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Despite the widespread expectation that additive manufacturing (AM) will become a disruptive technology to transform the spare parts supply chain, very limited research has been devoted to the quantitative modeling and analysis on how AM could fulfill the on-demand spare parts supply. On the other hand, the choice of using AM as a spare parts supply strategy over traditional inventory is a rising decision faced by manufacturers and requires quantitative analysis for their AM-or-stock decisions. The purpose of this paper is to develop a quantitative performance model for a generic powder bed fusion AM system in a spare parts supply chain, thus providing insights into this less-explored area in the literature.

In this study, analysis based on a discrete event simulation was carried out for the use of AM in replacement of traditional warehouse inventory for an on-demand spare parts supply system. Generic powder bed fusion AM system was used in the model, and the same modeling approach could be applied to other types of AM processes. Using this model, the impact of both spare parts demand characteristics (e.g. part size attributes, demand rates) and the AM operations characteristics (e.g. machine size and postpone strategy) on the performance of using AM to supply spare parts was studied.

The simulation results show that in many cases the AM operation is not as cost competitive compared to the traditional warehouse-based spare parts supply operation, and that the spare parts size characteristics could significantly affect the overall performance of the AM operations. For some scenarios of the arrival process of spare parts demand, the use of the batched AM production could potentially result in significant delay in parts delivery, which necessitates further investigations of production optimization strategies.

The findings demonstrate that the proposed simulation tool can not only provide insights on the performance characteristics of using AM in the spare parts supply chain, especially in comparison to the traditional warehousing system, but also can be used toward decision making for both the AM manufacturers and the spare parts service providers.

Focusing on the problem that the visual detection algorithm of navigation path line in intelligent harvester robot is susceptible to interference and low accuracy, a navigation path detection algorithm based on improved random sampling consensus is proposed.

First, inverse perspective mapping was applied to the original images of rice or wheat to restore the three-dimensional spatial geometric relationship between rice or wheat rows. Second, set the target region and enhance the image to highlight the difference between harvested and unharvested rice or wheat regions. Median filter is used to remove the intercrop gap interference and improve the anti-interference ability of rice or wheat image segmentation. The third step is to apply the method of maximum variance to thresholding the rice or wheat images in the operation area. The image is further segmented with the single-point region growth, and the harvesting boundary corner is detected to improve the accuracy of the harvesting boundary recognition. Finally, fitting the harvesting boundary corner point as the navigation path line improves the real-time performance of crop image processing.

The experimental results demonstrate that the improved random sampling consensus with an average success rate of 94.6% has higher reliability than the least square method, probabilistic Hough and traditional random sampling consensus detection. It can extract the navigation line of the intelligent combine robot in real time at an average speed of 57.1 ms/frame.

In the precision agriculture technology, the accurate identification of the navigation path of the intelligent combine robot is the key to realize accurate positioning. In the vision navigation system of harvester, the extraction of navigation line is its core and key, which determines the speed and precision of navigation.

A strong relationship exists in many cultures between ethnic identity and educational success. This study was conducted at a teacher training university in Southwest China in 1997. It examines how ethnic minority students, through a series of micro-level interactions, construct “scholar selves” within their families, villages, and schools. The study also looks at how macro-level structural supports, built into the Chinese education system, help minority students overcome obstacles to academic success. These supports include special schools and classes for ethnic students, training teachers for nationality areas, financial support for minority education and additional points awarded on national examinations. The chapter suggests what scholars and practitioners might learn from an educational system that demonstrates the characteristics of flexibility, inclusiveness and cohesiveness.

The purpose of this paper is to investigate the effect of strut size on the compressive response for selective laser-melted lattice structure with a body-centered cubic (BCC) unit cell.

Theoretical analysis and numerical simulation were used to predict the compressive stiffness and strength of the lattice structures with different struts, and compression testing was conducted to validate the predicted results. The effect of strut size on actual porosity was determined with the dry weighting method. Scanning electron microscopy was used to observe the fracture morphologies.

