FIRST LANGUAGE ACQUISITION AS A MODEL FOR SECOND LANGUAGE ACQUISITION: THE TOTAL PHYSICAL RESPONSE APPROACH IN SECOND LANGUAGE LEARNING AND TEACHING

L1 and L2 acquisition, in some respects, are similar. Language development in children goes hand in hand with physical and cognitive development. Children learn their first language by imitation, but not always and not only by imitation. There seems to be some "innate capacities" that make children start to speak at the same time they do and in the way they do it. Adults learning a second language usually are controlled more by their motivation. But language input is important for both L1 and L2 acquisition. Though there are differences between CL1 and between CL2 and AL2, the way in which these learners acquire some of the grammatical morphemes is similar. This, together with some other evidence, shows that it is not only children who can acquire language. Adults can also acquire a language. But when adults acquire a language, they should also learn it. Some of the ways in which children acquire their language can be used as a model for L2 acquisition, even for Chinese students whose language is unrelated to English and whose culture is different. Learning the culture of the English-speaking countries will benefit the learning of the language. Like children, listening should also be well in advance of speaking in L2 acquisition. To train listening comprehension skills, Asher’s TPR approach proves more effective. TPR approach is at the moment limited to the beginning stage only. In order for students to gain all the five skills in a second language learning, namely, listening, speaking, reading, writing, and interpreting/translating, other methods should be used at the same time, or at later stages.

In silico studies of magnesium-based implants: A review of the current stage and challenges

In silico methods to study biodegradable implants have recently received increasing attention due to their potential in reducing experimental time and cost. An important application case for in silico methods are magnesium(Mg)-based biodegradable implants, as they represent a powerful alternative to traditional materials used for temporary orthopaedic applications. Controlling Mg alloy degradation is critical to designing an implant that supports the bone healing process. To simulate different aspects of this biodegradation process, several mathematical models have been proposed with the ultimate aim of replacing laboratory experiments with computational modeling. In this review, we provide a comprehensive and critical discussion of the published models and their performance with respect to capturing the complexity of the biodegradation process. This complexity is presented initially. Additionally, the present review discusses the different approaches of optimizing and quantifying the different sources of errors and uncertainties within the proposed models.

A physical approach of the short-term wind power prediction based on CFD pre-calculated flow fields

A physical approach of the wind power prediction based on the CFD pre-calculated flow fields is proposed in this paper. The flow fields are obtained based on a steady CFD model with the discrete inflow wind conditions as the boundary conditions, and a database is established containing the important parameters including the inflow wind conditions, the flow fields and the corresponding wind power for each wind turbine. The power is predicted via the database by taking the Numerical Weather Prediction （NWP） wind as the input data. In order to evaluate the approach, the short-term wind power prediction for an actual wind farm is conducted as an example during the period of the year 2010. Compared with the measured power, the predicted results enjoy a high accuracy with the annual Root Mean Square Error （RMSE） of 15.2% and the annual MAE of 10.80%. A good performance is shown in predicting the wind power＇s changing trend. This approach is independent of the historical data and can be widely used for all kinds of wind farms including the newly-built wind farms. At the same time, it does not take much computation time while it captures the local air flows more precisely by the CFD model. So it is especially practical for engineering projects.

From electronic excited state theory to the property predictions of organic optoelectronic materials

We introduce here a work package for a National Natural Science Foundation of China Major Project. We propose to develop computational methodology starting from the theory of electronic excitation processes to predicting the opto-electronic property for organic materials, in close collaborations with experiments. Through developing methods for the electron dynamics, considering superexchange electronic couplings, spin-orbit coupling elements between excited states, electron-phonon relaxation, intermolecular Coulomb and exchange terms we combine the statistical physics approaches including dynamic Monte Carlo, Boltzmann transport equation and Boltzmann statistics to predict the macroscopic properties of opto-electronic materials such as light-emitting efficiency, charge mobility, and exciton diffusion length. Experimental synthesis and characterization of D-A type ambipolar transport material as well as novel carbon based material will provide a test ground for the verification of theory.