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To reduce production costs and enhance the high-temperature resistance of SiO2 aerogels, an aluminum-doped silica aerogel (ASA) was successfully prepared using the sol-gel method and atmospheric drying method. The composite silica sources included TEOS and inexpensive acidic silica sol, while the aluminum source was aluminum sol. The study investigated the influence of the molar ratio of acidic silica sol to TEOS, Al/Si, and calcination temperature on the composition, structure, and high-temperature resistance of the ASA. The results indicate that a sample with an acidic silica sol to TEOS molar ratio of 0.8 achieved a specific surface area of 683.204 m2·g−1. The Al/Si molar ratio significantly impacted the high-temperature resistance of the ASA, with the sample having a molar ratio of 0.02 Al/Si displaying the highest specific surface area of 705.956 m2·g−1 at 600 °C. Moreover, this surface area remained at 273.099 m2·g−1 after calcination at 1000 °C, notably higher than the sample without aluminum sol (16.082 m2·g−1). Mechanism analysis indicated that the addition of aluminum sol to the SiO2 aerogel inhibited phase transitions, and both acidic silica sol and aluminum sol particles enhanced the aerogel structure, contributing to a marked improvement in high-temperature resistance. [ABSTRACT FROM AUTHOR]

Based on the geological and geochemical analysis of volcanic rock and their surrounding rocks, combined with palaeontological data and hydrocarbon generation thermal simulation experiments, the mechanism of the impact of volcanic eruptions on the formation of organic matter and hydrocarbon generation in the Dongying Depression is investigated. According to the results, underwater volcanic eruptions play a significant role in promoting biological eruptions and water stratification by introducing heat, minerals, and metal elements. The formation of the basalt source rock symbiotic association went through three evolutionary stages, which were underwater eruption, late eruption, and normal sedimentation. These stages have influenced the generation of hydrocarbons from organic matter in the following ways: (1) The organic acid released from the source rock dissolves the adjacent basalt, and the transitional metal elements in the basalt become activated and enter the source rock, which catalyses the hydrocarbon generation from the source rock. (2) Alteration of olivine in basalt releases hydrogen, improving hydrocarbon generation efficiency of source rock. (3) The existence of a large number of carbonates improves the hydrocarbon generation rate and efficiency of source rocks and reduces the hydrocarbon generation threshold of source rocks. [ABSTRACT FROM AUTHOR]

This article examines a new trend of high school choice in China – parental choice of emerging international high-school curriculum programs (IHSCPs) recently established by Chinese elite public high schools. We add to the existing critical literature on school choice by providing a detailed empirical analysis of why, how, and under what conditions Chinese parents with high socio-economic status choose a particular IHSCP. Drawing on Pierre Bourdieu's conceptual triad of practice – capital, habitus, and field, the article points out that Chinese upper-middle- and upper-class parents engage in complicated conversion strategies surrounding their individual and collective struggles for elite education in both national and international fields of power. The study foregrounds the role of neoliberalism in articulating local and global education markets that allows privileged Chinese to transcend structural constraints in China and seek out opportunities in the international education field for their children. [ABSTRACT FROM AUTHOR]

Deep Hashing is widely used for large-scale image-retrieval tasks to speed up the retrieval process. Current deep hashing methods are mainly based on the Convolutional Neural Network (CNN) or Vision Transformer (VIT). They only use the local or global features for low-dimensional mapping and only use the similarity loss function to optimize the correlation between pairwise or triplet images. Therefore, the effectiveness of deep hashing methods is limited. In this paper, we propose a dual-stream correlation-enhanced deep hashing framework (DSCEH), which uses the local and global features of the image for low-dimensional mapping and optimizes the correlation of images from the model architecture. DSCEH consists of two main steps: model training and deep-hash-based retrieval. During the training phase, a dual-network structure comprising CNN and VIT is employed for feature extraction. Subsequently, feature fusion is achieved through a concatenation operation, followed by similarity evaluation based on the class token acquired from VIT to establish edge relationships. The Graph Convolutional Network is then utilized to enhance correlation optimization between images, resulting in the generation of high-quality hash codes. This stage facilitates the development of an optimized hash model for image retrieval. In the retrieval stage, all images within the database and the to-be-retrieved images are initially mapped to hash codes using the aforementioned hash model. The retrieval results are subsequently determined based on the Hamming distance between the hash codes. We conduct experiments on three datasets: CIFAR-10, MSCOCO, and NUSWIDE. Experimental results show the superior performance of DSCEH, which helps with fast and accurate image retrieval. [ABSTRACT FROM AUTHOR]