In-space cable-driven manipulators exhibit several advantages,such as a large range of motion,high dexterity,and lightweight structure.However,kinematic and dynamic analysis play an essential role in designing a cable...In-space cable-driven manipulators exhibit several advantages,such as a large range of motion,high dexterity,and lightweight structure.However,kinematic and dynamic analysis play an essential role in designing a cable-driven manipulator.In this paper,the kinematic analysis of a type of cable-driven manipulator is performed,and a motion planning scheme is conducted to actuate this manipulator.Moreover,a flexible multi-body dynamic model of a cable-driven manipulator considering the frictional contact between the cables and pulleys is established.To describe properties such as flexibility,vibration,and variable length of the cable,this paper utilizes reducedorder beam elements of the Absolute Nodal Coordinates Formulation(ANCF)in Arbitrary Lagrangian Eulerian(ALE)framework.Additionally,a virtual element is introduced to model the contact segment in the cable-pulley system.A tension decay factor is employed to account for the friction in the contact segment.To validate the proposed method,a semi-analytical model based on D'Alembert's principle is established.Cross-verification is performed to validate the accuracy of both models.The model is further applied to simulate the rotation of the cable-driven manipulator with different structural parameters and frictional factors.The results from the analyses provide valuable guidance for the design and motion control of the in-space cable-driven manipulator.Finally,a prototype of a single module is manufactured and tested.Ground experiments are carried out to verify the kinematic and dynamic models.展开更多
Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charg...Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charge(RIC)mode facilitates the ER driven by SEA to provide the required assistance and support for the subject.展开更多
In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typica...In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.展开更多
To assess the effectiveness of vaccination in contaminated environments,this study introduces a modeling framework that encompasses two transmission routes,namely direct human-to-human contact and indirect human-to-en...To assess the effectiveness of vaccination in contaminated environments,this study introduces a modeling framework that encompasses two transmission routes,namely direct human-to-human contact and indirect human-to-environment contact,as well as the implementation of new M72/AS01_(E)vaccine.Motivated by this,a coupled age-structured tuberculosis(TB)model is proposed.Its well-posedness requirement is verified using the integrated semigroup theory.Furthermore,this study presents a comprehensive analysis of threshold dynamics associated with the proposed model.Specifically,the global stability of the disease-free and positive steady states is demonstrated by employing Lyapunov functionals.Lastly,the effects of the vaccination with M72/AS01_(E)and contaminated environments on TB control are numerically simulated.Experimental results indicate that high concentrations of Mycobacterium tuberculosis in contaminated environments may somewhat impede TB control efforts,but that large-scale deployment of new vaccine could significantly reduce the prevalence of TB.展开更多
We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties i...We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties in each construction phase.Specifically,the algorithm is developed for the Government Assisted Owner Driven(GAOD)reconstruction system to simulate long-term recovery trajectory.SDES,as a flexible modeling approach,can simulate any housing recovery scenario that follows phased reconstruction.The 2015 M 7.8 Gorkha earthquake sequence in Nepal is considered the extreme event,with 796,245 buildings requiring reconstruction.We present some recovery trajectories from severely hit,crisis hit,and earthquake hit parishes,comparing them with the actual reconstruction progress.We also assess quality and improvement of reconstructed buildings using seismic fragility functions,compared to pre-earthquake constructions.Housing recovery uncertainties are dissected in relation to reconstruction pace.We conclude that the vast majority of the reconstructed buildings followed the Build Back Better(BBB)approach and missed the opportunity to pursue the Build Back Resilient(BBR)approach due to multifaceted challenges ranging from unclear policies to economic constraints.We critically assess the GAOD vs Owner Driven(OD)recovery framework and conclude that insurance-supported and technically assisted OD approach could be the most suitable model for post extreme event housing recovery.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
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformatio...The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.展开更多
Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol ...Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol based on zeroing neural networks(ZNNs)is proposed.First,a dynamic linearization data model(DLDM)is acquired via dynamic linearization technology(DLT).展开更多
Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Class...Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.展开更多
