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The purpose of this paper is to analyze the detailed content and research framework of a knowledge management audit, from the view of operation flow based on a cognition summary of knowledge management audit proposed by many scholars and organizations, in order to put forward some references for effective implementation of an organization knowledge management project.

The paper carries out a multi‐perspective analysis of the cognition of knowledge management audit summarizes the similarities and differences between the different viewpoints, also a contrasting analysis on the method proposed by various scholars and organizations from the point of view of flow, and then constructs the conceptive framework of knowledge management audit.

Knowledge management audit is the first important step in a knowledge management project and includes four phases: preparation, analysis, implementation, and summary. Its primary content involves knowledge management environment audit, knowledge property audit, knowledge management ability audit and knowledge management performance audit, and its main modules consist of knowledge demand analysis, knowledge inventory analysis, knowledge map and knowledge flow analysis.

The paper provides a theoretical construction, but has not conducted a case study. In future research development, the framework of the paper will be improved through the case analysis of organization knowledge management practice.

By utilizing the research framework and method of the paper, an organization may understand rapidly the basic content of a knowledge management audit, implement an effective knowledge management audit to help improve organization (including non‐profit organization) performance and implement a knowledge management strategy.

The paper provides a framework for implementing a knowledge management audit.

With the analysis of the causes of corruption, this study aims to investigate specific anti-corruption measures that can be implemented to reform the political system and the social climate of China.

This study examines 97 severe corruption cases of high-ranking officials in China, which occurred between 2012 and 2015. As this insinuates that both institutional and social corruption are major problems in China, the analysis delves into multiple facts of corruption, including different types, four primary underlying causes, and suggestions regarding the implementation of three significant governmental shifts that focus on investigation, prevention tactics and legal regulations.

China’s corruption is not only individual-based but also it has developed into institutional corruption and social corruption. Besides human nature and instinct, the causes of corruption can be organised into four categories, namely, social customs, social transitions, institutional designs and institutional operations. For the removed high-ranking officials, the formation of interest chains was an important underlying cause behind their corruption.

This study makes a significant contribution to the literature because this study provides a well-rounded approach to a complex issue by highlighting the significance of democracy and the rule of law as ways to regulate human behaviour to combat future corruption.

The purpose of this paper is to apply what can be learned from the emergence of nature tourism to understand some current and future trends of tourism.

This study adopted the evolutionary paradigm for investigation.

The emergence of nature tourism in early medieval China can be attributed to four major factors, including transformation of value orientations, seeking longevity, interest in suburbs and population migration.

Historical studies help understand the current and future trends. When the contributing factors for nature tourism are linked to the contemporary world, it can be found that these factors are still playing a part in shaping tourism trends or patterns in their original or alternative forms. These trends or patterns are worthy of scholarly investigations.

This paper offers a comprehensive understanding of the origins of nature tourism.

The purpose of this paper is to develop a dual peg-in-hole insertion strategy. Dual peg-in-hole insertion is the most common task in manufacturing. Most of the previous work develop the insertion strategy in a two- or three-dimensional space, in which they suppose the initial yaw angle is zero and only concern the roll and pitch angles. However, in some case, the yaw angle could not be ignored due to the pose uncertainty of the peg on the gripper. Therefore, there is a need to design the insertion strategy in a higher-dimensional configuration space.

In this paper, the authors handle the insertion problem by converting it into several sub-problems based on the attractive region formed by the constraints. The existence of the attractive region in the high-dimensional configuration space is first discussed. Then, the construction of the high-dimensional attractive region with its sub-attractive region in the low-dimensional space is proposed. Therefore, the robotic insertion strategy can be designed in the subspace to eliminate some uncertainties between the dual pegs and dual holes.

Dual peg-in-hole insertion is realized without using of force sensors. The proposed strategy is also used to demonstrate the precision dual peg-in-hole insertion, where the clearance between the dual-peg and dual-hole is about 0.02 mm.

The sensor-less insertion strategy will not increase the cost of the assembly system and also can be used in the dual peg-in-hole insertion.

The theoretical and experimental analyses for dual peg-in-hole insertion are proposed without using of force sensor.

To present a custom design and fabrication method for a novel hemi‐knee joint substitute composed of titanium alloy and porous bioceramics based on rapid prototyping (RP) and rapid tooling (RT) techniques.

The three‐dimensional (3D) freeform model of a femur bone was reconstructed based on computerized tomography images via reverse engineering and the 3D reconstruction accuracy was evaluated. The negative image of artificial bone was designed with interconnected microstructures (250‐300 μm). The epoxy resin mould of a hemi‐knee joint and the negative pattern of an artificial bone were fabricated on Stereolithography apparatus. Based on these moulds, a titanium‐alloy hemi‐knee joint and a porous‐bioceramic artificial bone were created by quick casting and powder sintering (known as RT) techniques, respectively. After assembling, a composite hemi‐knee joint substitute was obtained.

The 3D reconstructed freeform model of the femur bone conformed to the original anatomy within a maximum deviation 0.206 mm. The sintered artificial bone had interconnected micropores (250 μm) and microchannels (300 μm). After implanting in vivo, the composite hemi‐knee joint substitute matched well with the surrounding tissues and bones with sufficient mechanical strength.

Further in‐vivo research is needed to provide the evidence for tissue growth into the ceramic structures and long‐term viability and stability of the implant.

This method enhances the versatility of using RP in the fabrication of tissue‐engineered substitutes, especially when individual matching is considered. Although this paper took a customized hemi‐knee joint substitute as an example, it is capable of fabricating other artificial substitutes with a variety of biomaterials.

