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The study aims to provide a basis for the effective use of safety-related information data and a quantitative assessment way for the occurrence probability of the safety risk such as the fatigue fracture of the key components.

The fatigue crack growth rate is of dispersion, which is often used to accurately describe with probability density. In view of the external dispersion caused by the load, a simple and applicable probability expression of fatigue crack growth rate is adopted based on the fatigue growth theory. Considering the isolation among the pairs of crack length a and crack formation time t (a∼t data) obtained from same kind of structural parts, a statistical analysis approach of t distribution is proposed, which divides the crack length in several segments. Furthermore, according to the compatibility criterion of crack growth, that is, there is statistical development correspondence among a∼t data, the probability model of crack growth rate is established.

The results show that the crack growth rate in the stable growth stage can be approximately expressed by the crack growth control curve da/dt = Q•a, and the probability density of the crack growth parameter Q represents the external dispersion; t follows two-parameter Weibull distribution in certain a values.

The probability density f(Q) can be estimated by using the probability model of crack growth rate, and a calculation example shows that the estimation method is effective and practical.

This paper aims to examine the association between economic policy uncertainty (EPU) and audit effort by focusing on audit hours. This paper also explores whether significant political uncertainty might amplify the positive association between EPU and audit effort by focusing on Korea.

This study uses 21,543 Korean firm-year observations from 2005 to 2018 in an audit hour determinant model, as well as EPU following Baker et al. (2016) and audit hour to proxy audit effort.

EPU is positively associated with audit hours, indicating that auditors work more audit hours in response to firms’ high EPU resulting from higher earnings manipulation risk. Further, whether this positive association between EPU and audit effort might be altered by significant political uncertainty is investigated using a presidential election dummy. The empirical results show that auditors work additional audit hours during fiscal years in which presidential elections occur, given high EPU.

To the best of the authors’ knowledge, this paper might be the first empirical attempt to use audit hour data with EPU to provide practical implications to academia or auditors.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

The purposes of this paper are to prepare the carbon nanotube (CNT) grease, to contrast the tribology properties of the CNT grease with the original grease and to find the lubricating mechanism of the CNT grease.

The CNTs (single-wall and multi-wall) are added into the polyalphaolefin oils (DURASYN_166) to form stable and homogeneous CNT grease with potential heat transfer, conductive and lubricative properties. The friction of this new type of CNT grease was determined by wear experiments under three conditions: dry friction, with the base oil grease and with the CNT grease.

The research is about the tribological properties of CNT greases; it shows better lubricating performance and wear resistance than the base oil grease. The performance improvement of CNT grease is owing to the unique hexagonal structure and the high thermal conductivity of CNTs.

The paper documents that CNTs can obviously improve the lubricating effect of grease, and the lubricating mechanism of the CNT grease is also discussed.

This paper aims to introduce a high-temperature grease design method assisted by back propagation neural network (BPNN) and verify its application value.

First, the grease data sets were built by sorting out the base data of greases in a large number of literatures and textbooks. Second, the BPNN model was built, trained and tested. Then, the optimized BPNN model was used to search the unknown data space and find the composition of greases with excellent high-temperature performance. Finally, a grease was prepared according to the selected composition predicted by the model and the high-temperature physicochemical performance, high-temperature stability and tribological properties under different friction conditions were investigated.

Through high temperature tribology experiments, thermal gravimetric analysis and differential scanning calorimetry experiments, it is proved that the high temperature grease prepared based on BPNN has good high-temperature performance.

To the best of the authors’ knowledge, a new method of designing and exploring high-temperature greases is successfully proposed, which is useful and important for the industrial applications.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

Nanoparticles as the grease additives play an important role in anti-wear and friction-reducing property during the mechanical operation. To improve the lubrication action of grease, the tribological behavior of lithium-based greases with single (nanometer Al₂O₃ or nanometer ZnO) and composite additives (Al₂O₃–ZnO nanoparticles) were investigated in this paper.

The morphology and microstructure of nanoparticles were characterized by means of transmission electron microscope and X-ray diffraction. Tribological properties of different nanoparticles as additives in lithium-based greases were evaluated using a universal friction and wear testing machine. In addition, the friction coefficient (COF) and wear scar diameter were analyzed. The surface morphology and element overlay of the worn steel surface were analyzed by scanning electron microscopy (SEM) and energy dispersive spectrometer (EDS), respectively.

The results show that the greases with nanometer Al₂O₃ or nanometer ZnO and the composite nanoparticles additives both exhibit lower COFs and wear scar diameters than those of base grease. And the grease with Al₂O₃–ZnO composite nanoparticles possesses much lower COF and shows much better wear resistance than greases with single additives. When the additives contents are 0.4 Wt.% Al₂O₃ and 0.6 Wt.% ZnO, the composite nanoparticles-based grease exhibits the lowest mean COF (0.04) and wear scar diameter (0.65 mm), which is about 160% and 28% lower than those of base grease, respectively.

The main innovative thought of this work lies in dealing with the grease using single or composite nanoparticles. And through a serial contrast experiments, the anti-wear and friction-reducing property with different nanoparticles additives in lithium grease are evaluated.

The study aims to analyze factors impacting firms’ success and persistence in a digital platform competition using the structure-conduct-performance (SCP) framework. The study also includes real-life cases that are beneficial to academicians and practitioners to understand and develop strategies for success and persistence during uncertainty.

A literature review to identify the factors that impact success and persistence in a digital platform competition was conducted following Webster and Watson (2002). Findings were integrated into a SCP framework to examine and understand the identified factors’ relational impact.

While analyzing factors under the SCP framework, all factors were divided into three categories: those impacting positively, those impacting negatively and those with ambiguous impact on the success and persistence in digital platform competition. Digital platform firms can exploit the positively impacting factors to increase market share by being distinctive from other digital platform firms and becoming dominant by withstanding competition. On the other hand, negatively impacting factors increase barriers to entry, intensify competition and reduce the distinctiveness of digital platform firms. Lastly, a few factors may have either a positive or a negative impact depending upon the particular characteristics of the firm/industry.

The study opens the scope for future research on empirically testing the developed conceptual framework and relationships by developing propositions to posit the possible impact of these factors on digital platforms’ success and persistence.

The study contributed to the existing literature by using SCP framework to analyze the factors affecting firm’s success and persistence in a digital platform competition. Also, the study has discussed the relational impact of factors rather than their impact in isolation.

The purpose of this paper is to examine the efficiencies of China's foreign and domestic life insurance providers and to explore the relationship between ownership structure and the efficiencies of insurers while taking into consideration other firm attributes.

The data envelopment analysis (DEA) method is used to estimate the efficiencies of the insurers based on a panel data between 1999 and 2004.

The results indicate that the average efficiency scores for all the insurers are cyclical. Both technical and scale efficiency reached their peaks in 1999 and 2000 and gradually reduced for the rest of the period under examination until 2004 when average efficiency were improved again. The Tobit regression results show that the insurers' market power, the distribution channels used and the ownership structures may be attributed to the variation in the efficiencies.

Based on the research findings and the discussion, the study provides several recommendations for policy makers, regulators and senior executives of insurers.

The research results highlight the importance of deregulating the sector to allow a further expansion of each individual insurer or encourage mergers and acquisitions of insurers so more efficient resource utilization can be achieved through economies of scale. It also suggests that it is imperative for insurers to recruit motivated insurance agents and offer them on‐the‐job training as a part of the management strategies for gaining technical efficiency.

The paper reports the development within China's insurance industry and is one of the few studies analyzing the efficiencies of China's insurers.