Fluvial systems play a crucial role in coastal and riverine ecosystems, making it essential to understand their responses to sea level changes for preserving biodiversity and managing natural resources. The evolution ...Fluvial systems play a crucial role in coastal and riverine ecosystems, making it essential to understand their responses to sea level changes for preserving biodiversity and managing natural resources. The evolution of the modern Indus River Delta offers a rare opportunity to study the interplay between sea level fluctuations, tectonism, sediment supply, and the corresponding fluvial responses. This study employs the ‘SedSim' stratigraphic forward model to simulate the delta's evolution from 200 kyr to the next5 kyr, drawing on data from field observations, Landsat imagery, digital elevation models, and previous studies. The model consists of 205 layers, each representing a 1-kyr time step, covering the last two glacial-interglacial cycles. Between 200 kyr and 130 kyr, during a lowstand period, sedimentation on the delta plain continued due to partial flow from the Indus River. During the last interglacial(130–60 kyr), rising sea levels led to peak sediment deposition, characteristic of a highstand phase. From 60 kyr to 18 kyr, sea levels dropped to their lowest during the Last Glacial Maximum(LGM), resulting in extensive erosion and minimal deposition on the delta plain. From 18 kyr to the present, rapidly rising sea levels, coupled with intensified monsoon activity, increased sedimentation rates and triggered avulsion and aggradation processes. The model accurately predicted depositional thickness across the delta plain, indicating a maximum of ca. 200 m at the shoreline platform, ca. 175 m in the northeastern delta, and ca. 100 m in the central delta. The study underscores the delta's vulnerability to future sea level rise, which–at a projected rate of 1 m/kyr–could significantly influence the densely populated, low-lying delta plain. These findings offer valuable insights into the geomorphic evolution of the Indus Delta and emphasize the socioeconomic implications of sea level change, underscoring the importance of proactive management and adaptation strategies.展开更多
Driven critical dynamics in quantum phase transitions holds significant theoretical importance,and also has practical applications in fast-developing quantum devices.While scaling corrections have been shown to play i...Driven critical dynamics in quantum phase transitions holds significant theoretical importance,and also has practical applications in fast-developing quantum devices.While scaling corrections have been shown to play important roles in fully characterizing equilibrium quantum criticality,their impact on nonequilibrium critical dynamics has not been extensively explored.In this work,we investigate the driven critical dynamics in a two-dimensional quantum Heisenberg model.We find that in this model the scaling corrections arising from both finite system size and finite driving rate must be incorporated into the finite-time scaling form in order to properly describe the nonequilibrium scaling behaviors.In addition,improved scaling relations are obtained from the expansion of the full scaling form.We numerically verify these scaling forms and improved scaling relations for different starting states using the nonequilibrium quantum Monte Carlo algorithm.展开更多
Current research on robot calibration can be roughly classified into two categories,and both of them have certain inherent limitations.Model-based methods are difficult to model and compensate the pose errors arising ...Current research on robot calibration can be roughly classified into two categories,and both of them have certain inherent limitations.Model-based methods are difficult to model and compensate the pose errors arising from configuration-dependent geometric and non-geometric source errors,whereas the accuracy of data-driven methods depends on a large amount of measurement data.Using a 5-DOF(degrees of freedom)hybrid machining robot as an exemplar,this study presents a model data-driven approach for the calibration of robotic manipulators.An f-DOF realistic robot containing various source errors is visualized as a 6-DOF fictitious robot having error-free parameters,but erroneous actuated/virtual joint motions.The calibration process essentially involves four steps:(1)formulating the linear map relating the pose error twist to the joint motion errors,(2)parameterizing the joint motion errors using second-order polynomials in terms of nominal actuated joint variables,(3)identifying the polynomial coefficients using the weighted least squares plus principal component analysis,and(4)compensating the compensable pose errors by updating the nominal actuated joint variables.The merit of this approach is that it enables compensation of the pose errors caused by configuration-dependent geometric and non-geometric source errors using finite measurement configurations.Experimental studies on a prototype machine illustrate the effectiveness of the proposed approach.展开更多
Correction to:J.Iron Steel Res.Int.https://doi.org/10.1007/s42243-025-01545-x The publication of this article unfortunately contained mistakes.Equation(14)was not correct.The corrected equation is given below.
