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The purpose of this paper is to forecast the reliable storage life of a certain kind of equipment under the normal stress level.

Through the stepping stress acceleration life test and the failure mechanism analysis, this paper aims to confirm the stress level for the stepping stress acceleration life test of a certain kind of equipment and establish the data processing mathematical model and storage life forecasting method.

The stress level for the stepping stress acceleration life test of a certain kind of equipment is confirmed and the data processing mathematical model and storage life forecasting method is established.

Availability of data is the main limitation affecting which model will be applied.

Useful advice for products' storage life forecasting.

The paper presents a new approach to product storage life estimation.

The purpose of this paper is to put forward a method which uses a computer to make a large number of simulated tests and determine the reference value of a system reliability index according to a unit reliability index.

At the beginning of conducting an index demonstration and development on a new system, its unit reliability index can already reflect the unit reliability level accurately. Based on this characteristic, this index is used as the confidence lower limit of single side estimated in the unit reliability interval to find the failure distribution law of the unit. On this basis, a great number of simulated tests are made by a computer to calculate the unit failure number and reliability point estimated value every test.

The confidence lower limit of single side estimated in the system reliability interval is found by counting up the simulated test data of these units.

Availability of data is the main limitation regarding which model will be applied.

A useful advice for system reliability analysis and calculation.

This paper presents a new approach of system reliability analysis.

The purpose of this paper is to develop a comprehensive understanding of the public safety risks of international construction projects (ICPs) from the perspective of threat and vulnerability. A novel and comprehensive risk assessment approach is developed from a systemic perspective and applied to the Belt and Road Initiative (BRI) to improve the public safety risk management strategy for ICPs in BRI.

First, a public safety risk indicator system was constructed from the two dimensions, namely threat and vulnerability. Next, an integrated measurement model was constructed by combining the Genetic Algorithm-Backpropagation (GA-BP) neural network, fuzzy comprehensive evaluation method and matter-element extension (MME) method. Data from 49 countries involved in the BRI, as well as five typical projects, were used to validate the model. Finally, targeted risk prevention measures were identified for use at the national, enterprise and project levels.

The findings indicate that while the vulnerability risks of typical projects in each region of the BRI were generally low, threat risks were high in West Asia and North Africa, Commonwealth of Independent States (CIS) countries and South Asia.

First, the structure of the public safety risk system of ICPs was analyzed using vulnerability and system theories. The connotation of public safety risk was defined based on two dimensions, namely threat and vulnerability. The idea of measuring threat risk with public data and measuring vulnerability risk with project data was clarified, and the risk measurement was integrated into the measurement results to help researchers and managers understand and systematically consider the public safety risks of ICPs. Second, a public safety risk indicator system was constructed, including 18 threat risk indicators and 14 vulnerability risk indicators to address the gaps in the existing research. The MEE model was employed to overcome the problem of incompatible indicator systems and provide stable and credible integrated measurement results. Finally, the whole-process public safety risk management scheme designed in this study can help to both provide a reference point for the Chinese enterprises and oversea contractors in market selection as well as improve ICP public safety risk management.

This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain.

Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities.

In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition.

This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.

With the continuous deepening of China's mixed-ownership reform, the participants in the reform have gradually expanded from state-owned enterprises to private enterprises. Whether state-owned equity participation in private enterprises can facilitate the development of environmental, social and governance (ESG) performance in private enterprises is a question that needs urgent examination. This study aims to investigate the impact of state-owned equity participation on the ESG performance of private enterprises.

Using Chinese listed companies as the research sample, this study uses econometric methods such as multiple regression to analyze the relationship between state-owned equity and the ESG performance of private enterprises. Additionally, it explores the underlying mechanisms and influencing factors of this relationship.

There is a significant inverted U-shaped relationship between state-owned equity and the ESG performance of private enterprises. Mechanism analysis reveals that resource effects and governance effects play a mediating role in this nonlinear relationship. Furthermore, the authors find that environmental regulation and managers' attention to the environment positively moderate the relationship between state-owned equity participation and ESG performance.

A reasonable equity structure is crucial for enhancing corporate ESG performance. Moderate state-owned equity participation helps to leverage resource integration and governance advantages, which will assist private enterprises in maximizing ESG performance and achieving sustainable development.

In advancing the process of mixed-ownership reform, the government should maintain an appropriate proportion of state-owned equity to avoid excessive intervention in enterprise decision-making. At the same time, it should ensure that enterprises can genuinely undertake their social and environmental responsibilities while pursuing economic benefits. This is of great significance for promoting sustainable economic and social development.

This study integrates state-owned equity, ESG and nonlinear relationships into a single research framework. It explores the internal mechanisms and influencing factors of their relationship, overcoming the limitations of previous studies and provides a new perspective for understanding the impact of state-owned equity on corporate ESG performance.

