Bemisia tabaci has a wide range of host plants. Due to its short-distance solid migratory ability, there is a phenomenon of migration and damage among plants in different seasons. Through the study of the population g...Bemisia tabaci has a wide range of host plants. Due to its short-distance solid migratory ability, there is a phenomenon of migration and damage among plants in different seasons. Through the study of the population growth and decline rules of B. tabaci on different host plants and the prediction and forecast technology explores its sustainable prevention control technology. This article mainly discusses the research progress of B. tabaci forecasting, realizing the timely prevention and control of B. tabaci and comprehensive regional management, which can effectively reduce the population base of B. tabaci and reduce the number of pesticides used, which has a protective effect on the ecological environment.展开更多
Large-scale bed separation in bending strip upside cranny strip was brought by un-consistency of overlying strata subsidence movement due to under surface mining. Different characteristic of movement and deformation o...Large-scale bed separation in bending strip upside cranny strip was brought by un-consistency of overlying strata subsidence movement due to under surface mining. Different characteristic of movement and deformation of overlying strata and surface include periodic caving of bed separation interspaced along with work face mining. Conglomerate rock layer movement is the main power fountain of rock burst on the basis of locale observation. And rock burst moves periodically adapted to movement of deep conglomerate rock layer which had similar characteristic with main roof. Practice indicates this method that forecasting and prediction using correlation information of movement of strata and surface is feasible and has reference meaning for similar stope.展开更多
Earthquake prediction is considered impossible for there is no scientific way to find the date and time, the location, and the magnitude of an earthquake. A new idea is introduced in this paper—earth rotation harmoni...Earthquake prediction is considered impossible for there is no scientific way to find the date and time, the location, and the magnitude of an earthquake. A new idea is introduced in this paper—earth rotation harmonics triggered natural volcano and earthquake. With earth rotation harmonics response model for a location, it could be possible to calculate the earthquake date and time, and the magnitude. Properties of earth rotation harmonics triggered earthquake are discussed and verified with earthquake data from USGS website. Also, both earth tide and ocean tide effects on earthquake are discussed and verified with earthquake data—tides did not trigger the natural earthquake, they only affect the earthquake activities and time.展开更多
This comprehensive review explores the integration of artificial intelligence(AI) in shale gas and oil resource management,with a focus on the application of machine learning and deep learning techniques.Shale gas and...This comprehensive review explores the integration of artificial intelligence(AI) in shale gas and oil resource management,with a focus on the application of machine learning and deep learning techniques.Shale gas and oil extraction,once constrained by complex geological and petrophysical challenges,has benefited significantly from AI methodologies,which enhance reservoir characterization,fracture prediction,production forecasting,and optimization of hydraulic fracturing.The review examines the current state of AI applications,identifying key advances in AI-driven models that improve predictive accuracy,address data heterogeneity,and integrate diverse data sources.Special attention is given to the combination of AI with traditional physical simulation models,such as physics-informed neural networks,and the potential for hybrid modeling approaches.Despite these advancements,the review highlights several challenges,including data sparsity,model interpretability,and the need for continuous updates with real-time data.Moreover,the review discusses emerging trends,such as explainable AI and real-time monitoring systems,which offer the promise of improving model transparency and adaptability.Future research directions are proposed to enhance the scalability and robustness of AI models,particularly through the integration of advanced hybrid models,uncertainty quantification techniques,and collaborative efforts across academia and industry,ultimately aiming to foster sustainable and efficient shale resource management.展开更多
Eyewall replacement cycles(ERCs)greatly increase the destructive potential of tropical cyclones(TCs)by affecting the maximum wind speed,wind field size,and storm surge severity while simultaneously reducing confidence...Eyewall replacement cycles(ERCs)greatly increase the destructive potential of tropical cyclones(TCs)by affecting the maximum wind speed,wind field size,and storm surge severity while simultaneously reducing confidence in TC forecasts,most prominently in intensity forecasting.Machine learning(ML)presents new opportunities to improve current forecasting and predictive capabilities,and its application will benefit forecasters and ultimately the public.The objective of this project was to create a proof-of-concept ML convolutional neural network(CNN)to predict ERCs using the 89 GHz microwave band for training and testing.The training set was comprised of North Atlantic basin(NATL)storms from 1999 to 2009.The testing set included NATL storms from 2019 to 2022.Twelve models were created,together known as the CNN Ensemble for Predicting Eyewall Replacement Cycles(CE-PERCY),with each individual member achieving at least 80%in-training accuracy.Two versions were created:versions A and B.Using synthetic aperture radar,land-based radar,aircraft reconnaissance,Microwave-based Probability of ERC(M-PERC),National Hurricane Center reports,and microwave imagery,ERC analysis was conducted on the testing set.28 ERCs were identified throughout 14 hurricanes from 2019 to 2022.CE-PERCY performs well for a proof-of-concept,with versions A and B predicting 21 and 23 ERCs,respectively.This project successfully introduces a foundation for using ML CNNs in ERC prediction,demonstrates the viability of the technique,and proves that a large enough dataset of microwave imagery can be used in this specific application.展开更多
Rock mass mechanics can be classified into engineering rock mass mechanics and disaster rock mass mechanics based on science and application.Their conception,object,scientific essence and application were elaborated.T...Rock mass mechanics can be classified into engineering rock mass mechanics and disaster rock mass mechanics based on science and application.Their conception,object,scientific essence and application were elaborated.The connotation,studying method and theoretical framework of disaster rock mass mechanics were described.Disaster rock mass mechanics is a strongly nonlinear discipline which is a strong tool to study natural and artificially-induced disasters.The rock mass system where disasters happen exhibits extremely spatial-temporal nonlinearity in the critically unstable state.Hence,the potentially effective prediction and forecasting of disasters depends on statistical analysis of highly probable events.The direction of efforts for predicting and forecasting disasters could be to find the quantitative or semi-quantitative relationship between physical and biological information and instability of rock mass system.展开更多
This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management ...This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.展开更多
文摘Bemisia tabaci has a wide range of host plants. Due to its short-distance solid migratory ability, there is a phenomenon of migration and damage among plants in different seasons. Through the study of the population growth and decline rules of B. tabaci on different host plants and the prediction and forecast technology explores its sustainable prevention control technology. This article mainly discusses the research progress of B. tabaci forecasting, realizing the timely prevention and control of B. tabaci and comprehensive regional management, which can effectively reduce the population base of B. tabaci and reduce the number of pesticides used, which has a protective effect on the ecological environment.
