Focusing on the key scientific questions of deep space exploration which include the origin and evolution of the solar system and its planets, disastrous impact on the Earth by the solar activities and small bodies, e...Focusing on the key scientific questions of deep space exploration which include the origin and evolution of the solar system and its planets, disastrous impact on the Earth by the solar activities and small bodies, extraterrestrial life, this paper put forward a propose about the roadmap and scientific objectives of China's Deep-space Exploration before 2030.展开更多
eight planets,various asteroids and comets in the solar system.Amount of deep-space scientific experiments promoted people to understand about the origin and evolution of the universe.With the rapid developments of eq...eight planets,various asteroids and comets in the solar system.Amount of deep-space scientific experiments promoted people to understand about the origin and evolution of the universe.With the rapid developments of equipment and spacecraft with high-accuracy detector and long-term energy,more and more ambitious deep-space exploration plans have also been scheduled or under discussion about space resources utilization and space migration,e.g.,manned landing on the Mars,guard infrastructures on the Moon and human-flight to the edge of the solar system(>100 AU),etc.展开更多
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
The Neogene Shawan Formation in the Chepaizi Uplift of the Junggar Basin(NW China)has obtained high oil flow,demonstrating a good potential for oil and gas exploration.The multi-source hydrocarbon generation backgroun...The Neogene Shawan Formation in the Chepaizi Uplift of the Junggar Basin(NW China)has obtained high oil flow,demonstrating a good potential for oil and gas exploration.The multi-source hydrocarbon generation background and strong tectonic activity have led to the simultaneous production of heavy oil and light oil from multi-layer in the area,which makes it very difficult to identify oil origins,presently,the hot debate on the oil origins needs to be clarified.In this paper,due to the selective consumption of different types of compounds in crude oils by severe and intense biodegradation,the commonly used oilsource correlation tools are ineffective or may produce misleading results,this study adopted a biomarker recovery method based on the principle of mass conservation that uses the sum of the mass of the residual biomarkers and their corresponding biodegradation products to obtain the mass of the original biomarkers,improving the reliability of oil origins determination.Based on the nature and occurrence of crude oils,the investigated oils are subdivided into three types,Group A,Group B and Group C.Group A,light oils occurred mainly in lower structure Neogene Shawan Formation in the western Chepaizi Uplift,while Group B,heavy oils occurred mainly in higher structure Neogene Shawan Formation in the western Chepaizi Uplift.The two types of crude oils may come from the mixed source of Jurassic Badaowan Formation source rocks(J_(1)b)and Paleogene Anjihaihe Formation source rocks(E_(2-3)a)in the Sikeshu Sag,and Jurassic Badaowan Formation source rocks(J_(1)b)are the main source of crude oils.Group C,heavy oils occurred mainly in Neogene Shawan Formation in the eastern Chepaizi Uplift,showing good correlation with the Permian(P_(1)f and P_(2)w)source rocks in the Shawan Sag.At the same time,by combining stable carbon isotope and parameters related to triaromatic steroids,the accuracy of the oilsource correlation results by biomarker recovery method was further verified.展开更多
This study investigated the heterogeneous responses of organic matter(OM)in highly-to over-mature source rocks during thermal maturation.An integrated analysis was conducted on the Raman spectroscopic and geochemical ...This study investigated the heterogeneous responses of organic matter(OM)in highly-to over-mature source rocks during thermal maturation.An integrated analysis was conducted on the Raman spectroscopic and geochemical signatures of shales from the Lower Silurian Longmaxi Formation and the Lower Cambrian Qiongzhusi Formation,as well as anthracites from the Lower Permian Shanxi–Formation and the Upper Carboniferous Taiyuan Formation(collectively referred to as the Shanxi Taiyuan Formations).Additionally,burial and thermal evolution modeling was employed to support the analysis.A systematic assessment of Raman spectral parameters(e.g.,the positions and intensity ratio of the D and G bands)revealed robust correlations between the thermal history patterns of source rocks and molecular structural evolution parameters.The subsequent mechanistic quantification demonstrated that the maturation state of the source rocks was subjected to the hierarchical control of three principal factors:Peak heating temperature,the duration of sustained thermal intensity,and effective maturation duration.In addition,comparative analyses demonstrated that the anthracites attained higher structural ordering under sustained thermal conditions.This contrasts with the disordered carbon matrices observed in the intermittently heated shales.Raman spectroscopy further revealed broader variations in the D and G band intensities of the Longmaxi Formation compared to the Qiongzhusi Formation.This difference is associated with their different thermal histories.The thermal burial histories confirm that shales in the Longmaxi Formation underwent thermal exposure at lower peak temperatures over a shorter duration compared to those in the Qiongzhusi Formation.Finally,this study established a maturity calibration model for over-mature source rocks through a systematic correlation between Raman peak height ratios(R_(D/G))and vitrinite reflectance(R_(o)).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
Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressin...Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.展开更多
In 2025,the global rare earth exploration and development sector achieved breakthroughs across multiple fronts.Projects advanced intensively across the Americas,Oceania,Africa,and Europe,with significant growth in res...In 2025,the global rare earth exploration and development sector achieved breakthroughs across multiple fronts.Projects advanced intensively across the Americas,Oceania,Africa,and Europe,with significant growth in resources,continuous emergence of new deposits,and strong impetus injected into the industry by technological innovation and policy support.The global rare earth resource supply pattern was further optimized (Table 1).1.Fruitful results in resource growth and new deposit discoveriesBrazil emerged as a core region for resource growth.The Colossus rare earth deposit saw a 150%increase in resources and announced its first reserve estimate.The Caldeira rare earth deposit’s resource estimate grew by 50%.The combined ore resources in the Caladão rare earth deposit’s Zones A and B reached 5.72×10~8 tonnes,with a total rare earth oxide(TREO) grade of 0.1506%,concurrently hosting 2.29×10~4tonnes of gallium metal resources.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
文摘Focusing on the key scientific questions of deep space exploration which include the origin and evolution of the solar system and its planets, disastrous impact on the Earth by the solar activities and small bodies, extraterrestrial life, this paper put forward a propose about the roadmap and scientific objectives of China's Deep-space Exploration before 2030.