The purpose of this paper is to examine how Si-shu, a traditional form of local, private education grounded in classical instruction, responded to the rapid modernization of education during the late Qing dynasty and early Republic of China and to explain why these schools, once extraordinarily adaptable, finally disappeared.

The authors have examined both primary and secondary sources, including government reports, education yearbooks, professional annals, public archives, and published research to analyze the social, political and institutional changes that reshaped Si-shu in the context of China's late-19th- and early-20th-century educational modernization.

Si-shu went through four stages of institutional change during the last century. First, they faced increased competition from new-style (westernized) schools during the late Qing dynasty. Second, they engaged in a process of intense self-reform, particularly after the Xinhai Revolution of 1911. Third, they were marginalized by the new educational systems of the Republic of China, especially the Renxu School System of 1922 and the Wuchen School System of 1928. Finally, after the foundation of the People's Republic of China in 1949, they were considered remnants of feudal culture and forcibly replaced by modern schools.

This paper brings hitherto unexplored Chinese sources to an English-speaking audience in an effort to shed new light on the history of traditional Chinese education. The fate of Si-shu was part of the larger modernization of Chinese education – a development that had both advantages and disadvantages.

The purpose of this paper is to examine team external learning, particularly focusing on: how leader external learning behavior affects team external learning; how team external learning influences employee creativity; and whether team internal learning is a moderator between the cross‐level relationship of external learning and employee creativity in Chinese R&D teams.

Survey questionnaire were administered to a sample of 80 R&D teams comprising 331 employees from three medium‐sized Chinese high‐tech companies. HLM was used to test cross‐level relationships.

The paper hypothesizes that: leader external learning positively influences team external learning; team external learning leads to employee creativity; and team internal learning moderates the relationship between team external learning and employee creativity. Results support hypotheses 2 and 3, but not hypothesis 1. In addition, leader external learning is found to positively influence employee creativity.

This study has implications for both team leaders and top management. Although the context of this study is in Chinese R&D teams, all those teams facing similar environment to R&D teams can also learn from this study. There are several interesting implications for scholars who are studying teams, creativity and innovation.

Combining fields of team learning, boundary spanning, creativity and leadership, the paper addresses the following four questions, and provides further insights to the area of team external learning: First, what is the cross level relationship between team external learning and employee creativity? Second, is internal learning a moderator on the relationship between team external learning and employee creativity in R&D project teams? Third, is leader external learning behavior one of antecedents of team external learning? Fourth, is there anything unique about team external learning in Chinese culture? The paper provides research evidence for each of these questions.

The purpose of this paper is to propose a DC-port voltage balance strategy realizing it by logic combination modulation (LCM). This voltage balance strategy is brief and high efficient, which can be used in many power electronic devices adopting the cascaded H-bridge rectifier (CHBR) such as power electronic transformer (PET).

The CHBR is typically as a core component in the power electronic devices to implement the voltage or current conversion. The modulation method presented here is aiming to solve the voltage imbalance problem occurred in the CHBR with more stable work station and higher reliability in ordinary operating conditions. In particular, by changing the switch states smoothly and quickly, the DC-port voltage can be controlled as the ideal value even one of the modules in CHBR is facing the load-removed problem.

By using the voltage balance strategy of LCM, the problem of voltage imbalance occurring in three-phase cascaded rectifiers has been solved properly. With the lower modulation depth, the efficiency of the strategy is shown to be better and stronger. The strategy can work reliably and quickly no matter facing the problem as load-removed change or the ordinary operating conditions.

The limitation of the proposed DC-port voltage balance strategy is calculated and proved, in a three-module CHBR, the LCM could balance the DC-port voltage while one module facing the load-removed situation under 0.83 modulation depth.

This paper provides a useful and particular voltage balance strategy which can be used in the topology of three-phase cascaded rectifier. The value of the strategy is that a brief and reliable voltage balance method in the power electronic devices can be achieved. What is more, facing the problem, such as load-removed, in outport, the strategy can response quickly with no switch jump and switch frequency rising.

Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction for EIT is a nonlinear problem. A generalized inverse operator is usually ill-posed and ill-conditioned. Therefore, the solutions for EIT are not unique and highly sensitive to the measurement noise.

This paper develops a novel image reconstruction algorithm for EIT based on patch-based sparse representation. The sparsifying dictionary optimization and image reconstruction are performed alternately. Two patch-based sparsity, namely, square-patch sparsity and column-patch sparsity, are discussed and compared with the global sparsity.

Both simulation and experimental results indicate that the patch based sparsity method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages.

EIT image is reconstructed based on patch-based sparse representation. Square-patch sparsity and column-patch sparsity are proposed and compared. Sparse dictionary optimization and image reconstruction are performed alternately. The new method tolerates a relatively high level of noise in measured voltages.

The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult.

In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition.

The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition.

A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal.