Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),a...Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.展开更多
Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc...Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Permanent-magnet(PM)machines are the important driving components of various mechanical equipment and industrial applications,such as robot joints,aerospace equipment,electric vehicles,actuators,wind generators and el...Permanent-magnet(PM)machines are the important driving components of various mechanical equipment and industrial applications,such as robot joints,aerospace equipment,electric vehicles,actuators,wind generators and electric traction systems.The PM machines are usually expected to have high torque/power density,low torque ripple,reduced rotor mass,a large constant power speed range or strong anti-magnetization capability to match different requirements of industrial applications.The structural topology of the electric machines,including stator/rotor arrangements and magnet patterns of rotor,is one major concern to improve their electromagnetic performance.However,systematic reviews of structural topology are seldom found in literature.Therefore,the objective of this paper is to summarize the stator/rotor arrangements and magnet patterns of the permanent-magnet brushless machines,in depth.Specifically,the stator/rotor arrangements of the PM machines including radial-flux,axialflux and emerging hybrid axial-radial flux configurations are presented,and pros and cons of these topologies are discussed regarding their electromagnetic performance.The magnet patterns including various surface-mounted and interior magnet patterns,such as parallel magnetization pole pattern,Halbach arrays,spoke-type designs and their variants are summarized,and the characteristics of those magnet patterns in terms of flux-focusing effect,magnetic self-shielding effect,torque ripple,reluctance torque,magnet utilization ratio,and anti-demagnetization capability are compared.This paper can provide guidance and suggestion for the structure selection and design of PM brushless machines for high-performance industrial applications.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limite...The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.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
Arsenic(As)pollution in soils is a pervasive environmental issue.Biochar immobilization offers a promising solution for addressing soil As contamination.The efficiency of biochar in immobilizing As in soils primarily ...Arsenic(As)pollution in soils is a pervasive environmental issue.Biochar immobilization offers a promising solution for addressing soil As contamination.The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics of both the soil and the biochar.However,the influence of a specific property on As immobilization varies among different studies,and the development and application of arsenic passivation materials based on biochar often rely on empirical knowledge.To enhance immobilization efficiency and reduce labor and time costs,a machine learning(ML)model was employed to predict As immobilization efficiency before biochar application.In this study,we collected a dataset comprising 182 data points on As immobilization efficiency from 17 publications to construct three ML models.The results demonstrated that the random forest(RF)model outperformed gradient boost regression tree and support vector regression models in predictive performance.Relative importance analysis and partial dependence plots based on the RF model were conducted to identify the most crucial factors influencing As immobilization.These findings highlighted the significant roles of biochar application time and biochar pH in As immobilization efficiency in soils.Furthermore,the study revealed that Fe-modified biochar exhibited a substantial improvement in As immobilization.These insights can facilitate targeted biochar property design and optimization of biochar application conditions to enhance As immobilization efficiency.展开更多
Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been wide...Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been widely employed for liposome preparation.Although some studies have explored factors affecting liposomal size in microfluidic processes,most focus on small-sized liposomes,predominantly through experimental data analysis.However,the production of larger liposomes,which are equally significant,remains underexplored.In this work,we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning(ML)model capable of accurately predicting liposomal size.Experimental validation was conducted using a staggered herringbone micromixer(SHM)chip.Our findings reveal that most investigated variables significantly influence liposomal size,often interrelating in complex ways.We evaluated the predictive performance of several widely-used ML algorithms,including ensemble methods,through cross-validation(CV)for both lipo-some size and polydispersity index(PDI).A standalone dataset was experimentally validated to assess the accuracy of the ML predictions,with results indicating that ensemble algorithms provided the most reliable predictions.Specifically,gradient boosting was selected for size prediction,while random forest was employed for PDI prediction.We successfully produced uniform large(600 nm)and small(100 nm)liposomes using the optimised experimental conditions derived from the ML models.In conclusion,this study presents a robust methodology that enables precise control over liposome size distribution,of-fering valuable insights for medicinal research applications.展开更多
