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Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

Agricultural science and technology (AST) often need the form of industry-university-research cooperation in successfully completing breakthrough agricultural technology innovations. Therefore, AST industry-university-research cooperation is not only the need of the AST development strategy in China but also the only way for the development of agricultural colleges and universities, agricultural scientific research institutions and agricultural enterprises. Among them, in the process of cooperative breakthrough agricultural technology innovation, the correct selection of partners is the basis for ensuring its effective operation.

Aiming at the time-series characteristics and information ambiguity of decision information in the dynamic selection process of AST industry-university-research partners, this research introduces the dynamic intuitionistic fuzzy multi-criteria decision-making method of time degree and orthogonal projection. On this basis, field theory is used to construct the cooperative innovation capability field model of the partners, and the threshold for the partners to enter or exit the system is designed to dynamically select and eliminate the partners.

Results show that this method fully considers the situation of cooperative innovation resources within the AST industry-university-research system and the resource complementarity of candidate partners.

Combined with examples from agricultural scientific research institutions, the applicability and superiority of the model are verified.

The role of intermediate urban centers (IUCs) (like towns/small cities/rural counties) between large cities and villages in leading urbanization has been increasingly highlighted. This article conceptually and empirically examines what types of industrial agglomeration (specialized or diversified agglomeration) of rural county in China would provide a more inclusive and efficient local labor market for rural workers to be inclusively urbanized.

We employ a three-stage labor supply model to theoretically generalize the decisions of rural workers to migrate to large city, to undertake nonfarm jobs in rural counties and the length of nonfarm jobs. Then, combining three national-level datasets of China (National Economic Census (NEC), China Household Income Project (CHIP) and the China county statistical yearbook), we explore how the type of industrial agglomeration in a rural county affects the labor supply decision of rural workers.

We show that rural workers in rural counties with higher level of industrial specialization are more likely to: (1) work locally than migrating to large cities; (2) find nonfarm jobs by themselves instead of relying on social networks (3) find a more stable and formal nonfarm job. These effects are significant when rural counties specialize in industries that they have local comparative advantages compared to large cities. This work provides insights into achieving inclusive urbanization by developing industry specialization in rural counties in China, which may also be useful for other developing countries.

First, the conceptual framework of this paper extends the basic model of rural-urban migration longstanding in the literature, to a multi-choice and three-stage labor supply model. Second, we empirically quantify the industrial structure of rural counties using unique dataset, which has been emphasized in the development economics literature but neglected due to data limitations. Third, this paper expands our understanding of the role of rural counties in inclusive urbanization and contributes to the future development of the county-led urbanization policies.

What’s the flow path of knowledge sharing among members in online health community (OHC)? Exploration of this issue could shed light on mechanisms behind user knowledge sharing and interaction on OHC, but few studies have focused on it. This study is going to address this research gap and to provide richer support for subsequent knowledge management related research.

Based on the core-periphery effect, this study combines content analysis and social network analysis to portray the paths of different types of social support for core and periphery users from social support perspective.

Results reveal that the core users follow a pattern of high-stage and low-stage users with distinct needs, while the path pattern of the edge user group mainly consists support from high-stage to low-stage users. Results show that there is apparent distinction between the paths of emotional and informational support between core and periphery users. For core users, emotional support flows from lower stage users to higher stage users, while informational support follows the opposite direction. For periphery users, the paths of emotional support and informational support are identical, with both flowing from higher stage users to lower stage users.

This study explores the flow paths of information support and emotional support for core and periphery users, and discovers the different patterns of these two types of users, providing theoretical guidance for platform administrators to manage users more efficiently and ensure the sustainable development of the platform.

Wire arc additive manufacturing (WAAM) is one of the most researched and fastest-growing AM technique because of its capability to produce larger components with medium complexity. In recent times, the use of WAAM process has been increased because of its ability to produce complex components economically when compared with other AM techniques. The purpose of this study is to investigate the capabilities of wire arc additive manufacturing (WAAM), which has emerged as a recognized method for fabricating larger components with complex geometries.

This paper provides a review of process parameters for optimizing and analyzing mechanical properties, hardness, microstructure and corrosion behavior achieved through various WAAM-based techniques.

Limited analysis exists regarding the mechanical properties of various orientations of Inconel 625 alloy. Moreover, there is a lack of studies concerning the corrosion behavior of Inconel 625 alloy fabricated using WAAM.

The review identifies that the formation of intermetallic phases reduces the desirability of mechanical properties and corrosion resistance of WAAM-fabricated Inconel 625 alloy. Additionally, the study reported notable results obtained by various research studies and the improvements to be achieved in the future.

This study aims to develop a finite element method based co-simulation platform for the numerical analysis of motor drive system. With the rising requirement of industry, the comprehensive design of motor drive systems has attracted increasing attentions. An accurate model, which considers the coupling between motor and its drive system, is vital for the analysis and design of motor drive system.

Considering the coupling relationship between motor and its drive system, a flexible and extensible co-simulation platform of motor drive system is developed with the C++ language and finite element machine model to carry out the comprehensive analysis of motor drive system. The control system simulation program developed with C++ language adopts the same discrete form as the single-chip microcomputer and can simulate the interrupt mechanism, making the simulation closer to the actual control system. With the finite element analysis results of current step, the winding input voltage of next step is calculated by the executable program of control system and is fed into the finite element analysis, forming the two-way coupling analysis of drive system.

Preliminary studies, such as calculation of machine core losses fed by inverters, and control parameters optimization, are conducted with this platform, which shows the flexibility and expansibility of this platform.

The power inverter circuit along with the controller is modeled using the C++ language, and embedded into the finite element machine model to achieve more realistic motor drive system simulation and complex functions.

Defining and validating a map of related technologies is critical for managers, investors and inventors. Because of the increase in the applications of and demand for semiconductor lasers, analyzing the technological position of developers has become increasingly critical. Therefore, the purpose of this study is to adopt the technological position analysis to identify mainstream technologies and developments relevant to semiconductor lasers.

Correspondence analysis and k-means cluster analysis, which are data mining techniques, are used to reveal strategic groups of major competitors in the semiconductor laser market according to their Patent Cooperation Treaty (PCT) patent applications.

The results of this study reveal that PCT patent applications are generally obtained for masers, optical elements, semiconductor devices and methods for measuring and that technology developers have varying technological positions.

Through position analysis, this study identifies the technological focuses of different manufacturers to obtain information that can guide the allocation of research and development resources.