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Abstract Grottoes are a comprehensive art treasure trove that integrate architecture, sculpture, and murals. They showcase the historical spiritual civilization of humanity and provide a solid foundation for studying the dissemination and development of Buddhist culture. Gansu Province is an important node on the transmission route of Buddhist culture, constituting a relatively complete and rich history of Buddhist art and cultural history. This article uses GIS technology to systematically analyse the spatial distribution characteristics and spatiotemporal evolution patterns of grottoes in Gansu Province from the Wei Jin to the Ming and Qing dynasties and explores the main factors affecting their distribution. The results indicate the following: (1) the grottoes in Gansu Province exhibit clustering and uneven distribution characteristics, which form the core aggregation area of Qingyang Tianshui City and the secondary aggregation area of Wuwei and Zhangye City. (2) Spatiotemporal characteristics show significant changes in the number and focus of excavation and repair of grottoes in Gansu Province over the years. The Northern and Southern Dynasties, Sui and Tang Dynasties, and Ming and Qing Dynasties had more grottoes than the Wei, Jin, and Yuan Dynasties. The overall centre of gravity shifted from northwest to southeast. Natural factors such as topography, stratigraphy, and hydrology and cultural factors such as politics and transportation significantly impacted the spatial pattern of grotto heritage in Gansu Province. Exploring and studying the spatial layout of grotto heritage from the perspective of historical geography is beneficial for understanding the cultural development and historical changes in Buddhism and is of great significance for the development of landscape environmental protection and utilization of grotto heritage.

Analcime plays a significant role in sandstone reservoirs as an authigenic diagenetic mineral in the Junggar Basin (northwestern China). However, the origin and controls on the reservoirs have received remarkably little attention. This study investigates the formation mechanism of analcime in the Middle Permian strata in the Jinan sag (southern Junggar Basin) through petrography and geochemistry. The results show that analcime is formed through early alkaline hydrolysis of volcanic materials under specific temperature and pressure conditions. The reservoir rocks primarily consist of various lithic sandstones, including volcanic debris such as basalt, andesite, and tuff. Analcime is characterized as rich in aluminium and poor in sodium, classified as low- silica analcime with a low Si-Al ratio (1.98-2.38). Furthermore, various other diagenetic minerals, such as glauconite, chlorite, albite, and calcite have been identified. The primary reservoir space chiefly consists of intragranular dissolved pores of analcime, while secondary pores are formed by intragranular pores of feldspar and lithic, along with some remaining intergranular pores. Cementation of analcime during early diagenesis changes primary pore structures and reduces reservoir properties. The low-silica analcime dissolves due to acidic pore fluids associated with three stages of oil and gas charging, transforming into albite and creating numerous secondary pores, thereby enhancing reservoir quality. [ABSTRACT FROM AUTHOR]

In comparison with capturing channel state information (CSI) measurements via a laptop or desktop, using a smartphone to collect CSI measurements incurs the restriction of working with a single access point and significant signal distortions, resulting in limited information for smartphone localization. Therefore, this paper intends to leverage as much available localization information as possible by (⁠|$1$|⁠) shifting the WiFi frequency from |$2.4$| to |$5$| GHz; (⁠|$2$|⁠) calibrating the noisy CSI measurements and (⁠|$3$|⁠) fusing both amplitudes and phases of the CSI measurements, so as to enhance localization accuracy. Specifically, we first filter out distorted CSI measurements based on their distribution characteristics, then apply the advanced uniform manifold approximation and projection method to refine the mapping relations from a high-dimensional fingerprint space to a low-dimensional location space, and design a location fusion algorithm based on the continuous feature scaling model, which is able to distinguish two locations with similar fingerprints. Extensive experimental results show that the localization accuracy of the proposed approach outperforms the state-of-the-art counterparts by at least |$15.5$| and |$18.7\%$| using two off-the-shelf smartphones. [ABSTRACT FROM AUTHOR]

With the rapid development of advanced technologies, such as artificial intelligence and deep learning, educational informatization has entered a new era. However, the explosion of information has brought numerous challenges. Knowledge graphs, as a crucial component of artificial intelligence, can contribute to the quality of teaching. This study proposes an educational knowledge graph based on course information named CourseKG for precision teaching. Precision teaching seeks to individualize the curriculum for each learner and optimize learning efficiency. CourseKG aims to establish a correct and comprehensive curriculum knowledge system and promote personalized learning paths. CourseKG can address the issue that current general-purpose knowledge graphs are not suitable for the education field. Particularly, this study proposes a framework for educational entity recognition based on the pre-trained BERT model. This framework captures relevant information in the educational domain using the BERT model and combines it with the BiGRU and multi-head self-attention mechanism to extract multi-scale and multi-level global dependency relationships. In addition, the CRF is used for character-label decoding. Further, a relationship extraction method based on the BERT model, which integrates sentence features and educational entities and estimates the similarity between knowledge pairs using cosine similarity, is proposed. The proposed CourseKG is verified by experiments using real-world C programming course data. The experimental results demonstrate the effectiveness of CourseKG. Finally, the results show that the proposed CourseKG can significantly enhance the precision teaching quality and realize multi-directional adaptation among teachers, courses, and students. [ABSTRACT FROM AUTHOR]