With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbin...With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes.These models leverage the ability to capture complex,high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models.As a result,data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output.This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches.It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature.Subsequently,the related studies are categorized into four key areas:wind turbine power prediction,data-driven analytic wake models,wake field reconstruction,and the incorporation of explicit physical constraints.The accuracy of data-driven models is influenced by two primary factors:the quality of the training data and the performance of the model itself.Accordingly,both data accuracy and model structure are discussed in detail within the review.展开更多
Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to...Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.展开更多
Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be...Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.展开更多
This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns...This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational ow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, ow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody puri cation process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.展开更多
Sub-Saharan Africa(SSA)has the highest maternal and under-five mortality rates in the world.The advent of the coronavirus disease 2019 exacerbated the region's problems by overwhelming the health systems and affec...Sub-Saharan Africa(SSA)has the highest maternal and under-five mortality rates in the world.The advent of the coronavirus disease 2019 exacerbated the region's problems by overwhelming the health systems and affecting access to healthcare through travel restrictions and rechanelling of resources towards the containment of the pandemic.The region failed to achieve the Millenium Development Goals on maternal and child mortalities,and is poised to fail to achieve the same goals in the Sustainable Development Goals.To improve on the maternal and child health outcomes,many SSA countries introduced digital technologies for educating pregnant and nurs-ing women,making doctors'appointments and sending reminders to mothers and expectant mothers,as well as capturing information about patients and their illnesses.However,the collected epidemiological data are not being utilised to inform patient care and improve on the quality,efficiency and access to maternal,neonatal and child health(MNCH)care.To the researchers'best knowledge,no review paper has been published that focuses on digital health for MNCH care in SSA and proposes data-driven approaches to the same.Therefore,this study sought to:(1)identify digital systems for MNCH in SSA;(2)identify the applicability and weaknesses of the dig-ital MNCH systems in SSA;and(3)propose a data-driven model for diverging emerging technologies into MNCH services in SSA to make better use of data to improve MNCH care coverage,efficiency and quality.The PRISMA methodology was used in this study.The study revealed that there are no data-driven models for monitoring pregnant women and under-five children in Sub-Saharan Africa,with the available digital health technologies mainly based on SMS and websites.Thus,the current digital health systems in SSA do not support real-time,ubiquitous,pervasive and data-driven healthcare.Their main applicability is in non-real-time pregnancy moni-toring,education and information dissemination.Unless new and more effective approaches are implemented,SSA might remain with the highest and unacceptable maternal and under-five mortality rates globally.The study proposes feasible emerging technologies that can be used to provide data-driven healthcare for MNCH in SSA,and the recommendations on how to make the transition successful as well as the lessons learn from other regions.展开更多
This study explores the effectiveness of machine learning models in predicting the air-side performance of microchannel heat exchangers.The data were generated by experimentally validated Computational Fluid Dynam-ics...This study explores the effectiveness of machine learning models in predicting the air-side performance of microchannel heat exchangers.The data were generated by experimentally validated Computational Fluid Dynam-ics(CFD)simulations of air-to-water microchannel heat exchangers.A distinctive aspect of this research is the comparative analysis of four diverse machine learning algorithms:Artificial Neural Networks(ANN),Support Vector Machines(SVM),Random Forest(RF),and Gaussian Process Regression(GPR).These models are adeptly applied to predict air-side heat transfer performance with high precision,with ANN and GPR exhibiting notably superior accuracy.Additionally,this research further delves into the influence of both geometric and operational parameters—including louvered angle,fin height,fin spacing,air inlet temperature,velocity,and tube temperature—on model performance.Moreover,it innovatively incorporates dimensionless numbers such as aspect ratio,fin height-to-spacing ratio,Reynolds number,Nusselt number,normalized air inlet temperature,temperature difference,and louvered angle into the input variables.This strategic inclusion significantly refines the predictive capabilities of the models by establishing a robust analytical framework supported by the CFD-generated database.The results show the enhanced prediction accuracy achieved by integrating dimensionless numbers,highlighting the effectiveness of data-driven approaches in precisely forecasting heat exchanger performance.This advancement is pivotal for the geometric optimization of heat exchangers,illustrating the considerable potential of integrating sophisticated modeling techniques with traditional engineering metrics.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
