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The literature from inception to 2020 on the prevalence of epilepsy in autistic individuals was systematically reviewed and further explored by subgroup analyses and meta-regression models. This systematic review is registered with PROSPERO (CRD42020179725). A total of 66 studies from 53 articles were included. The updated pooled prevalence of epilepsy in autistic individuals was 10% (95% CI: 6-14). The respective prevalence estimate of epilepsy was 19% (95% CI: 6-35) in the clinical sample-based cross-sectional study, 7% (95% CI: 3-11) in the cohort study, and 9% (95% CI: 5-15) in the population-based cross-sectional study. The pooled prevalence of epilepsy was 7% (95% CI: 4-11) in autistic children and 19% (95% CI: 14-24) in autistic adults. Compared to the school-aged group, the adolescence group (OR: 1.15, 95% CI: 1.06-1.25) and the pre-school group (OR: 1.06, 95% CI: 0.94-1.19) were positively associated with the prevalence of epilepsy. The moderators of age, human development index of the country, gender, and intellectual function accounted for most of the heterogeneity. The prevalence estimates were associated with age, female gender, intellectual disability rate, and the human development index of countries. About 1/10 autistic individuals co-occurred with epilepsy, which was common in the clinical setting, adolescents, adults, females, or patients with intellectual disability, and less common in the country with high human development index.

In a 2013-2014 Australia-China Council project, middle school students in Australia and China shared digital stories about their everyday lives and local cultures, and traditional tales with a modern twist. This article reports on research that aimed to explore the successes and challenges associated with this digital story exchange between Australia and China as a pedagogical approach to support language learning and intercultural understanding. An interpretivist approach was taken, focusing on the perspectives of the teachers. According to the teachers, the exchange was successful to a degree in supporting students' learning in the areas of language, intercultural understanding and twenty-first-century skills, including digital literacies and technological skills, and helped teachers extend their pedagogical horizons. A number of challenges also arose. Analysis of interview data revealed that both the successes and challenges fell into four interrelated domains, which we have labelled structures, practices, capabilities and technologies. This article offers new insights into the exchange of multimodal digital stories as learning activities in the Australian-Chinese context and provides recommendations to guide educators in these four domains.

The high accuracy of short-term power load forecasting has a pivotal role in helping power companies to construct reasonable production scheduling plans and avoid resource waste. In this paper, a multi-model short-term power load prediction method based on Variational mode decomposition (VMD)-improved whale optimization algorithm (IWOA)-wavelet temporal convolutional network (WTCN)-bidirectional gated recurrent unit (BiGRU)-attention and CatBoost model fusion is proposed. First, VMD was employed to decompose the load data into different intrinsic mode functions. Second, a WTCN was utilized to extract the load data features, and multi-dimensional feature factors were integrated into the BiGRU network for model training. Moreover, an attention mechanism was added to enhance the influence degree of important information. The WTCN-BiGRU-attention model is improved by the WOA algorithm to optimize the hyperparameters of the network. Finally, the model was fused with the predicted data of the CatBoost network by the mean absolute percentage error-reciprocal weight (MAPE-RW) algorithm to construct the best fusion model. Compared with other forecasting models, the proposed multi-model fusion method has higher accuracy in short-term power load forecasting using the public data set for an Australian region.