Computational solid mechanics has become an indispensable approach in engineering,and numerical investigation of fracturing in composites is essential,as composites are widely used in structural applications.Crack evo...Computational solid mechanics has become an indispensable approach in engineering,and numerical investigation of fracturing in composites is essential,as composites are widely used in structural applications.Crack evolution in composites is the path to elucidating the relationship between microstructures and fracture performance,but crack-based finite-element methods are computationally expensive and time-consuming,which limits their application in computation-intensive scenarios.Consequently,this study proposes a deep learning framework called Crack-Net for instant prediction of the dynamic crack growth process,as well as its strain-stress curve.Specifically,Crack-Net introduces an implicit constraint technique,which incorporates the relationship between crack evolution and stress response into the network architecture.This technique substantially reduces data requirements while improving predictive accuracy.The transfer learning technique enables Crack-Net to handle composite materials with reinforcements of different strengths.Trained on high-accuracy fracture development datasets from phase field simulations,the proposed framework is capable of tackling intricate scenarios,involving materials with diverse interfaces,varying initial conditions,and the intricate elastoplastic fracture process.The proposed Crack-Net holds great promise for practical applications in engineering and materials science,in which accurate and efficient fracture prediction is crucial for optimizing material performance and microstructural design.展开更多
Ensuring the consistent mechanical performance of three-dimensional(3D)-printed continuous fiber-reinforced composites is a significant challenge in additive manufacturing.The current reliance on manual monitoring exa...Ensuring the consistent mechanical performance of three-dimensional(3D)-printed continuous fiber-reinforced composites is a significant challenge in additive manufacturing.The current reliance on manual monitoring exacerbates this challenge by rendering the process vulnerable to environmental changes and unexpected factors,resulting in defects and inconsistent product quality,particularly in unmanned long-term operations or printing in extreme environments.To address these issues,we developed a process monitoring and closed-loop feedback control strategy for the 3D printing process.Real-time printing image data were captured and analyzed using a well-trained neural network model,and a real-time control module-enabled closed-loop feedback control of the flow rate was developed.The neural network model,which was based on image processing and artificial intelligence,enabled the recognition of flow rate values with an accuracy of 94.70%.The experimental results showed significant improvements in both the surface performance and mechanical properties of printed composites,with three to six times improvement in tensile strength and elastic modulus,demonstrating the effectiveness of the strategy.This study provides a generalized process monitoring and feedback control method for the 3D printing of continuous fiber-reinforced composites,and offers a potential solution for remote online monitoring and closed-loop adjustment in unmanned or extreme space environments.展开更多
Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-...Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin,the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD).Because the analysis is costly and dependent on experts,a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data.However,the model’s performance was limited because of improper preprocessing of the intensity profile.Because preprocessing was applied to each feature,the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition.In this study,the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis.For 49 test data among the 488 data,the convolutional neural network(CNN)model improved the average absolute error and coefficient of determination by 41%and 46%,respectively,than those of CNN model with feature-based pre-processing.This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data.The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau.In addition,the overall procedure can be applied to any XRD data of sediments worldwide.展开更多
With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engi...With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.展开更多
Carbon nanotube-reinforced cement composites have gained significant attention due to their enhanced mechanical properties,particularly in compressive and flexural strength.Despite extensive research,the influence of ...Carbon nanotube-reinforced cement composites have gained significant attention due to their enhanced mechanical properties,particularly in compressive and flexural strength.Despite extensive research,the influence of various parameters on these properties remains inadequately understood,primarily due to the complex interactions within the composites.This study addresses this gap by employingmachine learning techniques to conduct a sensitivity analysis on the compressive and flexural strength of carbon nanotube-reinforced cement composites.It systematically evaluates nine data-preprocessing techniques and benchmarks eleven machine-learning algorithms to reveal tradeoffs between predictive accuracy and computational complexity,which has not previously been explored in carbon nanotube-reinforced cement composite research.In this regard,four main factors are considered in the sensitivity analysis,which are the machine learning model type,the data pre-processing technique,and the effect of the concrete constituent materials on the compressive and flexural strength both globally through feature importance assessment and locally through partial dependence analysis.Accordingly,this research optimizes ninety-nine models representing combinations of eleven machine learning algorithms and nine data preprocessing techniques to accurately predict the mechanical properties of carbon nanotube-reinforced cement composites.Moreover,the study aims to unravel the relationships between different parameters and their impact on the composite’s strength by utilizing feature importance and partial dependence analyses.This research is crucial as it provides a comprehensive understanding of the factors influencing the performance of carbon nanotube-reinforced cement composites,which is vital for their efficient design and application in construction.The use of machine learning in this context not only enhances predictive accuracy but also offers insights that are often challenging to obtain through traditional experimental methods.The findings contribute to the field by highlighting the potential of advanced data-driven approaches in optimizing and understanding advanced composite materials,paving the way for more durable and resilient construction materials.展开更多
