Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
Zenith wet delay(ZWD)is a key parameter for the precise positioning of global navigation satellite systems(GNSS)and occupies a central role in meteorological research.Currently,most models only consider the periodic v...Zenith wet delay(ZWD)is a key parameter for the precise positioning of global navigation satellite systems(GNSS)and occupies a central role in meteorological research.Currently,most models only consider the periodic variability of the ZWD,neglecting the effect of nonlinear factors on the ZWD estimation.This oversight results in a limited capability to reflect the rapid fluctuations of the ZWD.To more accurately capture and predict complicated variations in ZWD,this paper developed the CRZWD model by a combination of the GPT3 model and random forests(RF)algorithm using 5-year atmospheric profiles from 70 radiosonde(RS)stations across China.Taking the external 25 test stations data as reference,the root mean square(RMS)of the CRZWD model is 29.95 mm.Compared with the GPT3 model and another model using backpropagation neural network(BPNN),the accuracy has improved by 24.7%and 15.9%,respectively.Notably,over 56%of the test stations exhibit an improvement of more than 20%in contrast to GPT3-ZWD.Further temporal and spatial characteristic analyses also demonstrate the significant accuracy and stability advantages of the CRZWD model,indicating the potential prospects for GNSS-based applications.展开更多
A“water”accelerated metal-free catalytic system has been discovered for the Mukaiyama-aldol reaction.The system involves the use of B(C_(6)F_(5))_(3) as a catalyst,which is water-tolerant and able to activate the ca...A“water”accelerated metal-free catalytic system has been discovered for the Mukaiyama-aldol reaction.The system involves the use of B(C_(6)F_(5))_(3) as a catalyst,which is water-tolerant and able to activate the carbonyl group through a hydrogen bonding network generated by the catalyst.This activation method allows the reactions to be performed with very low catalyst loading,as low as 0.5 mol%.The scope of substrates is broad and a wide variety of functional groups are well tolerated.Diverse aliphatic aldehydes,aromatic aldehydes,unsaturated aldehydes and aromatic ketones coupled with silyl enol ethers/silyl ketone acetals to generate their correspondingβ-hydroxy carbonyl compounds in moderate to good yields.This discovery represents a significant advancement in the field of organic synthesis,as it provides a new,practical and sustainable solution for carbon-carbon bond formation in water.展开更多
Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transp...Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.展开更多
Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Ou...Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.展开更多
In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integr...In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.展开更多
In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technolog...In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technology and BIM(Building Information Modeling)model was discussed.Focused on the efficient acquisition of building geometric information using the fast-developing 3D point cloud technology,an improved deep learning-based 3D point cloud recognition method was proposed.The method optimised the network structure based on RandLA-Net to adapt to the large-scale point cloud processing requirements,while the semantic and instance features of the point cloud were integrated to significantly improve the recognition accuracy and provide a precise basis for BIM model remodeling.In addition,a visual BIM model generation system was developed,which systematically transformed the point cloud recognition results into BIM component parameters,automatically constructed BIM models,and promoted the open sharing and secondary development of models.The research results not only effectively promote the automation process of converting 3D point cloud data to refined BIM models,but also provide important technical support for promoting building informatisation and accelerating the construction of smart cities,showing a wide range of application potential and practical value.展开更多
Associative memory, one of the major cognitive functions in the hippocampal CA3 region, includes auto-associative memory and hetero-associative memory. Many previous studies have shown that Alzheimer＇s disease （AD）...Associative memory, one of the major cognitive functions in the hippocampal CA3 region, includes auto-associative memory and hetero-associative memory. Many previous studies have shown that Alzheimer＇s disease （AD） can lead to loss of functional synapses in the central nervous system, and associative memory functions in patients with AD are often impaired, but few studies have addressed the effect of AD on hetero-associative memory in the hippocampal CA3 region. In this study, based on a simplified anatomical structure and synaptic connections in the hippocampal CA3 region, a three-layered Hopfield-like neural network model of hippocampal CA3 was proposed and then used to simulate associative memory functions in three circumstances： normal, synaptic deletion and synaptic compensation, according to Ruppin＇s synaptic deletion and