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Developing an enabling bureaucratic structure for school organization has been an important aim of education governance reforms in China, like many societies across the globe, since the 1990s. However, there is a lack of valid measures to investigate the extent to which the Chinese education governance reforms facilitate the development of the enabling structure of school bureaucracy and examine the antecedents and consequences of enabling school bureaucracy. Thus, the study was conducted to validate the Chinese version of the Enabling Structure Scale (ESS-Ch), which is used to assess school bureaucracy in China.

The study surveyed 1,146 teachers enrolled in professional development courses provided by a Beijing university. The validation process involved two phases. In the first phase, the sample was divided into three subgroups for exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and cross-validation. In the second phase, reliability and validity were assessed utilizing the entire sample.

It indicated a four-factor model of the ESS-Ch: enabling formalization, coercive formalization, enabling centralization and hindering centralization. Factor loadings ranged from 0.72 to 0.88, composite reliabilities ranged from 0.82 to 0.95 and values of average variance extracted ranged from 0.61 to 0.80.

The study contributes to the international literature by validating the ESS-Ch so as to provide a standard measure that can be applied in comparative studies on enabling school bureaucracy between Chinese and Western cultures.

The study is original by validating the ESS-Ch based on a sample of 1,146 teachers in China.

Entrepreneurship educators have recently employed various computer- and game-based teaching methods to develop students’ entrepreneurship knowledge and competencies. However, our understanding of the learning outcomes (LOs) of such methods for students and specifically gamification teaching techniques is fragmented and underdeveloped. This chapter aimed to narrow the gap by systematically analyzing the peer-reviewed empirical studies on gamification and students’ entrepreneurship LOs (ELOs).

This study employed the systematic literature review method to examine the papers on the intersection between gamification and entrepreneurship education (EE). Some of 80 papers were retrieved from Google Scholar, Web of Science and Scopus databases and 16 papers were included in the final analysis. The papers were analyzed based on the key LOs that teaching entrepreneurship using gamification have for students.

This study found limited literature on the interrelationship between gamification and students’ ELOs. The majority of these studies suggested a positive association between gamification and students’ ELOs. These ELOs were classified into four key groups including cognitive, behavioral, social/interpersonal and skill-based LOs. This analysis explored the huge gap in empirical studies on the impact of gamification on students’ ELOs.

This exploratory study is limited to the systematic review of the empirical researches published in scientific journals. Of the numerous game-based and simulation teaching methods, this systematic analysis focused on gamification and its effects on cultivating entrepreneurial knowledge and competencies in students. Future studies should include published and unpublished papers in other sources (such as books, book chapters, working papers and theses) and other types of technology-based entrepreneurship teaching methods.

Educators and computer-based game designers may use the findings of this study to improve the effectiveness of gamified EE and training programs by connecting the objectives and content of the programs to students’ ELOs and examining if the programs create the intended ELOs in students.

This chapter is one of the first attempts that examines students’ LOs of gamification in EE. This chapter contributes to the limited validated knowledge and understanding of the impact of gamification on ELOs of students.

This paper takes Chinese A-share listed companies from 2007 to 2021 as research samples to investigate the influence of supplier concentration on debt maturity structure and its mechanism. It further analyzes whether the relationship between the two is different in the case of different monetary policies, collateral assets, and total debt. The research conclusion is of practical significance for enterprises to construct a balanced debt maturity structure and prevent financial risks.

This paper adopts the empirical research method. The data came from the CSMAR database, which eliminated ST and *ST and companies with missing data, resulting in a sample of 20,328. Stata16 was used for statistical analysis.

There is an inverted U-shaped relationship between supplier concentration and debt maturity structure, and market position and trade credit play an intermediary role. In the case of tight monetary policy, fewer collateral assets, and higher total debt, the inverse U-shaped relationship is more significant.

This paper examines the relationship between supplier concentration and debt maturity structure from a non-linear perspective for the first time, providing theoretical support for enterprises to form a reasonable debt structure, and deepening the theoretical cognition of the relationship between supplier concentration and corporate debt maturity structure.

This paper aims to propose a real-time augmented reality (AR)-based assembly assistance system using a coarse-to-fine marker-less tracking strategy. The system automatically adapts to tracking requirement when the topological structure of the assembly changes after each assembly step.

The prototype system’s process can be divided into two stages: the offline preparation stage and online execution stage. In the offline preparation stage, planning results (assembly sequence, parts position, rotation, etc.) and image features [gradient and oriented FAST and rotated BRIEF (ORB)features] are extracted automatically from the assembly planning process. In the online execution stage, too, image features are extracted and matched with those generated offline to compute the camera pose, and planning results stored in XML files are parsed to generate the assembly instructions for manipulators. In the prototype system, the working range of template matching algorithm, LINE-MOD, is first extended by using depth information; then, a fast and robust marker-less tracker that combines the modified LINE-MOD algorithm and ORB tracker is designed to update the camera pose continuously. Furthermore, to track the camera pose stably, a tracking strategy according to the characteristic of assembly is presented herein.

The tracking accuracy and time of the proposed marker-less tracking approach were evaluated, and the results showed that the tracking method could run at 30 fps and the position and pose tracking accuracy was slightly superior to ARToolKit.

