THE FACILITATING EFFECT OF TRANSLATION ON EFL (ENGLISH AS A FOREIGN LANGUAGE)WRITINGⅡ:WRITERS' ATTENTION PATTERNS

This study is undertaken to find out writers' attention patterns in these two different writing methods (the direct method and the translation-based method) during the writing process through the think-aloud method and EFL learners' perception of the advantages and disadvantages of those two writing methods.The results show that the subjects' attention patterns in the direct method and in the translation-based method are quite different and the differences are mainly embodied in the linguistic level attention and personal comment.As far as the subjects are concerned,writing directly in English is easier and faster than writing through translation,and the direct writing method often helps them learn English language and forces them to focus on English expression.In contrast,for the translation-based writing method,the subjects as a whole relate that they have a wide range of vocabulary and expressions,have a greater number of ideas,and can think through ideas clearly.

Research on Short-Term Electric Load Forecasting Using IWOA CNN-BiLSTM-TPA Model

Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.