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
BACKGROUND Non-erosive reflux disease(NERD),the main gastroesophageal reflux subtype,features reflux symptoms without mucosal damage.Anxiety links to visceral hypersensitivity in NERD,yet mechanisms and animal models ...BACKGROUND Non-erosive reflux disease(NERD),the main gastroesophageal reflux subtype,features reflux symptoms without mucosal damage.Anxiety links to visceral hypersensitivity in NERD,yet mechanisms and animal models are unclear.AIM To establish a translational NERD rat model with anxiety comorbidity via tail clamping and study corticotropin-releasing hormone(CRH)-mediated neuroimmune pathways in visceral hypersensitivity and esophageal injury.METHODS Sprague-Dawley(SD)and Wistar rats were grouped into sham,model,and modified groups(n=10 each).The treatments for the modified groups were as follows:SD rats received ovalbumin/aluminum hydroxide suspension+acid perfusion±tail clamping(40 minutes/day for 7 days),while Wistar rats received fructose water+tail clamping.Esophageal pathology,visceral sensitivity,and behavior were assessed.Serum CRH,calcitonin gene-related peptide(CGRP),5-hydroxytryptamine(5-HT),and mast cell tryptase(MCT)and central amygdala(CeA)CRH mRNA were measured via ELISA and qRT-PCR.RESULTS Tail clamping induced anxiety,worsening visceral hypersensitivity(lower abdominal withdrawal reflex thresholds,P<0.05)and esophageal injury(dilated intercellular spaces and mitochondrial edema).Both models showed raised serum CRH,CGRP,5-HT,and MCT(P<0.01)and CeA CRH mRNA expression(P<0.01).Behavioral tests confirmed anxiety-like phenotypes.NERD-anxiety rats showed clinical-like symptom severity without erosion.CONCLUSION Tail clamping induces anxiety in NERD models,worsening visceral hypersensitivity via CRH neuroimmune dysregulation,offering a translational model and highlighting CRH as a treatment target.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
Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of kn...Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.展开更多
In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD...In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD),with its emphasis on cross-functional teamwork,concurrent engineering,and data-driven decision-making,has been widely recognized for enhancing R&D efficiency and product quality.However,the unique characteristics of SOEs pose challenges to the effective implementation of IPD.The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models.This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs.By integrating data mining,machine learning,and other advanced analytical techniques,the model serves as a scientific and efficient decision-making tool.It aids SOEs in optimizing R&D resource allocation,shortening product development cycles,reducing R&D costs,and improving product quality and innovation.Moreover,this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.展开更多
Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in ...Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.展开更多
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb...The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.展开更多
The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzho...The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzhou RDP(19th Hangzhou Asian Games Research Development Project on Convective-scale Ensemble Prediction and Application)testbed,with the LBCs respectively sourced from National Centers for Environmental Prediction(NCEP)Global Forecast System(GFS)forecasts with 33 vertical levels(Exp_GFS),Pangu forecasts with 13 vertical levels(Exp_Pangu),Fuxi forecasts with 13 vertical levels(Exp_Fuxi),and NCEP GFS forecasts with the vertical levels reduced to 13(the same as those of Exp_Pangu and Exp_Fuxi)(Exp_GFSRDV).In general,Exp_Pangu performs comparably to Exp_GFS,while Exp_Fuxi shows slightly inferior performance compared to Exp_Pangu,possibly due to its less accurate large-scale predictions.Therefore,the ability of using data-driven networks to efficiently provide LBCs for convective-scale ensemble forecasts has been demonstrated.Moreover,Exp_GFSRDV has the worst convective-scale forecasts among the four experiments,which indicates the potential improvement of using data-driven networks for LBCs by increasing the vertical levels of the networks.However,the ensemble spread of the four experiments barely increases with lead time.Thus,each experiment has insufficient ensemble spread to present realistic forecast uncertainties,which will be investigated in a future study.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various op...The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions,involving the pressure,temperature,humidity,and stoichiometry ratio.In this article,recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed.The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described.The reduced-dimensional physics-based models(pseudo-twodimensional,one-dimensional numerical and zero dimensional analytical models)and the non-physics-based models(zero-dimensional empirical and data-driven models)have been systematically examined,and the comparison of these models has been performed.It is found that the current trends for the real-time control models are(i)to couple the single cell model with balance of plants to investigate the system performance,(ii)to incorporate aging effects to enable long-term performance prediction,(iii)to increase the computational speed(especially for one-dimensional numerical models),and(iv)to develop data-driven models with artificial intelligence/machine learning algorithms.This review will be beneficial for the development of physics or nonphysics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.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
The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie...The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No.52131102.
