In order to establish the relationship between the measured dynamic response and the health status of long-span bridges, a double-layer model updating method for steel-concrete composite beam cable-stayed bridges is p...In order to establish the relationship between the measured dynamic response and the health status of long-span bridges, a double-layer model updating method for steel-concrete composite beam cable-stayed bridges is proposed. Measured frequencies are selected as the first-layer reference data, and the mass of the bridge deck, the grid density, the modulus of concrete and the ballast on the side span are modified by using a manual tuning technique. Measured global positioning system （GPS） data is selected as the second-layer reference data, and the degradation of the integral structure stiffness EI of the whole bridge is taken into account for the second-layer model updating by using the finite element iteration algorithm. The Nanpu Bridge in Shanghai is taken as a case to verify the applicability of the proposed model updating method. After the first-layer model updating, the standard deviation of modal frequencies is smaller than 7%. After the second-layer model updating, the error of the deflection of the mid-span is smaller than 10%. The integral structure stiffness of the whole bridge decreases about 20%. The research results show a good agreement between the calculated response and the measured response.展开更多
Virtual organization is a new production patter and a principal part in advanced manufacturing systems such as agile manufacturing. Manufacturability evaluation is the necessary condition to form the virtual organizat...Virtual organization is a new production patter and a principal part in advanced manufacturing systems such as agile manufacturing. Manufacturability evaluation is the necessary condition to form the virtual organization. A new manufacturability evaluation approach is described in this paper, which is carried out based on every process feature under the double-layer model of manufacturing resources proposed by authors. The manufacturing resources that build up the virtual organization are selected according to the results of manufacturability evaluation.展开更多
Two-dimensional double-layer honeycomb(DLHC)materials are known for their diverse physical properties,but superconductivity has been a notably absent characteristic in this structure.We address this gap by investigati...Two-dimensional double-layer honeycomb(DLHC)materials are known for their diverse physical properties,but superconductivity has been a notably absent characteristic in this structure.We address this gap by investigating M_(2)N_(2)(M=Nb,Ta)with DLHC structure using first-principles calculations.Our results show that M_(2)N_(2)are stable and metallic,exhibiting superconducting behavior.Specifically,Nb_(2)N_(2)and Ta_(2)N_(2)display superconducting transition temperatures of 6.8 K and 8.8 K,respectively.Their electron-phonon coupling is predominantly driven by the coupling between metal d-orbitals and low-frequency metal-dominated vibration modes.Interestingly,two compounds also exhibit non-trivial band topology.Thus,M_(2)N_(2)are promising platforms for studying the interplay between topology and superconductivity and fill the gap in superconductivity research for DLHC materials.展开更多
To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃...To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃induced vibration response data of a three⁃span four⁃row double⁃layer cable PV support system.The wind⁃induced vibration characteristics with different PV module tilt angles,wind speeds,and wind direction angles were analyzed.The results showed that the double⁃layer cable large⁃span flexible PV support can effectively control the wind⁃induced vibration response and prevent the occur⁃rence of flutter under strong wind conditions.The maxi⁃mum value of the wind⁃induced vibration displacement of the flexible PV support system occurs in the windward first row.The upstream module has a significant shading effect on the downstream module,with a maximum effect of 23%.The most unfavorable wind direction angles of the structure are 0°and 180°.The change of the wind direction angle in the range of 0°to 30°has little effect on the wind vi⁃bration response.The change in the tilt angle of the PV modules has a greater impact on the wind vibration in the downwind direction and a smaller impact in the upwind di⁃rection.Special attention should be paid to the structural wind⁃resistant design of such systems in the upwind side span.展开更多
This paper proposes a type of double-layer charge liner fabricated using chemical vapor deposition(CVD)that has tungsten as its inner liner.The feasibility of this design was evaluated through penetration tests.Double...This paper proposes a type of double-layer charge liner fabricated using chemical vapor deposition(CVD)that has tungsten as its inner liner.The feasibility of this design was evaluated through penetration tests.Double-layer charge liners were fabricated by using CVD to deposit tungsten layers on the inner surfaces of pure T2 copper liners.The microstructures of the tungsten layers were analyzed using a scanning electron microscope(SEM).The feasibility analysis was carried out by pulsed X-rays,slug-retrieval test and static penetration tests.The shaped charge jet forming and penetration law of inner tungsten-coated double-layer liner were studied by numerical simulation method.The results showed that the double-layer liners could form well-shaped jets.The errors between the X-ray test results and the numerical results were within 11.07%.A slug-retrieval test was found that the retrieved slug was similar to a numerically simulated slug.Compared with the traditional pure copper shaped charge jet,the penetration depth of the double-layer shaped charge liner increased by 11.4% and>10.8% respectively.In summary,the test results are good,and the numerical simulation is in good agreement with the test,which verified the feasibility of using the CVD method to fabricate double-layer charge liners with a high-density and high-strength refractory metal as the inner liner.展开更多
