Accurate acquisition and prediction of acoustic parameters of seabed sediments are crucial in marine sound propagation research.While the relationship between sound velocity and physical properties of sediment has bee...Accurate acquisition and prediction of acoustic parameters of seabed sediments are crucial in marine sound propagation research.While the relationship between sound velocity and physical properties of sediment has been extensively studied,there is still no consensus on the correlation between acoustic attenuation coefficient and sediment physical properties.Predicting the acoustic attenuation coefficient remains a challenging issue in sedimentary acoustic research.In this study,we propose a prediction method for the acoustic attenuation coefficient using machine learning algorithms,specifically the random forest(RF),support vector machine(SVR),and convolutional neural network(CNN)algorithms.We utilized the acoustic attenuation coefficient and sediment particle size data from 52 stations as training parameters,with the particle size parameters as the input feature matrix,and measured acoustic attenuation as the training label to validate the attenuation prediction model.Our results indicate that the error of the attenuation prediction model is small.Among the three models,the RF model exhibited the lowest prediction error,with a mean squared error of 0.8232,mean absolute error of 0.6613,and root mean squared error of 0.9073.Additionally,when we applied the models to predict the data collected at different times in the same region,we found that the models developed in this study also demonstrated a certain level of reliability in real prediction scenarios.Our approach demonstrates that constructing a sediment acoustic characteristics model based on machine learning is feasible to a certain extent and offers a novel perspective for studying sediment acoustic properties.展开更多
As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan ba...As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.展开更多
N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning m...N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning methods (PCA, HCA, KNN, SIMCA, and SDA). The optimization of molecular structures was performed using the B3LYP/6-31G* approach. MEP maps and ligand-receptor interactions were used to investigate key structural features required for biological activities and likely interactions between N-11-azaartemisinins and heme, respectively. The supervised machine learning methods allowed the separation of the investigated compounds into two classes: cha and cla, with the properties ε<sub>LUMO+1</sub> (one level above lowest unoccupied molecular orbital energy), d(C<sub>6</sub>-C<sub>5</sub>) (distance between C<sub>6</sub> and C<sub>5</sub> atoms in ligands), and TSA (total surface area) responsible for the classification. The insights extracted from the investigation developed and the chemical intuition enabled the design of sixteen new N-11-azaartemisinins (prediction set), moreover, models built with supervised machine learning methods were applied to this prediction set. The result of this application showed twelve new promising N-11-azaartemisinins for synthesis and biological evaluation.展开更多
In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers a...In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.展开更多
An emerging real-time ground compaction and quality control, known as intelligent compaction(IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time asse...An emerging real-time ground compaction and quality control, known as intelligent compaction(IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional(3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are discussed.The proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.展开更多
Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere wavegu...Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere waveguide over long distances.In this study,we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning method.The differences(automatic−manual)in each ionosphere parameter between the automatic method and the manual method were−0.07±2.73 km,0.03±0.92 cm^(−3),and 91±1,068 km for the ionospheric reflection height(h),equivalent electron densities at reflection heights(Ne),and propagation distance(d),respectively.Moreover,the automatic method is capable of recognizing higher harmonic tweek sferics.The evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere.展开更多
Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content...Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content,young hypertensive patients admitted to the hospital from January 2022 to December 2024 were selected as the research object and at least 950 patients were included according to the sample size calculation.According to the existence of anxiety,950 patients were divided into control group(n=650)and observation group(n=300),and the clinical data of all patients were collected for univariate analysis and multivariate Logistic regression analysis to get the risk factors of hypertension patients complicated with anxiety in.All patients were randomly divided into a training set(n=665)and a test set(n=285)according to the ratio of 7:3,and the evaluation efficiency of different prediction models was obtained by using machine learning algorithm.To evaluate the clinical application effect of the prediction model.Results:(1)Univariate analysis showed that age,BMI,education background,marital status,smoking,drinking,sleep disorder,family history of hypertension,history of diabetes,history of hyperlipidemia,history of cerebral infarction,and TC were important risk factors for young hypertensive patients complicated with anxiety.(2)Multivariate Logistic regression analysis showed that hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors for young hypertensive patients complicated with anxiety.(3)Extra Trees has the highest predictive power for young people with hypertension complicated with anxiety,while Decision-Tree has the lowest predictive power.Conclusion:Hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors that affect the anxiety of young hypertensive patients.Extra Trees model has the best prediction efficiency among different groups of models.展开更多