BACKGROUND Esophageal cancer carries a poor prognosis with low 5-year survival rates and limited early detection options.The skeletal muscle index at the L3 vertebral level is a well-established prognostic marker in e...BACKGROUND Esophageal cancer carries a poor prognosis with low 5-year survival rates and limited early detection options.The skeletal muscle index at the L3 vertebral level is a well-established prognostic marker in esophageal cancer,but most follow-up computed tomography(CT)scans do not extend to L3 and limiting its utility.Radiomics has emerged as a powerful tool for extracting prognostic information from medical images.AIM To evaluate the influential features for esophageal cancer prognosis by integrating radiomic and body compositionbased indices of skeletal muscle and adipose tissue at the T12 level from both pretreatment and follow-up CT images,in order to assess their value in predicting overall survival(OS).METHODS This retrospective study included 212 esophageal cancer patients who underwent concurrent chemoradiotherapy,with both pretreatment and follow-up chest CT scans available.Body organ analysis(BOA)and radiomic features were extracted from skeletal muscle and adipose tissue at the T12 level using automated tools.Four feature subsets(no-radiomics,pretreatment only,follow-up only,and combined inputs)were developed using logistic regression(LR)with least absolute shrinkage and selection operator for feature selection,followed by Cox regression.Prognostic models-including nomogram,support vector classifier,LR,and extra trees classifier-were constructed to predict 1-,2-,and 3-year OS.RESULTS The model integrating both BOA and radiomics from pretreatment and follow-up CT,combined with clinical data,achieved the best performance for 2-year OS prediction,with an area under the time-dependent receiver operating characteristic curve of 0.91,sensitivity of 0.81,and specificity of 0.88 using the LR model.The most predictive features included both clinical variables,body composition indices,and radiomic features,particularly from follow-up VAT.Follow-up imaging contributed significantly to model performance,reinforcing its value in treatment response evaluation.CONCLUSION This is the first study to demonstrate that BOA indices and their corresponding radiomics at the T12-level from both pretreatment and follow-up CT scans-combined with clinical data-can provide accurate prognostic information for esophageal cancer.This approach offers a practical alternative when L3-level imaging is unavailable and supports the clinical integration of automated T12-based imaging biomarkers.The integration of these imaging features with clinical parameters enhances the prediction of survival outcomes and contributes to non-invasive,personalized treatment planning.展开更多
In thermal protection structures,controlling and optimizing the surface roughness of carbon/phenolic(C/Ph)composites can effectively improve thermal protection performance and ensure the safe operation of carriers in ...In thermal protection structures,controlling and optimizing the surface roughness of carbon/phenolic(C/Ph)composites can effectively improve thermal protection performance and ensure the safe operation of carriers in high-temperature environments.This paper introduces a machine learning(ML)framework to forecast the surface roughness of carbon-phenolic composites under various thermal conditions by employing an ML algorithm derived from historical experimental datasets.Firstly,ablation experiments and collection of surface roughness height data of C/Ph composites under different thermal environments were conducted in an electric arc wind tunnel.Then,an ML model based on Ridge regression is developed for surface roughness prediction.The model involves incorporating feature engineering to choose the most concise and pertinent features,as well as developing an ML model.The ML model considers thermal environment parameters and feature screened by feature engineering as inputs,and predicts the surface height as the output.The results demonstrate that the suggested ML framework effectively anticipates the surface shape and associated surface roughness parameters in various heat flow conditions.Compared with the conventional 3D confocal microscope scanning,the method can obtain the surface topography information of the same area in a much shorter time,thus significantly saving time and cost.展开更多
Determining the optimal ceramic content of the ceramics-in-polymer composite electrolytes and the appropriate stack pressure can effectively improve the interfacial contact of solid-state batteries(SSBs).Based on the ...Determining the optimal ceramic content of the ceramics-in-polymer composite electrolytes and the appropriate stack pressure can effectively improve the interfacial contact of solid-state batteries(SSBs).Based on the contact mechanics model and constructed by the conjugate gradient method,continuous convolution,and fast Fourier transform,this paper analyzes and compares the interfacial contact responses involving the polymers commonly used in SSBs,which provides the original training data for machine learning.A support vector regression model is established to predict the relationship between the content of ceramics and the interfacial resistance.The Bayesian optimization and K-fold cross-validation are introduced to find the optimal combination of hyperparameters,which accelerates the training process and improves the model’s accuracy.We found the relationship between the content of ceramics,the stack pressure,and the interfacial resistance.The results can be taken as a reference for the design of the low-resistance composite electrolytes for solid-state batteries.展开更多
Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resour...Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resource-intensive.To address this challenge,we implemented a three-stage framework integrating machine learning,Bayesian optimization,and experimental validation,utilizing a carefully curated dataset from the literature.Our ensemble-tree model(R^(2)>0.87)identified Zn-Zr and Cu-Mg binary mixed oxides as the most effective OXZEO systems,with their light olefin space-time yields confirmed by physically mixing with HSAPO-34 through experimental validation.Density functional theory calculations further elucidated the activity trends between Zn-Zr and Cu-Mg mixed oxides.Among 16 catalyst and reaction condition descriptors,the oxide/zeolite ratio,reaction temperature,and pressure emerged as the most significant factors.This interpretable,data-driven framework offers a versatile approach that can be applied to other catalytic processes,providing a powerful tool for experiment design and optimization in catalysis.展开更多
Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning dis...Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e., zeroshot fine-grained classification. In the first feature learning phase, we finetune deep convolutional neural networks using hierarchical semantic structure among fine-grained classes to extract discriminative deep visual features. Meanwhile, a domain adaptation structure is induced into deep convolutional neural networks to avoid domain shift from training data to test data. In the second label inference phase, a semantic directed graph is constructed over attributes of fine-grained classes. Based on this graph, we develop a label propagation algorithm to infer the labels of images in the unseen classes. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art zero-shot learning models. In addition, the features obtained by our feature learning model also yield significant gains when they are used by other zero-shot learning models, which shows the flexility of our model in zero-shot finegrained classification.展开更多