The Songliao Basin(SLB)covers an area of approximately 260,000 km2in northeastern Asia and preserves a continuous and complete Cretaceous terrestrial record(Wang et al.,2021).The region is the most important petrolife...The Songliao Basin(SLB)covers an area of approximately 260,000 km2in northeastern Asia and preserves a continuous and complete Cretaceous terrestrial record(Wang et al.,2021).The region is the most important petroliferous sedimentary basin in China because of its continual annual oil and gas equivalent production of tens of millions of tons(ca.220–440 million barrels per year)since 1959.The SLB was previously thought to have developed on Hercynian basement and accumulated continuous sedimentary deposits during the Late Jurassic and Cretaceous(Wan et al.,2013;Wang et al.,2016).展开更多
The high specific capacity and low negative electrochemical potential of lithium metal anodes(LMAs),may allow the energy density threshold of Li metal batteries(LMBs)to be pushed higher.However,the existing detrimenta...The high specific capacity and low negative electrochemical potential of lithium metal anodes(LMAs),may allow the energy density threshold of Li metal batteries(LMBs)to be pushed higher.However,the existing detrimental issues,such as dendritic growth and volume expansion,have hindered the practical implementation of LMBs.Introducing three-dimensional frameworks(e.g.,copper and nickel foam),have been regarded as one of the fundamental strategies to reduce the local current density,aiming to extend the Sand'time.Nevertheless,the local environment far from the skeleton is almost the same as the typical plane Li,due to macroporous space of metal foam.Herein,we built a double-layered 3D current collector of Li alloy anchored on the metal foam,with micropores interconnected macropores,via a viable thermal infiltration and cooling strategy.Due to the excellent electronic and ionic conductivity coupled with favorable lithiophilicity,the Li alloy can effectively reduce the nucleation barrier and enhance the Li^(+)transportation rate,while the metal foam can role as the primary promotor to enlarge the surface area and buffer the dimensional variation.Synergistically,the Li composite anode with hierarchical structure of primary and secondary scaffolds realized the even deposition behavior and minimum volume expansion,outputting preeminent prolonged cycling performances under high rate.展开更多
To further investigate the one-dimensional(1D)rheological consolidation mechanism of double-layered soil,the fractional derivative Merchant model(FDMM)and the non-Darcian flow model with the non-Newtonian index are re...To further investigate the one-dimensional(1D)rheological consolidation mechanism of double-layered soil,the fractional derivative Merchant model(FDMM)and the non-Darcian flow model with the non-Newtonian index are respectively introduced to describe the deformation of viscoelastic soil and the flow of pore water in the process of consolidation.Accordingly,an 1D rheological consolidation equation of double-layered soil is obtained,and its numerical analysis is performed by the implicit finite difference method.In order to verify its validity,the numerical solutions by the present method for some simplified cases are compared with the results in the related literature.Then,the influence of the revelent parameters on the rheological consolidation of double-layered soil are investigated.Numerical results indicate that the parameters of non-Darcian flow and FDMM of the first soil layer greatly influence the consolidation rate of double-layered soil.As the decrease of relative compressibility or the increase of relative permeability between the lower soil and the upper soil,the dissipation rate of excess pore water pressure and the settlement rate of the ground will be accelerated.Increasing the relative thickness of soil layer with high permeability or low compressibility will also accelerate the consolidation rate of double-layered soil.展开更多