文摘With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes.These models leverage the ability to capture complex,high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models.As a result,data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output.This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches.It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature.Subsequently,the related studies are categorized into four key areas:wind turbine power prediction,data-driven analytic wake models,wake field reconstruction,and the incorporation of explicit physical constraints.The accuracy of data-driven models is influenced by two primary factors:the quality of the training data and the performance of the model itself.Accordingly,both data accuracy and model structure are discussed in detail within the review.
基金funded by National Natural Science Foundation of China(52004238)China Postdoctoral Science Foundation(2019M663561).
文摘Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.
基金supported by the National Natural Science Foundation of China(Grant No.51809279)the Major National Science and Technology Program(Grant No.2016ZX05028-001-05)+1 种基金Program for Changjiang Scholars and Innovative Research Team in University(Grant No.IRT14R58)the Fundamental Research Funds for the Central Universities,that is,the Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment(Grant No.20CX02302A).
文摘Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.
文摘This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational ow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, ow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody puri cation process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.
文摘Sub-Saharan Africa(SSA)has the highest maternal and under-five mortality rates in the world.The advent of the coronavirus disease 2019 exacerbated the region's problems by overwhelming the health systems and affecting access to healthcare through travel restrictions and rechanelling of resources towards the containment of the pandemic.The region failed to achieve the Millenium Development Goals on maternal and child mortalities,and is poised to fail to achieve the same goals in the Sustainable Development Goals.To improve on the maternal and child health outcomes,many SSA countries introduced digital technologies for educating pregnant and nurs-ing women,making doctors'appointments and sending reminders to mothers and expectant mothers,as well as capturing information about patients and their illnesses.However,the collected epidemiological data are not being utilised to inform patient care and improve on the quality,efficiency and access to maternal,neonatal and child health(MNCH)care.To the researchers'best knowledge,no review paper has been published that focuses on digital health for MNCH care in SSA and proposes data-driven approaches to the same.Therefore,this study sought to:(1)identify digital systems for MNCH in SSA;(2)identify the applicability and weaknesses of the dig-ital MNCH systems in SSA;and(3)propose a data-driven model for diverging emerging technologies into MNCH services in SSA to make better use of data to improve MNCH care coverage,efficiency and quality.The PRISMA methodology was used in this study.The study revealed that there are no data-driven models for monitoring pregnant women and under-five children in Sub-Saharan Africa,with the available digital health technologies mainly based on SMS and websites.Thus,the current digital health systems in SSA do not support real-time,ubiquitous,pervasive and data-driven healthcare.Their main applicability is in non-real-time pregnancy moni-toring,education and information dissemination.Unless new and more effective approaches are implemented,SSA might remain with the highest and unacceptable maternal and under-five mortality rates globally.The study proposes feasible emerging technologies that can be used to provide data-driven healthcare for MNCH in SSA,and the recommendations on how to make the transition successful as well as the lessons learn from other regions.
基金supported by the National Natural Science Foundation of China(Grant No.52306026)the Wenzhou Municipal Science and Technology Research Program(Grant No.G20220012)+2 种基金the Special Innovation Project Fund of the Institute of Wenzhou,Zhejiang University(XMGL-KJZX202205)the State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation Open Project(Project No.ACSKL2021KT01)the Special Innovation Project Fund of the Institute of Wenzhou,Zhejiang University(XMGL-KJZX-202205).
