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
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
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
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a...Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.展开更多
Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of elec...Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of electric vehicles,and the continuous power supply of electronic devices.This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field.First,the battery degradation mechanisms and lightweight data acquisition are analyzed.Secondly,a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods,and the application characteristics and implementation limitations of different methods are analyzed in detail.An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction.Then,collaborative modelling of calendar ageing and cyclic ageing is discussed,revealing their coupled effects and corresponding RUL prediction methods.Finally,the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified,potential solutions are proposed,and the future development trends are outlined.展开更多
In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation ...In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.展开更多
Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interact...Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations.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
The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-super...The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.展开更多
Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work pr...Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work proposes Secured-FL,a blockchain-based defensive framework that combines smart contract-based authentication,clustering-driven outlier elimination,and dynamic threshold adjustment to defend against adversarial attacks.The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates.Large-scale simulation on the Cyber Data dataset,under up to 50%malicious client settings,demonstrates Secured-FL achieves 6%-12%higher accuracy,9%-15%lower latency,and approximately 14%less computational expense compared to the PPSS benchmark framework.Additional tests,including confusion matrices,ROC and Precision-Recall curves,and ablation tests,confirm the interpretability and robustness of the defense.Tests for scalability also show consistent performance up to 500 clients,affirming appropriateness to reasonably large deployments.These results make Secured-FL a feasible,adversarially resilient FL paradigm with promising potential for application in smart cities,medicine,and other mission-critical IoT deployments.展开更多
This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and bou...This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and boundary conditions,SHTM now leverages large-scale constraints from machine-learning weather prediction(MLWP)models,resulting in an ML–physics hybrid framework.During Typhoon Danas(2025),the hybrid SHTM achieves substantially lower track errors than both the advanced ECMWF Integrated Forecasting System(IFS)and leading MLWP models such as PanGu and FuXi.Furthermore,the hybrid SHTM consistently maintains mean track errors below 200 km up to a forecast lead time of 108 hours,representing a significant advancement in forecast accuracy.In addition,this study highlights the technical roadmap for transitioning from a physics-based typhoon model to a fully data-driven ML typhoon forecast system.It also emphasizes that advances in the physical modeling framework provide a critical foundation for further improving the performance of future data-driven ML typhoon models.展开更多
The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-ti...The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-time scenarios.This review begins with a concise overview of traditional tight-binding(TB)models,including both(semi-)empirical and first-principles approaches,establishing the foundation for understanding MLTB developments.We then present a systematic classification of existing MLTB methodologies,grouped into two major categories:direct prediction of TB Hamiltonian elements and inference of empirical parameters.A comparative analysis with other ML-based electronic structure models is also provided,highlighting the advancement of MLTB approaches.Finally,we explore the emerging MLTB application ecosystem,highlighting how the integration of MLTB models with a diverse suite of post-processing tools from linear-scaling solvers to quantum transport frameworks and molecular dynamics interfaces is essential for tackling complex scientific problems across different domains.The continued advancement of this integrated paradigm promises to accelerate materials discovery and open new frontiers in the predictive simulation of complex quantum phenomena.展开更多
Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and requir...Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.展开更多
Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecul...Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecular mechanisms involved in these types of scars and developing effective treatments requires the use of controlled ex-perimental models,especially animals,to overcome the limitations of clinical studies.The aim of this sistematic review is to critically analyze the animal models used in the last five years(2020-2025)for the study of pathological scars,highlighting their advantages,limitations and applicability in the development of new therapeutic strat-egies.Murine,rabbit and porcine models,as well as alternative models,offer varied perspectives on the formation and treatment of HTS and KS,with an emphasis on histological and molecular correlations with human pathology.By synthesizing recent data,the paper highlights the essential role of preclinical research in optimizing an-tifibrotic treatments and in advancing the translation of data into the clinical sphere.Overall,animal models remain essential for bridging mechanistic insights with clinical translation,supporting the development of more effective and personalized anti-scar therapies.展开更多
Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medic...Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medical practice,a rigorous and systematic evaluation of their medical competence is imperative.This study presents a comprehensive review of the established methodologies and benchmarks for evaluating the medical competence of LLMs,encompassing a thorough analysis of current assessment practices across medical knowledge,clinical practice competence,and ethical-safety considerations.By integrating clinician competency assessment frameworks into LLMs evaluation,we propose a structured tri-dimensional framework that systematically organizes existing evaluation approaches according to medical theoretical knowledge,clinical practice ability,and ethical-safety considerations.Furthermore,this research provides critical insights into future developmental trajectories while establishing foundational frameworks and standardization protocols for the integration of LLMs into medical practice.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 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.
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supported by the National Key R&D Program of China(Grant No.2023YFC3209504)Natural Science Foundation of Wuhan(Grant No.2024040801020271)the Fundamental Research Funds for Central Public Welfare Research Institutes(Grant No.CKSF2025718/YT).
文摘Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.
基金supported by the National Natural Science Foundation of China(No.U23A20651)the Central Government Guides Local Science and Technology Development Foundation(No.2023ZYDF022)+1 种基金the Sichuan Science and Technology Program(2024ZDZX0031)the Open Fund Project of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines(No.SKLMRDPC23KF19).
