Aiming at the current problems of poor dynamic reconstruction of UAV aerial remote sensing images and low image clarity, the dynamic reconstruction method of UAV aerial remote sensing images based on compression perception is proposed. Construct a quality reduction model for UAV aerial remote sensing images, obtain image feature information, and further noise reduction preprocessing of UAV aerial remote sensing images to better improve the resolution, spectral and multi-temporal trends of UAV aerial remote sensing images, and effectively solve the problems of resource waste such as large amount of sampled data, long sampling time and large amount of data transmission and storage. Maximize the UAV aerial remote sensing images sampling rate, reduce the complexity of dynamic reconstruction of UAV aerial remote sensing images, and effectively obtain the research requirements of high-quality image reconstruction. The experimental results show that the proposed dynamic reconstruction method of UAV aerial remote sensing images based on compressed sensing is correct and effective, which is better than the current mainstream methods.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.

Google Earth images in the Marche Region of Central Italy revealed a circular structure consisting of a ring system made up of concentric hills and valleys. Cartography, DEM, geological, and available geophysical data were used to constrain the possible origin of the structure. Located in the Messinian foredeep deposits of the Central Apennines, it has a rim diameter of 3.75 km and a central uplift connected to its southernmost part. As it was formed in the clays of the Lower Pliocene, and clays are believed to have emerged definitively after the Upper Pliocene, its age might be constrained to the Lower Pleistocene. Similar concentric structures are usually found in impact craters, sedimentary domes, and volcanic landforms. As salt domes and magmatic activity are not found in this region, this study seeks to validate the results of previous work that it was the result of an ancient impact crater of hydrological, brachyanticline, or clayey diapiric origins. Specifically, an observed second ring portion with a curvature radius about double the first in size will be investigated in this work. This second ring portion appears to be concentric to the first one and is visible along its northern and western parts. Although double concentric rings are usually due to impact craters, the absence of the ring portion in the other two directions and the probable deviation of a river, deduced by studying hydrography, support the hypothesis that it might be of clay diapir origin.

In order to strengthen the study of soil-landscape relationships in mountain areas, a digital soil mapping approach based on fuzzy set theory was applied. Initially, soil properties were estimated with the regression kriging (RK) method, combining soil data and auxiliary information derived from a digital elevation model (DEM) and satellite images. Subsequently, the grouping of soil properties in raster format was performed with the fuzzy c-means (FCM) algorithm, whose final product resulted in a fuzzy soil class variation model at a semi-detailed scale. The validation of the model showed an overall reliability of 88% and a Kappa index of 84%, which shows the usefulness of fuzzy clustering in the evaluation of soil-landscape relationships and in the correlation with soil taxonomic categories.

This study examines the relationship between Russian FDI carried out by large MNCs and investment development path (IDP). Although statistical analysis does not establish a significant relationship between outward FDI and GDP, the behavior of Russian outward FDI contradicts traditional models. Two primary factors contribute to this paradox. First, the complex business environment in Russia, characterized by a combination of both improvements and contradictions, has a significant impact on outward FDI behavior. Secondly, the duality of the Russian economy and society plays a decisive role. This segment resembles a high-income country with ample resources, while most face lower income levels, raising concerns about wealth distribution. Historical factors, including Russia’s transition from a state-controlled to a market-oriented economy, contribute to the internationalization of Russian MNCs. Both state-owned enterprises and privatized firms are influenced by the state, although to varying degrees. Government involvement in international business strategies increases the knowledge and experience of Russian MNCs, but also raises concerns about political influence.