Double-layered microcapsule corrosion inhibitors were developed by sodium monofluorophosphate as the core material,polymethyl methacrylate as the inner wall material,and polyvinyl alcohol as the outer wall material co...Double-layered microcapsule corrosion inhibitors were developed by sodium monofluorophosphate as the core material,polymethyl methacrylate as the inner wall material,and polyvinyl alcohol as the outer wall material combining the solvent evaporation method and spray drying method.The protection by the outer capsule wall was used to prolong the service life of the corrosion inhibitor.The dispersion,encapsulation,thermal stability of microcapsules,and the degradation rate of capsule wall in concrete pore solution were analyzed by ultra-deep field microscopy,scanning electron microscopy,thermal analyzer,and sodium ion release rate analysis.The microcapsules were incorporated into mortar samples containing steel reinforcement,and the effects of double-layered microcapsule corrosion inhibitors on the performance of the cement matrix and the actual corrosion-inhibiting effect were analyzed.The experimental results show that the double-layered microcapsules have a moderate particle size and uniform distribution,and the capsules were completely wrapped.The microcapsules as a whole have good thermal stability below 230 ℃.The monolayer membrane structure microcapsules completely broke within 1 day in the simulated concrete pore solution,and the double-layer membrane structure prolonged the service life of the microcapsules to 80 days in the simulated concrete pore solution before the core material was completely released.The mortar samples containing steel reinforcement incorporated with the double-layered microcapsule corrosion inhibitors still maintained a higher corrosion potential than the monolayer microcapsule corrosion inhibitors control group at 60 days.The incorporation of double-layered microcapsules into the cement matrix has no significant adverse effect on the setting time and early strength.展开更多
A theoretical model which couples the oscillation of cavitation bubbles with the equation of an acoustic wave is utilized to describe the sound fields in double-layer liquids, which can be used to realize the asymmetr...A theoretical model which couples the oscillation of cavitation bubbles with the equation of an acoustic wave is utilized to describe the sound fields in double-layer liquids, which can be used to realize the asymmetric transmission of acoustic waves. Numerical simulations show that the asymmetry is related to the properties of the host liquids and the input acoustic wave. Asymmetry can be enhanced if the maximum number density or the ambient radius of the cavitation bubbles in the low cavitation threshold liquid increases. Moreover, the direction of rectification will be reversed if the amplitude of the input acoustic wave becomes high enough.展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
基金The Special Project of the Ministry of Construction ofChina (No.20060909).
文摘In order to establish the relationship between the measured dynamic response and the health status of long-span bridges, a double-layer model updating method for steel-concrete composite beam cable-stayed bridges is proposed. Measured frequencies are selected as the first-layer reference data, and the mass of the bridge deck, the grid density, the modulus of concrete and the ballast on the side span are modified by using a manual tuning technique. Measured global positioning system （GPS） data is selected as the second-layer reference data, and the degradation of the integral structure stiffness EI of the whole bridge is taken into account for the second-layer model updating by using the finite element iteration algorithm. The Nanpu Bridge in Shanghai is taken as a case to verify the applicability of the proposed model updating method. After the first-layer model updating, the standard deviation of modal frequencies is smaller than 7%. After the second-layer model updating, the error of the deflection of the mid-span is smaller than 10%. The integral structure stiffness of the whole bridge decreases about 20%. The research results show a good agreement between the calculated response and the measured response.
文摘Virtual organization is a new production patter and a principal part in advanced manufacturing systems such as agile manufacturing. Manufacturability evaluation is the necessary condition to form the virtual organization. A new manufacturability evaluation approach is described in this paper, which is carried out based on every process feature under the double-layer model of manufacturing resources proposed by authors. The manufacturing resources that build up the virtual organization are selected according to the results of manufacturability evaluation.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12074213 and 11574108)the National Key R&D Program of China(Grant No.2022YFA1403103)+2 种基金the Major Basic Program of Natural Science Foundation of Shandong Province(Grant No.ZR2021ZD01)the Natural Science Foundation of Shandong Province(Grant No.ZR2023MA082)the Project of Introduction and Cultivation for Young Innovative Talents in Colleges and Universities of Shandong Province。
文摘Two-dimensional double-layer honeycomb(DLHC)materials are known for their diverse physical properties,but superconductivity has been a notably absent characteristic in this structure.We address this gap by investigating M_(2)N_(2)(M=Nb,Ta)with DLHC structure using first-principles calculations.Our results show that M_(2)N_(2)are stable and metallic,exhibiting superconducting behavior.Specifically,Nb_(2)N_(2)and Ta_(2)N_(2)display superconducting transition temperatures of 6.8 K and 8.8 K,respectively.Their electron-phonon coupling is predominantly driven by the coupling between metal d-orbitals and low-frequency metal-dominated vibration modes.Interestingly,two compounds also exhibit non-trivial band topology.Thus,M_(2)N_(2)are promising platforms for studying the interplay between topology and superconductivity and fill the gap in superconductivity research for DLHC materials.
