In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This s...In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites.展开更多
It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements,including shock wave loading.However,the dependence of phase transformations in a wide range ...It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements,including shock wave loading.However,the dependence of phase transformations in a wide range of crystallographic directions of shock loading has not been revealed.In this work,we calculated the shock Hugoniot for aluminum and copper in different crystallographic directions([100],[110],[111],[112],[102],[114],[123],[134],[221]and[401])of shock compression using molecular dynamics(MD)simulations.The results showed a high pressure(>160 GPa for Cu and>40 GPa for Al)of the FCC-to-BCC transition.In copper,different characteristics of the phase transition are observed depending on the loading direction with the[100]compression direction being the weakest.The FCC-to-BCC transition for copper is in the range of 150–220 GPa,which is consistent with the existing experimental data.Due to the high transition pressure,the BCC phase transition in copper competes with melting.In aluminum,the FCC-to-BCC transition is observed for all studied directions at pressures between 40 and 50 GPa far beyond the melting.In all considered cases we observe the coexistence of HCP and BCC phases during the FCC-to-BCC transition,which is consistent with the experimental data and atomistic calculations;this HCP phase forms in the course of accompanying plastic deformation with dislocation activity in the parent FCC phase.The plasticity incipience is also anisotropic in bothmetals,which is due to the difference in the projections of stress on the slip plane for different orientations of the FCC crystal.MD modeling results demonstrate a strong dependence of the FCC-to-BCC transition on the crystallographic direction,in which the material is loaded in the copper crystals.However,MD simulations data can only be obtained for specific points in the stereographic direction space;therefore,for more comprehensive understanding of the phase transition process,a feed-forward neural network was trained using MD modeling data.The trained machine learning model allowed us to construct continuous stereographic maps of phase transitions as a function of stress in the shock-compressed state of metal.Due to appearance and growth of multiple centers of new phase,the FCC-to-BCC transition leads to formation of a polycrystalline structure from the parent single crystal.展开更多
The effective channeling of fluid flow by fractures is a liability for enhanced oil recovery(EOR)methods like CO_(2) flooding or CO_(2) storage.Developing a distributed fracture model to understand the heterogeneity o...The effective channeling of fluid flow by fractures is a liability for enhanced oil recovery(EOR)methods like CO_(2) flooding or CO_(2) storage.Developing a distributed fracture model to understand the heterogeneity of the fracture network is essential in characterizing tight and low-permeability reservoirs.In the Ordos Basin,the Chang 8-1-2 layer of the Yanchang Formation is a typical tight and low permeability reservoir in the JH17 wellblock.The strong heterogeneity of distributed fractures,differing fracture scales and fracture types make it difficult to effectively characterize the fracture distribution within the Chang 8-1-2 layer.In this paper,multi-source and multi-attribute methods are used to integrate data into a neural network at different scales,and fuzzy logic control is used to judge the correlation of various attributes.The results suggest that attribute correlation between coherence and fracture indication is the best,followed by correlations with fault distance,north–south slope,and north–south curvature.Advantageous attributes from the target area are used to train the neural network,and the fracture density model and discrete fracture network(DFN)model are built at different scales.This method can be used to effectively predict the distribution characteristics of fractures in the study area.And any learning done by the neural network from this case study can be applied to fracture network modeling for reservoirs of the same type.展开更多
[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a...[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a guide,we simulated pyruvate production of E.coli,screened key genes in metabolic pathways,and developed gene editing procedures accordingly.We knocked out the acetate kinase gene ackA,phosphate acetyltransferase gene pta,alcohol dehydrogenase adhE,glycogen synthase gene glgA,glycogen phosphorylase gene glgP,phosphoribosyl pyrophosphate(PRPP)synthase gene prs,ribose 1,5-bisphosphate phosphokinase gene phnN,and transporter encoding gene proP.Furthermore,we knocked in the transporter encoding gene ompC,flavonoid toxin gene fldA,and D-serine ammonia lyase gene dsdA.[Results]A shake flask process with the genetically edited mutant strain MG1655-6-2 under anaerobic conditions produced pyruvate at a titer of 10.46 g/L and a yield of 0.69 g/g.Metabolomic analysis revealed a significant increase in the pyruvate level in the fermentation broth,accompanied by notable decreases in the levels of certain related metabolic byproducts.Through 5 L fed-batch fermentation and an adaptive laboratory evolution,the strain finally achieved a pyruvate titer of 45.86 g/L.[Conclusion]This study illustrated the efficacy of a gene editing strategy predicted by a genome-scale metabolic network model in enhancing pyruvate accumulation in E.coli under anaerobic conditions and provided novel insights for microbial metabolic engineering.展开更多
