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The purpose of this paper is to design a current transformer model based on the principle of B-dot. It can reflect the change of transmission line current and meet the requirement of automation and intelligence for current measurement in power system.

In this paper, a new type of current transformer is designed on the principle of B-dot, which has the structure of the inverse series of planar air core coils and the form of printed circuit board (PCB). With this structure, the current transformers can induce magnetic field quite well. The finite element simulation for the current transformer with n layers structure is conducted in the Maxwell, which help to optimize the design of the current transformer.

By setting up the experimental platform, the experiment of the current transformer is carried out. The results of the test show that the measurement accuracy can satisfy the requirement of measurement. Besides, the new current transformer has good transient characteristics and can meet the needs of the development of smart grid.

The new type of current transformer is based on the principle of B-dot, which is designed with a new type of non-contact PCB hollow coil current transformer. It has no iron core, no ferromagnetic effect and the phenomenon of ferromagnetic resonance. It has great progress in its insulation performance, volume and bandwidth response. In addition, the planar hollow coil of the inverse series structure can make the structure more accurate.

Scholars mainly propose and establish theoretical models of cumulative fatigue damage for their research fields. This review aims to select the applicable model from many fatigue damage models according to the actual situation. However, relatively few models can be generally accepted and widely used.

This review introduces the development of cumulative damage theory. Then, several typical models are selected from linear and nonlinear cumulative damage models to perform data analyses and obtain the fatigue life for the metal.

Considering the energy law and strength degradation, the nonlinear fatigue cumulative damage model can better reflect the fatigue damage under constant and multi-stage variable amplitude loading. In the following research, the complex uncertainty of the model in the fatigue damage process can be considered, as well as the combination of advanced machine learning techniques to reduce the prediction error.

This review compares the advantages and disadvantages of various mainstream cumulative damage research methods. It provides a reference for further research into the theories of cumulative fatigue damage.

The purpose of this paper is to comprehensively examine the influence of formal and informal institutional differences on enterprise investment margin, mode and result.

This paper is based on 2,440 micro samples of large-scale outbound investment from 609 Chinese enterprises from the years 2005 to 2016.

The study has found that formal institutional differences have little impact on investment scale, but significantly affect investment diversification. In order to avoid the management risks brought by formal institutional differences, enterprises tend to a full ownership structure. However, the choice between greenfield investment and cross-border mergers and acquisitions is not affected by formal institutional differences. In contrast, the impact of informal institutional differences is more extensive. Both formal and informal institutional differences significantly increase the probability of investment failure. Further research found that the Belt and Road Initiative (BRI) bridges the formal institutional differences.

The study concludes that developing the BRI, especially cultural exchanges with countries alongside the Belt and Road, will help enterprises to “go global” faster and better.

The Belt and Road Initiative (BRI) is the most comprehensive and substantial international cooperation platform, creating a new market influenced by economic and political factors. In this paper, the authors aim to examine whether and how the BRI impacts the Chinese enterprises' corporate environmental responsibility (CER).

Based on China's listed firms' database from 2011 to 2018, the authors use the PSM-DID method, an econometrics method combined with propensity score matching (PSM) and difference-in-differences (DID), to conduct causal inference between the BRI and Chinese enterprises' CER and conduct a series of robustness analyses. Moreover, the authors explore the mechanisms underlying the main effect from both market and non-market perspectives.

The results suggest that the BRI significantly increases Chinese enterprises' CER. Further analyses show that market competition and government support are two possible mechanisms through which the BRI has an effect on the enterprises' CER.

The research study supplements existing work on the environmental effects of the BRI at a microlevel and adds to the literature on the drivers of CER. The findings offer valuable insights into governments and scholars by demonstrating that CER is a crucial tool for Chinese enterprises to gain a competitive advantage in the increasingly competitive markets along the BRI.

Automatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an attention-based nested segmentation network, named DAU-Net. In total, two types of attention mechanisms are introduced to make the U-Net network focus on the key feature regions. The proposed network has a deep supervised encoder–decoder architecture and a redesigned dense skip connection. DAU-Net introduces an attention mechanism between convolutional blocks so that the features extracted at different levels can be merged with a task-related selection.

In the coding layer, the authors designed a channel attention module. It marks the importance of each feature graph in the segmentation task. In the decoding layer, the authors designed a spatial attention module. It marks the importance of different regional features. And by fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.

To verify the effectiveness of the DAU-Net, experiments were carried out on the BRATS 2018 brain tumor magnetic resonance imaging (MRI) database. The segmentation results show that the proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 89% in the complete tumor, which is an improvement of 8.04 and 4.02%, compared with fully convolutional network (FCN) and U-Net, respectively.

The experimental results show that the proposed method has good performance in the segmentation of brain tumors. The proposed method has potential clinical applicability.

To solve the problem that the traditional received signal strength indicator real-time location method does not test the attenuation characteristics of the electromagnetic wave transmission in the location area, which cannot guarantee the accuracy of the location, resulting in a large location error.

At present, the compressed sensing (CS) reconstruction algorithm can be roughly divided into the following two categories (Zhouzhou and Fubao, 2014; Lagunas et al., 2016): one is the greedy iterative algorithm proposed for combinatorial optimization problems, which includes matching pursuit algorithm (MP), positive cross matching tracking algorithm (OMP), greedy matching tracking algorithm, segmented orthogonal matching tracking algorithm (StOMP) and so on. The second kind is the convex optimization algorithm, which also called the optimization approximation method. The common method is the basic tracking algorithm, which uses the norm instead of the norm to solve the optimization problem. In this paper, based on the piecewise orthogonal MP algorithm, the improved StOMP reconstruction algorithm is obtained.

In this paper, the MP algorithm (OMP), the StOMP and the improved StOMP algorithm are used as simulation reconstruction algorithms to achieve the comparison of location performance. It can be seen that the estimated position of the target is very close to the original position of the target. It is concluded that the CS grid-based target stepwise location method in underground tunnel can accurately locate the target in such specific region.

In this paper, the offline fingerprint database in offline phase of location method is established and the measurement of the electromagnetic noise distribution in different localization areas is considered. Furthermore, the offline phase shares the work of the location process, which greatly reduces the algorithm complexity of the online phase location process and the power consumption of the reference node, meanwhile is easy to implement under the same conditions, as well as conforms to the location environment.

The purpose of the research paper is to investigate the relationship between personality traits and investment decisions in the crypto market, including cryptocurrencies and NFTs. The study aims to explore the effect of dark personalities and the big five personalities on investment decisions in the crypto market.

The research was conducted through two online questionnaire studies. In Study 1, data were collected from the general public, while in Study 2, data were collected from crypto investors. The researchers analyzed the effect of dark personalities and the big five personalities on investment decisions in the crypto market.

The present research found that Machiavellianism, narcissism, psychopath, sadism and extraversion have positive effects on having crypto investments. In addition, focusing on actual crypto investors, the present paper showed that personalities including Machiavellianism, narcissism, psychopath, consciousness and extraversion have statistically significant effect on investment decisions such as making investments in Bitcoin.

The study is original in exploring the relationship between personality traits and investment decisions in the newly emerging crypto market, including cryptocurrencies and NFTs. The research provides insights into how different personality traits affect investment decisions in the crypto market, which can be valuable for investors in making informed decisions.