With the rapid development of artificial intelligence,magnetocaloric materials as well as other materials are being developed with increased efficiency and enhanced performance.However,most studies do not take phase t...With the rapid development of artificial intelligence,magnetocaloric materials as well as other materials are being developed with increased efficiency and enhanced performance.However,most studies do not take phase transitions into account,and as a result,the predictions are usually not accurate enough.In this context,we have established an explicable relationship between alloy compositions and phase transition by feature imputation.A facile machine learning is proposed to screen candidate NiMn-based Heusler alloys with desired magnetic entropy change and magnetic transition temperature with a high accuracy R^(2)≈0.98.As expected,the measured properties of prepared NiMn-based alloys,including phase transition type,magnetic entropy changes and transition temperature,are all in good agreement with the ML predictions.As well as being the first to demonstrate an explicable relationship between alloy compositions,phase transitions and magnetocaloric properties,our proposed ML model is highly predictive and interpretable,which can provide a strong theoretical foundation for identifying high-performance magnetocaloric materials in the future.展开更多
In medium/high entropy alloys, their mechanical properties are strongly dependent on the chemicalelemental composition. Thus, searching for optimum elemental composition remains a critical issue to maximize the mechan...In medium/high entropy alloys, their mechanical properties are strongly dependent on the chemicalelemental composition. Thus, searching for optimum elemental composition remains a critical issue to maximize the mechanical performance. However, this issue solved by traditional optimization process via "trial and error" or experiences of domain experts is extremely difficult. Here we propose an approach based on high-throughput simulation combined machine learning to obtain medium entropy alloys with high strength and low cost. This method not only obtains a large amount of data quickly and accurately,but also helps us to determine the relationship between the composition and mechanical properties.The results reveal a vital importance of high-throughput simulation combined machine learning to find best mechanical properties in a wide range of elemental compositions for development of alloys with expected performance.展开更多
As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditio...As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a classification.Moreover,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version discrepancy.In this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 environment.Control experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and performance.Owing to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.展开更多
Mechanical metamaterials such as auxetic materials have attracted great interest due to their unusual properties that are dictated by their architectures.However,these architected materials usually have low stiffness ...Mechanical metamaterials such as auxetic materials have attracted great interest due to their unusual properties that are dictated by their architectures.However,these architected materials usually have low stiffness because of the bending or rotation deformation mechanisms in the microstructures.In this work,a convolutional neural network(CNN)based self-learning multi-objective optimization is performed to design digital composite materials.The CNN models have undergone rigorous training using randomly generated two-phase digital composite materials,along with their corresponding Poisson's ratios and stiffness values.Then the CNN models are used for designing composite material structures with the minimum Poisson's ratio at a given volume fraction constraint.Furthermore,we have designed composite materials with optimized stiffness while exhibiting a desired Poisson's ratio(negative,zero,or positive).The optimized designs have been successfully and efficiently obtained,and their validity has been confirmed through finite element analysis results.This self-learning multi-objective optimization model offers a promising approach for achieving comprehensive multi-objective optimization.展开更多
The nutritional management of patients with esophageal cancer(EC)presents significant complexities,with traditional approaches facing inherent limitations in data collection,real-time decision-making,and personalized ...The nutritional management of patients with esophageal cancer(EC)presents significant complexities,with traditional approaches facing inherent limitations in data collection,real-time decision-making,and personalized care.This narrative review explores the transformative potential of artificial intelligence(AI)and machine learning(ML),particularly deep learning(DL)and reinforcement learning(RL),in revolutionizing nutritional support for this vulnerable patient population.DL has demonstrated remarkable capabilities in enhancing the accuracy and objectivity of nutritional assessment through precise,automated body composition analysis from medical imaging,offering valuable prognostic insights.Concurrently,RL enables the dynamic optimization of nutritional interventions,adapting them in real time to individual patient responses,paving the way for truly personalized care paradigms.Although AI/ML offers potential advantages in efficiency,precision,and personalization by integrating multidimensional data for superior clinical decision support,its widespread adoption is accompanied by critical challenges.These include safeguarding data privacy and security,mitigating algorithmic bias,ensuring transparency and accountability,and establishing rigorous clinical validation.Early evidence suggests the feasibility of applying AI/ML in nutritional risk stratification and workflow optimization,but highquality prospective studies are needed to demonstrate the direct impact on clinical outcomes,including complications,readmissions,and survival.Overcoming these hurdles necessitates robust ethical governance,interdisciplinary collaboration,and continuous education.Ultimately,the strategic integration of AI/ML holds immense promise to profoundly improve patient outcomes,enhance quality of life,and optimize health care resource utilization in the nutritional management of esophageal cancer.展开更多
A prediction model leveraging machine learning was developed to forecast the tensile strength of wear-resistant steels,focusing on the relationship between composition,hot rolling process parameters and resulting prop...A prediction model leveraging machine learning was developed to forecast the tensile strength of wear-resistant steels,focusing on the relationship between composition,hot rolling process parameters and resulting properties.Multiple machine learning algorithms were compared,with the deep neural network(DNN)model outperforming others including random forests,gradient boosting regression,support vector regression,extreme gradient boosting,ridge regression,multi-layer perceptron,linear regression and decision tree.The DNN model was meticulously optimized,achieving a training set mean squared error(MSE)of 14.177 with a coefficient of determination(R2)of 0.973 and a test set MSE of 21.573 with an R2 of 0.960,reflecting its strong predictive capabilities and generalization to unseen data.In order to further confirm the predictive ability of the model,an experimental validation was carried out,involving the preparation of five different steel samples.The tensile strength of each sample was predicted and then compared to actual measurements,with the error of the results consistently below 5%.展开更多