文摘Large-scale bed separation in bending strip upside cranny strip was brought by un-consistency of overlying strata subsidence movement due to under surface mining. Different characteristic of movement and deformation of overlying strata and surface include periodic caving of bed separation interspaced along with work face mining. Conglomerate rock layer movement is the main power fountain of rock burst on the basis of locale observation. And rock burst moves periodically adapted to movement of deep conglomerate rock layer which had similar characteristic with main roof. Practice indicates this method that forecasting and prediction using correlation information of movement of strata and surface is feasible and has reference meaning for similar stope.
文摘Earthquake prediction is considered impossible for there is no scientific way to find the date and time, the location, and the magnitude of an earthquake. A new idea is introduced in this paper—earth rotation harmonics triggered natural volcano and earthquake. With earth rotation harmonics response model for a location, it could be possible to calculate the earthquake date and time, and the magnitude. Properties of earth rotation harmonics triggered earthquake are discussed and verified with earthquake data from USGS website. Also, both earth tide and ocean tide effects on earthquake are discussed and verified with earthquake data—tides did not trigger the natural earthquake, they only affect the earthquake activities and time.
基金supported by the Fuling Shale Gas Environmental Exploration Technology of National Science and Technology Special Project(No.2016ZX05060)the following projects:the“Efficient and Low-Carbon Green Resource Utilization Technology Development of Typical Mine Tailings in the Danjiangkou Water Source Area”(No.20231h0168)the“Key Technology Development for Low-Carbon and High-Value Resource Utilization of Oil-containing Urban Mining in the Danjiangkou Water Source Area”(No.2024BEB027)
文摘This comprehensive review explores the integration of artificial intelligence(AI) in shale gas and oil resource management,with a focus on the application of machine learning and deep learning techniques.Shale gas and oil extraction,once constrained by complex geological and petrophysical challenges,has benefited significantly from AI methodologies,which enhance reservoir characterization,fracture prediction,production forecasting,and optimization of hydraulic fracturing.The review examines the current state of AI applications,identifying key advances in AI-driven models that improve predictive accuracy,address data heterogeneity,and integrate diverse data sources.Special attention is given to the combination of AI with traditional physical simulation models,such as physics-informed neural networks,and the potential for hybrid modeling approaches.Despite these advancements,the review highlights several challenges,including data sparsity,model interpretability,and the need for continuous updates with real-time data.Moreover,the review discusses emerging trends,such as explainable AI and real-time monitoring systems,which offer the promise of improving model transparency and adaptability.Future research directions are proposed to enhance the scalability and robustness of AI models,particularly through the integration of advanced hybrid models,uncertainty quantification techniques,and collaborative efforts across academia and industry,ultimately aiming to foster sustainable and efficient shale resource management.
文摘Eyewall replacement cycles(ERCs)greatly increase the destructive potential of tropical cyclones(TCs)by affecting the maximum wind speed,wind field size,and storm surge severity while simultaneously reducing confidence in TC forecasts,most prominently in intensity forecasting.Machine learning(ML)presents new opportunities to improve current forecasting and predictive capabilities,and its application will benefit forecasters and ultimately the public.The objective of this project was to create a proof-of-concept ML convolutional neural network(CNN)to predict ERCs using the 89 GHz microwave band for training and testing.The training set was comprised of North Atlantic basin(NATL)storms from 1999 to 2009.The testing set included NATL storms from 2019 to 2022.Twelve models were created,together known as the CNN Ensemble for Predicting Eyewall Replacement Cycles(CE-PERCY),with each individual member achieving at least 80%in-training accuracy.Two versions were created:versions A and B.Using synthetic aperture radar,land-based radar,aircraft reconnaissance,Microwave-based Probability of ERC(M-PERC),National Hurricane Center reports,and microwave imagery,ERC analysis was conducted on the testing set.28 ERCs were identified throughout 14 hurricanes from 2019 to 2022.CE-PERCY performs well for a proof-of-concept,with versions A and B predicting 21 and 23 ERCs,respectively.This project successfully introduces a foundation for using ML CNNs in ERC prediction,demonstrates the viability of the technique,and proves that a large enough dataset of microwave imagery can be used in this specific application.
基金supported by the National Natural Science Foundation of China(Grant No.52122405)Shanxi major research program for science and technology(Grant No.202101060301024).
文摘Rock mass mechanics can be classified into engineering rock mass mechanics and disaster rock mass mechanics based on science and application.Their conception,object,scientific essence and application were elaborated.The connotation,studying method and theoretical framework of disaster rock mass mechanics were described.Disaster rock mass mechanics is a strongly nonlinear discipline which is a strong tool to study natural and artificially-induced disasters.The rock mass system where disasters happen exhibits extremely spatial-temporal nonlinearity in the critically unstable state.Hence,the potentially effective prediction and forecasting of disasters depends on statistical analysis of highly probable events.The direction of efforts for predicting and forecasting disasters could be to find the quantitative or semi-quantitative relationship between physical and biological information and instability of rock mass system.
文摘This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.
