To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets ...To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets of concealed ore bodies,three-dimensional Mineral Prospectivity Modeling(MPM)of the deposit has been conducted using the weights-of-evidence(WofE)method.Conditional independence between evidence layers was tested,and the outline results using the prediction-volume(P-V)and Student's t-statistic methods for delineating favorable mineralization areas from continuous posterior probability map were critically compared.Four exploration targets delineated ultimately by the Student's t-statistic method for the discovery of minable ore bodies in each of the target areas were discussed in detail.The main conclusions include:(1)three-dimensional modeling of a deposit using multi-source reconnaissance data is useful for MPM in interpreting their relationships with known ore bodies;(2)WofE modeling can be used as a straightforward tool for integrating deposit model and reconnaissance data in MPM;(3)the Student's t-statistic method is more applicable in binarizing the continuous prospectivity map for exploration targeting than the PV approach;and(4)two target areas within high potential to find undiscovered ore bodies were diagnosed to guide future near-mine exploration activities of the Jinshan deposit.展开更多
The global demand for in vitro respiratory airway models has surged due to the coronavirus disease 2019(COVID-19)pandemic.Current state-of-the-art models use polymer membranes to separate epithelial cells from other c...The global demand for in vitro respiratory airway models has surged due to the coronavirus disease 2019(COVID-19)pandemic.Current state-of-the-art models use polymer membranes to separate epithelial cells from other cell types,creating a nonphysiological barrier.In this study,we applied three-dimensional(3D)printing and bioprinting to develop an in vitro model where endothelial and epithelial cells were in direct contact,mimicking their natural arrangement.This proof-ofconcept model includes a culture chamber,with an endothelial bioink printed and perfused through an epithelial channel.In silico simulations of the air velocity within the channel revealed shear stress values ranging from 0.13 to 0.39 Pa,aligning with the desired in vivo shear stress observed in the bronchi regions(0.1–0.4 Pa).Biomechanical movements during resting breathing were mimicked by incorporating a textile mesh positioned away from the cell–cell interface.The epithelial channel demonstrated a capacity for compression and expansion of up to−14.7%and+6.4%,respectively.Microscopic images showed that the epithelial cells formed a uniform monolayer within the lumen of the channel close to the bioprinted endothelial cells.Our novel model offers a valuable tool for future research into respiratory diseases and potential treatments under conditions closely mimicking those in the lung.展开更多
The acetylpolyamine oxidase(APAO),spermine oxidase(SMO),and spermidine/spermine N1-acetyltransferase(SSAT)are pivotal enzymes in polyamine metabolism,exerting direct influence on polyamine homeostasis regulation.Dysfu...The acetylpolyamine oxidase(APAO),spermine oxidase(SMO),and spermidine/spermine N1-acetyltransferase(SSAT)are pivotal enzymes in polyamine metabolism,exerting direct influence on polyamine homeostasis regulation.Dysfunctions in these enzymes are intricately linked to inflammatory diseases and cancers.Establishing their three-dimensional structures is essential for exploring enzymatic catalytic mechanisms and designing inhibitors at the atomic level.This article primarily assesses the precision of AlphaFold2 and molecular dynamics simulations in determining the three-dimensional structures of these enzymes,utilizing protein conformation rationality assessment,residue correlation matrix,and other techniques.This provides robust models for subsequent polyamine catabolic metabolism calculations and offers valuable insights for modeling proteins that have yet to acquire crystal structures.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
Long-duration energy storage has become critical for renewable energy integration.While redox flow batteries,especially vanadium-based systems,are scaling up in capacity,their performance at the stack level remains in...Long-duration energy storage has become critical for renewable energy integration.While redox flow batteries,especially vanadium-based systems,are scaling up in capacity,their performance at the stack level remains insufficiently optimized,demanding more profound mechanistic studies and engineering refinements.To address the difficulties in resolving the flow inhomogeneity at the stack scale,this study establishes a multi-physics field coupling model and analyzes the pressure distributions,flow rate differences,active substance concentration,and electrochemical characteristics.The results show that the uneven cell pressure distribution is a key factor affecting the consistency of the system performance,and the increase in the flow rate improves the reactant homogeneity,with both the average concentration and the uniformity factor increasing with the flow rate.In contrast,high current densities lead to an increased imbalance between electrochemical depletion and reactant replenishment,resulting in a significant decrease in reactant concentration in the under-ribs region.In addition,a higher flow rate can expand the high-current-density region where the stack operates efficiently.This study provides a theoretical basis for optimizing the design of the stack components.展开更多