文摘eight planets,various asteroids and comets in the solar system.Amount of deep-space scientific experiments promoted people to understand about the origin and evolution of the universe.With the rapid developments of equipment and spacecraft with high-accuracy detector and long-term energy,more and more ambitious deep-space exploration plans have also been scheduled or under discussion about space resources utilization and space migration,e.g.,manned landing on the Mars,guard infrastructures on the Moon and human-flight to the edge of the solar system(>100 AU),etc.
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.
基金co-funded by the National Natural Science Foundation of China(42372160,42072172)。
文摘The Neogene Shawan Formation in the Chepaizi Uplift of the Junggar Basin(NW China)has obtained high oil flow,demonstrating a good potential for oil and gas exploration.The multi-source hydrocarbon generation background and strong tectonic activity have led to the simultaneous production of heavy oil and light oil from multi-layer in the area,which makes it very difficult to identify oil origins,presently,the hot debate on the oil origins needs to be clarified.In this paper,due to the selective consumption of different types of compounds in crude oils by severe and intense biodegradation,the commonly used oilsource correlation tools are ineffective or may produce misleading results,this study adopted a biomarker recovery method based on the principle of mass conservation that uses the sum of the mass of the residual biomarkers and their corresponding biodegradation products to obtain the mass of the original biomarkers,improving the reliability of oil origins determination.Based on the nature and occurrence of crude oils,the investigated oils are subdivided into three types,Group A,Group B and Group C.Group A,light oils occurred mainly in lower structure Neogene Shawan Formation in the western Chepaizi Uplift,while Group B,heavy oils occurred mainly in higher structure Neogene Shawan Formation in the western Chepaizi Uplift.The two types of crude oils may come from the mixed source of Jurassic Badaowan Formation source rocks(J_(1)b)and Paleogene Anjihaihe Formation source rocks(E_(2-3)a)in the Sikeshu Sag,and Jurassic Badaowan Formation source rocks(J_(1)b)are the main source of crude oils.Group C,heavy oils occurred mainly in Neogene Shawan Formation in the eastern Chepaizi Uplift,showing good correlation with the Permian(P_(1)f and P_(2)w)source rocks in the Shawan Sag.At the same time,by combining stable carbon isotope and parameters related to triaromatic steroids,the accuracy of the oilsource correlation results by biomarker recovery method was further verified.
基金supported by the National Natural Science Foundation of China(42362022)the Open Fund of the Shaanxi Key Laboratory of Petroleum Accumulation Geology(PAG-202406)the Open Fund of the Mine Geology and Environment Academician and Expert Workstation(2024OITYSZJGZZ-005)。
文摘This study investigated the heterogeneous responses of organic matter(OM)in highly-to over-mature source rocks during thermal maturation.An integrated analysis was conducted on the Raman spectroscopic and geochemical signatures of shales from the Lower Silurian Longmaxi Formation and the Lower Cambrian Qiongzhusi Formation,as well as anthracites from the Lower Permian Shanxi–Formation and the Upper Carboniferous Taiyuan Formation(collectively referred to as the Shanxi Taiyuan Formations).Additionally,burial and thermal evolution modeling was employed to support the analysis.A systematic assessment of Raman spectral parameters(e.g.,the positions and intensity ratio of the D and G bands)revealed robust correlations between the thermal history patterns of source rocks and molecular structural evolution parameters.The subsequent mechanistic quantification demonstrated that the maturation state of the source rocks was subjected to the hierarchical control of three principal factors:Peak heating temperature,the duration of sustained thermal intensity,and effective maturation duration.In addition,comparative analyses demonstrated that the anthracites attained higher structural ordering under sustained thermal conditions.This contrasts with the disordered carbon matrices observed in the intermittently heated shales.Raman spectroscopy further revealed broader variations in the D and G band intensities of the Longmaxi Formation compared to the Qiongzhusi Formation.This difference is associated with their different thermal histories.The thermal burial histories confirm that shales in the Longmaxi Formation underwent thermal exposure at lower peak temperatures over a shorter duration compared to those in the Qiongzhusi Formation.Finally,this study established a maturity calibration model for over-mature source rocks through a systematic correlation between Raman peak height ratios(R_(D/G))and vitrinite reflectance(R_(o)).
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.
基金the Collaborative Innovation Project of Shanghai,China for the financial support。
文摘Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.
文摘In 2025,the global rare earth exploration and development sector achieved breakthroughs across multiple fronts.Projects advanced intensively across the Americas,Oceania,Africa,and Europe,with significant growth in resources,continuous emergence of new deposits,and strong impetus injected into the industry by technological innovation and policy support.The global rare earth resource supply pattern was further optimized (Table 1).1.Fruitful results in resource growth and new deposit discoveriesBrazil emerged as a core region for resource growth.The Colossus rare earth deposit saw a 150%increase in resources and announced its first reserve estimate.The Caldeira rare earth deposit’s resource estimate grew by 50%.The combined ore resources in the Caladão rare earth deposit’s Zones A and B reached 5.72×10~8 tonnes,with a total rare earth oxide(TREO) grade of 0.1506%,concurrently hosting 2.29×10~4tonnes of gallium metal resources.
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。
