Sentiment analysis on Chinese microblogs has received extensive attention recently. Most previous studies focus on identifying sentiment orientation by encoding as many word properties as possible while they fail to consider contextual features (e.g., the long-range dependencies of words), which are, however, essentially important in the sentiment analysis. In this paper, we propose a Chinese sentiment analysis method by incorporating a word2vec model and a stacked bidirectional long short-term memory (Stacked Bi-LSTM) model. We first employ the word2vec model to capture semantic features of words and transfer words into high-dimensional word vectors. We evaluate the performance of two typical word2vec models: continuous bag-of-words (CBOW) and skip-gram. We then use the Stacked Bi-LSTM model to conduct the feature extraction of sequential word vectors. We next apply a binary softmax classifier to predict the sentiment orientation by using semantic and contextual features. Moreover, we also conduct extensive experiments on the real dataset collected from Weibo (i.e., one of the most popular Chinese microblogs). The experimental results show that our proposed approach achieves better performance than other machine-learning models.INDEX TERMS Long short-term memory (LSTM), stacked bi-directional LSTM, sentiment analysis, continuous bag-of-words, Chinese microblog, contextual features.

Hydrogels as wound dressings have received great attention in recent years. It is highly important yet challenging to develop hydrogel dressings that are biocompatible and that can promote wound healing by lowering the risk of inflammatory responses. In this work, we designed and prepared zwitterionic dextran-based hydrogels using carboxybetaine dextran (CB-Dex) and sulfobetaine dextran (SB-Dex) as raw materials, respectively. The efficacy of CB-Dex and SB-Dex hydrogels in promoting wound recovery was evaluated using a mouse skin wound model. Results suggested that the zwitterionic dextran wound dressings showed a faster healing rate than natural dextran hydrogel and a commercial wound dressing (Duoderm film) due to their excellent protein resistance and capacity to scavenge free hydroxyl radicals. In addition, both CB-Dex and SB-Dex hydrogel wound dressings showed excellent cytocompatibility with NIH3T3 and L929 cells, as well as antibacterial adhesion against Staphylococcus aureus and Escherichia coli. Furthermore, both zwitterionic hydrogels demonstrated self-healing properties and can be stretched to adapt to irregular full-thickness wound beds. More importantly, they can be removed from the wound site painlessly by washing with normal saline. Overall, this work provided a new pathway to fabricate multifunctional polysaccharide hydrogels for wound treatment and pain relief when changing wound dressings.

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Cloud computing extends Transportation Cyber-Physical Systems (T-CPS) with provision of enhanced computing and storage capability via offloading computing tasks to remote cloud servers. However, cloud computing cannot fulfill the requirements such as low latency and context awareness in T-CPS. The appearance of Mobile Edge Computing (MEC) can overcome the limitations of cloud computing via offloading the computing tasks at edge servers in approximation to users, consequently reducing the latency and improving the context awareness. Although MEC has the potential in improving T-CPS, it is incapable of processing computational-intensive tasks such as deep learning algorithms due to the intrinsic storage and computing-capability constraints. Therefore, we design and develop a lightweight deep learning model to support MEC applications in T-CPS. In particular, we put forth a stacked convolutional neural network (CNN) consisting of factorization convolutional layers alternating with compression layers (namely, lightweight CNN-FC). Extensive experimental results show that our proposed lightweight CNN-FC can greatly decrease the number of unnecessary parameters, thereby reducing the model size while maintaining the high accuracy in contrast to conventional CNN models. In addition, we also evaluate the performance of our proposed model via conducting experiments at a realistic MEC platform. Specifically, experimental results at this MEC platform show that our model can maintain the high accuracy while preserving the portable model size.

An accurate prediction is certainly significant in financial data analysis. Investors have used a series of econometric techniques on pricing, stock selection and risk management but few of them have found great success due to the fact that most of them only are purely based on a single scheme. Recent advances in deep learning methods have also demonstrated the outstanding performance in the fields of image recognition and sentiment analysis. In this paper, we originally propose a novel gold price forecast method based on the integration of Long Short-Term Memory Neural Networks (LSTM) and Convolutional Neural Networks (CNN) with Attention Mechanism (denoted to LSTM-Attention-CNN model). Particularly, the LSTM-Attention-CNN model consists of three components: the LSTM component, Attention Mechanism and the CNN component. The LSTM component enables to harness the sequential order of daily gold price. Meanwhile, the Attention Mechanism assigns different attention weights on the new encoding method from LSTM component to enhance the extraction of the temporal and spatial features. In addition, the CNN component enables to capture the local patterns and abstract the spatial features. Extensive experiments on real dataset collected from World Gold Council show that our proposed approach outperforms other conventional financial forecast methods.