Flexible polymer composite films are prepared by a solution cast method with polar polyvinylidene fluoride (PVDF) or non-polar epoxy as the polymer matrix. BaTiO 3 nanoparticles and BaTiO 3 nanofibers with large aspect ratio are used as dielectric fillers after surface modification by polydopamine. The effects of filler shape, surface modification and polarity of polymer matrix on the microstructure, dielectric constants and breakdown strength of polymer composites are investigated in detail. Surface modification by polydopamine improves the compatibility between BaTiO 3 and polymer as well as passivating the surfaces of BaTiO 3 . At the same volume fraction, composites filled with BaTiO 3 nanofibers exhibit greater dielectric constants than the composites filled with BaTiO 3 nanoparticles. The polydopamine layers on BaTiO 3 nanofibers give rise to stronger interfaces between the fillers and polymer matrices. Improved breakdown strengths are achieved in both composites. This work may provide a general strategy for flexible polymer nanocomposites with greatly enhanced dielectric constants and breakdown strength.

A carbon nanotube (CNT)/polymer composite is prepared with a CNT array using an electrospinning method and hot‐pressing technology. This composite exhibits a stable high dielectric permittivity and low dielectric loss over a wide frequency range, in addition to a large energy density.

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Data streams, which can be considered as one of the primary sources of what is called big data, arrive continuously with high speed. The biggest challenge in data streams mining is to deal with concept drifts, during which ensemble methods are widely employed. The ensembles for handling concept drift can be categorized into two different approaches: online and block-based approaches. The primary disadvantage of the block-based ensembles lies in the difficulty of tuning the block size to provide a tradeoff between fast reactions to drifts. Motivated by this challenge, we put forward an online ensemble paradigm, which aims to combine the best elements of block-based weighting and online processing. The algorithm uses the adaptive windowing as a change detector. Once a change is detected, a new classifier is built replacing the worst one in the ensemble. By experimental evaluations on both synthetic and real-world datasets, our method performs significantly better than other ensemble approaches.

Students' dropout rate is a key metric in online distance learning courses such as MOOCs. We propose a timeseries classification method to construct data based on students' behavior and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.