A numerical investigation on the effectiveness of the actuator disc method in producing the interactions of multiple tidal stream devices via the 3D-RANS finite element model Telemac3D is explored. The methodology for...A numerical investigation on the effectiveness of the actuator disc method in producing the interactions of multiple tidal stream devices via the 3D-RANS finite element model Telemac3D is explored. The methodology for the implementation of the source term to represent an array of 20 m rotor diameter turbines deployed in an idealized channel is reviewed and discussed in detail. Flow interactions between multiple turbines are investigated for a single row arrangement with only two turbines and a two row arrangement containing three turbines. The results demonstrate that the non-hydrostatic solver shows better agreement when validated against published experimental data. Notably,the mesh density at the device location can strongly influence the simulated thrust from the disc. Although the actuator disc model can generally replicate the wake interactions well, the results indicate that it cannot accurately characterize the flow for regions with high turbulences. While a model setup with the largest lateral spacing(1.5D) demonstrates excellent agreement with the experimental data, the 0.5D model(smallest gap) deviates by up to 25%. These findings demonstrate the effectiveness of the applied source term in reproducing the wake profile, which is comparable with the published data, and highlight the inherent nature of the RANS and actuator disc models.展开更多
Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation,mimicking physiological heterogeneous structures,and recreating the cellular ...Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation,mimicking physiological heterogeneous structures,and recreating the cellular microenvironment.However,the layer-by-layer printing method is significantly constrained by the rheological properties of the bioink,making it challenging to form complex three-dimensional vascular structures in low-viscosity soft materials.To overcome this limitation,we developed a cross-linkable biphasic embedding medium by mixing low-viscosity biomaterials with gelatin microgel.This medium possesses yield stress and self-healing properties,facilitating efficient and continuous three-dimensional shaping of sacrificial ink within it.By adjusting the printing speed,we controlled the filament diameter,achieving a range from 250μm to 1000μm,and ensuring precise control over ink deposition locations and filament shapes.Using the in situ endothelialization method,we constructed complex vascular structures and ensured close adhesion between hepatocytes and endothelial cells.In vitro experiments demonstrated that the vascularized liver tissue model exhibited enhanced protein synthesis and metabolic function compared to mixed liver tissue.We also investigated the impact of varying vascular densities on liver tissue function.Transcriptome sequencing revealed that liver tissues with higher vascular density exhibited upregulated gene expression in metabolic and angiogenesis-related pathways.In summary,this method is adaptable to various materials,allowing the rheological properties of the supporting bath and the tissue's porosity to be modified using microgels,thus enabling precise regulation of the liver tissue microenvironment.Additionally,it facilitates the rapid construction of three-dimensional vascular structures within liver tissue.The resulting vascularized liver tissue model exhibits enhanced biological functionality,opening new opportunities for biomedical applications.展开更多
Currently,there are a limited number of dynamic models available for braided composite plates with large overall motions,despite the incorporation of three-dimensional(3D)braided composites into rotating blade compone...Currently,there are a limited number of dynamic models available for braided composite plates with large overall motions,despite the incorporation of three-dimensional(3D)braided composites into rotating blade components.In this paper,a dynamic model of 3D 4-directional braided composite thin plates considering braiding directions is established.Based on Kirchhoff's plate assumptions,the displacement variables of the plate are expressed.By incorporating the braiding directions into the constitutive equation of the braided composites,the dynamic model of the plate considering braiding directions is obtained.The effects of the speeds,braiding directions,and braided angles on the responses of the plate with fixed-axis rotation and translational motion,respectively,are investigated.This paper presents a dynamic theory for calculating the deformation of 3D braided composite structures undergoing both translational and rotational motions.It also provides a simulation method for investigating the dynamic behavior of non-isotropic material plates in various applications.展开更多
