Superhydrophobic pillar arrays, which can generate the droplet pancake bouncing phenomenon with reduced liquid-solid contact time, have huge application prospects in anti-icing of aircraft wings from freezing rain. However, the previously reported pillar arrays, suitable for obtaining pancake bouncing, have a diameter ≤100 μm and height-diameter ratio >10, which are difficult to fabricate over a large area. Here, we have systematically studied the influence of the dimension of the superhydrophobic pillar arrays on the bouncing dynamics of water droplets. We show that the typical pancake bouncing with 57.8% reduction in contact time with the surface was observed on the superhydrophobic pillar arrays with 1.05 mm diameter, 0.8 mm height, and 0.25 mm space. Such pillar arrays with millimeter diameter and <1 height-diameter ratio can be easily fabricated over large areas. Further, a simple replication-spraying method was developed for the large-area fabrication of the superhydrophobic pillar arrays to induce pancake bouncing. No sacrificial layer was needed to reduce the adhesion in the replication processes. Since the bouncing dynamics were rather sensitive to the space between the pillars, a method to control the contact time, bouncing shape, horizontal bouncing direction, and reversible switch between pancake bouncing and conventional bouncing was realized by adjusting the inclination angle of the shape memory polymer pillars.

Key performance indicators (KPIs) are critical for manufacturing operation management and continuous improvement (CI). In modern manufacturing systems, KPIs are defined as a set of metrics to reflect operation performance, such as efficiency, throughput, availability, from productivity, quality and maintenance perspectives. Through continuous monitoring and measurement of KPIs, meaningful quantification and identification of different aspects of operation activities can be obtained, which enable and direct CI efforts. A set of 34 KPIs has been introduced in ISO 22400. However, the KPIs in a manufacturing system are not independent, and they may have intrinsic mutual relationships. The goal of this paper is to introduce a multi-level structure for identification and analysis of KPIs and their intrinsic relationships in production systems. Specifically, through such a hierarchical structure, we define and layer KPIs into levels of basic KPIs, comprehensive KPIs and their supporting metrics, and use it to investigate the relationships and dependencies between KPIs. Such a study can provide a useful tool for manufacturing engineers and managers to measure and utilize KPIs for CI.

A badminton training system based on body sensor networks has been proposed. The system may recognize different badminton strokes of badminton players. A two-layer hidden Markov model (HMM) classification algorithm is proposed to recognize 14 types of badminton strokes. In the first layer, we use acceleration magnitude of the right wrist to determine a threshold to detect strokes, and then, the HMM is applied to filter out nonstroke motions. In the second layer, we adopt the HMM to classify all the strokes into 14 categories. Experimental results show that the two-layer HMM can achieve good recognition accuracy. The effectiveness and feasibility of the two-layer HMM classification algorithm have been verified in a comparison. Index Terms-Badminton stroke recognition, body sensor networks (BSN), hidden Markov model (HMM).

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