compensation theory. The influences of AD on hetero-associative memory were further analyzed. The simulated results showed that the established three-layered Hopfield-like neural network model of hippocampal CA3 has both auto-associative and hetero-associative memory functions. With increasing synaptic deletion level, both associative memory functions were gradually impaired and the mean firing rates of the neurons within the network model were decreased. With gradual increasing synaptic compensation, the associative memory functions of the network were improved and the mean firing rates were increased. The simulated results suggest that the Hopfield-like neural network model can effectively simulate both associative memory functions of the hippocampal CA3 region. Synaptic deletion affects both auto-associative and hetero-associative memory functions in the hippocampal CA3 region, and can also result in memory dysfunction. To some extent, synaptic compensation measures can offset two kinds of associative memory dysfunction caused by synaptic deletion in the hippocampal CA3 area.展开更多
Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the ...Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.展开更多
Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock propert...Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock properties.Given the multiscale characteristics of rock pore structures,direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible.This study introduces a method for reconstructing porous media using multidimensional data,which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models.The pore network model(PNM)is stochastically reconstructed using one-dimensional parameters,and a generative adversarial network(GAN)is utilized to equip the PNM with pore morphologies derived from two-dimensional images.The digital rocks generated by this method possess excellent controllability.Using Berea sandstone and Grosmont carbonate samples,we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM.Pore structure parameters,permeability,and formation factors were calculated.The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology,pore structure,and physical properties.Furthermore,our method effectively supplements the micropores not captured in CT images,demonstrating its potential in multiscale carbonate samples.Thus,the proposed reconstruction method is promising for advancing porous media property research.展开更多
Three-dimensional geological modeling （3DGM） assists geologists to quantitatively study in three-dimensional （3D） space structures that define temporal and spatial relationships between geological objects. The 3D ...Three-dimensional geological modeling （3DGM） assists geologists to quantitatively study in three-dimensional （3D） space structures that define temporal and spatial relationships between geological objects. The 3D property model can also be used to infer or deduce causes of geological objects. 3DGM technology provides technical support for extraction of diverse geoscience information, 3D modeling, and quantitative calculation of mineral resources. Based on metallogenic concepts and an ore deposit model, 3DGM technology is applied to analyze geological characteristics of the Tongshan Cu deposit in order to define a metallogenic model and develop a virtual borehole technology; a BP neural network and a 3D interpolation technique were combined to integrate multiple geoscience information in a 3D environment. The results indicate： （1） on basis of the concept of magmatic-hydrothermal Cu polymetallic mineraliza- tion and a porphyry Cu deposit model, a spatial relational database of multiple geoscience information for mineralization in the study area （geology, geophysics, geochemistry, borehole, and cross-section data） was established, and 3D metallogenic geological objects including mineralization stratum, granodiorite, alteration rock, and magnetic anomaly were constructed; （2） on basis of the 3D ore deposit model, 23,800 effective surveys from 94 boreholes and 21 sections were applied to establish 3D orebody models with a kriging interpolation method; （3） combined 23,800 surveys involving 21 sections, using VC＋＋ and OpenGL platform, virtual borehole and virtual section with BP network, and an improved inverse distance interpolation （IDW） method were used to predict and delineate mineralization potential targets （Cu-grade of cell not less than 0.1%）; （4） comparison of 3D ore bodies, metallogenic geological objects of mineralization, and potential targets of mineralization models in the study area, delineated the 3D spatial and temporal relationship and causal processes among the ore bodies, alteration rock, metallo- genic stratum, intrusive rock, and the Tongshan Fault. This study provides important technical support and a scientific basis for assessment of the Tongshan Cu deposit and surrounding exploration and mineral resources.展开更多