Ni-based superalloys play a critical role in the aerospace industry due to their exceptional mechanical properties and oxidation resistance.However,the conventional development of new superalloys is often constrained ...Ni-based superalloys play a critical role in the aerospace industry due to their exceptional mechanical properties and oxidation resistance.However,the conventional development of new superalloys is often constrained by lengthy experimental cycles and high costs.To address these challenges,machine learning has emerged as an effective strategy for accelerating alloy design by efficiently exploring composition-property relationship,optimizing processing parameters,and enhancing predictive accuracy.This review summarizes recent progress in applying machine learning to composition optimization and mechanical property prediction of Ni-based superalloys,emphasizing the integration of theoretical modeling and experimental validation.The importance of feature engineering,including data collection,preprocessing,feature construction,and dimensionality reduction,was first highlighted.Subsequently,the machine learning approaches for novel alloy design and prediction of key properties including fatigue resistance,creep resistance,and oxidation resistance were discussed.Through data-driven approaches,machine learning not only enhances predictive capabilities but also uncovers complex composition-property relationship,which accelerates the development of next-generation Ni-based superalloys.We anticipate that the continued advancements in this field will drive more efficient and cost-effective alloy design,ultimately accelerating the transition from computational predictions to experimental realizations.展开更多
Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock m...Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock mainly focused on purebreds,and they yielded lower predict accuracy in hybrid.In this study,we presented a Multi-Layer Perceptron(MLP)model with multi-output regression framework specifically designed for genomic breed composition prediction of purebred and hybrid in pigs.Results We utilized a total of 8,199 pigs from breeding farms in eight provinces in China,comprising Yorkshire,Landrace,Duroc and hybrids of Yorkshire×Landrace.All the animals were genotyped with 1K,50K and 100K SNP chips.Comparing with random forest(RF),support vector regression(SVR)and Admixture,our results from five replicates of fivefold cross validation demonstrated that MLP achieved a breed identification accuracy of 100%for both hybrid and purebreds in 50K and 100K SNP chips,SVR performed comparable with MLP,they both outperformed RF and Admixture.In the independent testing,MLP yielded accuracy of 100%for all three pure breeds and hybrid across all SNP chips and panel,while SVR yielded 0.026%–0.121%lower accuracy than MLP.Compared with classification-based framework,the new strategy of multi-output regression framework in this study was helpful to improve the predict accuracy.MLP,RF and SVR,achieved consistent improvements across all six SNP chips/panel,especially in hybrid identification.Our results showed the determination threshold for purebred had different effects,SVR,RF and Admixture were very sensitive to threshold values,their optimal threshold fluctuated in different scenarios,while MLP kept optimal threshold 0.75 in all cases.The threshold of 0.65–0.75 is ideal for accurate breed identification.Among different density of SNP chips,the 1K SNP chip was most cost-effective as yielding 100%accuracy with enlarging training set.Hybrid individuals in the training set were useful for both purebred and hybrid identification.Conclusions Our new MLP strategy demonstrated its high accuracy and robust applicability across low-,medium-,and high-density SNP chips.Multi-output regression framework could universally enhance prediction accuracy for ML methods.Our new strategy is also helpful for breed identification in other livestock.展开更多
PM_(2.5)constitutes a complex and diversemixture that significantly impacts the environment,human health,and climate change.However,existing observation and numerical simulation techniques have limitations,such as a l...PM_(2.5)constitutes a complex and diversemixture that significantly impacts the environment,human health,and climate change.However,existing observation and numerical simulation techniques have limitations,such as a lack of data,high acquisition costs,andmultiple uncertainties.These limitations hinder the acquisition of comprehensive information on PM_(2.5)chemical composition and effectively implement refined air pollution protection and control strategies.In this study,we developed an optimal deep learning model to acquire hourly mass concentrations of key PM_(2.5)chemical components without complex chemical analysis.The model was trained using a randomly partitioned multivariate dataset arranged in chronological order,including atmospheric state indicators,which previous studies did not consider.Our results showed that the correlation coefficients of key chemical components were no less than 0.96,and the root mean square errors ranged from 0.20 to 2.11μg/m^(3)for the entire process(training and testing combined).The model accurately captured the temporal characteristics of key chemical components,outperforming typical machine-learning models,previous studies,and global reanalysis datasets(such asModern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)and Copernicus Atmosphere Monitoring Service ReAnalysis(CAMSRA)).We also quantified the feature importance using the random forest model,which showed that PM_(2.5),PM_(1),visibility,and temperature were the most influential variables for key chemical components.In conclusion,this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data,improved air pollution monitoring and source identification.This approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.展开更多
基金supported and partially funded by the National Natural Science Foundation of China(52288101)the China Postdoctoral Science Foundation(2024M761535)supported by the High Performance Computing Centers at Eastern Institute of Technology,Ningbo,and Ningbo Institute of Digital Twin.
文摘Computational solid mechanics has become an indispensable approach in engineering,and numerical investigation of fracturing in composites is essential,as composites are widely used in structural applications.Crack evolution in composites is the path to elucidating the relationship between microstructures and fracture performance,but crack-based finite-element methods are computationally expensive and time-consuming,which limits their application in computation-intensive scenarios.Consequently,this study proposes a deep learning framework called Crack-Net for instant prediction of the dynamic crack growth process,as well as its strain-stress curve.Specifically,Crack-Net introduces an implicit constraint technique,which incorporates the relationship between crack evolution and stress response into the network architecture.This technique substantially reduces data requirements while improving predictive accuracy.The transfer learning technique enables Crack-Net to handle composite materials with reinforcements of different strengths.Trained on high-accuracy fracture development datasets from phase field simulations,the proposed framework is capable of tackling intricate scenarios,involving materials with diverse interfaces,varying initial conditions,and the intricate elastoplastic fracture process.The proposed Crack-Net holds great promise for practical applications in engineering and materials science,in which accurate and efficient fracture prediction is crucial for optimizing material performance and microstructural design.
