Logical and Innovative Construction of Digital Twin City

This paper first introduces the background and basic concept of digital twin city,then analyzes the relationship between digital twin city and smart city.Next,it introduces the primary supporting technologies for the construction of a digital twin city,and finally summarizes the current application status and development trends regarding digital twin city.The authors argue that digital twin technology will face challenges in regards to data,basic knowledge base,system integration,and talent issues if it is to be more widely applied in the construction of the smart city.Additionally,the authors propose institutional and technical suggestions for solving these problems at the macro and micro levels.

Remnants of the amalgamation of the east and west Cathaysia blocks revealed by a short-period dense nodal array

The Cathaysia block located at the southeast South China block(SCB)is considered formed by the amalgamation of the east and west Cathaysia blocks along the Gaoyao-Huilai and Zhenghe-Dapu deep faults(here referred as GHF and ZDF,respectively).Although the extension of the ZDF to the northeast,which represents the amalgamation of the two sub-blocks has been confirmed,the development of the GHF to the southwest remains to be verified.To better constrain the detailed deep structure beneath the southwest Cathaysia,which hold great significance for revealing the evolution of the SCB,a linear seismic array with 331 nodal geophones was deployed across the Sanshui basin(SSB).Combining with the regional 10 permanent stations(PA),we obtained two profiles with teleseismic P-wave receiver function stacking.The most obvious feature in our results is the ascending Moho towards the coastal area,which is consistent with the passive margin continental and extensional tectonic setting.The stacking profile from the dense nodal array(DNA)shows that the Moho is offset beneath the transition zone of the Nanling orogeny and SSB.We deduce that this offset may be casued by the deep extension of the GHF,which represents the remnants of the amalgamation of the Cathaysia block.From the other evidences,we infer that the widespread and early erupted felsic magmas in the SSB may have resulted from lithospheric materials that were squeezed out to the surface.The relative higher Bouguer gravity and heat flow support the consolidation of magmas and the residual warm state in the shallow crustal scale beneath the SSB.The sporadic basaltic magmas in the middle SSB may have a close relation to deep extension of the GHF,which serves as a channel for upwelling hot materials.

Mapping essential urban land use categories in China(EULUC-China):preliminary results for 2018

Land use reflects human activities on land.Urban land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and urbanization.Urban areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2].However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city level.The mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping personnel.To address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods.This is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental administration.However,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same scale.Even at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://sdi4apps.eu/open_land_use/).

Integration of maximum crop response with machine learning regression model to timely estimate crop yield

Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan’s Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R^(2)), Root Mean Square Error (RMSE, t/ ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat;0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6- 8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress.