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The purpose of this paper is, based on leisure constraints and means-end theories, to identify the e-leisure constraints of using the video-sharing websites/apps; demonstrate how means-end theory can be used to reveal the differences between high- and low-leisure constraints in an e-leisure environment; and provide designers and marketers with valuable insights for developing e-leisure products and e-marketing strategies.

Both qualitative and quantitative approaches are employed to collect data. By eliminating three participants whose age range did not meet our criterion (15 to 24 years old), 57 one-on-one in-depth interviews were then content analyzed to design the survey questionnaire. A total of 514 valid samples were collected for hierarchical value map (HVM) construction.

By comparing the full HVM vs the e-leisure constraints HVM, the analytical results indicate that the importance of attributes, consequences and values for the young people using video-sharing websites/apps is quite different. “Unable to resume the video after leaving the screen,” “creating playlist,” “providing movies” and “location restrictions” are extremely important features that influence the willingness of such users with high e-leisure constraints to participate in e-leisure activities. By understanding the differences between these two HVMs, it is possible to provide marketers or designers with valuable insights for website/app design and marketing strategies.

This study only focused on young people’s perceptions of video-sharing websites/apps, so the findings are limited to those aged between 15 and 24 years old. Since managers today are challenged to design effective strategies that can meet target users’ demands across different ages with different economic, social and sub-cultural groups, future research may consider gathering a wider age range of respondents in order to obtain more robust results.

This is the first paper integrating leisure constraints theory and means-end theory to understand young people’s cognitive structure of using video-sharing websites/apps, especially when they encounter e-leisure constraints.

The purpose of this study was to explore the impact of the formation of industrial clusters on the obtainment of professional human resources, to verify the impact of human resources on clustering relationships and firm’s performance and to understand whether the formation of clusters can contribute to the obtainment of professional human resources and the improvement of competitiveness of enterprises. It was expected that solutions could be found to make new contributions through the verification of special economic zones (SEZs).

Using manufacturers in Taiwan’s SEZs as the subjects, this study explored the impact on the obtainment of professional human resources after the formation of industrial clusters in SEZs, through conducting and empirical study with a questionnaire survey.

The professional human resources are the essential factor for the formation of industrial clusters and the improvement of competitiveness. This study also confirmed that industries can have professional human resources by industrial clustering and that this will produce a positive impact on the enterprise clustering relationships, which can also have a positive impact on firm’s performance and can enhance the enterprise’s competitive advantage.

Industrial clustering is the key factor to attract professional human resources; industrial clusters can enhance firm’s performance; and professional human resources affect firm’s performance of enterprises.

No study has discussed the topic of clusters from the perspective of SEZs also including six export processing zone (EPZ) parks in Taiwan. This study discussed the topic using theories relating to clustering and human resources. The formation of industrial clusters can result in higher competitiveness in the face of the global market. The EPZ industrial cluster provides an excellent investment environment. Coupled with one-stop express services and geographic advantage, the land-use rate is up to 97 per cent and the per hectare output value amounts to NTD 3.2 billion, setting a successful example of an industrial cluster.

A mathematical model to predict the layered process error and an optimization algorithm to define the fabricating orientation based on the minimum process error for layered manufacturing fabrication has been developed. Case studies to determine the preferred orientation candidates for fabricating spherical objects, cube objects and objects with irregular geometrical shapes have been conducted and the results were used to validate the sensitivity, accuracy, and capability of the developed model and optimization algorithm. Different orientation candidates determined by minimum processing error and by minimum processing time were also compared. The developed model and the optimization algorithm can be used, in conjunction with other processing parameters such as processing time and support structure, to define an optimal processing planning for layered manufacturing fabrication.

A mathematical model to describe the principle of layered manufacturing and layered fabrication error is presented in this paper. In this model, the layered manufacturing process is characterized by the model decomposition and material accumulation. A 3D design model is represented by a set of points with sequence functions to correlate the layered processing information. Iso‐sequence planes are defined as the processing layers to collect points with the same processing sequence and to define the material accumulation along its gradient direction. Examples of using the proposed model to describe the layered manufacturing to process flat and no‐flat surfaces and the description of the layered processing error are also presented.