Floods are widespread and dangerous natural hazards worldwide.It is essential to grasp the causes of floods to mitigate their severe effects on people and society.The key drivers of flood susceptibility in rapidly urb...Floods are widespread and dangerous natural hazards worldwide.It is essential to grasp the causes of floods to mitigate their severe effects on people and society.The key drivers of flood susceptibility in rapidly urbanizing areas can vary depending on the specific context and require further investigation.This research developed an index system comprising 10 indicators associated with factors and environments that lead to disasters,and used machine learning methods to assess flood susceptibility.The core urban area of the Yangtze River Delta served as a case study.Four scenarios depicting separate and combined effects of climate change and human activity were evaluated using data from various periods,to measure the spatial variability in flood susceptibility.The findings demonstrate that the extreme gradient boosting model outperformed the decision tree,support vector machine,and stacked models in evaluating flood susceptibility.Both climate change and human activity were found to act as catalysts for flooding in the region.Areas with increasing susceptibility were mainly distributed to the northwest and southeast of Taihu Lake.Areas with increased flood susceptibility caused by climate change were significantly larger than those caused by human activity,indicating that climate change was the dominant factor influencing flood susceptibility in the region.By comparing the relationship between the indicators and flood susceptibility,the rising intensity and frequency of extreme precipitation as well as an increase in impervious surface areas were identified as important reasons of heightened flood susceptibility in the Yangtze River Delta region.This study emphasized the significance of formulating adaptive strategies to enhance flood control capabilities to cope with the changing environment.展开更多
This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine(TBM)dataset for performance prediction and boring efficiency optimization using machine le...This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine(TBM)dataset for performance prediction and boring efficiency optimization using machine learning methods.The big dataset was col-lected during the Yinsong water diversion project construction in China,covering the tunnel excavation of a 20 km-section with 199 items of monitoring metrics taken with an interval of one second.The research papers were the result of a call for contributions during a TBM machine learning contest in 2019 and covered a variety of topics related to the intelligent construction of TBM.This review com-prises two parts.Part I is concerned with the data processing,feature extraction,and machine learning methods applied by the contrib-utors.The review finds that the data-driven and knowledge-driven approaches in extracting important features applied by various authors are diversified,requiring further studies to achieve commonly accepted criteria.The techniques for cleaning and amending the raw data adopted by the contributors were summarized,indicating some highlights such as the importance of sufficiently high fre-quency of data acquisition(higher than 1 second),classification and standardization for the data preprocessing process,and the appro-priate selections of features in a boring cycle.The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers.The ensemble and deep learning methods have found wide applications.Part I highlights the impor-tant features of the individual methods applied by the contributors,including the structures of the algorithm,selection of hyperparam-eters,and model validation approaches.展开更多
Smart liquid gating membrane is a responsive structural material as a pressure-driven system that consists of solid membrane and dynamic liquid,responding to the external field.An accurate prediction of rheological an...Smart liquid gating membrane is a responsive structural material as a pressure-driven system that consists of solid membrane and dynamic liquid,responding to the external field.An accurate prediction of rheological and mechanical properties is important for the designs of liquid gating membranes for various applications.However,high predicted accuracy by the traditional sequential method requires a large amount of experimental data,which is not practical in some situations.To conquer these problems,artificial intelligence has promoted the rapid development of material science in recent years,bringing hope to solve these challenges.Here we propose a Kriging machine learning model with an active candidate region,which can be smartly updated by an expected improvement probability method to increase the local accuracy near the most sensitive search region,to predict the mechanical and rheolo-gical performance of liquid gating system with an active minimal size of ex-perimental data.Besides this,this new machine learning model can instruct our experiments with optimal size.The methods are then verified by liquid gating membrane with magnetorheological fluids,which would be of wide interest for the design of potential liquid gating applications in drug release,microfluidic logic,dynamic fluid control,and beyond.展开更多