基金supported by National Key Research and Development Program of China(Grant No.2023YFB4604100)National Key Research and Development Program of China(Grant No.2022YFB3806104)+4 种基金Key Research and Development Program in Shaanxi Province(Grant No.2021LLRH-08-17)Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001)K C Wong Education Foundation of ChinaYouth Innovation Team of Shaanxi Universities of ChinaKey Research and Development Program of Shaanxi Province(Grant 2021LLRH-08-3.1).
文摘Ensuring the consistent mechanical performance of three-dimensional(3D)-printed continuous fiber-reinforced composites is a significant challenge in additive manufacturing.The current reliance on manual monitoring exacerbates this challenge by rendering the process vulnerable to environmental changes and unexpected factors,resulting in defects and inconsistent product quality,particularly in unmanned long-term operations or printing in extreme environments.To address these issues,we developed a process monitoring and closed-loop feedback control strategy for the 3D printing process.Real-time printing image data were captured and analyzed using a well-trained neural network model,and a real-time control module-enabled closed-loop feedback control of the flow rate was developed.The neural network model,which was based on image processing and artificial intelligence,enabled the recognition of flow rate values with an accuracy of 94.70%.The experimental results showed significant improvements in both the surface performance and mechanical properties of printed composites,with three to six times improvement in tensile strength and elastic modulus,demonstrating the effectiveness of the strategy.This study provides a generalized process monitoring and feedback control method for the 3D printing of continuous fiber-reinforced composites,and offers a potential solution for remote online monitoring and closed-loop adjustment in unmanned or extreme space environments.
基金supported by the Gas Hydrate R&D Organization and the Korea Institute of Geoscience and Mineral Resources(KIGAM)(GP2021-010)supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2021R1C1C1004460)Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korean government(MOTIE)(20214000000500,Training Program of CCUS for Green Growth).
文摘Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin,the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD).Because the analysis is costly and dependent on experts,a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data.However,the model’s performance was limited because of improper preprocessing of the intensity profile.Because preprocessing was applied to each feature,the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition.In this study,the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis.For 49 test data among the 488 data,the convolutional neural network(CNN)model improved the average absolute error and coefficient of determination by 41%and 46%,respectively,than those of CNN model with feature-based pre-processing.This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data.The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau.In addition,the overall procedure can be applied to any XRD data of sediments worldwide.
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002,2019ZA052011).
文摘With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.
文摘Carbon nanotube-reinforced cement composites have gained significant attention due to their enhanced mechanical properties,particularly in compressive and flexural strength.Despite extensive research,the influence of various parameters on these properties remains inadequately understood,primarily due to the complex interactions within the composites.This study addresses this gap by employingmachine learning techniques to conduct a sensitivity analysis on the compressive and flexural strength of carbon nanotube-reinforced cement composites.It systematically evaluates nine data-preprocessing techniques and benchmarks eleven machine-learning algorithms to reveal tradeoffs between predictive accuracy and computational complexity,which has not previously been explored in carbon nanotube-reinforced cement composite research.In this regard,four main factors are considered in the sensitivity analysis,which are the machine learning model type,the data pre-processing technique,and the effect of the concrete constituent materials on the compressive and flexural strength both globally through feature importance assessment and locally through partial dependence analysis.Accordingly,this research optimizes ninety-nine models representing combinations of eleven machine learning algorithms and nine data preprocessing techniques to accurately predict the mechanical properties of carbon nanotube-reinforced cement composites.Moreover,the study aims to unravel the relationships between different parameters and their impact on the composite’s strength by utilizing feature importance and partial dependence analyses.This research is crucial as it provides a comprehensive understanding of the factors influencing the performance of carbon nanotube-reinforced cement composites,which is vital for their efficient design and application in construction.The use of machine learning in this context not only enhances predictive accuracy but also offers insights that are often challenging to obtain through traditional experimental methods.The findings contribute to the field by highlighting the potential of advanced data-driven approaches in optimizing and understanding advanced composite materials,paving the way for more durable and resilient construction materials.
基金Supported by Taiwan National Science and Technology Council,No.NSTC114-2221-E-035-036Taichung Veterans General Hospital/Feng Chia University Joint Research Program,No.TCVGH-FCU1148207.
文摘BACKGROUND Esophageal cancer carries a poor prognosis with low 5-year survival rates and limited early detection options.The skeletal muscle index at the L3 vertebral level is a well-established prognostic marker in esophageal cancer,but most follow-up computed tomography(CT)scans do not extend to L3 and limiting its utility.Radiomics has emerged as a powerful tool for extracting prognostic information from medical images.AIM To evaluate the influential features for esophageal cancer prognosis by integrating radiomic and body compositionbased indices of skeletal muscle and adipose tissue at the T12 level from both pretreatment and follow-up CT images,in order to assess their value in predicting overall survival(OS).METHODS This retrospective study included 212 esophageal cancer patients who underwent concurrent chemoradiotherapy,with both pretreatment and follow-up chest CT scans available.Body organ analysis(BOA)and radiomic features were extracted from skeletal muscle and adipose tissue at the T12 level using automated tools.Four feature subsets(no-radiomics,pretreatment only,follow-up only,and combined inputs)were developed using logistic regression(LR)with least absolute shrinkage and selection operator for feature selection,followed by Cox regression.Prognostic models-including nomogram,support vector classifier,LR,and extra trees classifier-were constructed to predict 1-,2-,and 3-year OS.RESULTS The model integrating both BOA and radiomics from pretreatment and follow-up CT,combined with clinical data,achieved the best performance for 2-year OS prediction,with an area under the time-dependent receiver operating characteristic curve of 0.91,sensitivity of 0.81,and specificity of 0.88 using the LR model.The most predictive features included both clinical variables,body composition indices,and radiomic features,particularly from follow-up VAT.Follow-up imaging contributed significantly to model performance,reinforcing its value in treatment response evaluation.CONCLUSION This is the first study to demonstrate that BOA indices and their corresponding radiomics at the T12-level from both pretreatment and follow-up CT scans-combined with clinical data-can provide accurate prognostic information for esophageal cancer.This approach offers a practical alternative when L3-level imaging is unavailable and supports the clinical integration of automated T12-based imaging biomarkers.The integration of these imaging features with clinical parameters enhances the prediction of survival outcomes and contributes to non-invasive,personalized treatment planning.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2241240,12172045,and 12221002).