The lithofacies classification is essential for oil and gas reservoir exploration and development.The traditional method of lithofacies classification is based on"core calibration logging"and the experience ...The lithofacies classification is essential for oil and gas reservoir exploration and development.The traditional method of lithofacies classification is based on"core calibration logging"and the experience of geologists.This approach has strong subjectivity,low efficiency,and high uncertainty.This uncertainty may be one of the key factors affecting the results of 3 D modeling of tight sandstone reservoirs.In recent years,deep learning,which is a cutting-edge artificial intelligence technology,has attracted attention from various fields.However,the study of deep-learning techniques in the field of lithofacies classification has not been sufficient.Therefore,this paper proposes a novel hybrid deep-learning model based on the efficient data feature-extraction ability of convolutional neural networks(CNN)and the excellent ability to describe time-dependent features of long short-term memory networks(LSTM)to conduct lithological facies-classification experiments.The results of a series of experiments show that the hybrid CNN-LSTM model had an average accuracy of 87.3%and the best classification effect compared to the CNN,LSTM or the three commonly used machine learning models(Support vector machine,random forest,and gradient boosting decision tree).In addition,the borderline synthetic minority oversampling technique(BSMOTE)is introduced to address the class-imbalance issue of raw data.The results show that processed data balance can significantly improve the accuracy of lithofacies classification.Beside that,based on the fine lithofacies constraints,the sequential indicator simulation method is used to establish a three-dimensional lithofacies model,which completes the fine description of the spatial distribution of tight sandstone reservoirs in the study area.According to this comprehensive analysis,the proposed CNN-LSTM model,which eliminates class imbalance,can be effectively applied to lithofacies classification,and is expected to improve the reality of the geological model for the tight sandstone reservoirs.展开更多
Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important con...Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important content of numerical simulation.A new 3-dimensional rough discrete fracture network(RDFN3D)model and its modeling method based on the Weierstrass-Mandelbrot(W-M)function were presented in this paper.The RDFN3D model,which improves and unifies the modelling methods for the complex structural planes,has been realized.The influence of fractal dimension,amplitude,and surface precision on the modeling parameters of RDFN3D was discussed.The reasonable W-M parameters suitable for the roughness coefficient of JRC were proposed,and the relationship between the mathematical model and the joint characterization was established.The RDFN3D together with the smooth 3-dimensional discrete fracture network(DFN3D)models were successfully exported to the drawing exchange format,which will provide a wide application in numerous numerical simulation codes including both the continuous and discontinuous methods.The numerical models were discussed using the COMSOL Multiphysics code and the 3-dimensional particle flow code,respectively.The reliability of the RDFN3D model was preliminarily discussed and analyzed.The roughness and spatial connectivity of the fracture networks have a dominant effect on the fluid flow patterns.The research results can provide a new geological model and analysis model for numerical simulation and engineering analysis of jointed rock mass.展开更多
The method of building 3D model was discussed at first. Aiming at the feature of mine vacant place,a method to build the 3D vacant place model based on multi TIN (triangular irregular network) was put forward, and the...The method of building 3D model was discussed at first. Aiming at the feature of mine vacant place,a method to build the 3D vacant place model based on multi TIN (triangular irregular network) was put forward, and the data structure and visualization of vacant place were discussed. Then some crucial technologies of realizing function in 3D-GIS were proposed. In addition,the software about special 3D mapping and assaying was introduced.展开更多
With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming a...With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.展开更多
In this paper,3D-GIS reconstruction and interpolation approach,additional virtual borehole technology and BP network technology are used to explore the concealed ore body.The virtual borehole has same function as real...In this paper,3D-GIS reconstruction and interpolation approach,additional virtual borehole technology and BP network technology are used to explore the concealed ore body.The virtual borehole has same function as reality borehole due to the multi-information check and validation in展开更多
As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important pos...As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing.Among algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric parameters.In this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles.The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles.In this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.展开更多
OBJECTIVE:To investigate the potential pharmacological mechanisms of Ganshuang granules(肝爽颗粒,GSG)in treating non-alcoholic fatty liver(NAFLD).METHODS:All the active components and targets of GSG were retrieved fro...OBJECTIVE:To investigate the potential pharmacological mechanisms of Ganshuang granules(肝爽颗粒,GSG)in treating non-alcoholic fatty liver(NAFLD).METHODS:All the active components and targets of GSG were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform.Protein-Protein interaction network,Kyoto Encyclopedia of Genes and Genomes and Gene Ontology function annotation of common targets were analyzed to predict the mechanisms of action of GSG in the treatment of NAFLD.Then,the mouse models of NAFLD were constructed in a diet-induced manner and treated with GSG.The levels of interleukin 6(IL-6),tumor necrosis factor-alpha(TNF-α)and phosphatidylinositol 3-kinase/protein kinase B(PI3K/AKT)pathway-related proteins in the liver of mice in each group were measured by enzyme linked immunosorbent assay and Western blot,respectively.RESULTS:Network pharmacology revealed a total of 159 potential targets of GSG for the treatment of NAFLD.Functional enrichment analysis indicated that the PI3K/AKT signaling pathway may be involved during GSG treatment of NAFLD.Further experiments showed that the significantly decreased alanine aminotransferase,aspartate aminotransferase,alkaline phosphatase,total cholesterol,triglyceride and low-density lipoprotein cholesterol levels in NAFLD model mice serum after GSG treatment,as well as the expression levels of IL-6 and TNF-αin the liver.Furthermore,drug intervention increased the protein expression levels of phosphorylated-PI3K(P-PI3K)and P-AKT in the liver of the model group mice,and decreased the protein expression level of sterol regulatory element-binding protein 1.CONCLUSION:We found that GSG is effective in treating NAFLD and the potential therapeutic targets may be involved in PI3K/AKT signaling pathway.展开更多
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金supported by the National Natural Science Foundation of China[42030109,42074012]the Scientific Study Project for institutes of Higher Learning,Ministry of Education,Liaoning Province[LJKMZ20220673]+2 种基金the Project supported by the State Key Laboratory of Geodesy and Earths'Dynamics,Innovation Academy for Precision Measurement Science and Technology[SKLGED2023-3-2]Liaoning Revitalization Talent Program[XLYC2203162]Natural Science Foundation of Hebei Province in China[D2023402024].
文摘Zenith wet delay(ZWD)is a key parameter for the precise positioning of global navigation satellite systems(GNSS)and occupies a central role in meteorological research.Currently,most models only consider the periodic variability of the ZWD,neglecting the effect of nonlinear factors on the ZWD estimation.This oversight results in a limited capability to reflect the rapid fluctuations of the ZWD.To more accurately capture and predict complicated variations in ZWD,this paper developed the CRZWD model by a combination of the GPT3 model and random forests(RF)algorithm using 5-year atmospheric profiles from 70 radiosonde(RS)stations across China.Taking the external 25 test stations data as reference,the root mean square(RMS)of the CRZWD model is 29.95 mm.Compared with the GPT3 model and another model using backpropagation neural network(BPNN),the accuracy has improved by 24.7%and 15.9%,respectively.Notably,over 56%of the test stations exhibit an improvement of more than 20%in contrast to GPT3-ZWD.Further temporal and spatial characteristic analyses also demonstrate the significant accuracy and stability advantages of the CRZWD model,indicating the potential prospects for GNSS-based applications.
基金financial support from the Start-up Grant of Nanjing Tech University(Nos.38274017103,38037037)financial support from Distinguished University Professor grant(Nanyang Technological University)+1 种基金the Agency for Science,Technology and Research(A∗STAR)under its MTC Individual Research Grants(No.M21K2c0114)RIE2025 MTC Programmatic Fund(No.M22K9b0049).
文摘A“water”accelerated metal-free catalytic system has been discovered for the Mukaiyama-aldol reaction.The system involves the use of B(C_(6)F_(5))_(3) as a catalyst,which is water-tolerant and able to activate the carbonyl group through a hydrogen bonding network generated by the catalyst.This activation method allows the reactions to be performed with very low catalyst loading,as low as 0.5 mol%.The scope of substrates is broad and a wide variety of functional groups are well tolerated.Diverse aliphatic aldehydes,aromatic aldehydes,unsaturated aldehydes and aromatic ketones coupled with silyl enol ethers/silyl ketone acetals to generate their correspondingβ-hydroxy carbonyl compounds in moderate to good yields.This discovery represents a significant advancement in the field of organic synthesis,as it provides a new,practical and sustainable solution for carbon-carbon bond formation in water.
基金the National Natural Science Foundation of China(Nos.62272063,62072056 and 61902041)the Natural Science Foundation of Hunan Province(Nos.2022JJ30617 and 2020JJ2029)+4 种基金Open Research Fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology,Nanjing University of Posts and Telecommunications(No.JZNY202102)the Traffic Science and Technology Project of Hunan Province,China(No.202042)Hunan Provincial Key Research and Development Program(No.2022GK2019)this work was funded by the Researchers Supporting Project Number(RSPD2023R681)King Saud University,Riyadh,Saudi Arabia.