The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying ge...The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying geotechnical responses(e.g.consolidation settlement)in a 3D spatial domain.However,traditional 3D numerical model updating approaches are computationally prohibitive and therefore difficult to update the 3D responses in real time.To address these challenges,this study proposes a novel machine learning framework called sparse dictionary learning(T-3D-SDL)for real-time updating of time-varying 3D geotechnical responses.In T-3D-SDL,a concerned dataset(e.g.time-varying 3D settlement)is approximated as a linear superposition of dictionary atoms generated from 3D random FEM analyses.Field monitoring data are then used to identify non-trivial atoms and estimate their weights within a Bayesian framework for model updating and prediction.The proposed approach enables the real-time update of temporally varying settlements with a high 3D spatial resolution and quantified uncertainty as field monitoring data evolve.The proposed approach is illustrated using an embankment construction project.The results show that the proposed approach effectively improves settlement predictions along temporal and 3D spatial dimensions,with minimal latency(e.g.within minutes),as monitoring data appear.In addition,the proposed approach requires only a reasonably small number of 3D FEM model evaluations,avoids the use of widely adopted yet often criticized surrogate models,and effectively addresses the limitations(e.g.computational inefficiency)of existing 3D model updating approaches.展开更多
Hypoxia is a typical feature of the tumor microenvironment,one of the most critical factors affecting cell behavior and tumor progression.However,the lack of tumor models able to precisely emulate natural brain tumor ...Hypoxia is a typical feature of the tumor microenvironment,one of the most critical factors affecting cell behavior and tumor progression.However,the lack of tumor models able to precisely emulate natural brain tumor tissue has impeded the study of the effects of hypoxia on the progression and growth of tumor cells.This study reports a three-dimensional(3D)brain tumor model obtained by encapsulating U87MG(U87)cells in a hydrogel containing type I collagen.It also documents the effect of various oxygen concentrations(1%,7%,and 21%)in the culture environment on U87 cell morphology,proliferation,viability,cell cycle,apoptosis rate,and migration.Finally,it compares two-dimensional(2D)and 3D cultures.For comparison purposes,cells cultured in flat culture dishes were used as the control(2D model).Cells cultured in the 3D model proliferated more slowly but had a higher apoptosis rate and proportion of cells in the resting phase(G0 phase)/gap I phase(G1 phase)than those cultured in the 2D model.Besides,the two models yielded significantly different cell morphologies.Finally,hypoxia(e.g.,1%O2)affected cell morphology,slowed cell growth,reduced cell viability,and increased the apoptosis rate in the 3D model.These results indicate that the constructed 3D model is effective for investigating the effects of biological and chemical factors on cell morphology and function,and can be more representative of the tumor microenvironment than 2D culture systems.The developed 3D glioblastoma tumor model is equally applicable to other studies in pharmacology and pathology.展开更多
Three-dimensional geochemical modeling of ore-forming elements is crucial for predicting deep mineralization.This approach provides key information for the quantitative prediction of deep mineral localization,three-di...Three-dimensional geochemical modeling of ore-forming elements is crucial for predicting deep mineralization.This approach provides key information for the quantitative prediction of deep mineral localization,three-dimensional fine interpolation,analysis of spatial distribution patterns,and extraction of quantitative mineral-seeking markers.The Yechangping molybdenum(Mo)deposit is a significant and extensive porphyry-skarn deposit in the East Qinling-Dabie Mo polymetallic metallogenic belt at the southern margin of the North China Block.Abundant borehole data on oreforming elements underpin deep geochemical predictions.The methodology includes the following steps:(1)Threedimensional geological modeling of the deposit was established.(2)Correlation,cluster,and factor analyses post delineation of mineralization stages and determination of mineral generation sequence to identify(Cu,Pb,Zn,Ag)and(Mo,W,mfe)assemblages.(3)A three-dimensional geochemical block model was constructed for Mo,W,mfe,Cu,Zn,Pb,and Ag using the ordinary kriging method,and the variational function was developed.(4)Spatial distribution and enrichment characteristics analysis of ore-forming elements are performed to extract geological information,employing the variogram and w(Cu+Pb+Zn+Ag)/w(Mo+W)as predictive indicators.(5)Identifying the western,northwestern,and southwestern areas of the mine with limited mineralization potential,contrasted by the northeastern and southeastern areas favorable for mineral exploration.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
Landslides are a type of natural disaster that can cause substantial harm to humanity.Monitoring and predicting the initiation of potential landslides is critical to avoiding losses due to disasters and economic activ...Landslides are a type of natural disaster that can cause substantial harm to humanity.Monitoring and predicting the initiation of potential landslides is critical to avoiding losses due to disasters and economic activities.The impact of the controlled-source audio-frequency magnetotelluric method on investigating landslide surfaces is assessed through numerical simulations with a finite element approach.A Dirichlet boundary condition is selected to match the truncated boundary,resulting in a remarkable improvement in simulation efficiency.Rederivation of the formulas for a layered medium adept to the controlled-source audiofrequency magnetotelluric method is necessary to determine the electromagnetic field at any location along the truncated boundary.After the reliability evaluation of the new codes,a landslide model with a slide surface is designed,and the characteristics of its electromagnetic field and the apparent resistivity are studied.Instead of the total electromagnetic field,which is strongly infl uenced by topography variation,the apparent resistivity should be used for sliding surface detection.The normalized pure anomalous electromagnetic field may also be employed to quickly assess the detectability of the sliding surface.Overall,this study demonstrates that the controlled-source audio-frequency magnetotelluric method can be employed for investigating landslides,and recommends survey parameters,including configuration,frequency range,and length of survey line in landslide exploration.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
