In recent years,with the increasing attention to issues related to carbon emissions,such as carbon tariffs and government netzero carbon emission policies,carbon emissions have become an important indicator that is be...In recent years,with the increasing attention to issues related to carbon emissions,such as carbon tariffs and government netzero carbon emission policies,carbon emissions have become an important indicator that is being prioritized by governments worldwide.The Google Environmental Insights Explorer(EIE)tool has been developed to facilitate the collection and integration of data in this context.This study focuses on Tainan City and utilizes EIE to analyze greenhouse gas emissions from transportation.By using EIE,the study obtains data on greenhouse gas emissions from transportation activities in Tainan City.EIE utilizes data collected by Google and simulation functions to estimate data based on actual measurements of transportation activities.This tool saves time and resources by eliminating the need for on-site investigations while providing data that closely represent the real emissions from transportation activities in urban areas.Transportation vehicles contribute to greenhouse gas emissions in two ways:through direct combustion of fossil fuels and through the consumption of electricity in electric vehicles(EVs).The level of greenhouse gas emissions in a city’s transportation industry depends on factors such as transportation modes,fuel types,fleet age and energy efficiency,total distance traveled,and annual mileage.EIE estimates the greenhouse gas emissions from Tainan City’s transportation industry in 2022 to be 3,320,000 metric tons,including emissions from buses,motorcycles,cars,walking,railways,bicycles,and other modes of transportation.展开更多
【目的/意义】基于Open Site Explorer平台数据,分析和揭示网站层的我国图书馆共链关系及结构。【过程/方法】从该平台下载我国169所高校图书馆与公共图书馆网站的根域名入链数据,基于网站层的共链关系矩阵绘制共链网络,运用社会网络分...【目的/意义】基于Open Site Explorer平台数据,分析和揭示网站层的我国图书馆共链关系及结构。【过程/方法】从该平台下载我国169所高校图书馆与公共图书馆网站的根域名入链数据,基于网站层的共链关系矩阵绘制共链网络,运用社会网络分析方法对共链网络的节点中心性、块模型与核心-边缘结构进行分析。【结果/结论】该平台数据能够清晰地展现我国图书馆网站之间的共链关系结构,图书馆类型、实力及其所在地区的发达程度,对图书馆网站在共链关系网络中的网络特征及其网络空间分布有较大影响,但在网络中影响力最大、最重要的图书馆网站却是实力处于中上水平和中西部地区的图书馆。该平台既能为图书馆网站建设提供依据,也能拓展网络计量学理论与方法。展开更多
During the final of the International Standardization Youth Star Competition 2025,China Standardization interviewed several teams.In these young students,we see stories of their exploration,perseverance and dreams.The...During the final of the International Standardization Youth Star Competition 2025,China Standardization interviewed several teams.In these young students,we see stories of their exploration,perseverance and dreams.The competition has come to an end,but the real journey has just begun:How will this experience change their future pursuit?展开更多
The Yuncheng Basin,located in the southern part of the Fenwei Rift,North China,exhibits obvious crust thinning(Moho uplift of 6-8 km)and shallow Curie point depth(less than 18 km)and hence holds great potential for ge...The Yuncheng Basin,located in the southern part of the Fenwei Rift,North China,exhibits obvious crust thinning(Moho uplift of 6-8 km)and shallow Curie point depth(less than 18 km)and hence holds great potential for geothermal resources.However,geothermal exploration within the Yuncheng Basin typically faces significant challenges due to civil and industrial noise from dense populations and industrial activities.To address these challenges,both Controlled-Source Audio-frequency Magnetotellurics(CSAMT)and radon measurements were employed in Baozigou village to investigate the geothermal structures and identify potential geothermal targets.The CSAMT method effectively delineated the structure of the subsurface hydrothermal system,identifying the reservoir as Paleogene sandstones and Ordovician and Cambrian limestones at elevations ranging from−800 m to−2500 m.In particular,two concealed normal faults(F_(a)and F_(b))were newly revealed by the combination of CSAMT and radon profiling;these previously undetected faults,which exhibit different scales and opposing dips,are likely to be responsible for controlling the convection of thermal water within the Basin’s subsurface hydrothermal system.Moreover,this study developed a preliminary conceptual geothermal model for the Fen River Depression within the Yuncheng Basin,which encompasses geothermal heat sources,cap rocks,reservoirs,and fluid pathways,providing valuable insights for future geothermal exploration.In conjunction with the 3D geological model constructed from CSAMT resistivity structures beneath Baozigou village,test drilling is recommended in the northwestern region of the Baozigou area to intersect the potentially deep fractured carbonates that may contain temperature-elevated geothermal water.This study establishes a good set of guidelines for future geothermal exploration in this region,indicating that high-permeability faults in the central segments of the Fen River Depression are promising targets.展开更多
The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This rev...The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.展开更多
