Learning to write words may strengthen orthographic representations and thus support word-specific recognition processes. This hypothesis applies especially to Chinese because its writing system encourages character-specific recognition that depends on accurate representation of orthographic form. We report 2 studies that test this hypothesis in adult learners of Chinese. In those studies, the researchers 1st compared the effects of an online writing tutor that included character handwriting with an instructional tutor that included reading only. The writing condition led to better performance on word recognition and on character-meaning links but not on the character-phonology link. In the 2nd experiment, we added an alphabetic (Pinyin) typing tutor to strengthen the phonology link and to control for manual motor activity during instruction. This experiment replicated the effects of writing on word recognition and charactermeaning links, whereas alphabetic (Pinyin) typing supported only phonological representations and the character-phonology link. Theoretically, the studies suggest constituent-specific effects: writing on orthography and alphabetic coding on phonology. We suggest the mechanism for the writing effect is the refinement of visual-spatial information needed for character recognition and the addition of a sensorymotor memory that accompanies writing. The practical implication is that an integration of character handwriting and Pinyin typing promotes learning to read Chinese in a second language learning context.

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Fast development of IT and ICT facilitate customers to post a large volume of their concerns and expectation online, which are widely accepted to be a valuable resource for product designers. However, it is found that only a small number of small and medium-sized enterprises (SMEs) have capabilities to leverage customer online insights for design innovation, which often demonstrate a significant share in national economies growth. To discover the beneath reasons regarding the barrier that prevent them to make effective utilization, in this study, as a concrete example, manufacturing SMEs in the South Wales and Greater Manchester industrial areas of the UK are focused and their potential motivations for using and knowledge of big data-based customer analytics are investigated. An exploratory survey was conducted in terms of the type of customer data they have, the storage approaches, the volume of customer data, etc. Next, a carefully devised exploratory study was undertaken to understand how SMEs perceive the relations between customer data and product design, how about their expectations from big customer data analytics and what really challenges SMEs to exploit the value of big customer data. Besides, a demonstration platform is developed to present SMEs an automatic process of analysing customer online reviews and the capacity on customer insights acquisition and strategic decision making. Finally, findings from two focus groups indicate the different managerial and technical considerations required for SMEs considering implementing big data and customer analytics. This study encourages SMEs to welcome big customer data and suggests that a cloud-based approach may be the most appropriate way of giving access to big data analytics techniques.

In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis. For a given problem, one capsule is built for each sentiment category e.g., 'positive' and 'negative'. Each capsule has an attribute, a state, and three modules: representation module, probability module, and reconstruction module. The attribute of a capsule is the assigned sentiment category. Given an instance encoded in hidden vectors by a typical RNN, the representation module builds capsule representation by the attention mechanism. Based on capsule representation, the probability module computes the capsule's state probability. A capsule's state is active if its state probability is the largest among all capsules for the given instance, and inactive otherwise. On two benchmark datasets (i.e., Movie Review and Stanford Sentiment Treebank) and one proprietary dataset (i.e., Hospital Feedback), we show that RNN-Capsule achieves state-of-the-art performance on sentiment classification. More importantly, without using any linguistic knowledge, RNN-Capsule is capable of outputting words with sentiment tendencies reflecting capsules' attributes. The words well reflect the domain specificity of the dataset.

In the present study, a simple and efficient method for the preparative separation of 3-CQA from the extract of Helianthus tuberosus leaves with macroporous resins was studied. ADS-21 showed much higher adsorption capacity and better adsorption/desorption properties for 3-CQA among the tested resins. The adsorption of 3-CQA on ADS-21 resin at 25°C was fitted best to the Langmuir isotherm model and pseudo-second-order kinetic model. Dynamic adsorption/desorption experiments were carried out in a glass column packed with ADS-21 to optimise the separation process of 3-CQA from H. tuberosus leaves extract. After one treatment with ADS-21, the content of 3-CQA in the product was increased 5.42-fold, from 12.0% to 65.2%, with a recovery yield of 89.4%. The results demonstrated that the method was suitable for large-scale separation and manufacture of 3-CQA from H. tuberosus leaves.