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Three different polyethylene/polypropylene (PE/PP) blends were microcellular foamed and their crystallinities and melt strengths were investigated. The relationship between crystallinity, melt strength, and cellular structure was studied. Experimental results showed that the three blends had similar variation patterns in respect of crystallinity, melt strength, and cellular structure, and these variation patterns were correlative for each blend. For all blends, the melt strength and PP melting point initially heightened and then lowered, the PP crystallinity first decreased, and then increased as the PE content increased. At PE content of 30%, the melt strength and PP melting point were highest and the PP crystallinity was least. The blend with lower PP crystallinity and higher melt strength had better cellular structure and broader microcellular foaming temperature range. So, three blends had best cellular structure at PE content of 30%. Furthermore, when compared with PE/homopolymer (hPP) blend, the PE/copolymer PP (cPP) blend had higher melt strength, better cellular structure, and wider microcellular foaming temperature range, so it was more suited to be microcellular foamed. Whereas LDPE/cPP blend had the broadest microcellular foaming temperature range because of its highest melt strength within three blends.

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Activity recognition has received considerable attention in many research fields, such as industrial and healthcare fields. However, many researches about activity recognition have focused on static activities and dynamic activities in current literature, while, the transitional activities, such as stand-to-sit and sit-to-stand, are more difficult to recognize than both of them. Consider that it may be important in real applications. Thus, a novel framework is proposed in this paper to recognize static activities, dynamic activities, and transitional activities by utilizing stacked denoising autoencoders (SDAE), which is able to extract features automatically as a deep learning model rather than utilize manual features extracted by conventional machine learning methods. Moreover, the resampling technique (random oversampling) is used to improve problem of unbalanced samples due to relatively short duration characteristic of transitional activity. The experiment protocol is designed to collect twelve daily activities (three types) by using wearable sensors from 10 adults in smart lab of Ulster University, the experiment results show the significant performance on transitional activity recognition and achieve the overall accuracy of 94.88% on three types of activities. The results obtained by comparing with other methods and performances on other three public datasets verify the feasibility and priority of our framework. This paper also explores the effect of multiple sensors (accelerometer and gyroscope) to determine the optimal combination for activity recognition.

In the traditional agricultural wireless sensor networks (WSNs), there is a large amount of redundant data and high latency on critical events (CEs) for data collection systems, which increases the time and energy consumption. In order to overcome these problems, an effective edge computing (EC) enabled data collection approach for CE in smart agriculture is proposed. First, the key features data types (KFDTs) are extracted from the historical dataset to keep the main information on CEs. Next, the KFDTs are selected as the collection data type of the software-defined wireless sensor network (SDWSN). Then, the event types are decided by searching the minimum average variance between the sensing data of active nodes and the average value of the key feature data obtained by EC. Furthermore, the sensing nodes are driven to sense the event-related data with a consideration of latency constraints by the SDWSN servers. A real-world testbed was set up in a smart greenhouse for experimental verification of the proposed approach. The results showed that the proposed approach could reduce the number of needed sensors, sensing time, collection data volume, communication time, and provide the low latency agricultural data collection system. Thus, the proposed approach can improve the efficiency of CE sensing in smart agriculture.