In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot al...In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.展开更多
Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as ...Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge.Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators,potentially accelerating the development process of energetic materials.In this background,impact sensitivity,detonation performances,and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out.Four machine learning algorithms were employed to predict various properties of energetic materials,including impact sensitivity,detonation velocity,detonation pressure,and Gurney energy.Analysis of Pearson coefficients and feature importance showed that the heat of explosion,oxygen balance,decomposition products,and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials.Oxygen balance,decomposition products,and density have a strong correlation with detonation performances.Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark,the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances:oxygen balance values should be between-40%and-30%,density should range from 1.66 to 1.72 g/cm^(3),HOMO energy levels should be between-6.34 and-6.31 eV,and lipophilicity should be between-1.0 and 0.1,4.49 and 5.59.These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials,but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity.展开更多
The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approac...The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
Recent studies have shown that synergistic precipitation of continuous precipitates(CPs)and discontinuous precipitates(DPs)is a promising method to simultaneously improve the strength and electrical conductivity of Cu...Recent studies have shown that synergistic precipitation of continuous precipitates(CPs)and discontinuous precipitates(DPs)is a promising method to simultaneously improve the strength and electrical conductivity of Cu-Ni-Si alloy.However,the complex relationship between precipitates and two-stage aging process presents a significant challenge for the optimization of process parameters.In this study,machine learning models were established based on orthogonal experiment to mine the relationship between two-stage aging parameters and properties of Cu-5.3Ni-1.3Si-0.12Nb alloy with preferred formation of DPs.Two-stage aging parameters of 400℃/75 min+400℃/30 min were then obtained by multi-objective optimization combined with an experimental iteration strategy,resulting in a tensile strength of 875 MPa and a conductivity of 41.43%IACS,respectively.Such an excellent comprehensive performance of the alloy is attributed to the combined precipitation of DPs and CPs(with a total volume fraction of 5.4%and a volume ratio of CPs to DPs of 6.7).This study could provide a new approach and insight for improving the comprehensive properties of the Cu-Ni-Si alloys.展开更多
To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.Howeve...To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.However,most of the studies had focused only on colored plastic fragments,ignoring colorless plastic fragments and the effects of different environmental media(backgrounds),thus underestimating their abundance.To address this issue,the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis(PLS-DA),extreme gradient boost,support vector machine and random forest classifier.The effects of polymer color,type,thickness,and background on the plastic fragments classification were evaluated.PLS-DA presented the best and most stable outcome,with higher robustness and lower misclassification rate.All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm.A two-stage modeling method,which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background,was proposed.The method presented an accuracy higher than 99%in different backgrounds.In summary,this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.展开更多
Integrating exhaled breath analysis into the diagnosis of cardiovascular diseases holds significant promise as a valuable tool for future clinical use,particularly for ischemic heart disease(IHD).However,current resea...Integrating exhaled breath analysis into the diagnosis of cardiovascular diseases holds significant promise as a valuable tool for future clinical use,particularly for ischemic heart disease(IHD).However,current research on the volatilome(exhaled breath composition)in heart disease remains underexplored and lacks sufficient evidence to confirm its clinical validity.Key challenges hindering the application of breath analysis in diagnosing IHD include the scarcity of studies(only three published papers to date),substantial methodological bias in two of these studies,and the absence of standardized protocols for clinical imple-mentation.Additionally,inconsistencies in methodologies—such as sample collection,analytical techniques,machine learning(ML)approaches,and result interpretation—vary widely across studies,further complicating their reprodu-cibility and comparability.To address these gaps,there is an urgent need to establish unified guidelines that define best practices for breath sample collection,data analysis,ML integration,and biomarker annotation.Until these challenges are systematically resolved,the widespread adoption of exhaled breath analysis as a reliable diagnostic tool for IHD remains a distant goal rather than an immi-nent reality.展开更多