The flow characteristics of coalbed methane(CBM)are influenced by the coal rock fracture network,which serves as the primary gas transport channel.This has a significant effect on the permeability performance of coal ...The flow characteristics of coalbed methane(CBM)are influenced by the coal rock fracture network,which serves as the primary gas transport channel.This has a significant effect on the permeability performance of coal reservoirs.In any case,the traditional techniques of coal rock fracture observation are unable to precisely define the flow of CBM.In this study,coal samples were subjected to an in situ loading scanning test in order to create a pore network model(PNM)and determine the pore and fracture dynamic evolution law of the samples in the loading path.On this basis,the structural characteristic parameters of the samples were extracted from the PNM and the impact on the permeability performance of CBM was assessed.The findings demonstrate that the coal samples'internal porosity increases by 2.039%under uniaxial loading,the average throat pore radius increases by 205.5 to 36.1μm,and the loading has an impact on the distribution and morphology of the pores in the coal rock.The PNM was loaded into the finite element program COMSOL for seepage modeling,and the M3 stage showed isolated pore connectivity to produce microscopic fissures,which could serve as seepage channels.In order to confirm the viability of the PNM and COMSOL docking technology,the streamline distribution law of pressure and velocity fields during the coal sample loading process was examined.The absolute permeability of the coal samples was also obtained in order for comparison with the measured results.The macroscopic CBM flow mechanism in complex lowpermeability coal rocks can be revealed through three-dimensional reconstruction of the microscopic fracture structure and seepage simulation.This study lays the groundwork for the fine description and evaluation of coal reservoirs as well as the precise prediction of gas production in CBM wells.展开更多
In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models...In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models,some existing discretized continuous neuron models,and discrete neural networks in simulating complex neural dynamics.It places particular emphasis on the importance of memristors in the composition of neural networks,especially their unique memory and nonlinear characteristics.The integration of memristors into discrete neural networks,including Hopfield networks and their fractional-order variants,cellular neural networks and discrete neuron models has enabled the study and construction of various neural models with memory.These models exhibit complex dynamic behaviors,including superchaotic attractors,hidden attractors,multistability,and synchronization transitions.Furthermore,the present paper undertakes an analysis of more complex dynamical properties,including synchronization,speckle patterns,and chimera states in discrete coupled neural networks.This research provides new theoretical foundations and potential applications in the fields of brain-inspired computing,artificial intelligence,image encryption,and biological modeling.展开更多
Current hyperelastic constitutive models of hydrogels face difficulties in capturing the stress-strain behaviors of hydrogels under extremely large deformation because the effect of non-affine deformation of the polym...Current hyperelastic constitutive models of hydrogels face difficulties in capturing the stress-strain behaviors of hydrogels under extremely large deformation because the effect of non-affine deformation of the polymer network inside is ambiguous.In this work,we construct periodic random network(PRN)models for the effective polymer network in hydrogels and investigate the non-affine deformation of polymer chains intrinsically originates from the structural randomness from bottom up.The non-affine deformation in PRN models is manifested as the actual stretch of polymer chains randomly deviated from the chain stretch predicted by affine assumption,and quantified by a non-affine ratio of each polymer chain.It is found that the non-affine ratios of polymer chains are closely related to bulk deformation state,chain orientation,and initial chain elongation.By fitting the non-affine ratio of polymer chains in all PRN models,we propose a non-affine constitutive model for the hydrogel polymer network based on micro-sphere model.The stress-strain curves of the proposed constitutive models under uniaxial tension condition agree with the simulation results of different PRN models of hydrogels very well.展开更多
The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only...The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only used for limited cases.In this letter,we propose Kolmogorov-Arnold networks(KAN)base models for wavenumber-frequency spectra of pressure fluctuations under turbulent boundary layers.The results are compared with DNS results.In turbulent channel flows,it is found that the KAN base model leads to a smooth wavenumber-frequency spectrum with sparse samples.In the turbulent flow over an axisymmetric body of revolution,the KAN base model captures the wavenumber-frequency spectra near the convective peak.展开更多
