Nowadays, online word-of-mouth has turned to be a very important resource for electronic businesses. How to analyze user generated reviews and to classify them into different sentiment classes is gradually becoming a question that people pay close attention to. In this field, special challenges are associated with the mining of traveler reviews. At present, there is some research on sentiment analysis for English traveler generated reviews, but very few studies pay attention to sentiment analysis for traveler reviews in Chinese. China is the largest country in terms of the number of Internet users. Internet technologies are gradually playing more and more important roles for many industries including tourism industry. The lack of sentiment analysis methods will block the use of word-of-mouth for tourism industry in China. To solve the problem, this study conducts an exploring research on sentiment analysis to Chinese traveler reviews by support vector machine (SVM) algorithm. The experiment data of Chinese reviews for hotels are downloaded from www.ctrip.com, the largest online travel agency in China. Empirical results indicate that, comparing to prior studies on English reviews, SVM algorithm can gain a very well performance of sentiment classification for traveler reviews in Chinese.

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Smart mobile devices are one of the core components of the wireless body area networks (WBANs). These devices shoulder the important task of collecting, integrating, and transmitting medical data. When a personal computer collects information from these devices, it needs to authenticate the identity of them. Some effective schemes have been put forward to the device authentication in WBANs. However, few researchers have studied the WBANs device authentication in emergency situations. In this paper, we present a novel system named emergency medical system without the assistance of doctors. Based on the system, we propose an identity-based fast authentication scheme for smart mobile devices in WBANs. The scheme can shorten the time of device authentication in an emergency to achieve fast authentication. The analysis of this paper proves the security and efficiency of the proposed scheme.

Sentiment classification aims at mining word-of-mouth, reviews of consumers, for a product or service by automatically classifying reviews as positive or negative. Few empirical studies have been conducted in comparing the different effects between machine learning and semantic orientation approaches on Chinese sentiment classification. This paper adopts three supervised learning approaches and a web-based semantic orientation approach, PMI-IR, to Chinese reviews. The results show that SVM outperforms naive bayes and N-gram model on various sizes of training examples, but does not obviously exceeds the semantic orientation approach when the number of training examples is smaller than 300.

Purpose Biological therapies targeting eosinophils have been shown to be effective in treating patients with severe eosinophilic asthma. Benralizumab (Fasenra ® , AstraZeneca) is a humanized monoclonal antibody binding to the alpha subunit of the interleukin-5 receptor, which rapidly depletes eosinophils via antibody-dependent cellular cytotoxicity. The aim of this Phase 1 study was to assess the safety, tolerability, and pharmacokinetics of benralizumab in healthy Chinese individuals. Materials and Methods In this randomized, single-blind study (NCT03928262), healthy Chinese adult participants aged 18 to 45 years, weighing 50 to 100 kg, were randomized 1:1:1 to receive a single subcutaneous (SC) injection of benralizumab 10 mg, 30 mg, or 100 mg in the upper arms on Day 1. Safety was monitored throughout the study (up to Day 85), and blood samples were taken to determine serum benralizumab concentrations and for detection of anti-drug antibody. A non-compartmental analysis was conducted to estimate the pharmacokinetic parameters. Results Thirty-six healthy participants were enrolled, 12 in each dose group (mean [SD] age 26 [6] years). Following a single SC injection of benralizumab, 13 adverse events were reported by 10 participants (28%), with one mild injection-site reaction assessed as related. The mean serum benralizumab concentrations increased in a dose proportional manner, followed by exponential decreases. The mean terminal half-lives were 15.1 days for the 10 mg dose, 14.4 days for the 30 mg dose, and 15.4 days for the 100 mg dose. All doses resulted in near-complete depletion of eosinophils on Day 2, which was maintained throughout the study to Day 85. Conclusion A single SC injection of benralizumab was well tolerated by healthy Chinese participants, with no new or unexpected safety findings. The pharmacokinetics of benralizumab in Chinese participants was dose-proportional and consistent with those of non-Chinese participants observed in previous studies. Clinical Trial Registration NCT03928262 ( https://clinicaltrials.gov/ct2/show/NCT03928262 )