In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim＇s and Shekari＇...In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim＇s and Shekari＇s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5＇ model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5＇ approach.展开更多
The strength characteristics of ice materials are crucial for the analysis of the interaction between ice and structure in ocean engineering and ice engineering.In this investigation,six machine learning methods were ...The strength characteristics of ice materials are crucial for the analysis of the interaction between ice and structure in ocean engineering and ice engineering.In this investigation,six machine learning methods were utilized to predict the strength of the envelope surface of ice materials.The database for the ice strength was first established by collecting 1,481 testing data reported in the previous literatures.A quadric strength criterion was adopted to describe failure behaviors of ice materials under different conditions of material property and laboratory.Three model parameters in this strength criterion were forecasted by using six machine learning methods.The prediction capacities of six machine learning methods were evaluated by three statics indices,and the integrated simulation ability of six machine learning methods was arranged.Three machine learning algorithms were selected to be improved and optimized,and the simulation capacity of the three algorithms was further explored.The optimization results indicate that the improved potential of the Ensemble algorithm is much higher than that of the SVM algorithm and the GPR algorithm for predicting the ice strength.展开更多
this study,the energy bands of quadrupole and octupole excited states are investigated.This is achieved by employing the Bohr Hamiltonian,incorporating quadrupole and octupole deformations whose variables are accurate...this study,the energy bands of quadrupole and octupole excited states are investigated.This is achieved by employing the Bohr Hamiltonian,incorporating quadrupole and octupole deformations whose variables are accurately separated.Subsequently,the Woods-Saxon potential is added to the problem.Because this problem cannot yield suitable solutions using conventional approximations,we solve it numerically using machine learning.A detailed description is given of how wave functions and their associated energies are obtained.Throughout this procedure,we demonstrate how machine learning aids us in easily accomplishing our objective.We examine and analyze the energy spectrum and possible multipole transitions for candidate isotopes^(226)Ra and^(226)Th.展开更多
This study presents the results of a research into the developing a methodology for assessing the adequacy of advanced electric power systems characterized by the integration of various innovative technologies,which c...This study presents the results of a research into the developing a methodology for assessing the adequacy of advanced electric power systems characterized by the integration of various innovative technologies,which complicates their analysis.The methodology development is aimed at solving two main problems:(1)increase the adequacy of modeling the processes that occur in the electric power system and (2)enhance the computational efficiency of the adequacy assessment methodology.This study proposes a new mathematical model to minimize the power shortage and enhance the adequacy of modeling the processes.The model considers quadratic power transmission losses and network coefficients.The computational efficiency of the adequacy assessment methodology is enhanced using efficient random-number generators to form the calculated states of electric power systems and machine learning methods to assess power shortages and other reliability characteristics in the calculated states.展开更多
Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urba...Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions.The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.展开更多
Photoelectric displacement sensors rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. If the sensor output is nonlinear, it will prod...Photoelectric displacement sensors rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. If the sensor output is nonlinear, it will produce a whole assortment of problems. This paper presents a method to compensate the nonlinearity of the photoelectric displacement sensor based on the extreme learning machine （ELM） method which significantly reduces the amount of time needed to train a neural network with the output voltage of the optical displacement sensor and the measured input displacement to eliminate the nonlinear errors in the training process. The use of this proposed method was demonstrated through computer simulation with the experimental data of the sensor. The results revealed that the proposed method compensated the presence of nonlinearity in the sensor with very low training time, lowest mean squared error （MSE） value, and better linearity. This research work involved less computational complexity, and it behaved a good performance for nonlinearity compensation for the photoelectric displacement sensor and has a good application prospect.展开更多
