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The purpose of this paper is to combine the entropy weight method with the cloud model and establish a fire risk assessment method for airborne lithium battery.

In this paper, the fire risk assessment index system is established by fully considering the influence of the operation process of airborne lithium battery. Then, the cloud model based on entropy weight improvement is used to analyze the indexes in the system, and the cloud image is output to discuss the risk status of airborne lithium batteries. Finally, the weight, expectation, entropy and hyperentropy are analyzed to provide risk prevention measures.

In the risk system, bad contact of charging port, mechanical extrusion and mechanical shock have the greatest impact on the fire risk of airborne lithium battery. The fire risk of natural factors is at a low level, but its instability is 25% higher than that of human risk cases and 150% higher than that of battery risk cases.

The method of this paper can evaluate any type of airborne lithium battery and provide theoretical support for airborne lithium battery safety management.

After the fire risk assessment is completed, the risk cases are ranked by entropy weight. By summarizing the rule, the proposed measures for each prevention level are given.

This paper aims to present a novel hybrid time integration approach for efficient numerical simulations of multiscale problems involving interactions of electromagnetic fields with fine structures.

The entire computational domain is discretized with a coarse grid and a locally refined subgrid containing the tiny objects. On the coarse grid, the time integration of Maxwell’s equations is realized by the conventional finite-difference technique, while on the subgrid, the unconditionally stable Krylov-subspace-exponential method is adopted to breakthrough the Courant–Friedrichs–Lewy stability condition.

It is shown that in contrast with the conventional finite-difference time-domain method, the proposed approach significantly reduces the memory costs and computation time while providing comparative results.

An efficient hybrid time integration approach for numerical simulations of multiscale electromagnetic problems is presented.

The purpose of this study is to gather insights into digital consumer behaviour related to Chinese restaurents by examining visual contents on Tripadvisor platform.

Using the deep learning approach, this research assessed consumer-posted online content of dining experiences by implementing image analysis and clustering. Text mining using word cloud analysis revealed the most frequently repeated keywords.

First, 4,000 photos of nine Chinese restaurants posted on Tripadvisor’s website were analyzed using image recognition via Inception V3 and Google’s deep learning network; this revealed 12 hierarchical image clusters. Then, an open-questionnaire survey of 125 Chinese respondents investigated consumers’ information needs before visiting a restaurant and after purchasing behavior (motives to share).

This study contributes to culinary marketing development by introducing a new analysis methodology and demonstrating its application by exploring a wide range of keywords and visual images published on the internet.

This research extends and contributes to the literature regarding visual user-generated content in culinary tourism.

The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems.

On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects.

The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model.

This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

This research helps firms pursue an open innovation strategy but want to minimize competitive pressure from other external entities. A theoretical framework is constructed to analyze the impact of openness on innovation performance, exploring different effect of firms' external search channels.

This paper employs a stepwise hierarchical regression approach to assess the effect of openness on technological innovation considering the role of information technology adoption and political ties. The effect is conducted using a large-scale sample of 1,073 Chinese manufacturing firms over the period 2011–2013 as empirical research objects.

There are two stages of the open technological innovation process while the information technology (IT) adoption and political ties are the key consideration in emerging markets. Openness is curvilinearly (taking an inverted U-shape) related to innovation performance. Both information technology adoption and political ties generally help firms to turn broadly sourced external knowledge into technological innovation performance. This will stimulate “one plus one is greater than two” effect not only in the process of achieving performance goals, but also in the process of technological innovation.

This quantitative research illustrates the importance relationship between firms' open behaviors and technological innovation performance in emerging markets. It helps us understand firms' current constrains of open strategy of technological innovation and helps domestic or foreign investors to make strategic collaboration choices in emerging economies according to the degree of openness, informatization level, political connections, which is equally important for research and practice.

The purpose of this study is to investigate the impact of board secretaries’ characteristics on annual report readability using an original method that evaluates the readability of Chinese characters.

The authors manually collect board secretaries’ characteristics from the China Securities Market and Accounting Research database and obtain annual reports from the China Information website. Ordinary least square regression is applied to evaluate the impact, and then robustness tests and additional regression analyses are conducted.

Board secretaries’ legal-professional expertise, international expertise and role duality improve annual report readability. However, their political connections are negatively associated with it. The effect of expertise (role duality) is more pronounced for firms with lower ex ante litigation risk (board secretaries with equity holdings). Furthermore, higher readability increases the compensation of board secretaries, whereas lower readability increases their turnover. Finally, annual report readability is positively related to firm performance.