文摘This study explores the effectiveness of machine learning models in predicting the air-side performance of microchannel heat exchangers.The data were generated by experimentally validated Computational Fluid Dynam-ics(CFD)simulations of air-to-water microchannel heat exchangers.A distinctive aspect of this research is the comparative analysis of four diverse machine learning algorithms:Artificial Neural Networks(ANN),Support Vector Machines(SVM),Random Forest(RF),and Gaussian Process Regression(GPR).These models are adeptly applied to predict air-side heat transfer performance with high precision,with ANN and GPR exhibiting notably superior accuracy.Additionally,this research further delves into the influence of both geometric and operational parameters—including louvered angle,fin height,fin spacing,air inlet temperature,velocity,and tube temperature—on model performance.Moreover,it innovatively incorporates dimensionless numbers such as aspect ratio,fin height-to-spacing ratio,Reynolds number,Nusselt number,normalized air inlet temperature,temperature difference,and louvered angle into the input variables.This strategic inclusion significantly refines the predictive capabilities of the models by establishing a robust analytical framework supported by the CFD-generated database.The results show the enhanced prediction accuracy achieved by integrating dimensionless numbers,highlighting the effectiveness of data-driven approaches in precisely forecasting heat exchanger performance.This advancement is pivotal for the geometric optimization of heat exchangers,illustrating the considerable potential of integrating sophisticated modeling techniques with traditional engineering metrics.
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.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金Supported by the National Key Specialty of Traditional Chinese Medicine(Spleen and Stomach Diseases),No.0500004National Natural Science Foundation of China,No.82205104 and No.82104850+1 种基金Hospital Capability Enhancement Project of Xiyuan Hospital,CACMS,No.XYZX0303-07the Fundamental Research Funds for the Central Public Welfare Research Institutes,Excellent Young Scientists Training Program of China Academy of Chinese Medical Sciences,No.ZZ16-YQ-002.
文摘BACKGROUND Non-erosive reflux disease(NERD),the main gastroesophageal reflux subtype,features reflux symptoms without mucosal damage.Anxiety links to visceral hypersensitivity in NERD,yet mechanisms and animal models are unclear.AIM To establish a translational NERD rat model with anxiety comorbidity via tail clamping and study corticotropin-releasing hormone(CRH)-mediated neuroimmune pathways in visceral hypersensitivity and esophageal injury.METHODS Sprague-Dawley(SD)and Wistar rats were grouped into sham,model,and modified groups(n=10 each).The treatments for the modified groups were as follows:SD rats received ovalbumin/aluminum hydroxide suspension+acid perfusion±tail clamping(40 minutes/day for 7 days),while Wistar rats received fructose water+tail clamping.Esophageal pathology,visceral sensitivity,and behavior were assessed.Serum CRH,calcitonin gene-related peptide(CGRP),5-hydroxytryptamine(5-HT),and mast cell tryptase(MCT)and central amygdala(CeA)CRH mRNA were measured via ELISA and qRT-PCR.RESULTS Tail clamping induced anxiety,worsening visceral hypersensitivity(lower abdominal withdrawal reflex thresholds,P<0.05)and esophageal injury(dilated intercellular spaces and mitochondrial edema).Both models showed raised serum CRH,CGRP,5-HT,and MCT(P<0.01)and CeA CRH mRNA expression(P<0.01).Behavioral tests confirmed anxiety-like phenotypes.NERD-anxiety rats showed clinical-like symptom severity without erosion.CONCLUSION Tail clamping induces anxiety in NERD models,worsening visceral hypersensitivity via CRH neuroimmune dysregulation,offering a translational model and highlighting CRH as a treatment target.
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funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation):project ID 431549029-SFB 1451the Marga-und-Walter-Boll-Stiftung(#210-10-15)(to MAR)a stipend from the'Gerok Program'(Faculty of Medicine,University of Cologne,Germany)。
文摘Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.