文摘In thermal protection structures,controlling and optimizing the surface roughness of carbon/phenolic(C/Ph)composites can effectively improve thermal protection performance and ensure the safe operation of carriers in high-temperature environments.This paper introduces a machine learning(ML)framework to forecast the surface roughness of carbon-phenolic composites under various thermal conditions by employing an ML algorithm derived from historical experimental datasets.Firstly,ablation experiments and collection of surface roughness height data of C/Ph composites under different thermal environments were conducted in an electric arc wind tunnel.Then,an ML model based on Ridge regression is developed for surface roughness prediction.The model involves incorporating feature engineering to choose the most concise and pertinent features,as well as developing an ML model.The ML model considers thermal environment parameters and feature screened by feature engineering as inputs,and predicts the surface height as the output.The results demonstrate that the suggested ML framework effectively anticipates the surface shape and associated surface roughness parameters in various heat flow conditions.Compared with the conventional 3D confocal microscope scanning,the method can obtain the surface topography information of the same area in a much shorter time,thus significantly saving time and cost.
基金the National Natural Science Foundation of China(12102085)the Postdoctoral Science Foundation of China(2023M730504)the Sichuan Province Regional Innovation and Cooperation Project(2024YFHZ0210).
文摘Determining the optimal ceramic content of the ceramics-in-polymer composite electrolytes and the appropriate stack pressure can effectively improve the interfacial contact of solid-state batteries(SSBs).Based on the contact mechanics model and constructed by the conjugate gradient method,continuous convolution,and fast Fourier transform,this paper analyzes and compares the interfacial contact responses involving the polymers commonly used in SSBs,which provides the original training data for machine learning.A support vector regression model is established to predict the relationship between the content of ceramics and the interfacial resistance.The Bayesian optimization and K-fold cross-validation are introduced to find the optimal combination of hyperparameters,which accelerates the training process and improves the model’s accuracy.We found the relationship between the content of ceramics,the stack pressure,and the interfacial resistance.The results can be taken as a reference for the design of the low-resistance composite electrolytes for solid-state batteries.
基金funded by the KRICT Project (KK2512-10) of the Korea Research Institute of Chemical Technology and the Ministry of Trade, Industry and Energy (MOTIE)the Korea Institute for Advancement of Technology (KIAT) through the Virtual Engineering Platform Program (P0022334)+1 种基金supported by the Carbon Neutral Industrial Strategic Technology Development Program (RS-202300261088) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea)Further support was provided by research fund of Chungnam National University。
文摘Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resource-intensive.To address this challenge,we implemented a three-stage framework integrating machine learning,Bayesian optimization,and experimental validation,utilizing a carefully curated dataset from the literature.Our ensemble-tree model(R^(2)>0.87)identified Zn-Zr and Cu-Mg binary mixed oxides as the most effective OXZEO systems,with their light olefin space-time yields confirmed by physically mixing with HSAPO-34 through experimental validation.Density functional theory calculations further elucidated the activity trends between Zn-Zr and Cu-Mg mixed oxides.Among 16 catalyst and reaction condition descriptors,the oxide/zeolite ratio,reaction temperature,and pressure emerged as the most significant factors.This interpretable,data-driven framework offers a versatile approach that can be applied to other catalytic processes,providing a powerful tool for experiment design and optimization in catalysis.
基金supported by National Basic Research Program of China (973 Program) (No. 2015CB352502)National Nature Science Foundation of China (No. 61573026)Beijing Nature Science Foundation (No. L172037)
文摘Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e., zeroshot fine-grained classification. In the first feature learning phase, we finetune deep convolutional neural networks using hierarchical semantic structure among fine-grained classes to extract discriminative deep visual features. Meanwhile, a domain adaptation structure is induced into deep convolutional neural networks to avoid domain shift from training data to test data. In the second label inference phase, a semantic directed graph is constructed over attributes of fine-grained classes. Based on this graph, we develop a label propagation algorithm to infer the labels of images in the unseen classes. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art zero-shot learning models. In addition, the features obtained by our feature learning model also yield significant gains when they are used by other zero-shot learning models, which shows the flexility of our model in zero-shot finegrained classification.
基金supported by the National Key R&D Program of China(No.2022YFE0109500)the National Natural Science Foundation of China(Nos.52071255,52301250,52171190 and 12304027)+2 种基金the Key R&D Project of Shaanxi Province(No.2022GXLH-01-07)the Fundamental Research Funds for the Central Universities(China)the World-Class Universities(Disciplines)and the Characteristic Development Guidance Funds for the Central Universities.