The main contributions of this work are as follows: First, the authors present a coarse-to-fine marker-less tracking method that uses modified state-of-the-art template matching algorithm, LINE-MOD, to find the coarse camera pose. Then, a feature point tracker ORB is activated to calculate the accurate camera pose. The whole tracking pipeline needs, on average, 24.35 ms for each frame, which can satisfy the real-time requirement for AR assembly. On basis of this algorithm, the authors present a generic tracking strategy according to the characteristics of the assembly and develop a generic AR-based assembly assistance platform. Second, the authors present a feature point mismatch-eliminating rule based on the orientation vector. By obtaining stable matching feature points, the proposed system can achieve accurate tracking results. The evaluation of the camera position and pose tracking accuracy result show that the study’s method is slightly superior to ARToolKit markers.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

The purpose of this study is to develop a cylindrical capacitive sensor that has the advantages of high resolution, small size and designability and can be easily installed on lubricant pipeline to monitor lubricant oil debris.

A theoretical model of the cylindrical capacitive sensor is presented to analyze several parameters’ effectiveness on the performance of sensor. Numerical simulations are then conducted to determine the optimal parameters for preliminary experiments. Experiments are finally carried out to demonstrate the detectability of developed capacitive sensors.

It is clear from experimental results that the developed capacitive sensor can monitor the debris in lubricant oil well, and the capacitance values increase almost linearly when the number and size of debris increase.

There is lot of further work to do to apply the presented method into the application. Especially, it is necessary to consider several factors’ influence on monitoring results. These factors include the flow rate of the lubricant oil, the temperature, the debris distribution and the vibration. Moreover, future work should consider the influence of the oil degradation to the capacitance change and other contaminations (e.g. water and dust).

This work conducts a feasibility study on application of capacitive sensing principle for detecting debris in aero engine lubricant oil.

The novelty of the presented capacitance sensor can be summarized into two aspects. One is that the sensor structure is simple and characterized by two coaxial cylinders as electrodes, while conventional capacitive sensors are composed of two parallel plates as electrodes. The other is that sensing mechanism and physical model of the presented sensor is verified and validated by the simulation and experiment.

This study highlights the demand for low-cost and high accuracy products through the design and development of new 3D printing technologies. Besides, significant progress has been made in this field. A comparative study helps to understand the latest development in materials and future prospect of this technology.

Nevertheless, a large amount of progress still remains to be made. While some of the works have focused on the performances of the materials, the rest have focused on the development of new methods and techniques in additive manufacturing.

This paper critically evaluates the current 3D printing technologies, including the development and optimizations made to the printing methods, as well as the printed objects. Meanwhile, previous developments in this area and contributions to the modern trend in manufacturing technology are summarized briefly.

The paper can be summarized in three sections. Firstly, the existing printing methods along with the frequently used printing materials, as well as the processing parameters, and the factors which influence the quality and mechanical performances of the printed objects are discussed. Secondly, the optimization techniques, such as topology, shape, structure and mechanical property, are described. Thirdly, the latest development and applications of additive manufacturing are depicted, and the scope of future research in the relevant area is put forward.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This paper aims to address the problem of uncertain product quality in digital light processing (DLP) three-dimensional (3D) printing, a scheme is proposed to qualitatively estimate whether a layer is printed with the qualified quality or not cured .

A thermochromic pigment whose color fades at 45°C is prepared as the indicator and it is mixed with the resin. A visual surveillance framework is proposed to monitor the visual variation in a period of the entire curing process. The exposure region is divided into 30 × 30 sub-regions; gray-level variation curves (curing curves) in all sub-regions are classified as normal or abnormal and a corresponding printing control strategy is designed to improve the percentage of qualified printed objects.

The temperature variation caused by the releasing reaction heat on the exposure surface is consistent in different regions under the homogenized light intensity. The temperature in depth begins to rise at different times. The temperature in the regions near the light source rises earlier, and that far from the light source rises later. Thus, the color of resin mixed with the thermochromic pigment fades gradually over a period of the entire solidification process. The color variation in the regions with defects of bubbles, insufficient material filling, etc., is much different from that in the normal curing regions.

A temperature-sensitive organic chromatic chemical pigment is prepared to present the visual variation over a period of the entire curing process. A novel 3D printing scheme with visual surveillance is proposed to monitor the layer-wise curing quality and to timely stop the possible unqualified printing resulted from bubbles, insufficient material filling, etc.

In recent years, additive manufacturing (AM) technology has been acknowledged as an efficient method for producing geometrical complex objects with a wide range of applications. However, dimensional inaccuracies and presence of defects hinder the broad adaption of AM procedures. These factors arouse concerns regarding the quality of the products produced with AM and the utilization of quality control (QC) techniques constitutes a must to further support this emerging technology. This paper aims to assist researchers to obtain a clear sight of what are the trends and what has been inspected so far concerning non-destructive testing (NDT) QC methods in AM.

In this paper, a survey on research advances on non-destructive QC procedures used in AM technology has been conducted. The paper is organized as follows: Section 2 discusses the existing NDT methods applied for the examination of the feedstock material, i.e. incoming quality control (IQC). Section 3 outlines the inspection methods for in situ QC, while Section 4 presents the methods of NDT applied after the manufacturing process i.e. outgoing QC methods. In Section 5, statistical QC methods used in AM technologies are documented. Future trends and challenges are included in Section 6 and conclusions are drawn in Section 7.

The primary scope of the study is to present the available and reliable NDT methods applied in every AM technology and all stages of the process. Most of the developed techniques so far are concentrated mainly in the inspection of the manufactured part during and post the AM process, compared to prior to the procedure. Moreover, material extrusion, direct energy deposition and powder bed processes are the focal points of the research in NDT methods applied in AM.

This literature review paper is the first to collect the latest and the most compatible techniques to evaluate the quality of parts produced by the main AM processes prior, during and after the manufacturing procedure.