Text-level discourse parsing remains a challenge: most approaches employ features that fail to capture the intentional, semantic, and syntactic aspects that govern discourse coherence. In this paper, we propose a recursive model for discourse parsing that jointly models distributed representations for clauses, sentences, and entire discourses. The learned representations can to some extent learn the semantic and intentional import of words and larger discourse units automatically,. The proposed framework obtains comparable performance regarding standard discoursing parsing evaluations when compared against current state-of-art systems.
Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we used all Sentinel-2 images on the Google Earth Engine cloud platform, and constructed an improved peak point detection method to extract the cropping intensity of a heterogeneous planting area combined with crop phenology. The crop growth cycle profiles were extracted from the multi-temporal normalized difference vegetation index (NDVI) and land surface water index (LSWI) datasets. Results show that by 2020, the area of single cropping, double cropping, and triple cropping in the Henan Province are 52,236.9 km2, 74,334.1 km2, and 1927.1 km2, respectively; the corresponding producer accuracies are 86.12%, 93.72%, and 91.41%, respectively; the corresponding user accuracies are 88.99%, 92.29%, and 71.26%, respectively. The overall accuracy is 90.95%, and the Kappa coefficient is 0.81. Using the sown area in the statistical yearbook data of cities in the Henan Province to verify the extraction results of this paper, the R2 is 0.9717, and the root mean square error is 1715.9 km2. This study shows that using all the Sentinel-2 data, the phenology algorithm, and cloud computing technology has great potential in producing a high spatio-temporal resolution dataset for crop remote sensing monitoring and agricultural policymaking in complex planting areas.
The purpose of this study is to diagnose mesoscale factors responsible for the formation and development of an extreme rainstorm that occurred on 20 July 2021 in Zhengzhou, China. The rainstorm produced 201.9 mm rainfall in one hour, breaking the record of mainland China for 1-h rainfall accumulation in the past 73 years. Using 2-km continuously cycled analyses with 6-min updates that were produced by assimilating observations from radar and dense surface networks with a four-dimensional variational (4DVar) data assimilation system, we illustrate that the modification of environmental easterlies by three mesoscale disturbances played a critical role in the development of the rainstorm. Among the three systems, a meso-beta-scale low pressure system (mesolow) that developed from an inverted trough southwest of Zhengzhou was key to the formation and intensification of the rainstorm. We show that the rainstorm formed via sequential merging of three convective cells, which initiated along the convergence bands in the mesolow. Further, we present evidence to suggest that the mesolow and two terrain-influenced flows near the Taihang mountains north of Zhengzhou, including a barrier jet and a downslope flow, contributed to the local intensification of the rainstorm and the intense 1-h rainfall. The three mesoscale features co-existed near Zhengzhou in the several hours before the extreme one-hour rainfall and enhanced local wind convergence and moisture transport synergistically. Our analysis also indicated that the strong midlevel south/southwesterly winds from the mesolow along with the gravity-current-modified low-level northeasterly barrier jet enhanced the vertical wind shear, which provided favorable local environment supporting the severe rainstorm.
The rapid development of the web geographic information system (Web GIS) has promoted new vitality in high school geography education, relieved the stress of geography teachers caused by software and technical problems, and made it possible for teachers to devote more energy to geography teaching and research activities. Natural disaster education is not only an important part of the geography curriculum, but also an indispensable aspect of education for sustainable development (ESD) for high school students. The application of Web GIS in the dynamic monitoring, forecast, and early warning of natural disasters is becoming more experienced. Therefore, the application of Web GIS in natural disaster education is quite feasible. How to build a bridge between them is the purpose of this paper. Thus, the paper selects ArcGIS Online, which is not limited by time and space, and analyzes several functions that apply it to geography teaching. These include smart mapping, story maps, 3D web maps, and mobile GIS. Meanwhile, it analyzes the knowledge structure of “natural disasters” in Chinese geography textbooks to guide the subsequent case design. Then, the Web GIS inquiry-based teaching case is formed based on “7.20 Zhengzhou Torrential Rain”. It contains knowledge about natural disasters and designs from many aspects, such as the causes, manifestations, and prevention and control of disasters. The discussion identifies a range of specific educational benefits of applying Web GIS to natural disaster education for teachers and schools. Ultimately, it can provide some reference values for geography teachers and other developers to explore curriculum resources and create quality educational models.
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