Objective:This study aimed to explore the applications of three-dimensional (3D) technology, including virtual reality, augmented reality (AR), and 3D printing system, in the field of medicine, particularly in renal i...Objective:This study aimed to explore the applications of three-dimensional (3D) technology, including virtual reality, augmented reality (AR), and 3D printing system, in the field of medicine, particularly in renal interventions for cancer treatment.Methods:A specialized software transforms 2D medical images into precise 3D digital models, facilitating improved anatomical understanding and surgical planning. Patient-specific 3D printed anatomical models are utilized for preoperative planning, intraoperative guidance, and surgical education. AR technology enables the overlay of digital perceptions onto real-world surgical environments.Results:Patient-specific 3D printed anatomical models have multiple applications, such as preoperative planning, intraoperative guidance, trainee education, and patient counseling. Virtual reality involves substituting the real world with a computer-generated 3D environment, while AR overlays digitally created perceptions onto the existing reality. The advances in 3D modeling technology have sparked considerable interest in their application to partial nephrectomy in the realm of renal cancer. 3D printing, also known as additive manufacturing, constructs 3D objects based on computer-aided design or digital 3D models. Utilizing 3D-printed preoperative renal models provides benefits for surgical planning, offering a more reliable assessment of the tumor's relationship with vital anatomical structures and enabling better preparation for procedures. AR technology allows surgeons to visualize patient-specific renal anatomical structures and their spatial relationships with surrounding organs by projecting CT/MRI images onto a live laparoscopic video. Incorporating patient-specific 3D digital models into healthcare enhances best practice, resulting in improved patient care, increased patient satisfaction, and cost saving for the healthcare system.展开更多
It is always a challenge for a model developer to verify a three-dimensional hydrodynamic model, especially for the baroclinic term over variable topography, due to a lack of observational data sets or suitable analyt...It is always a challenge for a model developer to verify a three-dimensional hydrodynamic model, especially for the baroclinic term over variable topography, due to a lack of observational data sets or suitable analytical solutions. In this paper, exact solutions for the periodic forcing by surface heat flux and wind stress are given by solving the linearized equations of motion neglecting the rotation, advection and horizontal diffusion terms. The temperature at the bottom is set to a prescribed periodic value and a slip condition on flow is enforced at the bottom. The geometry of the quarter annulus, which has been extensively studied for two- and three-dimensional analytical solutions of unstratified water bodies, is used with a general power law variation of the bottom slope in the radial direction and is constant in the azimuthal direction. The analytical solutions are derived in a cylindrical coordinate system, which describes the three-dimensional fluid field in a Cartesian coordinate system. The results presented in this paper should provide a foundation for studying and verifying the baroclinic term over a varied topography in a three-dimensional numerical model.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
基金financially supported by the Ministry of Science and Technology of China(Nos.2022YFF0801201,2021YFC2900300)the National Natural Science Foundation of China(Nos.41872245,U1911202)the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515010666)。
文摘To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets of concealed ore bodies,three-dimensional Mineral Prospectivity Modeling(MPM)of the deposit has been conducted using the weights-of-evidence(WofE)method.Conditional independence between evidence layers was tested,and the outline results using the prediction-volume(P-V)and Student's t-statistic methods for delineating favorable mineralization areas from continuous posterior probability map were critically compared.Four exploration targets delineated ultimately by the Student's t-statistic method for the discovery of minable ore bodies in each of the target areas were discussed in detail.The main conclusions include:(1)three-dimensional modeling of a deposit using multi-source reconnaissance data is useful for MPM in interpreting their relationships with known ore bodies;(2)WofE modeling can be used as a straightforward tool for integrating deposit model and reconnaissance data in MPM;(3)the Student's t-statistic method is more applicable in binarizing the continuous prospectivity map for exploration targeting than the PV approach;and(4)two target areas within high potential to find undiscovered ore bodies were diagnosed to guide future near-mine exploration activities of the Jinshan deposit.