BACKGROUND To investigate the preoperative factors influencing textbook outcomes(TO)in Intrahepatic cholangiocarcinoma(ICC)patients and evaluate the feasibility of an interpretable machine learning model for preoperat...BACKGROUND To investigate the preoperative factors influencing textbook outcomes(TO)in Intrahepatic cholangiocarcinoma(ICC)patients and evaluate the feasibility of an interpretable machine learning model for preoperative prediction of TO,we developed a machine learning model for preoperative prediction of TO and used the SHapley Additive exPlanations(SHAP)technique to illustrate the prediction process.AIM To analyze the factors influencing textbook outcomes before surgery and to establish interpretable machine learning models for preoperative prediction.METHODS A total of 376 patients diagnosed with ICC were retrospectively collected from four major medical institutions in China,covering the period from 2011 to 2017.Logistic regression analysis was conducted to identify preoperative variables associated with achieving TO.Based on these variables,an EXtreme Gradient Boosting(XGBoost)machine learning prediction model was constructed using the XGBoost package.The SHAP(package:Shapviz)algorithm was employed to visualize each variable's contribution to the model's predictions.Kaplan-Meier survival analysis was performed to compare the prognostic differences between the TO-achieving and non-TO-achieving groups.RESULTS Among 376 patients,287 were included in the training group and 89 in the validation group.Logistic regression identified the following preoperative variables influencing TO:Child-Pugh classification,Eastern Cooperative Oncology Group(ECOG)score,hepatitis B,and tumor size.The XGBoost prediction model demonstrated high accuracy in internal validation(AUC=0.8825)and external validation(AUC=0.8346).Survival analysis revealed that the disease-free survival rates for patients achieving TO at 1,2,and 3 years were 64.2%,56.8%,and 43.4%,respectively.CONCLUSION Child-Pugh classification,ECOG score,hepatitis B,and tumor size are preoperative predictors of TO.In both the training group and the validation group,the machine learning model had certain effectiveness in predicting TO before surgery.The SHAP algorithm provided intuitive visualization of the machine learning prediction process,enhancing its interpretability.展开更多
Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide.Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research probl...Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide.Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem.Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables.In this study,we propose a machine learning algorithm for carbon emissions,a Bayesian optimized XGboost regression model,using multi-year energy carbon emission data and nighttime lights(NTL)remote sensing data from Shaanxi Province,China.Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models,with an R^(2)of 0.906 and RMSE of 5.687.We observe an annual increase in carbon emissions,with high-emission counties primarily concentrated in northern and central Shaanxi Province,displaying a shift from discrete,sporadic points to contiguous,extended spatial distribution.Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns,with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering.Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissionsmore accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment.This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.展开更多
The application of machine learning in alloy design is increasingly widespread,yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships.This work proposes an ...The application of machine learning in alloy design is increasingly widespread,yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships.This work proposes an interpretable machine learning method based on data augmentation and reconstruction,excavating high-performance low-alloyed magnesium(Mg)alloys.The data augmentation technique expands the original dataset through Gaussian noise.The data reconstruction method reorganizes and transforms the original data to extract more representative features,significantly improving the model's generalization ability and prediction accuracy,with a coefficient of determination(R^(2))of 95.9%for the ultimate tensile strength(UTS)model and a R^(2)of 95.3%for the elongation-to-failure(EL)model.The correlation coefficient assisted screening(CCAS)method is proposed to filter low-alloyed target alloys.A new Mg-2.2Mn-0.4Zn-0.2Al-0.2Ca(MZAX2000,wt%)alloy is designed and extruded into bar at given processing parameters,achieving room-temperature strength-ductility synergy showing an excellent UTS of 395 MPa and a high EL of 17.9%.This is closely related to its hetero-structured characteristic in the as-extruded MZAX2000 alloy consisting of coarse grains(16%),fine grains(75%),and fiber regions(9%).Therefore,this work offers new insights into optimizing alloy compositions and processing parameters for attaining new high strong and ductile low-alloyed Mg alloys.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
文摘Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.
基金funded by the Directorate of Research and Community Service,Directorate General of Research and Development,Ministry of Higher Education,Science and Technologyin accordance with the Implementation Contract for the Operational Assistance Program for State Universities,Research Program Number:109/C3/DT.05.00/PL/2025.
文摘Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金Supported by National Natural Science Foundation of China(NSFC)(Grant No.52130505)Zhejiang Provincial Natural Science Foundation of China(Grant No.LD24E050005)+1 种基金Ningbo Key Scientific and Technological Project of China(Grant No.2022Z040)Academic Excellence Foundation of BUAA for PhD Students.