Data-driven research on recycled aggregate concrete(RAC)has long faced the challenge of lacking a unified testing standard dataset,hindering accurate model evaluation and trust in predictive outcomes.This paper review...Data-driven research on recycled aggregate concrete(RAC)has long faced the challenge of lacking a unified testing standard dataset,hindering accurate model evaluation and trust in predictive outcomes.This paper reviews critical parameters influencing mechanical properties in 35 RAC studies,compiles four datasets encompassing these parameters,and compiles the performance and key findings of 77 published data-driven models.Baseline capability tests are conducted on the nine most used models.The paper also outlines advanced methodological frameworks for future RAC research,examining the principles and challenges of physics-informed neural networks(PINNs)and generative adversarial networks(GANs),and employs SHAP and PDP tools to interpret model behaviour and enhance transparency.Findings indicate a clear trend toward integrated systems,hybrid models,and advanced optimization strategies,with integrated tree-based models showing superior performance across various prediction tasks.Based on this comprehensive review,we offer a recommendation for future research on how AI can be effectively oriented in RAC studies to support practical deployment and build confidence in data-driven approaches.展开更多
With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medici...With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medicine and transportation.In this paper,we systematically expound on the intelligent decision-making technology and prospects driven by large AI models.Specifically,we first review the development of large AI models in recent years.Then,from the perspective of methods,we introduce important theories and technologies of large decision models,such as model architecture and model adaptation.Next,from the perspective of applications,we introduce the cutting-edge applications of large decision models in various fields,such as autonomous driving and knowledge decision-making.Finally,we discuss existing challenges,such as security issues,decision bias and hallucination phenomenon as well as future prospects,from both technology development and domain applications.We hope this review paper can help researchers understand the important progress of intelligent decision-making driven by large AI models.展开更多
Accurate prediction of strip width is a key factor related to the quality of hot rolling manufacture.Firstly,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calc...Accurate prediction of strip width is a key factor related to the quality of hot rolling manufacture.Firstly,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calculation model is delineated for the optimization of process parameters via the particle swarm optimization algorithm.Subsequently,a hybrid strip width prediction model is proposed by effectively combining the respective advantages of the improved mechanism model and the data-driven model.In acknowledgment of prerequisite for positive error in strip width prediction,an adaptive width error compensation algorithm is proposed.Finally,comparative simulation experiments are designed on the actual rolling dataset after completing data cleaning and feature engineering.The experimental results show that the hybrid prediction model proposed has superior precision and robustness compared with the improved mechanism model and the other eight common data-driven models and satisfies the needs of practical applications.Moreover,the hybrid model can realize the complementary advantages of the mechanism model and the data-driven model,effectively alleviating the problems of difficult to improve the accuracy of the mechanism model and poor interpretability of the data-driven model,which bears significant practical implications for the research of strip width control.展开更多
Advancements in dynamic modeling methods of robotic manipulator are critical to the effective implementation of model-based control.Traditional approaches rely on rigorous first-principles-based dynamic modeling and p...Advancements in dynamic modeling methods of robotic manipulator are critical to the effective implementation of model-based control.Traditional approaches rely on rigorous first-principles-based dynamic modeling and precise parameter identification,while this paper explores an altemative through data-driven model reconstruction.To tackle the curse of dimensionality in the model reconstruction of a serial robotic manipulator with multi-degree-of-freedom,a relative activation indicator is proposed.Based on this indicator,the k-means clustering algorithm is utilized to classify the data under different working conditions.Sub-sequently,we leverage the fundamental prior knowledge to find the dynamical characteristics of each cluster and reconstruct the dynamic model in a stepwise manner using the method of sparse identification of nonlinear dynamics(SINDy).For the library generation of SINDy,the strategy of double-feature-set for serial manipulators with common joint types is proposed.Simula-tion results show that the stepwise model reconstruction approach not only reduces the size of the library of candidate functions but also decreases the impact of data noise on the reconstruction results.Finally,controllers based on the reconstructed mod.els are deployed on the experimental platform and the experimental results demonstrate the improvement in trajectory tracking performance and the potential of the proposed method in engineering applications.展开更多
Ghana’s Tema Port marked a historic milestone on 16 January 2024 with the arrival of Maersk Edirne,a 13,676-TEU container vessel-one of the largest of its kind globally-as part of Maersk’s newly launched Far East-We...Ghana’s Tema Port marked a historic milestone on 16 January 2024 with the arrival of Maersk Edirne,a 13,676-TEU container vessel-one of the largest of its kind globally-as part of Maersk’s newly launched Far East-West Africa express service.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.12102034 and 12125201)the Open Fund of State Key Laboratory of Robotics and Systems(HIT),China。
文摘In-space cable-driven manipulators exhibit several advantages,such as a large range of motion,high dexterity,and lightweight structure.However,kinematic and dynamic analysis play an essential role in designing a cable-driven manipulator.In this paper,the kinematic analysis of a type of cable-driven manipulator is performed,and a motion planning scheme is conducted to actuate this manipulator.Moreover,a flexible multi-body dynamic model of a cable-driven manipulator considering the frictional contact between the cables and pulleys is established.To describe properties such as flexibility,vibration,and variable length of the cable,this paper utilizes reducedorder beam elements of the Absolute Nodal Coordinates Formulation(ANCF)in Arbitrary Lagrangian Eulerian(ALE)framework.Additionally,a virtual element is introduced to model the contact segment in the cable-pulley system.A tension decay factor is employed to account for the friction in the contact segment.To validate the proposed method,a semi-analytical model based on D'Alembert's principle is established.Cross-verification is performed to validate the accuracy of both models.The model is further applied to simulate the rotation of the cable-driven manipulator with different structural parameters and frictional factors.The results from the analyses provide valuable guidance for the design and motion control of the in-space cable-driven manipulator.Finally,a prototype of a single module is manufactured and tested.Ground experiments are carried out to verify the kinematic and dynamic models.