文摘With the rapid development of artificial intelligence,magnetocaloric materials as well as other materials are being developed with increased efficiency and enhanced performance.However,most studies do not take phase transitions into account,and as a result,the predictions are usually not accurate enough.In this context,we have established an explicable relationship between alloy compositions and phase transition by feature imputation.A facile machine learning is proposed to screen candidate NiMn-based Heusler alloys with desired magnetic entropy change and magnetic transition temperature with a high accuracy R^(2)≈0.98.As expected,the measured properties of prepared NiMn-based alloys,including phase transition type,magnetic entropy changes and transition temperature,are all in good agreement with the ML predictions.As well as being the first to demonstrate an explicable relationship between alloy compositions,phase transitions and magnetocaloric properties,our proposed ML model is highly predictive and interpretable,which can provide a strong theoretical foundation for identifying high-performance magnetocaloric materials in the future.
基金supported financially by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 51621004)the National Natural Science Foundation of China (Nos. 51871092, 11772122, 51625404, 51771232+5 种基金51671217)the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body (No. 71865015)the State Key Laboratory of Powder Metallurgythe National Key Research and Development Program of China (Nos. 2016YFB0700300 and 2016YFB1100103)support of the U.S. Army Research Office Project (Nos. W911NF-13-1-0438 and W911NF-19-2-0049) with the program managers,Drs. M.P. Bakas,S.N. Mathaudhusupport from the National Science Foundation (Nos. DMR-1611180 and 1809640)with the program directors,Drs. J. Yang,J.G. Shiflet,and D. Farkas。
文摘In medium/high entropy alloys, their mechanical properties are strongly dependent on the chemicalelemental composition. Thus, searching for optimum elemental composition remains a critical issue to maximize the mechanical performance. However, this issue solved by traditional optimization process via "trial and error" or experiences of domain experts is extremely difficult. Here we propose an approach based on high-throughput simulation combined machine learning to obtain medium entropy alloys with high strength and low cost. This method not only obtains a large amount of data quickly and accurately,but also helps us to determine the relationship between the composition and mechanical properties.The results reveal a vital importance of high-throughput simulation combined machine learning to find best mechanical properties in a wide range of elemental compositions for development of alloys with expected performance.
基金supported by the General Program of the National Natural Science Foundation of China under Grant No.62172093the National Key R&D Program of China under Grant No.2018YFB1800602+1 种基金2019 Industrial Internet Innovation and Development Project,Ministry of Industry and Information Technology(MIIT)under Grant No.6709010003Ministry of Education-China Mobile Research Fund under Grant No.MCM20180506。
文摘As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a classification.Moreover,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version discrepancy.In this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 environment.Control experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and performance.Owing to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.
文摘Mechanical metamaterials such as auxetic materials have attracted great interest due to their unusual properties that are dictated by their architectures.However,these architected materials usually have low stiffness because of the bending or rotation deformation mechanisms in the microstructures.In this work,a convolutional neural network(CNN)based self-learning multi-objective optimization is performed to design digital composite materials.The CNN models have undergone rigorous training using randomly generated two-phase digital composite materials,along with their corresponding Poisson's ratios and stiffness values.Then the CNN models are used for designing composite material structures with the minimum Poisson's ratio at a given volume fraction constraint.Furthermore,we have designed composite materials with optimized stiffness while exhibiting a desired Poisson's ratio(negative,zero,or positive).The optimized designs have been successfully and efficiently obtained,and their validity has been confirmed through finite element analysis results.This self-learning multi-objective optimization model offers a promising approach for achieving comprehensive multi-objective optimization.
文摘The nutritional management of patients with esophageal cancer(EC)presents significant complexities,with traditional approaches facing inherent limitations in data collection,real-time decision-making,and personalized care.This narrative review explores the transformative potential of artificial intelligence(AI)and machine learning(ML),particularly deep learning(DL)and reinforcement learning(RL),in revolutionizing nutritional support for this vulnerable patient population.DL has demonstrated remarkable capabilities in enhancing the accuracy and objectivity of nutritional assessment through precise,automated body composition analysis from medical imaging,offering valuable prognostic insights.Concurrently,RL enables the dynamic optimization of nutritional interventions,adapting them in real time to individual patient responses,paving the way for truly personalized care paradigms.Although AI/ML offers potential advantages in efficiency,precision,and personalization by integrating multidimensional data for superior clinical decision support,its widespread adoption is accompanied by critical challenges.These include safeguarding data privacy and security,mitigating algorithmic bias,ensuring transparency and accountability,and establishing rigorous clinical validation.Early evidence suggests the feasibility of applying AI/ML in nutritional risk stratification and workflow optimization,but highquality prospective studies are needed to demonstrate the direct impact on clinical outcomes,including complications,readmissions,and survival.Overcoming these hurdles necessitates robust ethical governance,interdisciplinary collaboration,and continuous education.Ultimately,the strategic integration of AI/ML holds immense promise to profoundly improve patient outcomes,enhance quality of life,and optimize health care resource utilization in the nutritional management of esophageal cancer.
基金support of the National Natural Science Foundation of China(Nos.52161008 and 51961030)the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region of China(NJYT23003)+1 种基金the Project of Central Government Guiding Local Science and Technology Development(Nos.2022ZY0035 and 2022ZY0072)the Special Fund for Talent Revitalisation Plan of Yangjiang Key Industries of Alloy Materials and Hardware Knives and Scissors(RCZX2022020).