文摘Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.
文摘Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.
基金the management of Sierra Rutile Company for providing the drillhole dataset used in this studythe Japanese Ministry of Education Science and Technology (MEXT) Scholarship for academic funding
文摘In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.
文摘In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technology and BIM(Building Information Modeling)model was discussed.Focused on the efficient acquisition of building geometric information using the fast-developing 3D point cloud technology,an improved deep learning-based 3D point cloud recognition method was proposed.The method optimised the network structure based on RandLA-Net to adapt to the large-scale point cloud processing requirements,while the semantic and instance features of the point cloud were integrated to significantly improve the recognition accuracy and provide a precise basis for BIM model remodeling.In addition,a visual BIM model generation system was developed,which systematically transformed the point cloud recognition results into BIM component parameters,automatically constructed BIM models,and promoted the open sharing and secondary development of models.The research results not only effectively promote the automation process of converting 3D point cloud data to refined BIM models,but also provide important technical support for promoting building informatisation and accelerating the construction of smart cities,showing a wide range of application potential and practical value.
基金the National Natural Science Foundation of China,No.30870649the Natural Science Foundation of Tianjin,No.08JCYBJC03300
文摘Associative memory, one of the major cognitive functions in the hippocampal CA3 region, includes auto-associative memory and hetero-associative memory. Many previous studies have shown that Alzheimer＇s disease （AD） can lead to loss of functional synapses in the central nervous system, and associative memory functions in patients with AD are often impaired, but few studies have addressed the effect of AD on hetero-associative memory in the hippocampal CA3 region. In this study, based on a simplified anatomical structure and synaptic connections in the hippocampal CA3 region, a three-layered Hopfield-like neural network model of hippocampal CA3 was proposed and then used to simulate associative memory functions in three circumstances： normal, synaptic deletion and synaptic compensation, according to Ruppin＇s synaptic deletion and compensation theory. The influences of AD on hetero-associative memory were further analyzed. The simulated results showed that the established three-layered Hopfield-like neural network model of hippocampal CA3 has both auto-associative and hetero-associative memory functions. With increasing synaptic deletion level, both associative memory functions were gradually impaired and the mean firing rates of the neurons within the network model were decreased. With gradual increasing synaptic compensation, the associative memory functions of the network were improved and the mean firing rates were increased. The simulated results suggest that the Hopfield-like neural network model can effectively simulate both associative memory functions of the hippocampal CA3 region. Synaptic deletion affects both auto-associative and hetero-associative memory functions in the hippocampal CA3 region, and can also result in memory dysfunction. To some extent, synaptic compensation measures can offset two kinds of associative memory dysfunction caused by synaptic deletion in the hippocampal CA3 area.
基金the Project of the Key Open Laboratory of Atmospheric Detection,China Meteorological Administration(2023KLAS02M)the Second Batch of Science and Technology Project of China Meteorological Administration("Jiebangguashuai"):the Research and Development of Short-term and Near-term Warning Products for Severe Convective Weather in Beijing-Tianjin-Hebei Region(CMAJBGS202307).
文摘Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.
基金supported by the Shandong Provincial Natural Science Foundation(ZR2024MD116)National Natural Science Foundation of China(Grant Nos.42174143,42004098)Technology Innovation Leading Program of Shaanxi(No.2024 ZC-YYDP-27).
文摘Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock properties.Given the multiscale characteristics of rock pore structures,direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible.This study introduces a method for reconstructing porous media using multidimensional data,which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models.The pore network model(PNM)is stochastically reconstructed using one-dimensional parameters,and a generative adversarial network(GAN)is utilized to equip the PNM with pore morphologies derived from two-dimensional images.The digital rocks generated by this method possess excellent controllability.Using Berea sandstone and Grosmont carbonate samples,we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM.Pore structure parameters,permeability,and formation factors were calculated.The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology,pore structure,and physical properties.Furthermore,our method effectively supplements the micropores not captured in CT images,demonstrating its potential in multiscale carbonate samples.Thus,the proposed reconstruction method is promising for advancing porous media property research.