基金supported in part by the National Natural Science Foundation of China(62173048,62373065,61873304,62106023)the Key Science and Technology Projects of Jilin Province,China(20230204081YY)the Research and Innovation Team of Anhui Province(2024AH010023)。
文摘Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charge(RIC)mode facilitates the ER driven by SEA to provide the required assistance and support for the subject.
基金supported in part by the National Natural Science Foundation of China(62373070 and 52272388)in part by the Chongqing Natural Science Foundation(CSTB2024NSCQ-QCXMX0054,CSTB2022NSCQ-MSX1225 and CSTC2024YCJH-BGZXM0042)in part by the Key Research and Development Project of Anhui Province(202304a05020060).
文摘In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.
文摘To assess the effectiveness of vaccination in contaminated environments,this study introduces a modeling framework that encompasses two transmission routes,namely direct human-to-human contact and indirect human-to-environment contact,as well as the implementation of new M72/AS01_(E)vaccine.Motivated by this,a coupled age-structured tuberculosis(TB)model is proposed.Its well-posedness requirement is verified using the integrated semigroup theory.Furthermore,this study presents a comprehensive analysis of threshold dynamics associated with the proposed model.Specifically,the global stability of the disease-free and positive steady states is demonstrated by employing Lyapunov functionals.Lastly,the effects of the vaccination with M72/AS01_(E)and contaminated environments on TB control are numerically simulated.Experimental results indicate that high concentrations of Mycobacterium tuberculosis in contaminated environments may somewhat impede TB control efforts,but that large-scale deployment of new vaccine could significantly reduce the prevalence of TB.
文摘We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties in each construction phase.Specifically,the algorithm is developed for the Government Assisted Owner Driven(GAOD)reconstruction system to simulate long-term recovery trajectory.SDES,as a flexible modeling approach,can simulate any housing recovery scenario that follows phased reconstruction.The 2015 M 7.8 Gorkha earthquake sequence in Nepal is considered the extreme event,with 796,245 buildings requiring reconstruction.We present some recovery trajectories from severely hit,crisis hit,and earthquake hit parishes,comparing them with the actual reconstruction progress.We also assess quality and improvement of reconstructed buildings using seismic fragility functions,compared to pre-earthquake constructions.Housing recovery uncertainties are dissected in relation to reconstruction pace.We conclude that the vast majority of the reconstructed buildings followed the Build Back Better(BBB)approach and missed the opportunity to pursue the Build Back Resilient(BBR)approach due to multifaceted challenges ranging from unclear policies to economic constraints.We critically assess the GAOD vs Owner Driven(OD)recovery framework and conclude that insurance-supported and technically assisted OD approach could be the most suitable model for post extreme event housing recovery.
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funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12322203).
文摘The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.
基金supported by the National Nature Science Foundation of China(U21A20166)the Science and Technology Development Foundation of Jilin Province(20230508095RC)+2 种基金the Major Science and Technology Projects of Jilin Province and Changchun City(20220301033GX)the Development and Reform Commission Foundation of Jilin Province(2023C034-3)the Interdisciplinary Integration and Innovation Project of JLU(JLUXKJC2020202).
文摘Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol based on zeroing neural networks(ZNNs)is proposed.First,a dynamic linearization data model(DLDM)is acquired via dynamic linearization technology(DLT).
基金funded by grants from the National Key Research and Development Program of China(Grant Nos.:2022YFE0205600 and 2022YFC3400504)the National Natural Science Foundation of China(Grant Nos.:82373792 and 82273857)the Fundamental Research Funds for the Central Universities,China,and the East China Normal University Medicine and Health Joint Fund,China(Grant No.:2022JKXYD07001).
