Research on Procedures and Advantages of Flipped Classroom Based on WeChat: Taking English Teaching in Open University as an Example

Flipped Classroom Teaching model has attracted much attention in China’s education system, because it reversed the traditional teaching process and innovated a new teaching structure. This study focused on Flipped Classroom English teaching based on Wechat. First it constructed a flipped classroom teaching model based on WeChat and showed three procedures including before-the-class, during-the-class and after-the-class. It has also revealed three advantages which are focusing English core competences, creating a U-learning environment and sharing digital opportunities. At last suggestions for further study were given

In-situ growth of nanowire WO2.72 on carbon cloth as a binder-free electrode for flexible asymmetric supercapacitors with high performance

For the first time, WO2.72 nanowires were in-situ grown on carbon cloth by a simple solvothermal reaction. The nanowire WO2.72/carbon cloth (NW WO2.72/CC) electrode showed good electrochemical performance with specific capacitance (C s) reaching up to 398 F g-1 at a current density of 2 A g-1. The capacitance of 240 F g-1 was retained at a high current density of 16 A g-1. To further evaluate the energy storage performance, flexible asymmetric supercapacitors (FASCs) were fabricated using the activated carbon/carbon cloth (AC/CC) as negative electrode and NW WO2.72/CC as positive electrode, respectively. The FASCs delivered a high energy density of 28 Wh kg-1 at a power density of 745 W kg-1 and 13 Wh kg-1 even at a high power density of 22.5 kW kg-1. More impressively, 81% of the specific capacitance of the FASCs was retained after 10,000 cycles, indicating excellent cycle stability. This work indicates the NW WO2.72/CC holds a great potential for application in energy storage devices

OAG-BERT: Pre-train Heterogeneous Entity-augmented Academic Language Models

To enrich language models with domain knowledge is crucial but difficult. Based on the world's largest public academic graph Open Academic Graph (OAG), we pre-train an academic language model, namely OAG-BERT, which integrates massive heterogeneous entities including paper, author, concept, venue, and affiliation. To better endow OAG-BERT with the ability to capture entity information, we develop novel pre-training strategies including heterogeneous entity type embedding, entity-aware 2D positional encoding, and span-aware entity masking. For zero-shot inference, we design a special decoding strategy to allow OAG-BERT to generate entity names from scratch. We evaluate the OAG-BERT on various downstream academic tasks, including NLP benchmarks, zero-shot entity inference, heterogeneous graph link prediction, and author name disambiguation. Results demonstrate the effectiveness of the proposed pre-training approach to both comprehending academic texts and modeling knowledge from heterogeneous entities. OAG-BERT has been deployed to multiple real-world applications, such as reviewer recommendations and paper tagging in the AMiner system. It is also available to the public through the CogDL package

In-N-Out Generative Learning for Dense Unsupervised Video Segmentation

In this paper, we focus on the unsupervised learning for Video Object Segmentation (VOS) which learns visual correspondence (i.e., similarity between pixel-level features) from unlabeled videos. Previous methods are mainly based on the contrastive learning paradigm, which optimize either in image level or pixel level. Image-level optimization (e.g., the spatially pooled feature of ResNet) learns robust high-level semantics but is sub-optimal since the pixel-level features are optimized implicitly. By contrast, pixel-level optimization is more explicit, however, it is sensitive to the visual quality of training data and is not robust to object deformation. To complementarily perform these two levels of optimization in a unified framework, we propose the In-aNd-Out (INO) generative learning from a purely generative perspective with the help of naturally designed class tokens and patch tokens in Vision Transformer (ViT). Specifically, for image-level optimization, we force the out-view imagination from local to global views on class tokens, which helps capturing high-level semantics, and we name it as out-generative learning. As to pixel-level optimization, we perform in-view masked image modeling on patch tokens, which recovers the corrupted parts of an image via inferring its fine-grained structure, and we term it as in-generative learning. To better discover the temporal information, we additionally force the inter-frame consistency from both feature level and affinity matrix level. Extensive experiments on DAVIS-2017 val and YouTube-VOS 2018 val show that our INO outperforms previous state-of-the-art methods by significant margins

Au@h-Al2O3 Analogic Yolk–Shell Nanocatalyst for Highly Selective Synthesis of Biomass-Derived D-xylonic Acid via Regulation of Structure Effects

Selective oxidation of biomass-based monosaccharides into value-added sugar acids is highly desired, but limited success of producing D-xylonic acid has been achieved. Herein, we report an efficient catalyst system, viz., Au nanoparticles anchored on the inner walls of hollow Al2O3 nanospheres (Au@h- Al2O3), which could catalyze the selective oxidation of D-xylose into D-xylonic acid under base-free conditions. The mesoporous Al2O3 shell as the adsorbent first adsorbed D-xylose. Then, the interface of Au nanoparticles and Al2O3 as active sites spontaneously dissociated O2, and the exposed Au nanoparticle surface as the catalytic site drove the transformation. With this catalyst system, the valuable D-xylonic acid was produced with excellent yields in the aerobic oxidation of D-xylose. Extensive investigation showed that Au@h- Al2O3 is an efficient catalyst with high stability and recyclability

Ecological risk assessment of fifty pharmaceuticals and personal care products (PPCPs) in Chinese surface waters: a proposed multiple-level system

Interest in the risks posed by trace concentrations of pharmaceuticals and personal care products (PPCPs) in surface waters is increasing, particularly with regard to potential effects of long-term, low-dose exposures of aquatic organisms. In most cases, the actual studies on PPCPs were risk assessments at screening-level, and accurate estimates were scarce. In this study, exposure and ecotoxicity data of 50 PPCPs were collected based on our previous studies, and a multiple-level environmental risk assessment was performed. The 50 selected PPCPs are likely to be frequently detected in surface waters of China, with concentrations ranging from the ng L−1 to the low-g L−1, and the risk quotients based on median concentrations ranged from 2046 for nonylphenol to 0 for phantolide. A semi-probabilistic approach screened 33 PPCPs that posed potential risks to aquatic organisms, among which 15 chemicals (nonylphenol, sulfamethoxazole, di (2-ethylhexyl) phthalate, 17β-ethynyl estradiol, caffeine, tetracycline, 17β-estradiol, estrone, dibutyl phthalate, ibuprofen, carbamazepine, tonalide, galaxolide, triclosan, and bisphenol A) were categorized as priority compounds according to an optimized risk assessment, and then the refined probabilistic risk assessment indicated 12 of them posed low to high risk to aquatic ecosystem, with the maximum risk products ranged from 1.54% to 17.38%. Based on these results, we propose that the optimized risk assessment was appropriate for screening priority contaminants at national scale, and when a more accurate estimation is required, the refined probability risk assessment is useful. The methodology and process might provide reference for other research of chemical evaluation and management for rivers, lakes, and sea waters