基金supported by the National Basic Research Program of China(Grant No.1212010881001 )the National Scicnce of the 12th "Five-Year Technology Support Program"(Grant No.2010BAE00281-6)+1 种基金the National Natural Science Foundation of China(Grant Nos.40772157,40972232, 41072070)the State Key Laboratory of Geological Processes and Mineral Resources(Grant Nos.GPMR0941,200624)
文摘Three-dimensional geological modeling （3DGM） assists geologists to quantitatively study in three-dimensional （3D） space structures that define temporal and spatial relationships between geological objects. The 3D property model can also be used to infer or deduce causes of geological objects. 3DGM technology provides technical support for extraction of diverse geoscience information, 3D modeling, and quantitative calculation of mineral resources. Based on metallogenic concepts and an ore deposit model, 3DGM technology is applied to analyze geological characteristics of the Tongshan Cu deposit in order to define a metallogenic model and develop a virtual borehole technology; a BP neural network and a 3D interpolation technique were combined to integrate multiple geoscience information in a 3D environment. The results indicate： （1） on basis of the concept of magmatic-hydrothermal Cu polymetallic mineraliza- tion and a porphyry Cu deposit model, a spatial relational database of multiple geoscience information for mineralization in the study area （geology, geophysics, geochemistry, borehole, and cross-section data） was established, and 3D metallogenic geological objects including mineralization stratum, granodiorite, alteration rock, and magnetic anomaly were constructed; （2） on basis of the 3D ore deposit model, 23,800 effective surveys from 94 boreholes and 21 sections were applied to establish 3D orebody models with a kriging interpolation method; （3） combined 23,800 surveys involving 21 sections, using VC＋＋ and OpenGL platform, virtual borehole and virtual section with BP network, and an improved inverse distance interpolation （IDW） method were used to predict and delineate mineralization potential targets （Cu-grade of cell not less than 0.1%）; （4） comparison of 3D ore bodies, metallogenic geological objects of mineralization, and potential targets of mineralization models in the study area, delineated the 3D spatial and temporal relationship and causal processes among the ore bodies, alteration rock, metallo- genic stratum, intrusive rock, and the Tongshan Fault. This study provides important technical support and a scientific basis for assessment of the Tongshan Cu deposit and surrounding exploration and mineral resources.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.300102278402)。
文摘The lithofacies classification is essential for oil and gas reservoir exploration and development.The traditional method of lithofacies classification is based on"core calibration logging"and the experience of geologists.This approach has strong subjectivity,low efficiency,and high uncertainty.This uncertainty may be one of the key factors affecting the results of 3 D modeling of tight sandstone reservoirs.In recent years,deep learning,which is a cutting-edge artificial intelligence technology,has attracted attention from various fields.However,the study of deep-learning techniques in the field of lithofacies classification has not been sufficient.Therefore,this paper proposes a novel hybrid deep-learning model based on the efficient data feature-extraction ability of convolutional neural networks(CNN)and the excellent ability to describe time-dependent features of long short-term memory networks(LSTM)to conduct lithological facies-classification experiments.The results of a series of experiments show that the hybrid CNN-LSTM model had an average accuracy of 87.3%and the best classification effect compared to the CNN,LSTM or the three commonly used machine learning models(Support vector machine,random forest,and gradient boosting decision tree).In addition,the borderline synthetic minority oversampling technique(BSMOTE)is introduced to address the class-imbalance issue of raw data.The results show that processed data balance can significantly improve the accuracy of lithofacies classification.Beside that,based on the fine lithofacies constraints,the sequential indicator simulation method is used to establish a three-dimensional lithofacies model,which completes the fine description of the spatial distribution of tight sandstone reservoirs in the study area.According to this comprehensive analysis,the proposed CNN-LSTM model,which eliminates class imbalance,can be effectively applied to lithofacies classification,and is expected to improve the reality of the geological model for the tight sandstone reservoirs.