Using a modified subgradient extragradient algorithm, this paper proposed a novel approach to solving a supply chain network equilibrium model. The method extends the scope of optimisation and improves the accuracy at...Using a modified subgradient extragradient algorithm, this paper proposed a novel approach to solving a supply chain network equilibrium model. The method extends the scope of optimisation and improves the accuracy at each iteration by incorporating adaptive parameter selection and a more general subgradient projection operator. The advantages of the proposed method are highlighted by the proof of strong convergence presented in the paper. Several concrete examples are given to demonstrate the effectiveness of the algorithm, with comparisons illustrating its superior CPU running time compared to alternative techniques. The practical applicability of the algorithm is also demonstrated by applying it to a realistic supply chain network model.展开更多
Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying clima...Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.展开更多
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression...Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.展开更多
Based on the percolation network model characterizing reservoir rock＇s pore structure and fluid characteristics, this paper qualitatively studies the effects of pore size, pore shape, pore connectivity, and the amoun...Based on the percolation network model characterizing reservoir rock＇s pore structure and fluid characteristics, this paper qualitatively studies the effects of pore size, pore shape, pore connectivity, and the amount of micropores on the I - Sw curve using numerical modeling. The effects of formation water salinity on the electrical resistivity of the rock are discussed. Then the relative magnitudes of the different influencing factors are discussed. The effects of the different factors on the I - Sw curve are analyzed by fitting simulation results. The results show that the connectivity of the void spaces and the amount of micropores have a large effect on the I - S, curve, while the other factors have little effect. The formation water salinity has a large effect on the absolute resistivity values. The non-Archie phenomenon is prevalent, which is remarkable in rocks with low permeability.展开更多
A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional an...A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network node.Similarly,a road node is abstracted as the linkage of two ordered single-directional roads.This model can describe turn restrictions,circular roads,and other real scenarios usually described using a super-graph.Then a computing framework for optimal path finding（OPF）is presented.It is proved that classical Dijkstra and A algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD network.Finally,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.展开更多
Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation proced...Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.展开更多
The permeability in the methane hydrate reservoir is one of the key parameters in estimating the gas production performance and the flow behavior of gas and water during dissociation.In this paper,a three-dimensional ...The permeability in the methane hydrate reservoir is one of the key parameters in estimating the gas production performance and the flow behavior of gas and water during dissociation.In this paper,a three-dimensional cubic pore-network model based on invasion percolation is developed to study the effect of hydrate particle formation and growth habit on the permeability.The variation of permeability in porous media with different hydrate saturation is studied by solving the network problem.The simulation results are well consistent with the experimental data.The proposed model predicts that the permeability will reduce exponentially with the increase of hydrate saturation,which is crucial in developing a deeper understanding of the mechanism of hydrate formation and dissociation in porous media.展开更多
Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of t...Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.展开更多
The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in Chin...The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in China have done researches concerning this problem. Based on previous researches, this paper analyzed characteristics, tendencies, and causes of annual runoff variations in the Yingluo Gorge （1944-2005） and the Zhengyi Gorge （1954-2005）, which are the boundaries of the upper reaches, the middle reaches, and the lower reaches of the Heihe River drainage basin, by wavelet analysis, wavelet neural network model, and GIS spatial analysis. The results show that： （1） annual runoff variations of the Yingluo Gorge have principal periods of 7 years and 25 years, and its increasing rate is 1.04 m^3/s.10y; （2） annual runoff variations of the Zhengyi Gorge have principal periods of 6 years and 27 years, and its decreasing rate is 2.25 m^3/s.10y; （3） prediction results show that： during 2006-2015, annual runoff variations of the Yingluo and Zhengyi gorges have ascending tendencies, and the increasing rates are respectively 2.04 m^3/s.10y and 1.61 m^3/s.10y; （4） the increase of annual runoff in the Yingluo Gorge has causal relationship with increased temperature and precipitation in the upper reaches, and the decrease of annual runoff in the Zhengyi Gorge in the past decades was mainly caused by the increased human consumption of water resources in the middle researches. The study results will provide scientific basis for making rational use and allocation schemes of water resources in the Heihe River drainage basin.展开更多