文摘In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD),with its emphasis on cross-functional teamwork,concurrent engineering,and data-driven decision-making,has been widely recognized for enhancing R&D efficiency and product quality.However,the unique characteristics of SOEs pose challenges to the effective implementation of IPD.The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models.This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs.By integrating data mining,machine learning,and other advanced analytical techniques,the model serves as a scientific and efficient decision-making tool.It aids SOEs in optimizing R&D resource allocation,shortening product development cycles,reducing R&D costs,and improving product quality and innovation.Moreover,this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.
文摘Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.
基金supported by the Grant PID2021-126715OB-IOO financed by MCIN/AEI/10.13039/501100011033 and"ERDFA way of making Europe"by the Grant PI22CⅢ/00055 funded by Instituto de Salud CarlosⅢ(ISCⅢ)+6 种基金the UFIECPY 398/19(PEJ2018-004965) grant to RGS funded by AEI(Spain)the UFIECPY-396/19(PEJ2018-004961)grant financed by MCIN (Spain)FI23CⅢ/00003 grant funded by ISCⅢ-PFIS Spain) to PMMthe UFIECPY 328/22 (PEJ-2021-TL/BMD-21001) grant to LM financed by CAM (Spain)the grant by CAPES (Coordination for the Improvement of Higher Education Personnel)through the PDSE program (Programa de Doutorado Sanduiche no Exterior)to VSCG financed by MEC (Brazil)
文摘The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.
基金supported by the Strategic Research and Consulting Project of the Chinese Academy of Engineering[grant number 2024-XBZD-14]the National Natural Science Foundation of China[grant numbers 42192553 and 41922036]the Fundamental Research Funds for the Central Universities–Cemac“GeoX”Interdisciplinary Program[grant number 020714380207]。
文摘The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzhou RDP(19th Hangzhou Asian Games Research Development Project on Convective-scale Ensemble Prediction and Application)testbed,with the LBCs respectively sourced from National Centers for Environmental Prediction(NCEP)Global Forecast System(GFS)forecasts with 33 vertical levels(Exp_GFS),Pangu forecasts with 13 vertical levels(Exp_Pangu),Fuxi forecasts with 13 vertical levels(Exp_Fuxi),and NCEP GFS forecasts with the vertical levels reduced to 13(the same as those of Exp_Pangu and Exp_Fuxi)(Exp_GFSRDV).In general,Exp_Pangu performs comparably to Exp_GFS,while Exp_Fuxi shows slightly inferior performance compared to Exp_Pangu,possibly due to its less accurate large-scale predictions.Therefore,the ability of using data-driven networks to efficiently provide LBCs for convective-scale ensemble forecasts has been demonstrated.Moreover,Exp_GFSRDV has the worst convective-scale forecasts among the four experiments,which indicates the potential improvement of using data-driven networks for LBCs by increasing the vertical levels of the networks.However,the ensemble spread of the four experiments barely increases with lead time.Thus,each experiment has insufficient ensemble spread to present realistic forecast uncertainties,which will be investigated in a future study.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金This work received financial support from Toyota Motor Engineering&Manufacturing North America,Inc.,Toyota Motor Manufacturing Canada,and Natural Sciences and Engineering Research Council of Canada through a Collaborative Research and Development Grant with the project number of CRDPJ 543945-19.
文摘The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions,involving the pressure,temperature,humidity,and stoichiometry ratio.In this article,recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed.The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described.The reduced-dimensional physics-based models(pseudo-twodimensional,one-dimensional numerical and zero dimensional analytical models)and the non-physics-based models(zero-dimensional empirical and data-driven models)have been systematically examined,and the comparison of these models has been performed.It is found that the current trends for the real-time control models are(i)to couple the single cell model with balance of plants to investigate the system performance,(ii)to incorporate aging effects to enable long-term performance prediction,(iii)to increase the computational speed(especially for one-dimensional numerical models),and(iv)to develop data-driven models with artificial intelligence/machine learning algorithms.This review will be beneficial for the development of physics or nonphysics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金supported by the National Key Research and Development Program of China(2023YFB3307801)the National Natural Science Foundation of China(62394343,62373155,62073142)+3 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)under Grant B17017the Fundamental Research Funds for the Central Universities,Science Foundation of China University of Petroleum,Beijing(No.2462024YJRC011)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024B70).
文摘The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.