The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the...The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.展开更多
The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NC...The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are analyzed.The sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical results.The results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV.While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second type.The third type is atypical and is not analyzed in detail.Sensitivity tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the observations.In contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.展开更多
Previous studies have revealed that patients with hypertrophic cardiomyopathy(HCM)exhibit differences in symptom severity and prognosis,indicating potential HCM subtypes among these patients.Here,793 patients with HCM...Previous studies have revealed that patients with hypertrophic cardiomyopathy(HCM)exhibit differences in symptom severity and prognosis,indicating potential HCM subtypes among these patients.Here,793 patients with HCM were recruited at an average follow-up of 32.78±27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features.Furthermore,we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data.Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings.Consequently,two subtypes characterized by different clinical outcomes were identified in HCM.Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course,while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression.Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities.Furthermore,the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction.By employing echocardiography and genetic screening for the 46 genes,HCM can be classified into two subtypes with distinct clinical outcomes.展开更多
The development of functional relationships between the observed deposition rate and the experimental conditions is an important step toward understanding and optimizing low-pressure chemical vapor deposition(LPCVD)or...The development of functional relationships between the observed deposition rate and the experimental conditions is an important step toward understanding and optimizing low-pressure chemical vapor deposition(LPCVD)or low-pressure chemical vapor infiltration(LPCVI).In the field of ceramic matrix composites(CMCs),methyltrichlorosilane(CH3 SiCl3,MTS)is the most widely used source gas system for SiC,because stoichiometric SiC deposit can be facilitated at 900°C–1300°C.However,the reliability and accuracy of existing numerical models for these processing conditions are rarely reported.In this study,a comprehensive transport model was coupled with gas-phase and surface kinetics.The resulting gas-phase kinetics was confirmed via the measured concentration of gaseous species.The relationship between deposition rate and 24 gaseous species has been effectively evaluated by combining the special superiority of the novel extreme machine learning method and the conventional sticking coefficient method.Surface kinetics were then proposed and shown to reproduce the experimental results.The proposed simulation strategy can be used for different material systems.展开更多
基金funded by the Basic Scientific Fund for National Public Research Institutes of China(No.2022 S01)the National Natural Science Foundation of China(Nos.42176191,42049902,and U22A2012)+5 种基金the Shandong Provincial Natural Science Foundation,China(No.ZR2022YQ40)the National Key R&D Program of China(No.2021YFF0501202)the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(No.SML2023 SP232)the Fundamental Research Funds for the Central Universities,Sun Yat-sen University(No.241gqb006)Data acquisition and sample collections were supported by the National Natural Science Foundation of China Open Research Cruise(Cruise No.NORC2021-02+NORC2021301)funded by the Shiptime Sharing Project of the National Natural Science Foundation of China。
文摘Accurate acquisition and prediction of acoustic parameters of seabed sediments are crucial in marine sound propagation research.While the relationship between sound velocity and physical properties of sediment has been extensively studied,there is still no consensus on the correlation between acoustic attenuation coefficient and sediment physical properties.Predicting the acoustic attenuation coefficient remains a challenging issue in sedimentary acoustic research.In this study,we propose a prediction method for the acoustic attenuation coefficient using machine learning algorithms,specifically the random forest(RF),support vector machine(SVR),and convolutional neural network(CNN)algorithms.We utilized the acoustic attenuation coefficient and sediment particle size data from 52 stations as training parameters,with the particle size parameters as the input feature matrix,and measured acoustic attenuation as the training label to validate the attenuation prediction model.Our results indicate that the error of the attenuation prediction model is small.Among the three models,the RF model exhibited the lowest prediction error,with a mean squared error of 0.8232,mean absolute error of 0.6613,and root mean squared error of 0.9073.Additionally,when we applied the models to predict the data collected at different times in the same region,we found that the models developed in this study also demonstrated a certain level of reliability in real prediction scenarios.Our approach demonstrates that constructing a sediment acoustic characteristics model based on machine learning is feasible to a certain extent and offers a novel perspective for studying sediment acoustic properties.