This is the first of a three-part series of pape rs which introduces a general background of building trajectory-oriented road net work data models, including motivation, related works, and basic concepts. The p urpos...This is the first of a three-part series of pape rs which introduces a general background of building trajectory-oriented road net work data models, including motivation, related works, and basic concepts. The p urpose of the series is to develop a trajectory-oriented road network data mode l, namely carriageway-based road network data model (CRNM). Part 1 deals with t he modeling background. Part 2 proposes the principle and architecture of the CR NM. Part 3 investigates the implementation of the CRNM in a case study. In the p resent paper, the challenges of managing trajectory data are discussed. Then, de veloping trajectory-oriented road network data models is proposed as a solution and existing road network data models are reviewed. Basic representation approa ches of a road network are introduced as well as its constitution.展开更多
This is the second of a three-part series of papers which presents the principle and architecture of the CRNM, a trajectory-oriented, carriageway-based road network data model. The first part of the series has introdu...This is the second of a three-part series of papers which presents the principle and architecture of the CRNM, a trajectory-oriented, carriageway-based road network data model. The first part of the series has introduced a general background of building trajectory-oriented road network data models, including motivation, related works, and basic concepts. Based on it, this paper describs the CRNM in detail. At first, the notion of basic roadway entity is proposed and discussed. Secondly, carriageway is selected as the basic roadway entity after compared with other kinds of roadway, and approaches to representing other roadways with carriageways are introduced. At last, an overall architecture of the CRNM is proposed.展开更多
基金supported by the National Level Project of China(No.KJSP2023020201)the Foundation of Science and Technology on Aerospace Flight Dynamics Laboratory of China(No.kjw6142210240202)+1 种基金the Beijing Institute of Technology Research Fund Program for Young Scholars of Chinathe Fundamental Research Funds for Central Universities of China。
文摘In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites.
基金founded by the Ministry of Science and Higher Education of the Russian Federation,State assignments for research,registration No.1024032600084-8-1.3.2Study of the grain growth and the formation of polycrystalline structure as a result of phase transition(Section 6)was founded by the Russian Science Foundation,Project No.24-71-00078+3 种基金https://rscf.ru/en/project/24-71-00078/(accessed on 01 December 2025).Study of the orientation dependence of the phase transition of aluminum in Section 3 was founded by the Russian Science Foundation,Project No.24-19-00684https://rscf.ru/en/project/24-19-00684/(accessed on 01 December 2025).
文摘It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements,including shock wave loading.However,the dependence of phase transformations in a wide range of crystallographic directions of shock loading has not been revealed.In this work,we calculated the shock Hugoniot for aluminum and copper in different crystallographic directions([100],[110],[111],[112],[102],[114],[123],[134],[221]and[401])of shock compression using molecular dynamics(MD)simulations.The results showed a high pressure(>160 GPa for Cu and>40 GPa for Al)of the FCC-to-BCC transition.In copper,different characteristics of the phase transition are observed depending on the loading direction with the[100]compression direction being the weakest.The FCC-to-BCC transition for copper is in the range of 150–220 GPa,which is consistent with the existing experimental data.Due to the high transition pressure,the BCC phase transition in copper competes with melting.In aluminum,the FCC-to-BCC transition is observed for all studied directions at pressures between 40 and 50 GPa far beyond the melting.In all considered cases we observe the coexistence of HCP and BCC phases during the FCC-to-BCC transition,which is consistent with the experimental data and atomistic calculations;this HCP phase forms in the course of accompanying plastic deformation with dislocation activity in the parent FCC phase.The plasticity incipience is also anisotropic in bothmetals,which is due to the difference in the projections of stress on the slip plane for different orientations of the FCC crystal.MD modeling results demonstrate a strong dependence of the FCC-to-BCC transition on the crystallographic direction,in which the material is loaded in the copper crystals.However,MD simulations data can only be obtained for specific points in the stereographic direction space;therefore,for more comprehensive understanding of the phase transition process,a feed-forward neural network was trained using MD modeling data.The trained machine learning model allowed us to construct continuous stereographic maps of phase transitions as a function of stress in the shock-compressed state of metal.Due to appearance and growth of multiple centers of new phase,the FCC-to-BCC transition leads to formation of a polycrystalline structure from the parent single crystal.