基金supported by the Volkswagen Foundation(Grant No.Az 99078 to DDC,ALT,and MT)funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy–2082/1–390761711(to DDC)part of the research training group GRK 2415–Mechanobiology in Epithelial 3D Tissue Constructs(project number 363055819,to ALT and SJ).
文摘The global demand for in vitro respiratory airway models has surged due to the coronavirus disease 2019(COVID-19)pandemic.Current state-of-the-art models use polymer membranes to separate epithelial cells from other cell types,creating a nonphysiological barrier.In this study,we applied three-dimensional(3D)printing and bioprinting to develop an in vitro model where endothelial and epithelial cells were in direct contact,mimicking their natural arrangement.This proof-ofconcept model includes a culture chamber,with an endothelial bioink printed and perfused through an epithelial channel.In silico simulations of the air velocity within the channel revealed shear stress values ranging from 0.13 to 0.39 Pa,aligning with the desired in vivo shear stress observed in the bronchi regions(0.1–0.4 Pa).Biomechanical movements during resting breathing were mimicked by incorporating a textile mesh positioned away from the cell–cell interface.The epithelial channel demonstrated a capacity for compression and expansion of up to−14.7%and+6.4%,respectively.Microscopic images showed that the epithelial cells formed a uniform monolayer within the lumen of the channel close to the bioprinted endothelial cells.Our novel model offers a valuable tool for future research into respiratory diseases and potential treatments under conditions closely mimicking those in the lung.
基金National Natural Science Foundation of China(22073023)Natural Science Foundation of Henan Province(242300421134)+1 种基金the Young Backbone Teacher in Colleges and Universities of Henan Province(2021GGJS020)Foundation of State Key Laboratory of Antiviral Drugs。
文摘The acetylpolyamine oxidase(APAO),spermine oxidase(SMO),and spermidine/spermine N1-acetyltransferase(SSAT)are pivotal enzymes in polyamine metabolism,exerting direct influence on polyamine homeostasis regulation.Dysfunctions in these enzymes are intricately linked to inflammatory diseases and cancers.Establishing their three-dimensional structures is essential for exploring enzymatic catalytic mechanisms and designing inhibitors at the atomic level.This article primarily assesses the precision of AlphaFold2 and molecular dynamics simulations in determining the three-dimensional structures of these enzymes,utilizing protein conformation rationality assessment,residue correlation matrix,and other techniques.This provides robust models for subsequent polyamine catabolic metabolism calculations and offers valuable insights for modeling proteins that have yet to acquire crystal structures.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
基金supported by National Natural Science Foundation of China(No.524B2078,12426307,51906203)Guangdong Major Project of Basic and Applied Basic Research(2023B0303000002)+6 种基金Guangdong Basic and Applied Basic Research Foundation(2023B1515120005)Natural Science Foundation of Shenzhen(JCYJ20241202125327036,JCYJ20240813100103005)Shenzhen Engineering Research Center of Redox Flow Battery for Energy Storage(XMHT20230208003)Research Project on Medium-and Long-Duration Flow Battery Energy Storage Technology(2024KJTW0015)China Association for Science and Technology(OR2308010)High level of special funds(G03034K001)supported by the Center for Computational Science and Engineering at the Southern University of Science and Technology.