基金supported by China Postdoctoral Science Foundation(2019M651240)National Natural Science Foundation of China(31670559).
文摘As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.
文摘N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning methods (PCA, HCA, KNN, SIMCA, and SDA). The optimization of molecular structures was performed using the B3LYP/6-31G* approach. MEP maps and ligand-receptor interactions were used to investigate key structural features required for biological activities and likely interactions between N-11-azaartemisinins and heme, respectively. The supervised machine learning methods allowed the separation of the investigated compounds into two classes: cha and cla, with the properties ε<sub>LUMO+1</sub> (one level above lowest unoccupied molecular orbital energy), d(C<sub>6</sub>-C<sub>5</sub>) (distance between C<sub>6</sub> and C<sub>5</sub> atoms in ligands), and TSA (total surface area) responsible for the classification. The insights extracted from the investigation developed and the chemical intuition enabled the design of sixteen new N-11-azaartemisinins (prediction set), moreover, models built with supervised machine learning methods were applied to this prediction set. The result of this application showed twelve new promising N-11-azaartemisinins for synthesis and biological evaluation.
基金supported by the National Key R&D Program of China(2017YFF0205600)the International Research Cooperation Seed Fund of Beijing University of Technology(2018A08)+1 种基金Science and Technology Project of Beijing Municipal Commission of Transport(2018-kjc-01-213)the Construction of Service Capability of Scientific and Technological Innovation-Municipal Level of Fundamental Research Funds(Scientific Research Categories)of Beijing City(PXM2019_014204_500032).
文摘In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.
文摘An emerging real-time ground compaction and quality control, known as intelligent compaction(IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional(3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are discussed.The proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.
基金supported by the Chinese Academy of Sciences(CAS)Project of Stable Support for Youth Team in Basic Research Field(Grant No.YSRR-018)the National Key R&D Program of China(Grant No.2019YFC1510103)+1 种基金the National Natural Science Foundation of China(Grant Nos.41875006 and U1938115)the Chinese Meridian Project,and the International Partnership Program of CAS(Grant No.183311KYSB20200003).
文摘Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere waveguide over long distances.In this study,we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning method.The differences(automatic−manual)in each ionosphere parameter between the automatic method and the manual method were−0.07±2.73 km,0.03±0.92 cm^(−3),and 91±1,068 km for the ionospheric reflection height(h),equivalent electron densities at reflection heights(Ne),and propagation distance(d),respectively.Moreover,the automatic method is capable of recognizing higher harmonic tweek sferics.The evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere.