基金supported by the National Science and Technology Project of China(No.2024ZD1004300)。
文摘The effective channeling of fluid flow by fractures is a liability for enhanced oil recovery(EOR)methods like CO_(2) flooding or CO_(2) storage.Developing a distributed fracture model to understand the heterogeneity of the fracture network is essential in characterizing tight and low-permeability reservoirs.In the Ordos Basin,the Chang 8-1-2 layer of the Yanchang Formation is a typical tight and low permeability reservoir in the JH17 wellblock.The strong heterogeneity of distributed fractures,differing fracture scales and fracture types make it difficult to effectively characterize the fracture distribution within the Chang 8-1-2 layer.In this paper,multi-source and multi-attribute methods are used to integrate data into a neural network at different scales,and fuzzy logic control is used to judge the correlation of various attributes.The results suggest that attribute correlation between coherence and fracture indication is the best,followed by correlations with fault distance,north–south slope,and north–south curvature.Advantageous attributes from the target area are used to train the neural network,and the fracture density model and discrete fracture network(DFN)model are built at different scales.This method can be used to effectively predict the distribution characteristics of fractures in the study area.And any learning done by the neural network from this case study can be applied to fracture network modeling for reservoirs of the same type.
基金supported by the Hebei Provincial Key Research and Development Project(21372803D)。
文摘[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a guide,we simulated pyruvate production of E.coli,screened key genes in metabolic pathways,and developed gene editing procedures accordingly.We knocked out the acetate kinase gene ackA,phosphate acetyltransferase gene pta,alcohol dehydrogenase adhE,glycogen synthase gene glgA,glycogen phosphorylase gene glgP,phosphoribosyl pyrophosphate(PRPP)synthase gene prs,ribose 1,5-bisphosphate phosphokinase gene phnN,and transporter encoding gene proP.Furthermore,we knocked in the transporter encoding gene ompC,flavonoid toxin gene fldA,and D-serine ammonia lyase gene dsdA.[Results]A shake flask process with the genetically edited mutant strain MG1655-6-2 under anaerobic conditions produced pyruvate at a titer of 10.46 g/L and a yield of 0.69 g/g.Metabolomic analysis revealed a significant increase in the pyruvate level in the fermentation broth,accompanied by notable decreases in the levels of certain related metabolic byproducts.Through 5 L fed-batch fermentation and an adaptive laboratory evolution,the strain finally achieved a pyruvate titer of 45.86 g/L.[Conclusion]This study illustrated the efficacy of a gene editing strategy predicted by a genome-scale metabolic network model in enhancing pyruvate accumulation in E.coli under anaerobic conditions and provided novel insights for microbial metabolic engineering.
基金The National Key R&D Program,Grant/Award Number:2023YFC2907203National Natural Science Foundation of China,Grant/Award Numbers:52374121,52074121。
文摘The flow characteristics of coalbed methane(CBM)are influenced by the coal rock fracture network,which serves as the primary gas transport channel.This has a significant effect on the permeability performance of coal reservoirs.In any case,the traditional techniques of coal rock fracture observation are unable to precisely define the flow of CBM.In this study,coal samples were subjected to an in situ loading scanning test in order to create a pore network model(PNM)and determine the pore and fracture dynamic evolution law of the samples in the loading path.On this basis,the structural characteristic parameters of the samples were extracted from the PNM and the impact on the permeability performance of CBM was assessed.The findings demonstrate that the coal samples'internal porosity increases by 2.039%under uniaxial loading,the average throat pore radius increases by 205.5 to 36.1μm,and the loading has an impact on the distribution and morphology of the pores in the coal rock.The PNM was loaded into the finite element program COMSOL for seepage modeling,and the M3 stage showed isolated pore connectivity to produce microscopic fissures,which could serve as seepage channels.In order to confirm the viability of the PNM and COMSOL docking technology,the streamline distribution law of pressure and velocity fields during the coal sample loading process was examined.The absolute permeability of the coal samples was also obtained in order for comparison with the measured results.The macroscopic CBM flow mechanism in complex lowpermeability coal rocks can be revealed through three-dimensional reconstruction of the microscopic fracture structure and seepage simulation.This study lays the groundwork for the fine description and evaluation of coal reservoirs as well as the precise prediction of gas production in CBM wells.
