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The substrate coupling and loss in integrated circuits are analyzed. Then, the authors extract impedances between any numbers of embedded contacts. The paper aims to discuss these issues.

The paper proposes a new substrate network 3D extraction technique, adapted from a transmission line method or Green kernels, but in the whole volume.

Extracting impedances between any numbers of embedded contacts with variable shapes or/and through silicon via. This 3D method is much faster comparing with FEM

Previous works consider TSVs alone, contacts onto the substrate. The authors do study entanglement between the substrate and the interconnections.

Considering two-dimensional features in the body shape classification system cannot fully reflect the three-dimensional (3D) morphological characteristics of human body. The purpose of this paper is to propose a 3D feature based method to characterize and classify the upper body shape of women, and then obtained the corresponding garment block and improved the fitness of clothing.

In this study, the [TC]² 3D scanner was used to obtain human data, and 15 layers of cross-sections of young females’ upper body were extracted. In total, 240 space vectors were obtained with the center of the bust cross-section as the original point. By using the principal component analysis and K-means clustering analysis, the body shape classification based on the space vectors length was realized. The garment block corresponding to three body types was obtained using the 3D scanning data and the cross-section convex hull, and compared with existing garment block and evaluated fitness of the blocks.

In total, 11 main components used to characterize the 3D morphological features of young women were obtained, which could explain 95.28 percent features of young women’s upper body. By cluster analysis, the body shape of women was divided into three categories. The block of three body types was obtained by the construction of the convex hull model.

This paper investigates a classification method of the body shape based on space vector length, which can effectively reflect the difference of surface shape of human body and further improve the matching degree of human body and clothing.

The purpose of this study is to examine the fretting wear property of ultra-high molecular weight polyethylene (UHMWPE)-based composites reinforced by different content of attapulgite.

A series of composites were prepared by a hot-pressing method. Fretting tests were carried out using an SRV-IV oscillating reciprocating friction wear tester with a load of 10 N and a frequency of 100 Hz. The morphology of the fracture structure and the worn surface was observed by field-emission scanning electron microscopy, X-ray diffraction and a non-contact three dimensional surface profiler.

With the addition of attapulgite, the microstructure of the composites become more regular, and their heat resistance improved. Furthermore, the friction coefficient and the specific wear rate of the composites with lower filler content reduced compared with that of neat UHMWPE, and the optimum filler content is 1 per cent.

The study investigated the fretting resistance mechanism of the attapulgite in the UHMWPE matrix. The results could help to provide some experimental evidence for the broader application of silicates on the fretting wear resistance of polymers.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2019-0420/

Considering only two-dimensional (2D) ease allowance cannot fully reflect the three-dimensional (3D) relationship between the position of clothing and the human body. The purpose of this paper is to propose a method with a 3D space vector and corresponding distance ease to characterize fitting garments and then used to construct personalized clothing for similar shape body.

Firstly, a 3D scanner was used to obtain mannequin and fitted garment data, and 17 layers of cross-sections of the upper body were extracted. Then, 37 space vectors and corresponding space angles on each cross-section were obtained with the original point. Secondly, the detailed distance ease between the mannequin and garment was constructed due to the difference between garment vectors and body vectors. Thirdly, the distance ease mathematical models were achieved and used to calculate distance ease on a similar shape body. Additionally, the fit garment is constructed, and the garment pattern is altered by the geometric pattern alteration method.

The results show that 3D space vectors can explain the relationship between body skin and garment surface of the upper body properly. The distance ease is modeled by mathematic expressions and successfully used to make a new garment to fit a similar shape body.

The proposed method of constructing garments based on distance ease and 3D space vectors can create a fitted garment for a similar shape body effectively and accurately. It is useful for the personalized garment design and suitable for the manufacturing process.

It is an important style classification way to sort out suits according to the button arrangement. However, since the different dressing ways of suit cause the buttons to be easily occluded, the traditional identification methods are difficult to identify the details of suits, and the recognition accuracy is not ideal. The purpose of this paper is to solve the problem of fine-grained classification of suit by button arrangement. Taking men's suits as an example, a method of coordinate position discrimination algorithm combined faster region-based convolutional neural network (R-CNN) algorithm is proposed to achieve accurate batch classification of suit styles under different dressing modes.

