and Rayleigh wave exploration are widely used in the exploration of near-surface structure, but both have limitations. This study aims to make full use of the advantages of the two methods, reduce the multiple solutions of single inversion and improve the accuracy of the inversion. Thus, a nonlinear joint inversion method of VES and Rayleigh wave exploration based on improved differential evolution (DE) algorithm was proposed.

Based on the DE algorithm, a new initialization strategy was proposed. Then, taking AK-type with high-velocity interlayer model and HA-type with low-velocity interlayer model near the surface as examples, the inversion results of different methods were compared and analyzed. Then, the proposed method was applied to the field data in Chengde, Hebei Province, China. The stratum structure was accurately depicted and verified by drilling.

The synthetic data and field data results showed that the joint inversion of VES and Rayleigh wave data based on the improved DE algorithm can effectively improve the interpretation accuracy of the single-method inversion and had strong stability and large generalizable ability in near-surface engineering problems.

A joint inversion method of VES and Rayleigh wave data based on improved DE algorithm is proposed, which can improve the accuracy of single-method inversion.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

Multifilamentary Bi₂Sr₂CaCu₂O_x (Bi2212) superconductor composite wires are the only high-temperature superconducting round wires (RW) with sufficient critical current density (J_c) for superconducting magnets generating magnetic fields greater than 25 Tesla. Very complex microstructures of Bi2212 RWs including the voids or gas bubbles, filament to filament bridges and wire architecture strongly influence their electrical behavior. Especially, a large number of voids in Bi2212 superconducting filaments is believed to be the major current-limiting mechanism. However, the effect of the void structure on the Jc is not well understood yet.

In this paper, the authors first statistically analyzed the size and distribution of voids in filaments using the reported microscopic data, obtaining the essential statistical regularities. An electrical model was further developed to predict the J_c of multifilamentary wires while taking into account of the current limiting mechanisms of the void structure in filaments, and the current sharing roles of filament to filament bridges.

The model predicts the quantitative dependence of J_c on the number of Bi2212 filaments in each bundle of a double-restack wire and porosity. The results are useful optimizing design and fabrication of Bi2212 multifilamentary wires.

For the complex structure of voids and interfilamentary bridges inside Bi2212 multifilamentary superconducting wires, the authors took a statistical characterization and studied its effect on the critical current density Jc (the key index of evaluating the current carrying capacity).