基金supported by the Natural Science Foundation of Hunan Province(Grant No.2025JJ50368)the Scientific Research Fund of Hunan Provincial Education Department(Grant No.24A0248)the Guiding Science and Technology Plan Project of Changsha City(Grant No.kzd2501129)。
文摘In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models,some existing discretized continuous neuron models,and discrete neural networks in simulating complex neural dynamics.It places particular emphasis on the importance of memristors in the composition of neural networks,especially their unique memory and nonlinear characteristics.The integration of memristors into discrete neural networks,including Hopfield networks and their fractional-order variants,cellular neural networks and discrete neuron models has enabled the study and construction of various neural models with memory.These models exhibit complex dynamic behaviors,including superchaotic attractors,hidden attractors,multistability,and synchronization transitions.Furthermore,the present paper undertakes an analysis of more complex dynamical properties,including synchronization,speckle patterns,and chimera states in discrete coupled neural networks.This research provides new theoretical foundations and potential applications in the fields of brain-inspired computing,artificial intelligence,image encryption,and biological modeling.
基金supported by the National Natural Science Foundation of China(Grant Nos.12202339 and 12172273)Xi’an Jiaotong University Tang Scholar.
文摘Current hyperelastic constitutive models of hydrogels face difficulties in capturing the stress-strain behaviors of hydrogels under extremely large deformation because the effect of non-affine deformation of the polymer network inside is ambiguous.In this work,we construct periodic random network(PRN)models for the effective polymer network in hydrogels and investigate the non-affine deformation of polymer chains intrinsically originates from the structural randomness from bottom up.The non-affine deformation in PRN models is manifested as the actual stretch of polymer chains randomly deviated from the chain stretch predicted by affine assumption,and quantified by a non-affine ratio of each polymer chain.It is found that the non-affine ratios of polymer chains are closely related to bulk deformation state,chain orientation,and initial chain elongation.By fitting the non-affine ratio of polymer chains in all PRN models,we propose a non-affine constitutive model for the hydrogel polymer network based on micro-sphere model.The stress-strain curves of the proposed constitutive models under uniaxial tension condition agree with the simulation results of different PRN models of hydrogels very well.
基金supported by the National Natural Science Foundation of China Basic Science Center Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.11988102)the National Natural Science Foundation of China(Grant Nos.92252203,12102439,and 12425207)+1 种基金the Chinese Academy of Sciences Project for Young Scientists in Basic Research(Grant No.YSBR-087)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB0620102).
文摘The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only used for limited cases.In this letter,we propose Kolmogorov-Arnold networks(KAN)base models for wavenumber-frequency spectra of pressure fluctuations under turbulent boundary layers.The results are compared with DNS results.In turbulent channel flows,it is found that the KAN base model leads to a smooth wavenumber-frequency spectrum with sparse samples.In the turbulent flow over an axisymmetric body of revolution,the KAN base model captures the wavenumber-frequency spectra near the convective peak.
文摘Using a modified subgradient extragradient algorithm, this paper proposed a novel approach to solving a supply chain network equilibrium model. The method extends the scope of optimisation and improves the accuracy at each iteration by incorporating adaptive parameter selection and a more general subgradient projection operator. The advantages of the proposed method are highlighted by the proof of strong convergence presented in the paper. Several concrete examples are given to demonstrate the effectiveness of the algorithm, with comparisons illustrating its superior CPU running time compared to alternative techniques. The practical applicability of the algorithm is also demonstrated by applying it to a realistic supply chain network model.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3002803)the National Key Research and Development Program of China(Grant No.2024YFF0808402)the National Natural Science Foundation of China(Grant No.42375169)。
文摘Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.
基金supported by the Science and Technology Innovation Key R&D Program of Chongqing(CSTB2025TIAD-STX0032)National Key Research and Development Program of China(2024YFF0908200)+1 种基金the Chongqing Technology Innovation and Application Development Special Key Project(CSTB2024TIAD-KPX0018)the Southwest University Graduate Student Research Innovation(SWUB24051)。
文摘Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.
基金This project is sponsored by National Natural Science Foundation of China, No. 40574030.