The actual porosities in all the specimens turned out to be a little lower than the values expected from design. The maximum deviation appears at the strut size of 1.25 mm. The theoretical analysis reveals that the junctions of BCC unit cells are the most loaded points, and the maximum compression resistance load is proportional to the strut size. The stress–strain curves and collapse modes predicted by numerical simulation are in good agreement with the theoretical calculation and experimental results. The compression stress increases monotonously in strut size of 0.50–2.00 mm. The fracture morphologies reflect a transition from a mixed to ductile fracture mechanism. The lattice structure shows a stable plastic deformation without a destructive fracture for the strut size of 2.00 mm.

The findings of this study can provide theoretical and experimental support for the choice of strut size under different stress conditions. In addition, they are conductive to in-depth study of the compressive properties for lattice structures with different geometrical dimensions fabricated by selective laser melting.

To reduce the effect of parameter uncertainties and input saturation on path tracking control for autonomous combine harvester, a path tracking controller is proposed, which integrates an adaptive neural network estimator and a saturation-aided system.

First, to analyze and compensate the influence of external factors, the vehicle model is established combining a dynamic model and a kinematic model. Meanwhile, to make the model simple, a comprehensive error is used, weighting heading error and position error simultaneously. Second, an adaptive neural network estimator is presented to calculate uncertain parameters which eventually improve the dynamic model. Then, the path tracking controller based on the improved dynamic model is designed by using the backstepping method, and its stability is proved by the Lyapunov theorem. Third, to mitigate round-trip operation of the actuator due to input saturation, a saturation-aided variable is presented during the control design process.

To verify the tracking accuracy and environmental adaptability of the proposed controller, numerical simulations are carried out under three different cases, and field experiments are performed in harvesting wheat and paddy. The experimental results demonstrate the tracking errors of the proposed controller that are reduced by more than 28% with contrast to the conventional controllers.

An adaptive neural network-based path tracking control is proposed, which considers both parameter uncertainties and input saturation. As far as we know, this is the first time a path tracking controller is specifically designed for the combine harvester with full consideration of working characteristics.

This paper aims to analyse how both Lin’s birthplace identity and his Christian identity contributed to his fruitful public career and to ascertain which identity became the most significant.

Archival research is the main method used in this paper. The most important archives drawn from are the Daniel Tse Collection in the Special Collection and Archives of the Hong Kong Baptist University Library. Oral history has also been used in this paper to uncover more material that has not yet been discussed in existing scholarly works.

This paper argues that although Lin’s birthplace identity and social networks helped him to start his business career in Nam Pak Hong and develop into a leader in the local Chaozhou communities, these factors were insufficient to his becoming a respectable member of the Chinese elite in post-war Hong Kong. He became well known not because of his leading position in local Chaozhou communities or any great achievement he had obtained in business but because of his contribution to the development of Christian education. These achievements earned him a reputation as a “Christian educator”. Thus Lin’s Christian identity became more important than his birthplace identity in contributing to his successful public career.

This paper has value in showing how Christian influences interacted with various cultural factors in early Hong Kong. It also offers insights into Lin’s life and motivations as well as the history of the institutions he contributed to/founded. It not only furthers our understanding of the Chinese Christian business elite in early Hong Kong but also provides us with insights when further studying this group of people in other British colonies in Asia.

The purpose of this paper is to discuss how the rush of technological change will consolidate the worldwide reach of the internet with more capacity, specifically to control the physical world, including the machines, industrial facilities and frameworks that characterize cutting-edge technology.

The data were collected from 203 respondents predominantly from emerging economies, specifically India and SEA. Most of the participants are working professionals. Structural equation modelling was used to analyze data, as it is a popular statistical technique because of its ability to model selected independent variables and take into account all possible forms of measurement error to test an entire theory.

The Industrial Internet of Things (IIOT) platform comprises four fundamental capabilities: connectivity, big data, advanced analytics and application development. The IIOT has the potential to provide a high level of synergies between the 4 Ms of manufacturing, namely, man, machine, material and method.

The collected data are predominately from India and SEA (close to 75 per cent), while contributions from other regions are comparatively less, so the findings cannot be generalized to the global context.

It is in the interest of service providers to collaborate and provide a universal solution to retain legacy systems to minimize the investment and reduce the security threat, which could boost IIOT adoption while ensuring that manufacturers are able to leverage this new technology efficiently.

The framework obtained has good quality of validity and reliability indicators. Thus, an alternative framework has been added to customer expectation which is currently a popular topic in the technological changes.