文摘A prediction model leveraging machine learning was developed to forecast the tensile strength of wear-resistant steels,focusing on the relationship between composition,hot rolling process parameters and resulting properties.Multiple machine learning algorithms were compared,with the deep neural network(DNN)model outperforming others including random forests,gradient boosting regression,support vector regression,extreme gradient boosting,ridge regression,multi-layer perceptron,linear regression and decision tree.The DNN model was meticulously optimized,achieving a training set mean squared error(MSE)of 14.177 with a coefficient of determination(R2)of 0.973 and a test set MSE of 21.573 with an R2 of 0.960,reflecting its strong predictive capabilities and generalization to unseen data.In order to further confirm the predictive ability of the model,an experimental validation was carried out,involving the preparation of five different steel samples.The tensile strength of each sample was predicted and then compared to actual measurements,with the error of the results consistently below 5%.
基金financially supported by the National Natural Science Foundation of China(Nos.52201203 and 52471004)the Fundamental Research Funds for the Central Universities(No.N2423030)the Science and Technology Project of Hebei Education Department(No.QN2023155).
文摘Ni-based superalloys play a critical role in the aerospace industry due to their exceptional mechanical properties and oxidation resistance.However,the conventional development of new superalloys is often constrained by lengthy experimental cycles and high costs.To address these challenges,machine learning has emerged as an effective strategy for accelerating alloy design by efficiently exploring composition-property relationship,optimizing processing parameters,and enhancing predictive accuracy.This review summarizes recent progress in applying machine learning to composition optimization and mechanical property prediction of Ni-based superalloys,emphasizing the integration of theoretical modeling and experimental validation.The importance of feature engineering,including data collection,preprocessing,feature construction,and dimensionality reduction,was first highlighted.Subsequently,the machine learning approaches for novel alloy design and prediction of key properties including fatigue resistance,creep resistance,and oxidation resistance were discussed.Through data-driven approaches,machine learning not only enhances predictive capabilities but also uncovers complex composition-property relationship,which accelerates the development of next-generation Ni-based superalloys.We anticipate that the continued advancements in this field will drive more efficient and cost-effective alloy design,ultimately accelerating the transition from computational predictions to experimental realizations.
基金supported by grants from Key R&D Program of Shandong Province(2022LZGC003)China Agriculture Research System of MOF and MARA,the National Key Research and Development Project(2023YFD1300200 and 2023YFF1001104)+1 种基金the Science and Technology Program of Sichuan Province(2024ZHCG0109)the 2115 Talent Development Program of China Agricultural University.
文摘Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock mainly focused on purebreds,and they yielded lower predict accuracy in hybrid.In this study,we presented a Multi-Layer Perceptron(MLP)model with multi-output regression framework specifically designed for genomic breed composition prediction of purebred and hybrid in pigs.Results We utilized a total of 8,199 pigs from breeding farms in eight provinces in China,comprising Yorkshire,Landrace,Duroc and hybrids of Yorkshire×Landrace.All the animals were genotyped with 1K,50K and 100K SNP chips.Comparing with random forest(RF),support vector regression(SVR)and Admixture,our results from five replicates of fivefold cross validation demonstrated that MLP achieved a breed identification accuracy of 100%for both hybrid and purebreds in 50K and 100K SNP chips,SVR performed comparable with MLP,they both outperformed RF and Admixture.In the independent testing,MLP yielded accuracy of 100%for all three pure breeds and hybrid across all SNP chips and panel,while SVR yielded 0.026%–0.121%lower accuracy than MLP.Compared with classification-based framework,the new strategy of multi-output regression framework in this study was helpful to improve the predict accuracy.MLP,RF and SVR,achieved consistent improvements across all six SNP chips/panel,especially in hybrid identification.Our results showed the determination threshold for purebred had different effects,SVR,RF and Admixture were very sensitive to threshold values,their optimal threshold fluctuated in different scenarios,while MLP kept optimal threshold 0.75 in all cases.The threshold of 0.65–0.75 is ideal for accurate breed identification.Among different density of SNP chips,the 1K SNP chip was most cost-effective as yielding 100%accuracy with enlarging training set.Hybrid individuals in the training set were useful for both purebred and hybrid identification.Conclusions Our new MLP strategy demonstrated its high accuracy and robust applicability across low-,medium-,and high-density SNP chips.Multi-output regression framework could universally enhance prediction accuracy for ML methods.Our new strategy is also helpful for breed identification in other livestock.
基金supported by the National Key Research and Development Program for Young Scientists of China(No.2022YFC3704000)the National Natural Science Foundation of China(No.42275122)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab).
文摘PM_(2.5)constitutes a complex and diversemixture that significantly impacts the environment,human health,and climate change.However,existing observation and numerical simulation techniques have limitations,such as a lack of data,high acquisition costs,andmultiple uncertainties.These limitations hinder the acquisition of comprehensive information on PM_(2.5)chemical composition and effectively implement refined air pollution protection and control strategies.In this study,we developed an optimal deep learning model to acquire hourly mass concentrations of key PM_(2.5)chemical components without complex chemical analysis.The model was trained using a randomly partitioned multivariate dataset arranged in chronological order,including atmospheric state indicators,which previous studies did not consider.Our results showed that the correlation coefficients of key chemical components were no less than 0.96,and the root mean square errors ranged from 0.20 to 2.11μg/m^(3)for the entire process(training and testing combined).The model accurately captured the temporal characteristics of key chemical components,outperforming typical machine-learning models,previous studies,and global reanalysis datasets(such asModern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)and Copernicus Atmosphere Monitoring Service ReAnalysis(CAMSRA)).We also quantified the feature importance using the random forest model,which showed that PM_(2.5),PM_(1),visibility,and temperature were the most influential variables for key chemical components.In conclusion,this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data,improved air pollution monitoring and source identification.This approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.