文摘Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.
基金the Science and Technology Innovation Project of the Laoshan Laboratory (No. LSKJ202203402)the Major Research Project on the Tethys Geodynamic System from the National Science Foundation of China (No. 92055204)。
文摘Fluvial systems play a crucial role in coastal and riverine ecosystems, making it essential to understand their responses to sea level changes for preserving biodiversity and managing natural resources. The evolution of the modern Indus River Delta offers a rare opportunity to study the interplay between sea level fluctuations, tectonism, sediment supply, and the corresponding fluvial responses. This study employs the ‘SedSim' stratigraphic forward model to simulate the delta's evolution from 200 kyr to the next5 kyr, drawing on data from field observations, Landsat imagery, digital elevation models, and previous studies. The model consists of 205 layers, each representing a 1-kyr time step, covering the last two glacial-interglacial cycles. Between 200 kyr and 130 kyr, during a lowstand period, sedimentation on the delta plain continued due to partial flow from the Indus River. During the last interglacial(130–60 kyr), rising sea levels led to peak sediment deposition, characteristic of a highstand phase. From 60 kyr to 18 kyr, sea levels dropped to their lowest during the Last Glacial Maximum(LGM), resulting in extensive erosion and minimal deposition on the delta plain. From 18 kyr to the present, rapidly rising sea levels, coupled with intensified monsoon activity, increased sedimentation rates and triggered avulsion and aggradation processes. The model accurately predicted depositional thickness across the delta plain, indicating a maximum of ca. 200 m at the shoreline platform, ca. 175 m in the northeastern delta, and ca. 100 m in the central delta. The study underscores the delta's vulnerability to future sea level rise, which–at a projected rate of 1 m/kyr–could significantly influence the densely populated, low-lying delta plain. These findings offer valuable insights into the geomorphic evolution of the Indus Delta and emphasize the socioeconomic implications of sea level change, underscoring the importance of proactive management and adaptation strategies.
基金supported by the National Natural Science Foundation of China(Grant Nos.12104109,12222515,and 12075324)the Science and Technology Projects in Guangzhou(Grant No.2024A04J2092)the Science and Technology Projects in Guangdong Province(Grant No.211193863020).
文摘Driven critical dynamics in quantum phase transitions holds significant theoretical importance,and also has practical applications in fast-developing quantum devices.While scaling corrections have been shown to play important roles in fully characterizing equilibrium quantum criticality,their impact on nonequilibrium critical dynamics has not been extensively explored.In this work,we investigate the driven critical dynamics in a two-dimensional quantum Heisenberg model.We find that in this model the scaling corrections arising from both finite system size and finite driving rate must be incorporated into the finite-time scaling form in order to properly describe the nonequilibrium scaling behaviors.In addition,improved scaling relations are obtained from the expansion of the full scaling form.We numerically verify these scaling forms and improved scaling relations for different starting states using the nonequilibrium quantum Monte Carlo algorithm.
基金Supported by National Natural Science Foundation of China(Grant Nos.52325501,U24B2047).
文摘Current research on robot calibration can be roughly classified into two categories,and both of them have certain inherent limitations.Model-based methods are difficult to model and compensate the pose errors arising from configuration-dependent geometric and non-geometric source errors,whereas the accuracy of data-driven methods depends on a large amount of measurement data.Using a 5-DOF(degrees of freedom)hybrid machining robot as an exemplar,this study presents a model data-driven approach for the calibration of robotic manipulators.An f-DOF realistic robot containing various source errors is visualized as a 6-DOF fictitious robot having error-free parameters,but erroneous actuated/virtual joint motions.The calibration process essentially involves four steps:(1)formulating the linear map relating the pose error twist to the joint motion errors,(2)parameterizing the joint motion errors using second-order polynomials in terms of nominal actuated joint variables,(3)identifying the polynomial coefficients using the weighted least squares plus principal component analysis,and(4)compensating the compensable pose errors by updating the nominal actuated joint variables.The merit of this approach is that it enables compensation of the pose errors caused by configuration-dependent geometric and non-geometric source errors using finite measurement configurations.Experimental studies on a prototype machine illustrate the effectiveness of the proposed approach.
文摘Correction to:J.Iron Steel Res.Int.https://doi.org/10.1007/s42243-025-01545-x The publication of this article unfortunately contained mistakes.Equation(14)was not correct.The corrected equation is given below.
