~cr:The regularity of the distribution of higher categories in Chinese Soil Taxonomy(CST) were studied through analyzing the diagrmstic lxa'izons and characteristics and the variation of soil forming factors in China. The results indicate that the distribution regularity of higher categories in CST is different f~ that in the past zonal chssifieation, which is inferred on the basis of typical profiles and biodimatic conditions. Among the 14 soil orders available in CSI', 6 belong to the basic types, which show the regularly continuous distribution, and can be deduced into three larger groups: aridic, ustic and udic. The other 8 soil orders belong to the special types with the band-shaped, spot-shaped, chessboard-shaped, patch-shaped distributions and soon. Moreover, there is the regularity of vertical distribution in nuxmtains. KEY WORld: Chinese Soil Taxon~ny, The distribution regularity Soil classification plays important roles in soil survey, soil mapping and agricultural technology transfer in the light of local conditions. It is also the medium of soil science communication among domestic and international scholars. Moreover, the survey and evaluation of agricultural natural resources are always based on the soil classification: firstly, the properties, types and distributions of soils must be surveyed; then the soil adaptation and production potential can be evaluated. This is one of the most important and basic work in the investigation of agricultural situation.
The relics of ancient rice have been regarded as the most important objective evidence of the origination and spread of rice cultivation. Based on the records of 280 rice relics sites and the rice cropping regionalization as well as the distribution map of paddy soils, the current study compiled the temporal and spatial distribution map of ancient rice distribution in China. The map shows that the distribution of ancient rice is spatially extensive and meantime comparatively concentrated, temporarily covering a long and relatively continuous time-span. The rice relics in the Central China double and single rice cropping regions are among the earliest and the most abundant ones, possessing continuity in time sequence. Combined with the discovery of ancient rice and paddy filed relics, soil micromorphology, pollen combination and element geochemistry, it is suggested that Central China was the origin center of rice cultivation in China. Rice had been spread to the rest part of China in three major waves, also to the East Asian part like Korea and Japan. The temporal and spatial distribution of ancient rice reflects the past environmental change, which is also meaningful to the current rice regionalization and planning as well as food security in China.
In this paper, we propose a novel two-level hierarchical framework for threedimensional (3D) skeleton-based action recognition, in order to tackle the challenges of high intra-class variance, movement speed variability and high computational costs of action recognition. In the first level, a new partbased clustering module is proposed. In this module, we introduce a partbased five-dimensional (5D) feature vector to explore the most relevant joints of body parts in each action sequence, upon which action sequences are automatically clustered and the high intra-class variance is mitigated. In the second level, there are two modules, motion feature extraction and action graphs. In the module of motion feature extraction, we utilize the clusterrelevant joints only and present a new statistical principle to decide the time scale of motion features, to reduce computational costs and adapt to variable movement speed. In the action graphs module, we exploit these 3D skeletonbased motion features to build action graphs, and devise a new score function
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.