This paper aims to propose a deep learning model that can be used to expand the number of samples. In the process of manufacturing and assembling electronic components on the printed circuit board in the surface mount technology production line, it is relatively easy to collect non-defective samples, but it is difficult to collect defective samples within a certain period of time. Therefore, the number of non-defective components is much greater than the number of defective components. In the process of training the defect detection method of electronic components based on deep learning, a large number of defective and non-defective samples need to be input at the same time.

To obtain enough electronic components samples required for training, a method based on the generative adversarial network (GAN) to generate training samples is proposed, and then the generated samples and real samples are used to train the convolutional neural networks (CNN) together to obtain the best detection results.

The experimental results show that the defect recognition method using GAN and CNN can not only expand the sample images of the electronic components required for the training model but also accurately classify the defect types.

To solve the problem of unbalanced sample types in component inspection, a GAN-based method is proposed to generate different types of training component samples and then the generated samples and real samples are used to train the CNN together to obtain the best detection results.

Conservation of green space is crucial to the sustainable urban development of China. Nanjing is a central city in the Yangtze River Delta cluster in eastern China. Owing to urbanization, the green spaces in this city have been affected significantly in terms of their scale and structure. Assessing such changes can provide a basis for environmental protection measures and sustainable development. Using Landsat remote-sensing image data from 1998 and 2007, we constructed a distribution map of the patches of green spaces in Nanjing, and analyzed and assessed the changes in scale and spatial layout of the green spaces using various landscape metrics including CA, PLAND, PD, MPS, LPI, NP, PD, TE, ED, MPI, and LSI. Coinciding with a sharp increase in urban population, the area of green spaces in Nanjing decreased by 27.06% between 1998 and 2007. The patches of green space increased by 39%, and the average area per patch decreased by about 47.52%. Green space patches were found to be unevenly distributed among the districts of Nanjing. In urban areas, significant numbers of such patches were found primarily in various types of parks, while in suburban areas, they were concentrated mainly in the hilly forestlands. Green spaces exhibited tendencies towards fragmentation, miniaturization, and discretization.

The purpose of this paper is to fabricate and characterize osteochondral beta‐tricalcium phosphate/collagen scaffold with bio‐inspired design by ceramic stereolithography (CSL) and gel casting.

Histological analysis was applied to explore the morphological characteristics of the transitional structure between the bone and the cartilage. The acquired data were used to design biomimetic biphasic scaffolds, which include the bone phase, cartilage phase, and their transitional structure. The engineered scaffolds were fabricated from β‐TCP‐collagen by CSL and gel casting. The cartilage phase was added to the ceramic phase by gel‐casting and freeze drying.

The resulting ceramic scaffolds were composed of a bone phase with the following properties: 700‐900 μm pore size, 200‐500 μm interconnected pores size, 50‐65 percent porosity, fully interconnected, ∼12 Mpa compressive strength. A suitable binding force between cartilage phase and ceramic phase was achieved by physical locking that was created by the biomimetic transitional structure. Cellular evaluation showed satisfactory results.

This study is the first try to apply CSL to fabricate biological implants with β‐TCP and type‐I collagen. There are still some defects in the composition of the slurry and the fabrication process.

This strategy of osteochondral scaffold fabrication can be implemented to construct an osteochondral complex that is similar to native tissue.

The CSL technique is highly accurate, as well as biologically secure, when fabricating ceramic tissue engineering scaffolds and may be a promising method to construct hard tissue with delicate structures. The present strategy enhances the versatility of scaffold fabrication by RP.

Rare earths are essential materials for many high-tech industries critical to both economic development and national defense. China, the world's dominant supplier of rare earths, has recently been imposing stricter controls over its production and export. The purpose of this paper is to examine the domestic roots of the changes in China's rare earth industry production and exports in its three-decade rise to the current global monopoly.

This paper adopts the historical institutionalism approach to analyze the trajectory of industry and trade development. The author analyzes data collected from government whitepapers and reputed scholarly and news sources.

This paper argues that the Chinese rare earth industry has gone through three periods of development, in which the state attempted to control the market and industry through reformulating rules and institutions to achieve state goals. Domestic state institutions, combined with macroeconomic environment and state governance strategy shaped the three-decade experience of rare earth industry and trade development in China.

This paper builds on existing findings about Chinese state regulations to provide a novel analytical framework to analyze the role of the state in industry and trade development in the rare earth industry. The focus on a single strategic industry seldom studied in the current literature also provides ample empirical value to further scholarly understanding about this industry.

This paper aims to explore the relationship between the characteristics of social media health information and its adoption. The purpose is to identify information characteristics that can be used to estimate the level of health information adoption in advance.

According to the Information Adoption Model (IAM), the study extracted ten information characteristics from the aspects of information quality and information source credibility. The sample data was collected from the top ten influential health accounts based on the Impact List of Sina Weibo to test the effectiveness of these characteristics in distinguishing information at different levels of adoption. The forecasting of information adoption level is regarded as a binary classification question in the study and support vector machine (SVM) is used to do the research.

The results indicate that ten information characteristics chosen in this study are related to information adoption. Based on these information characteristics, it is feasible to estimate the level of health information adoption, and the estimation accuracy is relatively high.

A lot of work has been done in previous researches to reveal the factors that influence information adoption. The theoretical contribution of this work is to further discuss how to use the influencing factors to do some predictive work for information adoption. In practice, it will help health information publishers to disseminate high-quality health information more effectively as well as promote the adoption of health information.