文摘Data-driven research on recycled aggregate concrete(RAC)has long faced the challenge of lacking a unified testing standard dataset,hindering accurate model evaluation and trust in predictive outcomes.This paper reviews critical parameters influencing mechanical properties in 35 RAC studies,compiles four datasets encompassing these parameters,and compiles the performance and key findings of 77 published data-driven models.Baseline capability tests are conducted on the nine most used models.The paper also outlines advanced methodological frameworks for future RAC research,examining the principles and challenges of physics-informed neural networks(PINNs)and generative adversarial networks(GANs),and employs SHAP and PDP tools to interpret model behaviour and enhance transparency.Findings indicate a clear trend toward integrated systems,hybrid models,and advanced optimization strategies,with integrated tree-based models showing superior performance across various prediction tasks.Based on this comprehensive review,we offer a recommendation for future research on how AI can be effectively oriented in RAC studies to support practical deployment and build confidence in data-driven approaches.
基金supported by the National Natural Science Foundation of China(Grant 62293545)Shenzhen Science and Technology Program(Grant ZDSYS20220323112000001).
文摘With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medicine and transportation.In this paper,we systematically expound on the intelligent decision-making technology and prospects driven by large AI models.Specifically,we first review the development of large AI models in recent years.Then,from the perspective of methods,we introduce important theories and technologies of large decision models,such as model architecture and model adaptation.Next,from the perspective of applications,we introduce the cutting-edge applications of large decision models in various fields,such as autonomous driving and knowledge decision-making.Finally,we discuss existing challenges,such as security issues,decision bias and hallucination phenomenon as well as future prospects,from both technology development and domain applications.We hope this review paper can help researchers understand the important progress of intelligent decision-making driven by large AI models.
基金supported by the National Natural Science Foundation of China(No.62273234)Key Research and Development Program of Shaanxi(Program No.2022GY-306)Technology Innovation Leading Program of Shaanxi(Program No.2022QFY01-16).
文摘Accurate prediction of strip width is a key factor related to the quality of hot rolling manufacture.Firstly,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calculation model is delineated for the optimization of process parameters via the particle swarm optimization algorithm.Subsequently,a hybrid strip width prediction model is proposed by effectively combining the respective advantages of the improved mechanism model and the data-driven model.In acknowledgment of prerequisite for positive error in strip width prediction,an adaptive width error compensation algorithm is proposed.Finally,comparative simulation experiments are designed on the actual rolling dataset after completing data cleaning and feature engineering.The experimental results show that the hybrid prediction model proposed has superior precision and robustness compared with the improved mechanism model and the other eight common data-driven models and satisfies the needs of practical applications.Moreover,the hybrid model can realize the complementary advantages of the mechanism model and the data-driven model,effectively alleviating the problems of difficult to improve the accuracy of the mechanism model and poor interpretability of the data-driven model,which bears significant practical implications for the research of strip width control.
基金supported by the National Natural Science Foundation of China(Grant Nos.12072237,12472022,12372022,12372065,and U2441202)the Fundamental Research Funds for the Central Universities(Grant No.22120220590)。
文摘Advancements in dynamic modeling methods of robotic manipulator are critical to the effective implementation of model-based control.Traditional approaches rely on rigorous first-principles-based dynamic modeling and precise parameter identification,while this paper explores an altemative through data-driven model reconstruction.To tackle the curse of dimensionality in the model reconstruction of a serial robotic manipulator with multi-degree-of-freedom,a relative activation indicator is proposed.Based on this indicator,the k-means clustering algorithm is utilized to classify the data under different working conditions.Sub-sequently,we leverage the fundamental prior knowledge to find the dynamical characteristics of each cluster and reconstruct the dynamic model in a stepwise manner using the method of sparse identification of nonlinear dynamics(SINDy).For the library generation of SINDy,the strategy of double-feature-set for serial manipulators with common joint types is proposed.Simula-tion results show that the stepwise model reconstruction approach not only reduces the size of the library of candidate functions but also decreases the impact of data noise on the reconstruction results.Finally,controllers based on the reconstructed mod.els are deployed on the experimental platform and the experimental results demonstrate the improvement in trajectory tracking performance and the potential of the proposed method in engineering applications.
文摘Ghana’s Tema Port marked a historic milestone on 16 January 2024 with the arrival of Maersk Edirne,a 13,676-TEU container vessel-one of the largest of its kind globally-as part of Maersk’s newly launched Far East-West Africa express service.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