基金The National Natural Science Foundation of China(No.52338011).
文摘To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃induced vibration response data of a three⁃span four⁃row double⁃layer cable PV support system.The wind⁃induced vibration characteristics with different PV module tilt angles,wind speeds,and wind direction angles were analyzed.The results showed that the double⁃layer cable large⁃span flexible PV support can effectively control the wind⁃induced vibration response and prevent the occur⁃rence of flutter under strong wind conditions.The maxi⁃mum value of the wind⁃induced vibration displacement of the flexible PV support system occurs in the windward first row.The upstream module has a significant shading effect on the downstream module,with a maximum effect of 23%.The most unfavorable wind direction angles of the structure are 0°and 180°.The change of the wind direction angle in the range of 0°to 30°has little effect on the wind vi⁃bration response.The change in the tilt angle of the PV modules has a greater impact on the wind vibration in the downwind direction and a smaller impact in the upwind di⁃rection.Special attention should be paid to the structural wind⁃resistant design of such systems in the upwind side span.
基金funded by the China Postdoctoral Science Foundation(Grant No.2022M721614)the opening project of State Key Laboratory of Explosion Science and Technology,Beijing Institute of Technology(Grant No.KFJJ23-07M)。
文摘This paper proposes a type of double-layer charge liner fabricated using chemical vapor deposition(CVD)that has tungsten as its inner liner.The feasibility of this design was evaluated through penetration tests.Double-layer charge liners were fabricated by using CVD to deposit tungsten layers on the inner surfaces of pure T2 copper liners.The microstructures of the tungsten layers were analyzed using a scanning electron microscope(SEM).The feasibility analysis was carried out by pulsed X-rays,slug-retrieval test and static penetration tests.The shaped charge jet forming and penetration law of inner tungsten-coated double-layer liner were studied by numerical simulation method.The results showed that the double-layer liners could form well-shaped jets.The errors between the X-ray test results and the numerical results were within 11.07%.A slug-retrieval test was found that the retrieved slug was similar to a numerically simulated slug.Compared with the traditional pure copper shaped charge jet,the penetration depth of the double-layer shaped charge liner increased by 11.4% and>10.8% respectively.In summary,the test results are good,and the numerical simulation is in good agreement with the test,which verified the feasibility of using the CVD method to fabricate double-layer charge liners with a high-density and high-strength refractory metal as the inner liner.
基金supports from the International Continental Scientific Drilling Programfunded by the National Natural Science Foundation of China(Grant Nos.41790453,41472304,42102129,42102135 and 41972313)+2 种基金Natural Science Foundation of Jilin Province(Grant No.20170101001JC)the National Key Research&Development Program of China(Grant No.2019YFC0605402)China Geological Survey(Grant No.DD20189702)。
文摘The Songliao Basin(SLB)covers an area of approximately 260,000 km2in northeastern Asia and preserves a continuous and complete Cretaceous terrestrial record(Wang et al.,2021).The region is the most important petroliferous sedimentary basin in China because of its continual annual oil and gas equivalent production of tens of millions of tons(ca.220–440 million barrels per year)since 1959.The SLB was previously thought to have developed on Hercynian basement and accumulated continuous sedimentary deposits during the Late Jurassic and Cretaceous(Wan et al.,2013;Wang et al.,2016).