文摘Permanent-magnet(PM)machines are the important driving components of various mechanical equipment and industrial applications,such as robot joints,aerospace equipment,electric vehicles,actuators,wind generators and electric traction systems.The PM machines are usually expected to have high torque/power density,low torque ripple,reduced rotor mass,a large constant power speed range or strong anti-magnetization capability to match different requirements of industrial applications.The structural topology of the electric machines,including stator/rotor arrangements and magnet patterns of rotor,is one major concern to improve their electromagnetic performance.However,systematic reviews of structural topology are seldom found in literature.Therefore,the objective of this paper is to summarize the stator/rotor arrangements and magnet patterns of the permanent-magnet brushless machines,in depth.Specifically,the stator/rotor arrangements of the PM machines including radial-flux,axialflux and emerging hybrid axial-radial flux configurations are presented,and pros and cons of these topologies are discussed regarding their electromagnetic performance.The magnet patterns including various surface-mounted and interior magnet patterns,such as parallel magnetization pole pattern,Halbach arrays,spoke-type designs and their variants are summarized,and the characteristics of those magnet patterns in terms of flux-focusing effect,magnetic self-shielding effect,torque ripple,reluctance torque,magnet utilization ratio,and anti-demagnetization capability are compared.This paper can provide guidance and suggestion for the structure selection and design of PM brushless machines for high-performance industrial applications.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金the National Key Research and Development Program of China(2021YFC2900300)the Natural Science Foundation of Guangdong Province(2024A1515030216)+2 种基金MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(GPMR202437)the Guangdong Province Introduced of Innovative R&D Team(2021ZT09H399)the Third Xinjiang Scientific Expedition Program(2022xjkk1301).
文摘The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.
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 by the National Key Research and Development Program of China(No.2020YFC1808701).
文摘Arsenic(As)pollution in soils is a pervasive environmental issue.Biochar immobilization offers a promising solution for addressing soil As contamination.The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics of both the soil and the biochar.However,the influence of a specific property on As immobilization varies among different studies,and the development and application of arsenic passivation materials based on biochar often rely on empirical knowledge.To enhance immobilization efficiency and reduce labor and time costs,a machine learning(ML)model was employed to predict As immobilization efficiency before biochar application.In this study,we collected a dataset comprising 182 data points on As immobilization efficiency from 17 publications to construct three ML models.The results demonstrated that the random forest(RF)model outperformed gradient boost regression tree and support vector regression models in predictive performance.Relative importance analysis and partial dependence plots based on the RF model were conducted to identify the most crucial factors influencing As immobilization.These findings highlighted the significant roles of biochar application time and biochar pH in As immobilization efficiency in soils.Furthermore,the study revealed that Fe-modified biochar exhibited a substantial improvement in As immobilization.These insights can facilitate targeted biochar property design and optimization of biochar application conditions to enhance As immobilization efficiency.
基金supported by the National Key Research and Development Plan of the Ministry of Science and Technology,China(Grant No.:2022YFE0125300)the National Natural Science Foundation of China(Grant No:81690262)+2 种基金the National Science and Technology Major Project,China(Grant No.:2017ZX09201004-021)the Open Project of National facility for Translational Medicine(Shanghai),China(Grant No.:TMSK-2021-104)Shanghai Jiao Tong University STAR Grant,China(Grant Nos.:YG2022ZD024 and YG2022QN111).
文摘Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been widely employed for liposome preparation.Although some studies have explored factors affecting liposomal size in microfluidic processes,most focus on small-sized liposomes,predominantly through experimental data analysis.However,the production of larger liposomes,which are equally significant,remains underexplored.In this work,we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning(ML)model capable of accurately predicting liposomal size.Experimental validation was conducted using a staggered herringbone micromixer(SHM)chip.Our findings reveal that most investigated variables significantly influence liposomal size,often interrelating in complex ways.We evaluated the predictive performance of several widely-used ML algorithms,including ensemble methods,through cross-validation(CV)for both lipo-some size and polydispersity index(PDI).A standalone dataset was experimentally validated to assess the accuracy of the ML predictions,with results indicating that ensemble algorithms provided the most reliable predictions.Specifically,gradient boosting was selected for size prediction,while random forest was employed for PDI prediction.We successfully produced uniform large(600 nm)and small(100 nm)liposomes using the optimised experimental conditions derived from the ML models.In conclusion,this study presents a robust methodology that enables precise control over liposome size distribution,of-fering valuable insights for medicinal research applications.
