Second language (L2) learners need to continually learn new L2 words as well as additional meanings of previously learned L2 words. The present study investigated the influence of semantic similarity on the growth curve of learning of artificially paired new meanings of previously known L2 words in Chinese–English bilinguals. The results of a translation recognition task showed that related meanings are learned faster and more accurately than unrelated meanings. The advantage of learning related new meaning persisted and increased for a week after learning the new meanings. These results suggest that semantic similarities impact the learning of new meanings for known L2 words, and that the shared features between previously known and new meanings of a word facilitate the process of incorporating the related new meaning into the lexical semantic network. Our results are discussed under the framework of the connectionist model.

a b s t r a c tAlthough vertical circulation well (VCW) has been widely applied for aquifer remediation and hydrogeological parameter estimation for three decades, few studies have focused on the hydraulic zone visualization, and the spatial scale of hydraulic zones as functions of VCW parameters. This study provided a numerical method to visualize 3D steady-state flow field induced by VCW in a confined aquifer in presence of an ambient flow. Mathematical model for velocity field is established based on the superposition principle, and the fourth-order Runge-Kutta method is adopted to perform particle tracking. Visualization results illustrate that 3D flow regime can be divided into Recirculation, Capture, and Discharge zones. An integrated dimensionless parameter Q D is introduced to account for the relative strength of VCW flow against ambient flow. Comparison is performed through simulations by different Q D -d D combinations, where d D is the dimensionless distance between injection and extraction screens. Results demonstrate that: (1) a Q D -d D breakthrough line can be established to mark the occurrence of circulatory flow; (2) the effect of Q D on the size of recirculation zone is significant when Q D is smaller than 3.5, while d D becomes more influential if Q D is larger; (3) the extent of capture and discharge zone enlarges as Q D and/or d D increases.

No abstract

Objective Forgiveness includes processes that involve a decision to stop bitterness and thoughts of revenge (i.e., decisional forgiveness), which further motivates the forgiver towards the restoration of positive emotions (i.e., emotional forgiveness). Using stress and coping framework, this study investigated intrapersonal and interpersonal facilitators of decisional and emotional forgiveness in a Chinese marital context. Method Participants were 154 respondents who had experienced or were experiencing spousal infidelity. Results Solidarity‐oriented personality and perceived partner's reconciliation motivation facilitated benign attributions and empathy, then facilitated higher levels of decisional forgiveness, which promoted emotional forgiveness. Strength of marital bond before the infidelity directly predicted higher levels of emotional forgiveness. Conclusions Our findings provide evidence for the differentiated decisional and emotional forgiveness processes after spousal infidelity and delineate different coping mechanism that triggers them, thus lending culturally appropriate evidence for clinicians who work with clients facing spousal infidelity.

Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based) model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to be addressed. In this study, a new energy demand forecasting framework is presented at first. On the basis of historical annual data of electricity usage over the period of 1985-2015, the coefficients of linear and quadratic forms of the AI-based model are optimized by combining an adaptive genetic algorithm and a cointegration analysis shown as an example. Prediction results of the proposed model indicate that the annual growth rate of electricity demand in China will slow down. However, China will continue to demand about 13 trillion kilowatt hours in 2030 because of population growth, economic growth, and urbanization. In addition, the model has greater accuracy and reliability compared with other single optimization methods.