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This study aims to develop magnetic field-controlled friction braking technology, the preparation process of hard magnetic brake friction material was optimized and analyzed in this paper.

NdFeB, a rigid magnetic material, was selected as additive. Magnetic field orientation, a part of material preparation, was added to the preparation process. Experiments investigated the tribological properties of each brake lining sample. The preparation process of the hard magnetic friction material was optimized based on fuzzy theory by using analytic hierarchy process (AHP) methods and SPSS software. The microscopic morphology and the distribution and content of elements of friction lining samples prepared with or without orientation excitation voltage were analyzed by scanning electron microscope and energy dispersive X-ray microanalysis.

The results showed that the tribological properties of brake lining samples could be improved by process optimization and the oriented excitation voltage can effectively improve the properties of the brake lining.

The magnetic field orientation was added into the traditional preparation process, and a set of process parameters with the best tribological properties were obtained through optimization. It is believed that this research will be of great theoretical and practical significance to develop both new brake materials and active control technology of the braking process in the future.

It is a challenge to print a model with the size that is larger than the working volume of a three-dimensional (3D) printer. The purpose of this paper is to present a feasible approach to divide a large model into small printing parts to fit the volume of a printer and then assemble these parts into the final model.

The proposed approach is based on the skeletonization and the minima rule. The skeleton of a printing model is first extracted using the mesh contraction and the principal component analysis. The 3D model is then partitioned preliminarily into many smaller parts using the space sweep method and the minima rule. The preliminary partition is finally optimized using the greedy algorithm.

The skeleton of a 3D model can effectively represent a simplified version of the geometry of the 3D model. Using a model’s skeleton to partition the model is an efficient way. As it is generally desirable to have segmentations at concave creases and seams, the cutting position should be located in the concave region. The proposed approach can partition large models effectively to well retain the integrity of meaningful parts.

The proposed approach is new in the rapid prototyping field using the model skeletonization and the minima rule. Based on the authors’ knowledge, there is no method that concerns the integrity of meaningful parts for partitioning. The proposed method can achieve satisfactory results by the integrity of meaningful parts and assemblability for most 3D models.

The purpose of this paper is to introduce a method of the bottleneck detection for Emergency Department (ED) improvement using benchmarking and design of experiments (DOE) in simulation model.

Four procedures of treatments are used to represent ED activities of the patient flow. Simulation modeling is applied as a cost-effective tool to analyze the ED operation. Benchmarking provides the achievable goal for the improvement. DOE speeds up the process of bottleneck search.

It is identified that the long waiting time is accumulated by previous arrival patients waiting for treatment in the ED. Comparing the processing time of each treatment procedure with the benchmark reveals that increasing the treatment time mainly happens in treatment in progress and emergency room holding (ERH) procedures. It also indicates that the to be admitted time caused by the transfer delay is a common case.

The current research is conducted in the ED only. Activities in the ERH require a close cooperation of several medical teams to complete patients’ condition evaluations. The current model may be extended to the related medical units to improve the model detail.

ED overcrowding is an increasingly significant public healthcare problem. Bottlenecks that affect ED overcrowding have to be detected to improve the patient flow.

Integration of benchmarking and DOE in simulation modeling proposed in this research shows the promise in time-saving for bottleneck detection of ED operations.

This study aims to explore the associations between leadership styles (i.e. transformational and ethical) and innovative work behavior within higher education institutions (HEIs), additionally investigating the mediating role of work engagement and the moderating role of psychological empowerment.

The basis of this study rested upon adopting a cross-sectional research framework. The data were acquired from a sample comprising 825 academic staff and 275 leaders across 226 HEIs in China. Employing a quantitative methodology, the researchers used AMOS version 26.0 and SPSS version 22.0 for statistical analysis.

The results indicated that leadership styles (i.e. transformational and ethical) positively affected innovative work behavior, both directly and indirectly (via work engagement). Also, psychological empowerment moderated the linkage between leadership styles and innovative work behavior but not the association between work engagement and innovative work behavior.

Despite some existing literature having discussed the correlation between leadership styles and innovative work behavior, there appears to be a conspicuous dearth of research endeavoring to explore the mediator (i.e. work engagement) and the moderator (i.e. psychological empowerment) within the above nexus, especially in the context of HEIs. Thus, this study can be considered original, introducing novel perspectives and substantial contributions to the management literature on HEIs. In addition, it offers insights for organizational managers.

The purpose of this paper is to quantify external and internal uncertainties in product design process. The research addresses the measure of product future changes.

Two methods are proposed to model and quantify uncertainty in the product life cycle. Changes of user preferences are considered as the external uncertainty. Changes stemming from dependencies between components are addressed as the internal uncertainty. Both methods use developed mechanisms to capture and treat changes of user preferences. An agent-based model is developed to simulate sociotechnical events in the product life cycle for the external uncertainty. An innovative application of Big Data Analytics (BDA) is proposed to evaluate the external and internal uncertainties in product design. The methods can identify the most affected product components under uncertainty.

The results show that the proposed method could identify product changes during its life cycle, particularly using the proposed BDA method.

It is essential for manufacturers in the competitive market to know their product changes under uncertainty. Proposed methods have potential to optimize design parameters in complex environments.

This research bridges the gap of literature in the accurate estimation of uncertainty. The research integrates the change prediction and change transferring, applies data management methods innovatively, and utilizes the proposed methods practically.