基金supported by the SP2024/089 Project by the Faculty of Materials Science and Technology,VˇSB-Technical University of Ostrava.
文摘In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2682024GF019)。
文摘Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge.Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators,potentially accelerating the development process of energetic materials.In this background,impact sensitivity,detonation performances,and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out.Four machine learning algorithms were employed to predict various properties of energetic materials,including impact sensitivity,detonation velocity,detonation pressure,and Gurney energy.Analysis of Pearson coefficients and feature importance showed that the heat of explosion,oxygen balance,decomposition products,and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials.Oxygen balance,decomposition products,and density have a strong correlation with detonation performances.Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark,the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances:oxygen balance values should be between-40%and-30%,density should range from 1.66 to 1.72 g/cm^(3),HOMO energy levels should be between-6.34 and-6.31 eV,and lipophilicity should be between-1.0 and 0.1,4.49 and 5.59.These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials,but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity.
基金supported by the National Natural Science Foundation of China(No.U21A20290)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011656)+2 种基金the Projects of Talents Recruitment of GDUPT(No.2023rcyj1003)the 2022“Sail Plan”Project of Maoming Green Chemical Industry Research Institute(No.MMGCIRI2022YFJH-Y-024)Maoming Science and Technology Project(No.2023382).
文摘The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
基金financially supported by the National Key Research and Development Program of China(No.2023YFB3812601)the National Natural Science Foundation of China(Nos.51925401,92066205 and 92266301)the Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC001).
文摘Recent studies have shown that synergistic precipitation of continuous precipitates(CPs)and discontinuous precipitates(DPs)is a promising method to simultaneously improve the strength and electrical conductivity of Cu-Ni-Si alloy.However,the complex relationship between precipitates and two-stage aging process presents a significant challenge for the optimization of process parameters.In this study,machine learning models were established based on orthogonal experiment to mine the relationship between two-stage aging parameters and properties of Cu-5.3Ni-1.3Si-0.12Nb alloy with preferred formation of DPs.Two-stage aging parameters of 400℃/75 min+400℃/30 min were then obtained by multi-objective optimization combined with an experimental iteration strategy,resulting in a tensile strength of 875 MPa and a conductivity of 41.43%IACS,respectively.Such an excellent comprehensive performance of the alloy is attributed to the combined precipitation of DPs and CPs(with a total volume fraction of 5.4%and a volume ratio of CPs to DPs of 6.7).This study could provide a new approach and insight for improving the comprehensive properties of the Cu-Ni-Si alloys.
基金supported by the National Natural Science Foundation of China(No.22276139)the Shanghai’s Municipal State-owned Assets Supervision and Administration Commission(No.2022028).
文摘To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.However,most of the studies had focused only on colored plastic fragments,ignoring colorless plastic fragments and the effects of different environmental media(backgrounds),thus underestimating their abundance.To address this issue,the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis(PLS-DA),extreme gradient boost,support vector machine and random forest classifier.The effects of polymer color,type,thickness,and background on the plastic fragments classification were evaluated.PLS-DA presented the best and most stable outcome,with higher robustness and lower misclassification rate.All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm.A two-stage modeling method,which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background,was proposed.The method presented an accuracy higher than 99%in different backgrounds.In summary,this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.
基金Supported by The government assignment,No.1023022600020-6The Ministry of Science and Higher Education of the Russian Federation Within The Framework of State Support for The Creation and Development of World-Class Research Center“Digital Biodesign and Personalized Healthcare,”No.075-15-2022-304RSF grant,No.24-15-00549.
文摘Integrating exhaled breath analysis into the diagnosis of cardiovascular diseases holds significant promise as a valuable tool for future clinical use,particularly for ischemic heart disease(IHD).However,current research on the volatilome(exhaled breath composition)in heart disease remains underexplored and lacks sufficient evidence to confirm its clinical validity.Key challenges hindering the application of breath analysis in diagnosing IHD include the scarcity of studies(only three published papers to date),substantial methodological bias in two of these studies,and the absence of standardized protocols for clinical imple-mentation.Additionally,inconsistencies in methodologies—such as sample collection,analytical techniques,machine learning(ML)approaches,and result interpretation—vary widely across studies,further complicating their reprodu-cibility and comparability.To address these gaps,there is an urgent need to establish unified guidelines that define best practices for breath sample collection,data analysis,ML integration,and biomarker annotation.Until these challenges are systematically resolved,the widespread adoption of exhaled breath analysis as a reliable diagnostic tool for IHD remains a distant goal rather than an immi-nent reality.