This paper aims to study the effect of processing parameters and the fundamental mechanism of surface morphologies during electron beam selective melting.

From the powder-scale level, first, the discrete element method is used to obtain the powder bed distribution that is comparable with the practical condition; then, the finite volume method is used to simulate the particle melting and flowing process. A physically reliable energy distribution of the electron beam is applied and the volume of fluid method is coupled to capture the free boundary flow. Twelve sets of parameters grouped into three categories are examined, focusing on the effect of scan speed, input powder and energy density.

According to the results, both melting pool width and depth have a positive relation with the energy density, whereas the melting pool length is insensitive to the scan velocity change. The balling effect is attributed to either an insufficient energy input or the flow instability; the hump effect originates from the mismatch between electron beam moving and the fluid flow. The scan speed is a key parameter closely related to melting pool size and surface morphologies.

Through a number of case studies, this paper gives a comprehensive insight of the parameter effects and mechanisms of different surface morphologies, which helps to better control the manufacturing quality of electron beam selective melting.

This paper aims to investigate the collision avoidance problem for a mobile robot by constructing an artificial potential field (APF) based on geometrically modelling the obstacles with a new method named the obstacle envelope modelling (OEM).

The obstacles of arbitrary shapes are enveloped in OEM using the primitive, which is an ellipse in a two-dimensional plane or an ellipsoid in a three-dimensional space. As the surface details of obstacles are neglected elegantly in OEM, the workspace of a mobile robot is made simpler so as to increase the capability of APF in a clustered environment.

Further, a dipole is applied to the construction of APF produced by each obstacle, among which the positive pole pushes the robot away and the negative pole pulls the robot close.

As a whole, the dipole leads the robot to make a derivation around the obstacle smoothly, which greatly reduces the local minima and trajectory oscillations. Computer simulations are conducted to demonstrate the effectiveness of the proposed approach.

The purpose of this paper is to promote the efficiency of RGB-depth (RGB-D)-based object recognition in robot vision and find discriminative binary representations for RGB-D based objects.

To promote the efficiency of RGB-D-based object recognition in robot vision, this paper applies hashing methods to RGB-D-based object recognition by utilizing the approximate nearest neighbors (ANN) to vote for the final result. To improve the object recognition accuracy in robot vision, an “Encoding+Selection” binary representation generation pattern is proposed. “Encoding+Selection” pattern can generate more discriminative binary representations for RGB-D-based objects. Moreover, label information is utilized to enhance the discrimination of each bit, which guarantees that the most discriminative bits can be selected.

The experiment results validate that the ANN-based voting recognition method is more efficient and effective compared to traditional recognition method in RGB-D-based object recognition for robot vision. Moreover, the effectiveness of the proposed bit selection method is also validated to be effective.

Hashing learning is applied to RGB-D-based object recognition, which significantly promotes the recognition efficiency for robot vision while maintaining high recognition accuracy. Besides, the “Encoding+Selection” pattern is utilized in the process of binary encoding, which effectively enhances the discrimination of binary representations for objects.

This paper introduces a new subject called bio‐manufacturing. Bio‐manufacturing combines life science with manufacturing science, and uses manufacturing method to form materials with bio‐activity and bio‐degradability into scaffolds. In this paper, we discuss the hierarchy of bio‐manufacturing: the lower grade uses undegradable bio‐material to form permanent organ replacement such as auricular cartilage and the higher grade uses biodegradable bio‐materials to repair organ damage or organ replacement which degrades after embedded in the human body. They all adopt jetting/extrusion deposition process (fused deposition modelling or 3D printer), the distinct different point being the temperature of the forming chamber. The samples of bones and auricular cartilage produced by those processes had been practiced on dogs and rabbits, repaired their damage.

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.