城中村信息三维构建是改造和管理城中村的前提和基础,本文以淮南市某城中村为例,介绍了基于ArcGIS Explorer建立三维虚拟城中村的研究过程,并重点探讨了系统体系结构、三维建模与系统功能等的技术与方法,结合Arcgis Explorer SDK进行定...城中村信息三维构建是改造和管理城中村的前提和基础,本文以淮南市某城中村为例,介绍了基于ArcGIS Explorer建立三维虚拟城中村的研究过程,并重点探讨了系统体系结构、三维建模与系统功能等的技术与方法,结合Arcgis Explorer SDK进行定制开发,实现查询属性等功能。展开更多
文摘In recent years,with the increasing attention to issues related to carbon emissions,such as carbon tariffs and government netzero carbon emission policies,carbon emissions have become an important indicator that is being prioritized by governments worldwide.The Google Environmental Insights Explorer(EIE)tool has been developed to facilitate the collection and integration of data in this context.This study focuses on Tainan City and utilizes EIE to analyze greenhouse gas emissions from transportation.By using EIE,the study obtains data on greenhouse gas emissions from transportation activities in Tainan City.EIE utilizes data collected by Google and simulation functions to estimate data based on actual measurements of transportation activities.This tool saves time and resources by eliminating the need for on-site investigations while providing data that closely represent the real emissions from transportation activities in urban areas.Transportation vehicles contribute to greenhouse gas emissions in two ways:through direct combustion of fossil fuels and through the consumption of electricity in electric vehicles(EVs).The level of greenhouse gas emissions in a city’s transportation industry depends on factors such as transportation modes,fuel types,fleet age and energy efficiency,total distance traveled,and annual mileage.EIE estimates the greenhouse gas emissions from Tainan City’s transportation industry in 2022 to be 3,320,000 metric tons,including emissions from buses,motorcycles,cars,walking,railways,bicycles,and other modes of transportation.
文摘【目的/意义】基于Open Site Explorer平台数据,分析和揭示网站层的我国图书馆共链关系及结构。【过程/方法】从该平台下载我国169所高校图书馆与公共图书馆网站的根域名入链数据,基于网站层的共链关系矩阵绘制共链网络,运用社会网络分析方法对共链网络的节点中心性、块模型与核心-边缘结构进行分析。【结果/结论】该平台数据能够清晰地展现我国图书馆网站之间的共链关系结构,图书馆类型、实力及其所在地区的发达程度,对图书馆网站在共链关系网络中的网络特征及其网络空间分布有较大影响,但在网络中影响力最大、最重要的图书馆网站却是实力处于中上水平和中西部地区的图书馆。该平台既能为图书馆网站建设提供依据,也能拓展网络计量学理论与方法。
文摘During the final of the International Standardization Youth Star Competition 2025,China Standardization interviewed several teams.In these young students,we see stories of their exploration,perseverance and dreams.The competition has come to an end,but the real journey has just begun:How will this experience change their future pursuit?
基金supported by the Shanxi Province Basic Research Program(No.20210302123374)Yuncheng University Doctoral Research Initiation Fund(No.YQ-2021008)+3 种基金Excellent doctors come to Shanxi to reward scientific research projects(No.QZX-2023020)Open Fund of State Key Laboratory of Precision Geodesy(No.SKLPG2025-1-1)Joint Open Fund of the Research Platforms of School of Computer Science,China University of Geosciences,Wuhan(No.PTLH2024-B-03)Hubei Provincial Natural Science Foundation Project(No.2025AFC095).
文摘The Yuncheng Basin,located in the southern part of the Fenwei Rift,North China,exhibits obvious crust thinning(Moho uplift of 6-8 km)and shallow Curie point depth(less than 18 km)and hence holds great potential for geothermal resources.However,geothermal exploration within the Yuncheng Basin typically faces significant challenges due to civil and industrial noise from dense populations and industrial activities.To address these challenges,both Controlled-Source Audio-frequency Magnetotellurics(CSAMT)and radon measurements were employed in Baozigou village to investigate the geothermal structures and identify potential geothermal targets.The CSAMT method effectively delineated the structure of the subsurface hydrothermal system,identifying the reservoir as Paleogene sandstones and Ordovician and Cambrian limestones at elevations ranging from−800 m to−2500 m.In particular,two concealed normal faults(F_(a)and F_(b))were newly revealed by the combination of CSAMT and radon profiling;these previously undetected faults,which exhibit different scales and opposing dips,are likely to be responsible for controlling the convection of thermal water within the Basin’s subsurface hydrothermal system.Moreover,this study developed a preliminary conceptual geothermal model for the Fen River Depression within the Yuncheng Basin,which encompasses geothermal heat sources,cap rocks,reservoirs,and fluid pathways,providing valuable insights for future geothermal exploration.In conjunction with the 3D geological model constructed from CSAMT resistivity structures beneath Baozigou village,test drilling is recommended in the northwestern region of the Baozigou area to intersect the potentially deep fractured carbonates that may contain temperature-elevated geothermal water.This study establishes a good set of guidelines for future geothermal exploration in this region,indicating that high-permeability faults in the central segments of the Fen River Depression are promising targets.
文摘The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.