文摘Long-duration energy storage has become critical for renewable energy integration.While redox flow batteries,especially vanadium-based systems,are scaling up in capacity,their performance at the stack level remains insufficiently optimized,demanding more profound mechanistic studies and engineering refinements.To address the difficulties in resolving the flow inhomogeneity at the stack scale,this study establishes a multi-physics field coupling model and analyzes the pressure distributions,flow rate differences,active substance concentration,and electrochemical characteristics.The results show that the uneven cell pressure distribution is a key factor affecting the consistency of the system performance,and the increase in the flow rate improves the reactant homogeneity,with both the average concentration and the uniformity factor increasing with the flow rate.In contrast,high current densities lead to an increased imbalance between electrochemical depletion and reactant replenishment,resulting in a significant decrease in reactant concentration in the under-ribs region.In addition,a higher flow rate can expand the high-current-density region where the stack operates efficiently.This study provides a theoretical basis for optimizing the design of the stack components.
文摘A numerical investigation on the effectiveness of the actuator disc method in producing the interactions of multiple tidal stream devices via the 3D-RANS finite element model Telemac3D is explored. The methodology for the implementation of the source term to represent an array of 20 m rotor diameter turbines deployed in an idealized channel is reviewed and discussed in detail. Flow interactions between multiple turbines are investigated for a single row arrangement with only two turbines and a two row arrangement containing three turbines. The results demonstrate that the non-hydrostatic solver shows better agreement when validated against published experimental data. Notably,the mesh density at the device location can strongly influence the simulated thrust from the disc. Although the actuator disc model can generally replicate the wake interactions well, the results indicate that it cannot accurately characterize the flow for regions with high turbulences. While a model setup with the largest lateral spacing(1.5D) demonstrates excellent agreement with the experimental data, the 0.5D model(smallest gap) deviates by up to 25%. These findings demonstrate the effectiveness of the applied source term in reproducing the wake profile, which is comparable with the published data, and highlight the inherent nature of the RANS and actuator disc models.
基金the funding from the National Natural Science Foundation of China No.52275294the National Key Research and Development Program of China(No.2018YFA0703000)。
文摘Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation,mimicking physiological heterogeneous structures,and recreating the cellular microenvironment.However,the layer-by-layer printing method is significantly constrained by the rheological properties of the bioink,making it challenging to form complex three-dimensional vascular structures in low-viscosity soft materials.To overcome this limitation,we developed a cross-linkable biphasic embedding medium by mixing low-viscosity biomaterials with gelatin microgel.This medium possesses yield stress and self-healing properties,facilitating efficient and continuous three-dimensional shaping of sacrificial ink within it.By adjusting the printing speed,we controlled the filament diameter,achieving a range from 250μm to 1000μm,and ensuring precise control over ink deposition locations and filament shapes.Using the in situ endothelialization method,we constructed complex vascular structures and ensured close adhesion between hepatocytes and endothelial cells.In vitro experiments demonstrated that the vascularized liver tissue model exhibited enhanced protein synthesis and metabolic function compared to mixed liver tissue.We also investigated the impact of varying vascular densities on liver tissue function.Transcriptome sequencing revealed that liver tissues with higher vascular density exhibited upregulated gene expression in metabolic and angiogenesis-related pathways.In summary,this method is adaptable to various materials,allowing the rheological properties of the supporting bath and the tissue's porosity to be modified using microgels,thus enabling precise regulation of the liver tissue microenvironment.Additionally,it facilitates the rapid construction of three-dimensional vascular structures within liver tissue.The resulting vascularized liver tissue model exhibits enhanced biological functionality,opening new opportunities for biomedical applications.
基金Project supported by the National Natural Science Foundation of China(Nos.12372071 and 12372070)the Aeronautical Science Fund of China(No.2022Z055052001)the Foundation of China Scholarship Council(No.202306830079)。
文摘Currently,there are a limited number of dynamic models available for braided composite plates with large overall motions,despite the incorporation of three-dimensional(3D)braided composites into rotating blade components.In this paper,a dynamic model of 3D 4-directional braided composite thin plates considering braiding directions is established.Based on Kirchhoff's plate assumptions,the displacement variables of the plate are expressed.By incorporating the braiding directions into the constitutive equation of the braided composites,the dynamic model of the plate considering braiding directions is obtained.The effects of the speeds,braiding directions,and braided angles on the responses of the plate with fixed-axis rotation and translational motion,respectively,are investigated.This paper presents a dynamic theory for calculating the deformation of 3D braided composite structures undergoing both translational and rotational motions.It also provides a simulation method for investigating the dynamic behavior of non-isotropic material plates in various applications.