文摘Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content,young hypertensive patients admitted to the hospital from January 2022 to December 2024 were selected as the research object and at least 950 patients were included according to the sample size calculation.According to the existence of anxiety,950 patients were divided into control group(n=650)and observation group(n=300),and the clinical data of all patients were collected for univariate analysis and multivariate Logistic regression analysis to get the risk factors of hypertension patients complicated with anxiety in.All patients were randomly divided into a training set(n=665)and a test set(n=285)according to the ratio of 7:3,and the evaluation efficiency of different prediction models was obtained by using machine learning algorithm.To evaluate the clinical application effect of the prediction model.Results:(1)Univariate analysis showed that age,BMI,education background,marital status,smoking,drinking,sleep disorder,family history of hypertension,history of diabetes,history of hyperlipidemia,history of cerebral infarction,and TC were important risk factors for young hypertensive patients complicated with anxiety.(2)Multivariate Logistic regression analysis showed that hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors for young hypertensive patients complicated with anxiety.(3)Extra Trees has the highest predictive power for young people with hypertension complicated with anxiety,while Decision-Tree has the lowest predictive power.Conclusion:Hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors that affect the anxiety of young hypertensive patients.Extra Trees model has the best prediction efficiency among different groups of models.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52209019,52379010)the Natural Science Foundation of Guangdong Province(Grant Nos.2023B1515020087,2022A1515240071)+1 种基金the Fund of Science and Technology Program of Guangzhou(2023A04J1595)the Open Research Fund of Key Laboratory of Water Security Guarantee in the Guangdong-Hong Kong-Marco Greater Bay Area of Ministry of Water Resources(WSGBAKJ2023010)。
文摘Floods are widespread and dangerous natural hazards worldwide.It is essential to grasp the causes of floods to mitigate their severe effects on people and society.The key drivers of flood susceptibility in rapidly urbanizing areas can vary depending on the specific context and require further investigation.This research developed an index system comprising 10 indicators associated with factors and environments that lead to disasters,and used machine learning methods to assess flood susceptibility.The core urban area of the Yangtze River Delta served as a case study.Four scenarios depicting separate and combined effects of climate change and human activity were evaluated using data from various periods,to measure the spatial variability in flood susceptibility.The findings demonstrate that the extreme gradient boosting model outperformed the decision tree,support vector machine,and stacked models in evaluating flood susceptibility.Both climate change and human activity were found to act as catalysts for flooding in the region.Areas with increasing susceptibility were mainly distributed to the northwest and southeast of Taihu Lake.Areas with increased flood susceptibility caused by climate change were significantly larger than those caused by human activity,indicating that climate change was the dominant factor influencing flood susceptibility in the region.By comparing the relationship between the indicators and flood susceptibility,the rising intensity and frequency of extreme precipitation as well as an increase in impervious surface areas were identified as important reasons of heightened flood susceptibility in the Yangtze River Delta region.This study emphasized the significance of formulating adaptive strategies to enhance flood control capabilities to cope with the changing environment.
基金supported by the National Key R&D Program of China(Grant No.2018YFB1702504)the National Natural Science Foundation of China(Grant Nos.52179121,51879284)+3 种基金the State Key Laboratory of Simulations and Regulation of Water Cycle in River Basin,China(Grant No.SKL2022ZD05)the IWHR Research&Development Support Program,China(Grant No.GE0145B012021)the Natural Science Foundation of Shaanxi Province,China(Grant No.2021JLM-50)the National Key R&D Program of China(Grant No.2022YFE0200400).
文摘This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine(TBM)dataset for performance prediction and boring efficiency optimization using machine learning methods.The big dataset was col-lected during the Yinsong water diversion project construction in China,covering the tunnel excavation of a 20 km-section with 199 items of monitoring metrics taken with an interval of one second.The research papers were the result of a call for contributions during a TBM machine learning contest in 2019 and covered a variety of topics related to the intelligent construction of TBM.This review com-prises two parts.Part I is concerned with the data processing,feature extraction,and machine learning methods applied by the contrib-utors.The review finds that the data-driven and knowledge-driven approaches in extracting important features applied by various authors are diversified,requiring further studies to achieve commonly accepted criteria.The techniques for cleaning and amending the raw data adopted by the contributors were summarized,indicating some highlights such as the importance of sufficiently high fre-quency of data acquisition(higher than 1 second),classification and standardization for the data preprocessing process,and the appro-priate selections of features in a boring cycle.The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers.The ensemble and deep learning methods have found wide applications.Part I highlights the impor-tant features of the individual methods applied by the contributors,including the structures of the algorithm,selection of hyperparam-eters,and model validation approaches.
基金This study was supported by the National Natural Science Foundation of China(52025132,21975209,and 21621091)the National Key R&D Program of China(2018YFA0209500).