The authors only investigate listed firms in China from 2007 to 2017 because of the difficulties of obtaining data and text mining.

The authors provide managerial insights for regulators aiming to establish an effective governance mechanism with Chinese characteristics. First, certain requirements for board secretaries’ expertise can improve annual report readability. Further, firms can consider appointing board members or senior executives as board secretaries to enhance disclosure quality.

To the best of the authors’ knowledge, this study is the first to verify the effect of board secretaries’ characteristics on disclosure quality, especially annual report readability. Moreover, this study proposes a novel measure of annual report readability for Chinese texts.

In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

The purpose of this study is to establish an assembly accuracy analysis model of deployable arms based on Jacobian–Torsor theory to improve the assembly accuracy. Spacecraft deployable arm is one of the core components of spacecraft. Reducing the errors in assembly process is the main method to improve the assembly accuracy of spacecraft deployable arms.

First, the influence of composite connecting rod, root joint and arm joint on assembly accuracy in the tandem assembly process is analyzed to propose the assembly accuracy analysis model. Second, a non-tandem assembly process of “two joints fixed-composite rod installed-flange gasket compensated” is proposed and analyzed to improve the assembly accuracy of deployable arms. Finally, the feasibility of non-tandem assembly process strategy is verified by assembly experiment.

The experiential results show that the assembly errors are reduced compared with the tandem assembly process. The errors on axes x, y and z directions decreased from 14.1009 mm, 14.2424 mm and 0.8414 mm to 0.922 mm, 0.671 mm and 0.2393 mm, respectively. The errors round axes x and y directions also decreased from 0.0050° and 0.0053° to 0.00292° and 0.00251°, respectively.

This paper presents an assembly accuracy analysis model of deployable arms and applies the model to calculate assembly errors in tandem assembly process. In addition, a non-tandem assembly process is proposed based on the model. The experimental results show that the non-tandem assembly process can improve the assembly accuracy of deployable arms.

Over the decades, simultaneous localization and mapping (SLAM) techniques have been extensively researched and applied in robotic mapping. In complex environments, SLAM systems using a single sensor, such as a camera or light detection and ranging (LiDAR), often cannot meet the accuracy and map consistency requirements. This study aims to propose a tightly-coupled LiDAR-inertial SLAM system, which aims to achieve higher accuracy and map consistency for robotic mapping in complex environments.

This paper presents TC-Mapper, a tightly coupled LiDAR-inertial SLAM system based on LIO-SAM. The authors introduce the normal distribution-based loop closure detection method to the original one (i.e. the radius search-based method), which can enhance the accuracy and map consistency for robotic mapping. To further suppress map drift in complex environments, this paper incorporates a gravity factor into the original factor graph. In addition, TC-Mapper introduces incremental voxels (iVox) as the point cloud spatial data structure.

Extensive experiments in public and self-collected data sets demonstrate that TC-Mapper has high accuracy and map consistency.

TC-Mapper has two types of loop closure detections: the normal distribution-based method for correcting large drifts and the radius search-based method for fine-stitching, which can achieve higher accuracy and map consistency. The authors introduce iVox as the point cloud spatial data structure, which strives to attain a balance between precision and efficiency to the greatest extent feasible.

Value co-creation (VCC) helps platforms establish competitive advantages. Unlike their traditional counterparts, social attribute is a key concept of social e-commerce platforms. This study integrates VCC and social network theories, introduces relational embeddedness and divides this variable into economic and social relational embeddedness to explore its impact on VCC intention. This study also explores the mediating and moderating roles of customers' psychological ownership (CPO) and regulatory focus, respectively.

A questionnaire survey was conducted among users of mainstream social e-commerce platforms in China, and the relationship among the variables was revealed through a structural equation modeling of 464 valid responses.

The dimensions of relational embeddedness positively affect CPO and VCC intention, with social relational embeddedness exerting the strongest effect. CPO positively affects VCC intention and partially mediates the relationship between relational embeddedness and VCC intention. Promotion and prevention focus positively and negatively moderate the relationship between CPO and VCC intention, respectively.

This study expands the VCC research perspective and links the VCC concepts to social network dynamics. From the relational embeddedness perspective, this study identifies the type and intensity of relational embeddedness that promotes users' VCC intention and contributes to theoretical research on VCC and relational embeddedness. This study also introduces CPO as an intermediary variable, thus opening the black box of this mechanism, and confirms the moderating role of regulatory focus as the key psychological factor motivating users' VCC intention.