基金supported by a grant from the Research Grant Council of Hong Kong Special Administrative Region(Project No.11207724).
文摘The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying geotechnical responses(e.g.consolidation settlement)in a 3D spatial domain.However,traditional 3D numerical model updating approaches are computationally prohibitive and therefore difficult to update the 3D responses in real time.To address these challenges,this study proposes a novel machine learning framework called sparse dictionary learning(T-3D-SDL)for real-time updating of time-varying 3D geotechnical responses.In T-3D-SDL,a concerned dataset(e.g.time-varying 3D settlement)is approximated as a linear superposition of dictionary atoms generated from 3D random FEM analyses.Field monitoring data are then used to identify non-trivial atoms and estimate their weights within a Bayesian framework for model updating and prediction.The proposed approach enables the real-time update of temporally varying settlements with a high 3D spatial resolution and quantified uncertainty as field monitoring data evolve.The proposed approach is illustrated using an embankment construction project.The results show that the proposed approach effectively improves settlement predictions along temporal and 3D spatial dimensions,with minimal latency(e.g.within minutes),as monitoring data appear.In addition,the proposed approach requires only a reasonably small number of 3D FEM model evaluations,avoids the use of widely adopted yet often criticized surrogate models,and effectively addresses the limitations(e.g.computational inefficiency)of existing 3D model updating approaches.
基金supported by the National Natural Science Foundation of China (No. 52275291)the Fundamental Research Funds for the Central Universitiesthe Program for Innovation Team of Shaanxi Province,China (No. 2023-CX-TD-17)
文摘Hypoxia is a typical feature of the tumor microenvironment,one of the most critical factors affecting cell behavior and tumor progression.However,the lack of tumor models able to precisely emulate natural brain tumor tissue has impeded the study of the effects of hypoxia on the progression and growth of tumor cells.This study reports a three-dimensional(3D)brain tumor model obtained by encapsulating U87MG(U87)cells in a hydrogel containing type I collagen.It also documents the effect of various oxygen concentrations(1%,7%,and 21%)in the culture environment on U87 cell morphology,proliferation,viability,cell cycle,apoptosis rate,and migration.Finally,it compares two-dimensional(2D)and 3D cultures.For comparison purposes,cells cultured in flat culture dishes were used as the control(2D model).Cells cultured in the 3D model proliferated more slowly but had a higher apoptosis rate and proportion of cells in the resting phase(G0 phase)/gap I phase(G1 phase)than those cultured in the 2D model.Besides,the two models yielded significantly different cell morphologies.Finally,hypoxia(e.g.,1%O2)affected cell morphology,slowed cell growth,reduced cell viability,and increased the apoptosis rate in the 3D model.These results indicate that the constructed 3D model is effective for investigating the effects of biological and chemical factors on cell morphology and function,and can be more representative of the tumor microenvironment than 2D culture systems.The developed 3D glioblastoma tumor model is equally applicable to other studies in pharmacology and pathology.
基金supported by the Key Research Project of China Geological Survey(Grant No.DD20230564)the Research Project of Natural Resources Department of Gansu Province(Grant No.202219)。
文摘Three-dimensional geochemical modeling of ore-forming elements is crucial for predicting deep mineralization.This approach provides key information for the quantitative prediction of deep mineral localization,three-dimensional fine interpolation,analysis of spatial distribution patterns,and extraction of quantitative mineral-seeking markers.The Yechangping molybdenum(Mo)deposit is a significant and extensive porphyry-skarn deposit in the East Qinling-Dabie Mo polymetallic metallogenic belt at the southern margin of the North China Block.Abundant borehole data on oreforming elements underpin deep geochemical predictions.The methodology includes the following steps:(1)Threedimensional geological modeling of the deposit was established.(2)Correlation,cluster,and factor analyses post delineation of mineralization stages and determination of mineral generation sequence to identify(Cu,Pb,Zn,Ag)and(Mo,W,mfe)assemblages.(3)A three-dimensional geochemical block model was constructed for Mo,W,mfe,Cu,Zn,Pb,and Ag using the ordinary kriging method,and the variational function was developed.(4)Spatial distribution and enrichment characteristics analysis of ore-forming elements are performed to extract geological information,employing the variogram and w(Cu+Pb+Zn+Ag)/w(Mo+W)as predictive indicators.(5)Identifying the western,northwestern,and southwestern areas of the mine with limited mineralization potential,contrasted by the northeastern and southeastern areas favorable for mineral exploration.