文摘Smart liquid gating membrane is a responsive structural material as a pressure-driven system that consists of solid membrane and dynamic liquid,responding to the external field.An accurate prediction of rheological and mechanical properties is important for the designs of liquid gating membranes for various applications.However,high predicted accuracy by the traditional sequential method requires a large amount of experimental data,which is not practical in some situations.To conquer these problems,artificial intelligence has promoted the rapid development of material science in recent years,bringing hope to solve these challenges.Here we propose a Kriging machine learning model with an active candidate region,which can be smartly updated by an expected improvement probability method to increase the local accuracy near the most sensitive search region,to predict the mechanical and rheolo-gical performance of liquid gating system with an active minimal size of ex-perimental data.Besides this,this new machine learning model can instruct our experiments with optimal size.The methods are then verified by liquid gating membrane with magnetorheological fluids,which would be of wide interest for the design of potential liquid gating applications in drug release,microfluidic logic,dynamic fluid control,and beyond.
文摘In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim＇s and Shekari＇s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5＇ model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5＇ approach.
基金supported by the National Key R&D Program of China(2022YFC2903903)the National Natural Science Foundation of China(42271153,42471164)+2 种基金Western light project of Chinese Academy of Sciences of China(xbzg-zdsys-202311)The Science and Technology program of Gansu Province(23ZDFA017)Natural Science Foundation of Gansu province of China(24JRRA102)。
文摘The strength characteristics of ice materials are crucial for the analysis of the interaction between ice and structure in ocean engineering and ice engineering.In this investigation,six machine learning methods were utilized to predict the strength of the envelope surface of ice materials.The database for the ice strength was first established by collecting 1,481 testing data reported in the previous literatures.A quadric strength criterion was adopted to describe failure behaviors of ice materials under different conditions of material property and laboratory.Three model parameters in this strength criterion were forecasted by using six machine learning methods.The prediction capacities of six machine learning methods were evaluated by three statics indices,and the integrated simulation ability of six machine learning methods was arranged.Three machine learning algorithms were selected to be improved and optimized,and the simulation capacity of the three algorithms was further explored.The optimization results indicate that the improved potential of the Ensemble algorithm is much higher than that of the SVM algorithm and the GPR algorithm for predicting the ice strength.
基金Supported by the Natural Science Foundation of China(12275141)the Natural Science Foundation of Tianjin,China(20JCYBJC01510)。
文摘this study,the energy bands of quadrupole and octupole excited states are investigated.This is achieved by employing the Bohr Hamiltonian,incorporating quadrupole and octupole deformations whose variables are accurately separated.Subsequently,the Woods-Saxon potential is added to the problem.Because this problem cannot yield suitable solutions using conventional approximations,we solve it numerically using machine learning.A detailed description is given of how wave functions and their associated energies are obtained.Throughout this procedure,we demonstrate how machine learning aids us in easily accomplishing our objective.We examine and analyze the energy spectrum and possible multipole transitions for candidate isotopes^(226)Ra and^(226)Th.
基金the framework of the project under state assignment (No. FWEU-2021-0003) of the RF Basic Research Program for 2021-2030financial support from the Russian Foundation for Basic Research within the framework of the scientific project No 20-08-00550
文摘This study presents the results of a research into the developing a methodology for assessing the adequacy of advanced electric power systems characterized by the integration of various innovative technologies,which complicates their analysis.The methodology development is aimed at solving two main problems:(1)increase the adequacy of modeling the processes that occur in the electric power system and (2)enhance the computational efficiency of the adequacy assessment methodology.This study proposes a new mathematical model to minimize the power shortage and enhance the adequacy of modeling the processes.The model considers quadratic power transmission losses and network coefficients.The computational efficiency of the adequacy assessment methodology is enhanced using efficient random-number generators to form the calculated states of electric power systems and machine learning methods to assess power shortages and other reliability characteristics in the calculated states.
文摘Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions.The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.
