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The establishment of the "three-dimension-in-one" objectives in the new curriculums has changed the "knowledge-centered" objective model in the traditional teaching syllabuses. This change will cause the transformation of knowledge learning ideology, i.e. from stressing the explicit knowledge acquisition to concerning the value of tacit knowledge. In return, the transformation of knowledge learning ideology will shed great influence on teaching processes, learning styles and assessing standards.

Dendrites and side reactions of Zn anodes severely restrict the application of aqueous Zn‐based batteries for grid‐scale energy storage. While surface/interface modification strategies have shown some progress in improving Zn anode reversibility, they still fall short in addressing the overall regulation and intrinsic mechanisms from the bulk‐phase perspective. Herein, a bulk‐phase composite Zn/CNTs anodes fabricated by a powder‐metallurgy‐based strategy is introduced. Benefiting from the regulation of grain boundary engineering on local electric conductivity, electric field distributions, and Zn atom absorption energy, the Zn/CNTs anodes effectively suppress dendrite growth and enhance corrosion resistance during Zn stripping/plating cycles. Symmetrical cells equipped with Zn/CNT4 anodes exhibit extended cycling stability with minimal voltage hysteresis (only 22 mV). Furthermore, the full cells incorporating Zn/CNT4 with commercial MnO2 demonstrate superior rate performance and specific capacitance retention after 500 h cycling. This breakthrough opens up new avenues for optimizing metallic anodes at the bulk phase level using powder metallurgy, enabling scalable manufacturing processes, and providing valuable insights for various metal anode systems. [ABSTRACT FROM AUTHOR]

The internet is a huge resource base and a rich knowledge base. Aiming at the problem of small agricultural samples, the utilization technology of network resources was studied in the research, which would provide an idea and method for the research and application of crop disease identification and diagnosis. The knowledge transfer and deep learning methods to carry out research and experiments on public data sets (ImageNet, PlantVillage) and laboratory small sample disease data (AES-IMAGE) were introduced: first the batch normalization algorithm was applied to the AlexNet and VGG of Convolutional Neural Network (CNN) models to improve the over-fitting problem of the network; second the transfer learning strategy using parameter fine-tuning: The PlantVillage large-scale plant disease dataset was used to obtain the pre-trained model. On the improved network (AlexNet, VGG model), the pre-trained model was adjusted by our small sample dataset AES-IMAGE to obtain the disease identification model of cucumber and rice; third the transfer learning strategy was used for the bottleneck feature extraction: using the ImageNet big dataset to obtain the network parameters, CNN model (Inception-v3 and Mobilenet) was used as feature extractor to extract disease features. This method requires only a quick identification of the disease on the CPU and does not require a lot of training time, which can quickly complete the process of disease identification on the CPU. The experimental results show that: first in the transfer learning strategy of parameter fine-tuning: the highest accuracy rate was 98.33%, by using the VGG network parameter fine-tuning strategy; second in the transfer learning strategy of bottleneck feature extraction, using the Mobilenet model for bottleneck layer feature extraction and identification could obtain 96.8% validation accuracy. The results indicate that the combination of CNN and transfer learning is effective for the identification of small sample crop diseases.

SO2 emissions have brought serious hidden danger to human health and environmental quality, thus hindering sustainable economic development. The development of high-speed rail indirectly has an important impact on SO2 emissions through its economic effects. Controlling SO2 emissions from the source has increasingly become the focus of many scholars, and it is very important to assess the environmental effects of high-speed rail on SO2 emissions reduction. We use the panel data of 285 cities in China from 2007 to 2017, and adopt the spatial Difference-in-Differences model to study the impact of the opening of high-speed rail on SO2 emissions. We also introduce an improved spatial DID model that distinguishes neighboring treatment groups and neighboring control groups to test the spatial spillover effect of high-speed rail on neighboring heterogeneous samples. We find that the opening of high-speed rail significantly reduces the city's SO2 emissions through the internal accumulation effect of technological innovation and industrial structure optimization and the urban external interaction mechanism of the cross-regional flow of production factors. Moreover, the spatial spillover effect of the opening of high-speed rail on neighboring cities is significantly positive, especially the spatial spillover effect of HSR on SO2 emissions from neighboring cities without HSR. In addition, heterogeneity analysis shows that the effect varies with the different cities' tiers and income levels. These findings are conducive to accurately assessing the environmental effects of high-speed rail, and provide important policy references for achieving sustainable development and reducing SO2 emissions. [ABSTRACT FROM AUTHOR]

As an integral part of urban forests, urban parks play a vital role in mitigating urban heat islands (UHI) and providing residents with comfortable outdoor recreational plots. For high-quality use of the trees in regulating the thermal comfort of urban parks, previous studies primarily focused on the microclimate variations caused by tree coverage and morphological features separately. However, there is still a lack of systematic understanding of how tree canopy coverage (TCC) and its morphological elements, including leaf area index (LAI), trunk height (TH), and crown diameter (CD), combined affect the thermal comfort in the urban park. This study employed an orthogonal experiment design and ENVI-met software to simulate the microclimate of various multi-factor combination models in the case of a typical urban park in a temperate continental climate zone in China, analyzing the simulated result through physiological equivalent temperature (PET). Results show that the contribution ratio of various elements to the thermal environment vary over time. In studied elements, the contribution ratio of TCC to PET is consistently higher than 50% during the morning, midday, and evening periods, reaching a peak of 67% in the evening. The maximum contribution ratios for CD, TH, and LAI occur during midday, morning, and midday, respectively, with corresponding contribution ratios of approximately 22%, 10%, and 9%, respectively. The ranking of elements affecting thermal comfort in the urban park generally is TCC, CD, LAI, TH throughout the day, apart from the morning, when the influence of TH is greater than LAI. The optimal combination of elements is 85% TCC, 4m TH, 3.9 LAI, and 7m CD, and thirteen combinations of element cases meet the thermal comfort requirements during summer. The research findings highlight the significance of optimizing the configuration of trees in creating a more comfortable and inviting space for human activities. [ABSTRACT FROM AUTHOR]