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supported by the Project 42374170,XDA0430101.and 2022YFF0706200.
文摘Landslides are a type of natural disaster that can cause substantial harm to humanity.Monitoring and predicting the initiation of potential landslides is critical to avoiding losses due to disasters and economic activities.The impact of the controlled-source audio-frequency magnetotelluric method on investigating landslide surfaces is assessed through numerical simulations with a finite element approach.A Dirichlet boundary condition is selected to match the truncated boundary,resulting in a remarkable improvement in simulation efficiency.Rederivation of the formulas for a layered medium adept to the controlled-source audiofrequency magnetotelluric method is necessary to determine the electromagnetic field at any location along the truncated boundary.After the reliability evaluation of the new codes,a landslide model with a slide surface is designed,and the characteristics of its electromagnetic field and the apparent resistivity are studied.Instead of the total electromagnetic field,which is strongly infl uenced by topography variation,the apparent resistivity should be used for sliding surface detection.The normalized pure anomalous electromagnetic field may also be employed to quickly assess the detectability of the sliding surface.Overall,this study demonstrates that the controlled-source audio-frequency magnetotelluric method can be employed for investigating landslides,and recommends survey parameters,including configuration,frequency range,and length of survey line in landslide exploration.
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.
文摘Objective:This study aimed to explore the applications of three-dimensional (3D) technology, including virtual reality, augmented reality (AR), and 3D printing system, in the field of medicine, particularly in renal interventions for cancer treatment.Methods:A specialized software transforms 2D medical images into precise 3D digital models, facilitating improved anatomical understanding and surgical planning. Patient-specific 3D printed anatomical models are utilized for preoperative planning, intraoperative guidance, and surgical education. AR technology enables the overlay of digital perceptions onto real-world surgical environments.Results:Patient-specific 3D printed anatomical models have multiple applications, such as preoperative planning, intraoperative guidance, trainee education, and patient counseling. Virtual reality involves substituting the real world with a computer-generated 3D environment, while AR overlays digitally created perceptions onto the existing reality. The advances in 3D modeling technology have sparked considerable interest in their application to partial nephrectomy in the realm of renal cancer. 3D printing, also known as additive manufacturing, constructs 3D objects based on computer-aided design or digital 3D models. Utilizing 3D-printed preoperative renal models provides benefits for surgical planning, offering a more reliable assessment of the tumor's relationship with vital anatomical structures and enabling better preparation for procedures. AR technology allows surgeons to visualize patient-specific renal anatomical structures and their spatial relationships with surrounding organs by projecting CT/MRI images onto a live laparoscopic video. Incorporating patient-specific 3D digital models into healthcare enhances best practice, resulting in improved patient care, increased patient satisfaction, and cost saving for the healthcare system.
文摘It is always a challenge for a model developer to verify a three-dimensional hydrodynamic model, especially for the baroclinic term over variable topography, due to a lack of observational data sets or suitable analytical solutions. In this paper, exact solutions for the periodic forcing by surface heat flux and wind stress are given by solving the linearized equations of motion neglecting the rotation, advection and horizontal diffusion terms. The temperature at the bottom is set to a prescribed periodic value and a slip condition on flow is enforced at the bottom. The geometry of the quarter annulus, which has been extensively studied for two- and three-dimensional analytical solutions of unstratified water bodies, is used with a general power law variation of the bottom slope in the radial direction and is constant in the azimuthal direction. The analytical solutions are derived in a cylindrical coordinate system, which describes the three-dimensional fluid field in a Cartesian coordinate system. The results presented in this paper should provide a foundation for studying and verifying the baroclinic term over a varied topography in a three-dimensional numerical model.
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.