The detection algorithm of suit buttons proposed in this paper includes faster R-CNN algorithm and coordinate position discrimination algorithm. Firstly, a small sample base was established, which includes six suit styles in different dressing states. Secondly, buttons and buttonholes in the image were marked, and the image features were extracted by the residual network to identify the object. The anchors regression coordinates in the sample were obtained through convolution, pooling and other operations. Finally, the position coordinate relation of buttons and buttonholes was used to accurately judge and distinguish suit styles under different dressing ways, so as to eliminate the wrong results of direct classification by the network and achieve accurate classification.

The experimental results show that this method could be used to accurately classify suits based on small samples. The recognition accuracy rate reaches 95.42%. It can effectively solve the problem of machine misjudgment of suit style due to the cover of buttons, which provides an effective method for the fine-grained classification of suit style.

A method combining coordinate position discrimination algorithm with convolutional neural network was proposed for the first time to realize the fine-grained classification of suit style. It solves the problem of machine misreading, which is easily caused by buttons occluded in different suits.

This paper aims to study the influence of load and environment medium on the fretting behavior of SAF 2507 SDSS.

In this study, the effect of load on the fretting behavior of SAF 2507 SDSS in air and sea water were studied. The fretting wear tests under different loads were conducted with a ball-on-flat contact configuration. The friction coefficient, wear volume, surface morphology and oxidation component were determined.

With the increase of applied load, the friction coefficient decreases both in air and sea water. The fretting mechanism is gradually transformed from partial slip regime to slip regime in air while the fretting counterparts are all in the state of gross slip in sea water. In sea water, the friction coefficient is lower while the wear loss is higher compared with that in air.

This research suggests that the fretting behavior of SAF 2507 SDSS is related to load and environment medium.

The results may help us to choose the appropriate load under different environments.

The main originality of the research is to reveal the fretting behavior of SAF 2507 SDSS under different loads in air and sea water, which would help us to realize fretting behavior of SAF 2507 SDSS is controlled by the combination of applied load and lubricating environment.

The peer review history for this article is available at: http://dx.doi.org/10.1108/ILT-08-2019-0335.

This paper examines the role of professional associations, governmental agencies, and international accounting and auditing bodies in promulgating standards to deter and detect fraud, domestically and abroad. Specifically, it focuses on the role played by the US Securities and Exchange Commission (SEC), the American Institute of Certified Public Accountants (AICPA), the Institute of Internal Auditors (IIA), the Institute of Management Accountants (IMA), the Association of Certified Fraud Examiners (ACFE), the US Government Accounting Office (GAO), and other national and foreign professional associations, in promulgating auditing standards and procedures to prevent fraud in financial statements and other white‐collar crimes. It also examines several fraud cases and the impact of management and employee fraud on the various business sectors such as insurance, banking, health care, and manufacturing, as well as the role of management, the boards of directors, the audit committees, auditors, and fraud examiners and their liability in the fraud prevention and investigation.

China has a new rural revitalization strategy to stimulate rural transformation through modernizing rural areas and resolving their social contradictions. While social capital is recognized as an important element to rural revitalization and entrepreneurship, research into the role of psychological capital is less developed. Therefore, this paper assesses the impact of both social and psychological capital on entrepreneurial performance of Chinese new-generation rural migrant entrepreneurs (NGRMEs) who have returned to their homes to develop businesses as part of the rural revitalization revolution.

Based on a survey, data were collected from 525 NGRMEs in Shaanxi province. This paper uses factor analysis to determine variables for a multiple linear regression model to investigate the impacts of dimensions of both social capital and psychological capital on NGRMEs’ entrepreneurial performance.

Through the factor analysis, social capital of these entrepreneurs consists of five dimensions (reputation, participation, networks, trust and support), psychological capital has three dimensions (innovation and risk-taking, self-efficacy and entrepreneurial happiness) and entrepreneurial performance contains four dimensions (financial, customer, learning and growth, and internal business process). Furthermore, the multiple linear regression model empirically verifies that both social capital and psychological capital significantly influence and positively correlate with NGRMEs' entrepreneurial performance.

This study shows the importance of how a mixture of interrelated social and psychological dimensions influence entrepreneurial performance that may contribute to the success of the Chinese rural revitalization strategy. This has serious implications when attempting to improve the lives of over 100 million rural Chinese citizens.