文摘Photoelectric displacement sensors rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. If the sensor output is nonlinear, it will produce a whole assortment of problems. This paper presents a method to compensate the nonlinearity of the photoelectric displacement sensor based on the extreme learning machine （ELM） method which significantly reduces the amount of time needed to train a neural network with the output voltage of the optical displacement sensor and the measured input displacement to eliminate the nonlinear errors in the training process. The use of this proposed method was demonstrated through computer simulation with the experimental data of the sensor. The results revealed that the proposed method compensated the presence of nonlinearity in the sensor with very low training time, lowest mean squared error （MSE） value, and better linearity. This research work involved less computational complexity, and it behaved a good performance for nonlinearity compensation for the photoelectric displacement sensor and has a good application prospect.
基金This research was jointly supported by the National Natural Science Foundation of China(Grant No.42005037)the Liaoning Provincial Natural Science Foundation Project(PhD Start-up Research Fund 2019-BS-214),the Special Scientific Research Project for the Forecaster(Grant No.CMAYBY2018-018)+2 种基金a Key Technical Project of Liaoning Meteorological Bureau(Grant No.LNGJ201903)the National Key Research and Development Project(Grant No.2018YFC1505601)the Open Foundation Project of the Institute of Atmospheric Environment,China Meteorological Administration(Grant Nos.2020SYIAE08 and 2020SYIAEZD5).
文摘The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.
基金jointly supported by the National Natural Science Foundation of China (Grant No. 42005037)Special Project of Innovative Development, CMA (CXFZ2021J022, CXFZ2022J008, and CXFZ2021J028)+1 种基金Liaoning Provincial Natural Science Foundation Project (Ph.D. Start-up Research Fund 2019-BS214)Research Project of the Institute of Atmospheric Environment, CMA (2021SYIAEKFMS08, 2020SYIAE08 and 2021SYIAEKFMS09)
文摘The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are analyzed.The sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical results.The results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV.While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second type.The third type is atypical and is not analyzed in detail.Sensitivity tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the observations.In contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.
基金the National Key R&D Program of China(No.2017YFC0909400)the National Natural Science Foundation of China(Nos.91439203,91839302,and 81700413)+1 种基金Shanghai Municipal Science and Technology Major Project(No.2017SHZDZX01)the Fundamental Research Funds for the Central Universities,HUST(No.2016JCTD117).
文摘Previous studies have revealed that patients with hypertrophic cardiomyopathy(HCM)exhibit differences in symptom severity and prognosis,indicating potential HCM subtypes among these patients.Here,793 patients with HCM were recruited at an average follow-up of 32.78±27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features.Furthermore,we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data.Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings.Consequently,two subtypes characterized by different clinical outcomes were identified in HCM.Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course,while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression.Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities.Furthermore,the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction.By employing echocardiography and genetic screening for the 46 genes,HCM can be classified into two subtypes with distinct clinical outcomes.
基金the National Key R&D Program of China(Grants No.2017YFB0703200)National Natural Science Foundation of China(Grants Nos.51702100,51972268)China Postdoctoral Science Foundation(Grants No.2018M643075)for financial support。
文摘The development of functional relationships between the observed deposition rate and the experimental conditions is an important step toward understanding and optimizing low-pressure chemical vapor deposition(LPCVD)or low-pressure chemical vapor infiltration(LPCVI).In the field of ceramic matrix composites(CMCs),methyltrichlorosilane(CH3 SiCl3,MTS)is the most widely used source gas system for SiC,because stoichiometric SiC deposit can be facilitated at 900°C–1300°C.However,the reliability and accuracy of existing numerical models for these processing conditions are rarely reported.In this study,a comprehensive transport model was coupled with gas-phase and surface kinetics.The resulting gas-phase kinetics was confirmed via the measured concentration of gaseous species.The relationship between deposition rate and 24 gaseous species has been effectively evaluated by combining the special superiority of the novel extreme machine learning method and the conventional sticking coefficient method.Surface kinetics were then proposed and shown to reproduce the experimental results.The proposed simulation strategy can be used for different material systems.