文摘Based on the percolation network model characterizing reservoir rock＇s pore structure and fluid characteristics, this paper qualitatively studies the effects of pore size, pore shape, pore connectivity, and the amount of micropores on the I - Sw curve using numerical modeling. The effects of formation water salinity on the electrical resistivity of the rock are discussed. Then the relative magnitudes of the different influencing factors are discussed. The effects of the different factors on the I - Sw curve are analyzed by fitting simulation results. The results show that the connectivity of the void spaces and the amount of micropores have a large effect on the I - S, curve, while the other factors have little effect. The formation water salinity has a large effect on the absolute resistivity values. The non-Archie phenomenon is prevalent, which is remarkable in rocks with low permeability.
基金The National Key Technology R&D Program of China during the 11th Five Year Plan Period(No.2008BAJ11B01)
文摘A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network node.Similarly,a road node is abstracted as the linkage of two ordered single-directional roads.This model can describe turn restrictions,circular roads,and other real scenarios usually described using a super-graph.Then a computing framework for optimal path finding（OPF）is presented.It is proved that classical Dijkstra and A algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD network.Finally,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.
文摘Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.
基金supported by the National High Technology Research and Development (863) Program of China (Grant Nos.2006AA09A209-5)the National Natural Science Foundation of China (Grant Nos. 90510003)the Major Research Project of the Ministry of Education (Grant Nos. 306005)
文摘The permeability in the methane hydrate reservoir is one of the key parameters in estimating the gas production performance and the flow behavior of gas and water during dissociation.In this paper,a three-dimensional cubic pore-network model based on invasion percolation is developed to study the effect of hydrate particle formation and growth habit on the permeability.The variation of permeability in porous media with different hydrate saturation is studied by solving the network problem.The simulation results are well consistent with the experimental data.The proposed model predicts that the permeability will reduce exponentially with the increase of hydrate saturation,which is crucial in developing a deeper understanding of the mechanism of hydrate formation and dissociation in porous media.
文摘Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.
基金National Natural Science Foundation of China, No.40335046
文摘The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in China have done researches concerning this problem. Based on previous researches, this paper analyzed characteristics, tendencies, and causes of annual runoff variations in the Yingluo Gorge （1944-2005） and the Zhengyi Gorge （1954-2005）, which are the boundaries of the upper reaches, the middle reaches, and the lower reaches of the Heihe River drainage basin, by wavelet analysis, wavelet neural network model, and GIS spatial analysis. The results show that： （1） annual runoff variations of the Yingluo Gorge have principal periods of 7 years and 25 years, and its increasing rate is 1.04 m^3/s.10y; （2） annual runoff variations of the Zhengyi Gorge have principal periods of 6 years and 27 years, and its decreasing rate is 2.25 m^3/s.10y; （3） prediction results show that： during 2006-2015, annual runoff variations of the Yingluo and Zhengyi gorges have ascending tendencies, and the increasing rates are respectively 2.04 m^3/s.10y and 1.61 m^3/s.10y; （4） the increase of annual runoff in the Yingluo Gorge has causal relationship with increased temperature and precipitation in the upper reaches, and the decrease of annual runoff in the Zhengyi Gorge in the past decades was mainly caused by the increased human consumption of water resources in the middle researches. The study results will provide scientific basis for making rational use and allocation schemes of water resources in the Heihe River drainage basin.
文摘This is the first of a three-part series of pape rs which introduces a general background of building trajectory-oriented road net work data models, including motivation, related works, and basic concepts. The p urpose of the series is to develop a trajectory-oriented road network data mode l, namely carriageway-based road network data model (CRNM). Part 1 deals with t he modeling background. Part 2 proposes the principle and architecture of the CR NM. Part 3 investigates the implementation of the CRNM in a case study. In the p resent paper, the challenges of managing trajectory data are discussed. Then, de veloping trajectory-oriented road network data models is proposed as a solution and existing road network data models are reviewed. Basic representation approa ches of a road network are introduced as well as its constitution.
文摘This is the second of a three-part series of papers which presents the principle and architecture of the CRNM, a trajectory-oriented, carriageway-based road network data model. The first part of the series has introduced a general background of building trajectory-oriented road network data models, including motivation, related works, and basic concepts. Based on it, this paper describs the CRNM in detail. At first, the notion of basic roadway entity is proposed and discussed. Secondly, carriageway is selected as the basic roadway entity after compared with other kinds of roadway, and approaches to representing other roadways with carriageways are introduced. At last, an overall architecture of the CRNM is proposed.
