Liquid leakage of PCM and thermophysical performance defects seriously affect the application prospect of PCMs. Aerogels provide an excellent solution for packaging and performance improvement of PCMs with its ultra-high specific surface area and low density and give PCMs other functions besides energy storage, such as energy conversion (photothermal/electrothermal conversion, magnetic thermal/acoustic thermal conversion), thermal management (battery thermal management, electronic thermal management), thermal infrared stealth, building materials, etc. In this paper, firstly, the preparation method and multifunctional response mechanism of aerogel-based PCMs are systematically described, and the improvement of thermophysical and mechanical properties of various aerogel-based PCMs is reviewed from the perspective of aerogel preparation. Then, according to the different application scenarios of aerogel-based PCMs, the advanced functions of aerogel-based PCMs are reviewed, and the multifunctional effects of different materials in aerogel-based PCMs are compared. Finally, some insightful guidance and suggestions for the research and development of aerogel-based PCMs are put forward.
As the requirements for the optimal control of building systems increase, the accuracy and speed of load predictions should also increase. However, the accuracy of load predictions is related to not only the prediction algorithm, but also the feature set construction. Therefore, this study develops a short-term building cooling load prediction model based on feature set construction. The impacts of four different feature set construction methods-feature extraction, correlation analysis, K-means clustering, and discrete wavelet transform (DWT)-on the prediction accuracy are compared. To ensure that the effect of the feature set construction method is universal, three different prediction algorithms are used. The influences of the sample dimension and prediction time horizon on the prediction accuracy are also analysed. The prediction model is developed based on an ensemble learning algorithm utilising the cubist algorithm, and the performance of the prediction model is improved when DWT is used for constructing the feature set. Compared with other commonly used prediction models, the proposed model exhibits the best performance, with R-squared and CV-RMSE values of 99.8% and 1.5%, respectively.
Weakly supervised image segmentation trained with image-level labels usually suffers from inaccurate coverage of object areas during the generation of the pseudo groundtruth. This is because the object activation maps are trained with the classification objective and lack the ability to generalize. To improve the generality of the objective activation maps, we propose a region prototypical network RPNet to explore the cross-image object diversity of the training set. Similar object parts across images are identified via region feature comparison. Object confidence is propagated between regions to discover new object areas while background regions are suppressed. Experiments show that the proposed method generates more complete and accurate pseudo object masks, while achieving state-of-the-art performance on PASCAL VOC 2012 and MS COCO. In addition, we investigate the robustness of the proposed method on reduced training sets.
In light of carbon peak and carbon neutrality goals, China has attached great importance to energy savings and carbon reduction. Carbon reduction in the transport sector is critical to achieving the two-carbon target, as it accounts for 9.41% of total carbon emissions. As the sharing economy grows, car sharing is considered to present excellent carbon reduction potential in the transportation sector. However, the current research is focused on car sharing usage, with a lack of research on the carbon reduction capability of car sharing in China. Hence, this study aims to investigate the carbon reduction capacity of car sharing, including usage rates of car-share services and changes in travel behavior, through an online questionnaire combined with carbon emission data from the transportation sector. The study aims to analyze the contribution of car-share services to carbon reduction in the transportation sector under the current model. The well-to-wheel (WTW) approach is employed, including the energy consumption of vehicles and carbon emissions in the production process. The research results indicate that the introduction of car-sharing services increases driving energy consumption; however, this increase is offset by the decrease in carbon emissions as a result of the production process. Therefore, the overall effect is a reduction in carbon emissions of 1.058971 million tons in 2021, accounting for 1.95 percent of total transport carbon emissions. In addition, the impact on different modes on carbon emission reduction is also explored in this study. The results demonstrate that the private car disposal rate shows the most significant influence on traffic carbon emissions; a 10% reduction in the number of private cars can lead to a 2.48% carbon reduction. The relevant conclusions of this study can provide support for the future development of car sharing in China and the reduction of carbon emissions in the transportation sector.
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