基金This work was financially supported by the National Key R&D Program of China(No.2021YFC2900500)the National Natural Science Foundation of China(Nos.52074020 and 42202306)+2 种基金the Open Fund of State Key Laboratory of Water Resource Protection and Utilization in Coal Mining(No.WPUKFJJ2019-06)the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)(No.FRF-IDRY-21001)the Natural Science Foundation of Jiangsu Province,China(No.BK20200993).
文摘Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important content of numerical simulation.A new 3-dimensional rough discrete fracture network(RDFN3D)model and its modeling method based on the Weierstrass-Mandelbrot(W-M)function were presented in this paper.The RDFN3D model,which improves and unifies the modelling methods for the complex structural planes,has been realized.The influence of fractal dimension,amplitude,and surface precision on the modeling parameters of RDFN3D was discussed.The reasonable W-M parameters suitable for the roughness coefficient of JRC were proposed,and the relationship between the mathematical model and the joint characterization was established.The RDFN3D together with the smooth 3-dimensional discrete fracture network(DFN3D)models were successfully exported to the drawing exchange format,which will provide a wide application in numerous numerical simulation codes including both the continuous and discontinuous methods.The numerical models were discussed using the COMSOL Multiphysics code and the 3-dimensional particle flow code,respectively.The reliability of the RDFN3D model was preliminarily discussed and analyzed.The roughness and spatial connectivity of the fracture networks have a dominant effect on the fluid flow patterns.The research results can provide a new geological model and analysis model for numerical simulation and engineering analysis of jointed rock mass.
文摘The method of building 3D model was discussed at first. Aiming at the feature of mine vacant place,a method to build the 3D vacant place model based on multi TIN (triangular irregular network) was put forward, and the data structure and visualization of vacant place were discussed. Then some crucial technologies of realizing function in 3D-GIS were proposed. In addition,the software about special 3D mapping and assaying was introduced.
文摘With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.
文摘In this paper,3D-GIS reconstruction and interpolation approach,additional virtual borehole technology and BP network technology are used to explore the concealed ore body.The virtual borehole has same function as reality borehole due to the multi-information check and validation in
基金National Natural Science Foundation of China(41901297,41806209)Science and Technology Key Project of Henan Province(202102310017)+1 种基金Key Research Projects for the Universities of Henan Province(20A170013)China Postdoctoral Science Foundation(2021M693201)。
文摘As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing.Among algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric parameters.In this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles.The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles.In this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.
基金the Chengdu University of Traditional Chinese Medicine:Application of A Mouse Model to Explore the Mechanism of Therapeutic Action of Safranin in Type 2 Diabetes Mellitus(No.YXRC2018003)。
文摘OBJECTIVE:To investigate the potential pharmacological mechanisms of Ganshuang granules(肝爽颗粒,GSG)in treating non-alcoholic fatty liver(NAFLD).METHODS:All the active components and targets of GSG were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform.Protein-Protein interaction network,Kyoto Encyclopedia of Genes and Genomes and Gene Ontology function annotation of common targets were analyzed to predict the mechanisms of action of GSG in the treatment of NAFLD.Then,the mouse models of NAFLD were constructed in a diet-induced manner and treated with GSG.The levels of interleukin 6(IL-6),tumor necrosis factor-alpha(TNF-α)and phosphatidylinositol 3-kinase/protein kinase B(PI3K/AKT)pathway-related proteins in the liver of mice in each group were measured by enzyme linked immunosorbent assay and Western blot,respectively.RESULTS:Network pharmacology revealed a total of 159 potential targets of GSG for the treatment of NAFLD.Functional enrichment analysis indicated that the PI3K/AKT signaling pathway may be involved during GSG treatment of NAFLD.Further experiments showed that the significantly decreased alanine aminotransferase,aspartate aminotransferase,alkaline phosphatase,total cholesterol,triglyceride and low-density lipoprotein cholesterol levels in NAFLD model mice serum after GSG treatment,as well as the expression levels of IL-6 and TNF-αin the liver.Furthermore,drug intervention increased the protein expression levels of phosphorylated-PI3K(P-PI3K)and P-AKT in the liver of the model group mice,and decreased the protein expression level of sterol regulatory element-binding protein 1.CONCLUSION:We found that GSG is effective in treating NAFLD and the potential therapeutic targets may be involved in PI3K/AKT signaling pathway.
