Focusing on the massive open online course (MOOC) platform, the purpose of this study is to realize personalized adaptive learning according to the needs and abilities of each learner. To this end, the author created a personalized adaptive learning behaviour analysis model, and designed a personalized MOOC platform based on the model. Through the analysis of learning behaviours on the MOOC platform, the model digs deep into the pattern of learning behaviours, and lays the basis for personalized intervention in the learning process. The comparison ex-periments show that our prediction method is more accurate than the other predic-tion algorithms. This research sheds new light on the design of learner-specific MOOC platform.

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This paper developed a deep architecture to predict the short-term traffic flow in an urban traffic network. The architecture consists of three main modules: a pretraining module, which generates initialized weights and provides a rough learning of the features firstly with the training set in an unsupervised manner; a classification module, which performs the data classification operation through adding the logistic regression on top of the pretrained architecture to distinguish the traffic state; and a fine-tuning module, which predicts the traffic flow with supervised training based on the initialized weights in the first module. The classification module provides the fine-tuning modules with two classified datasets for more accurate forecasting. Furthermore, both upstream and downstream data are utilized to improve the prediction performance. The effectiveness of the proposed model was verified by the traffic prediction of the road segments of Nanming District of Guiyang. And with the comparison analysis over the existing approaches, the proposed model shows superiority in short-term traffic prediction, especially under incident conditions.

The impact of process induced variation on the response of SOI FinFET to heavy ion irradiation is studied through 3-D TCAD simulation for the first time. When FinFET biased at OFF state configuration (V gs D 0, V ds D V dd / is struck by a heavy ion, the drain collects ionizing charges under the electric field and a current pulse (single event transient, SET) is consequently formed. The results reveal that with the presence of line-edge roughness (LER), which is one of the major variation sources in nano-scale FinFETs, the device-to-device variation in terms of SET is observed. In this study, three types of LER are considered: type A has symmetric fin edges, type B has irrelevant fin edges and type C has parallel fin edges. The results show that type A devices have the largest SET variation while type C devices have the smallest variation. Further, the impact of the two main LER parameters, correlation length and root mean square amplitude, on SET variation is discussed as well. The results indicate that variation may be a concern in radiation effects with the down scaling of feature size.