基金supported by Huzhou Natural Science Foundation Project(Nos.2022YZ04 and 2022YZ21)S&T Special Program of Huzhou(No.2023GZ03)National Natural Science Foundation of China(No.52172184)。
文摘The high specific capacity and low negative electrochemical potential of lithium metal anodes(LMAs),may allow the energy density threshold of Li metal batteries(LMBs)to be pushed higher.However,the existing detrimental issues,such as dendritic growth and volume expansion,have hindered the practical implementation of LMBs.Introducing three-dimensional frameworks(e.g.,copper and nickel foam),have been regarded as one of the fundamental strategies to reduce the local current density,aiming to extend the Sand'time.Nevertheless,the local environment far from the skeleton is almost the same as the typical plane Li,due to macroporous space of metal foam.Herein,we built a double-layered 3D current collector of Li alloy anchored on the metal foam,with micropores interconnected macropores,via a viable thermal infiltration and cooling strategy.Due to the excellent electronic and ionic conductivity coupled with favorable lithiophilicity,the Li alloy can effectively reduce the nucleation barrier and enhance the Li^(+)transportation rate,while the metal foam can role as the primary promotor to enlarge the surface area and buffer the dimensional variation.Synergistically,the Li composite anode with hierarchical structure of primary and secondary scaffolds realized the even deposition behavior and minimum volume expansion,outputting preeminent prolonged cycling performances under high rate.
基金Project(51578511)supported by the National Natural Science Foundation of China。
文摘To further investigate the one-dimensional(1D)rheological consolidation mechanism of double-layered soil,the fractional derivative Merchant model(FDMM)and the non-Darcian flow model with the non-Newtonian index are respectively introduced to describe the deformation of viscoelastic soil and the flow of pore water in the process of consolidation.Accordingly,an 1D rheological consolidation equation of double-layered soil is obtained,and its numerical analysis is performed by the implicit finite difference method.In order to verify its validity,the numerical solutions by the present method for some simplified cases are compared with the results in the related literature.Then,the influence of the revelent parameters on the rheological consolidation of double-layered soil are investigated.Numerical results indicate that the parameters of non-Darcian flow and FDMM of the first soil layer greatly influence the consolidation rate of double-layered soil.As the decrease of relative compressibility or the increase of relative permeability between the lower soil and the upper soil,the dissipation rate of excess pore water pressure and the settlement rate of the ground will be accelerated.Increasing the relative thickness of soil layer with high permeability or low compressibility will also accelerate the consolidation rate of double-layered soil.
基金Fund by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No.2018YFD1101002-03)。
文摘Double-layered microcapsule corrosion inhibitors were developed by sodium monofluorophosphate as the core material,polymethyl methacrylate as the inner wall material,and polyvinyl alcohol as the outer wall material combining the solvent evaporation method and spray drying method.The protection by the outer capsule wall was used to prolong the service life of the corrosion inhibitor.The dispersion,encapsulation,thermal stability of microcapsules,and the degradation rate of capsule wall in concrete pore solution were analyzed by ultra-deep field microscopy,scanning electron microscopy,thermal analyzer,and sodium ion release rate analysis.The microcapsules were incorporated into mortar samples containing steel reinforcement,and the effects of double-layered microcapsule corrosion inhibitors on the performance of the cement matrix and the actual corrosion-inhibiting effect were analyzed.The experimental results show that the double-layered microcapsules have a moderate particle size and uniform distribution,and the capsules were completely wrapped.The microcapsules as a whole have good thermal stability below 230 ℃.The monolayer membrane structure microcapsules completely broke within 1 day in the simulated concrete pore solution,and the double-layer membrane structure prolonged the service life of the microcapsules to 80 days in the simulated concrete pore solution before the core material was completely released.The mortar samples containing steel reinforcement incorporated with the double-layered microcapsule corrosion inhibitors still maintained a higher corrosion potential than the monolayer microcapsule corrosion inhibitors control group at 60 days.The incorporation of double-layered microcapsules into the cement matrix has no significant adverse effect on the setting time and early strength.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11334005,11574150 and 11564006
文摘A theoretical model which couples the oscillation of cavitation bubbles with the equation of an acoustic wave is utilized to describe the sound fields in double-layer liquids, which can be used to realize the asymmetric transmission of acoustic waves. Numerical simulations show that the asymmetry is related to the properties of the host liquids and the input acoustic wave. Asymmetry can be enhanced if the maximum number density or the ambient radius of the cavitation bubbles in the low cavitation threshold liquid increases. Moreover, the direction of rectification will be reversed if the amplitude of the input acoustic wave becomes high enough.
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