文摘Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of electric vehicles,and the continuous power supply of electronic devices.This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field.First,the battery degradation mechanisms and lightweight data acquisition are analyzed.Secondly,a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods,and the application characteristics and implementation limitations of different methods are analyzed in detail.An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction.Then,collaborative modelling of calendar ageing and cyclic ageing is discussed,revealing their coupled effects and corresponding RUL prediction methods.Finally,the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified,potential solutions are proposed,and the future development trends are outlined.
文摘In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.
基金supported by the National Key R&D Program of China[2022YFF0902703]the State Administration for Market Regulation Science and Technology Plan Project(2024MK033).
文摘Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations.
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supported by the National Natural Science Foundation of China(32471964)。
文摘The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.
文摘Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work proposes Secured-FL,a blockchain-based defensive framework that combines smart contract-based authentication,clustering-driven outlier elimination,and dynamic threshold adjustment to defend against adversarial attacks.The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates.Large-scale simulation on the Cyber Data dataset,under up to 50%malicious client settings,demonstrates Secured-FL achieves 6%-12%higher accuracy,9%-15%lower latency,and approximately 14%less computational expense compared to the PPSS benchmark framework.Additional tests,including confusion matrices,ROC and Precision-Recall curves,and ablation tests,confirm the interpretability and robustness of the defense.Tests for scalability also show consistent performance up to 500 clients,affirming appropriateness to reasonably large deployments.These results make Secured-FL a feasible,adversarially resilient FL paradigm with promising potential for application in smart cities,medicine,and other mission-critical IoT deployments.
基金supported by the Special Project-Original Exploration(Grant No.42450163)the National Youth Science Foundation of China Project(Grant No.4240050560)the Research and Development of Key Technologies for Artificial Intelligence Regional Typhoon Forecasting Model project.
文摘This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and boundary conditions,SHTM now leverages large-scale constraints from machine-learning weather prediction(MLWP)models,resulting in an ML–physics hybrid framework.During Typhoon Danas(2025),the hybrid SHTM achieves substantially lower track errors than both the advanced ECMWF Integrated Forecasting System(IFS)and leading MLWP models such as PanGu and FuXi.Furthermore,the hybrid SHTM consistently maintains mean track errors below 200 km up to a forecast lead time of 108 hours,representing a significant advancement in forecast accuracy.In addition,this study highlights the technical roadmap for transitioning from a physics-based typhoon model to a fully data-driven ML typhoon forecast system.It also emphasizes that advances in the physical modeling framework provide a critical foundation for further improving the performance of future data-driven ML typhoon models.
基金supported by the Advanced Materials-National Science and Technology Major Project(Grant No.2025ZD0618401)the National Natural Science Foundation of China(Grant No.12504285)+1 种基金the Natural Science Foundation of Jiangsu Province(Grant No.BK20250472)NFSG grant from BITS-Pilani,Dubai campus。
文摘The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-time scenarios.This review begins with a concise overview of traditional tight-binding(TB)models,including both(semi-)empirical and first-principles approaches,establishing the foundation for understanding MLTB developments.We then present a systematic classification of existing MLTB methodologies,grouped into two major categories:direct prediction of TB Hamiltonian elements and inference of empirical parameters.A comparative analysis with other ML-based electronic structure models is also provided,highlighting the advancement of MLTB approaches.Finally,we explore the emerging MLTB application ecosystem,highlighting how the integration of MLTB models with a diverse suite of post-processing tools from linear-scaling solvers to quantum transport frameworks and molecular dynamics interfaces is essential for tackling complex scientific problems across different domains.The continued advancement of this integrated paradigm promises to accelerate materials discovery and open new frontiers in the predictive simulation of complex quantum phenomena.
基金funded by Fundación CajaCanarias and Fundación Bancaria“la Caixa”,grant number 2023DIG11.
文摘Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.
基金Ministry of Research,Innovation and Digitization,CCCDI-UEFISCDI,Grant/Award Number:PN-IV-P7-7.1-PED-2024-1578,within PNCDI Ⅳ.
文摘Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecular mechanisms involved in these types of scars and developing effective treatments requires the use of controlled ex-perimental models,especially animals,to overcome the limitations of clinical studies.The aim of this sistematic review is to critically analyze the animal models used in the last five years(2020-2025)for the study of pathological scars,highlighting their advantages,limitations and applicability in the development of new therapeutic strat-egies.Murine,rabbit and porcine models,as well as alternative models,offer varied perspectives on the formation and treatment of HTS and KS,with an emphasis on histological and molecular correlations with human pathology.By synthesizing recent data,the paper highlights the essential role of preclinical research in optimizing an-tifibrotic treatments and in advancing the translation of data into the clinical sphere.Overall,animal models remain essential for bridging mechanistic insights with clinical translation,supporting the development of more effective and personalized anti-scar therapies.
基金Guangzhou Science and Technology Program,Grant/Award Numbers:2025B03J0110,2024A03J1074,2024A03J0927。
文摘Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medical practice,a rigorous and systematic evaluation of their medical competence is imperative.This study presents a comprehensive review of the established methodologies and benchmarks for evaluating the medical competence of LLMs,encompassing a thorough analysis of current assessment practices across medical knowledge,clinical practice competence,and ethical-safety considerations.By integrating clinician competency assessment frameworks into LLMs evaluation,we propose a structured tri-dimensional framework that systematically organizes existing evaluation approaches according to medical theoretical knowledge,clinical practice ability,and ethical-safety considerations.Furthermore,this research provides critical insights into future developmental trajectories while establishing foundational frameworks and standardization protocols for the integration of LLMs into medical practice.