基金Supported by National Key Research and Development Program,No.2022YFC2407304Major Research Project for Middle-Aged and Young Scientists of Fujian Provincial Health Commission,No.2021ZQNZD013+2 种基金The National Natural Science Foundation of China,No.62275050Fujian Province Science and Technology Innovation Joint Fund Project,No.2019Y9108Major Science and Technology Projects of Fujian Province,No.2021YZ036017.
文摘BACKGROUND To investigate the preoperative factors influencing textbook outcomes(TO)in Intrahepatic cholangiocarcinoma(ICC)patients and evaluate the feasibility of an interpretable machine learning model for preoperative prediction of TO,we developed a machine learning model for preoperative prediction of TO and used the SHapley Additive exPlanations(SHAP)technique to illustrate the prediction process.AIM To analyze the factors influencing textbook outcomes before surgery and to establish interpretable machine learning models for preoperative prediction.METHODS A total of 376 patients diagnosed with ICC were retrospectively collected from four major medical institutions in China,covering the period from 2011 to 2017.Logistic regression analysis was conducted to identify preoperative variables associated with achieving TO.Based on these variables,an EXtreme Gradient Boosting(XGBoost)machine learning prediction model was constructed using the XGBoost package.The SHAP(package:Shapviz)algorithm was employed to visualize each variable's contribution to the model's predictions.Kaplan-Meier survival analysis was performed to compare the prognostic differences between the TO-achieving and non-TO-achieving groups.RESULTS Among 376 patients,287 were included in the training group and 89 in the validation group.Logistic regression identified the following preoperative variables influencing TO:Child-Pugh classification,Eastern Cooperative Oncology Group(ECOG)score,hepatitis B,and tumor size.The XGBoost prediction model demonstrated high accuracy in internal validation(AUC=0.8825)and external validation(AUC=0.8346).Survival analysis revealed that the disease-free survival rates for patients achieving TO at 1,2,and 3 years were 64.2%,56.8%,and 43.4%,respectively.CONCLUSION Child-Pugh classification,ECOG score,hepatitis B,and tumor size are preoperative predictors of TO.In both the training group and the validation group,the machine learning model had certain effectiveness in predicting TO before surgery.The SHAP algorithm provided intuitive visualization of the machine learning prediction process,enhancing its interpretability.
基金supported by the Key Research and Development Program in Shaanxi Province,China(No.2022ZDLSF07-05)the Fundamental Research Funds for the Central Universities,CHD(No.300102352901)。
文摘Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide.Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem.Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables.In this study,we propose a machine learning algorithm for carbon emissions,a Bayesian optimized XGboost regression model,using multi-year energy carbon emission data and nighttime lights(NTL)remote sensing data from Shaanxi Province,China.Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models,with an R^(2)of 0.906 and RMSE of 5.687.We observe an annual increase in carbon emissions,with high-emission counties primarily concentrated in northern and central Shaanxi Province,displaying a shift from discrete,sporadic points to contiguous,extended spatial distribution.Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns,with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering.Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissionsmore accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment.This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.
基金funded by the National Natural Science Foundation of China(No.52204407)the Natural Science Foundation of Jiangsu Province(No.BK20220595)+1 种基金the China Postdoctoral Science Foundation(No.2022M723689)the Industrial Collaborative Innovation Project of Shanghai(No.XTCX-KJ-2022-2-11)。
文摘The application of machine learning in alloy design is increasingly widespread,yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships.This work proposes an interpretable machine learning method based on data augmentation and reconstruction,excavating high-performance low-alloyed magnesium(Mg)alloys.The data augmentation technique expands the original dataset through Gaussian noise.The data reconstruction method reorganizes and transforms the original data to extract more representative features,significantly improving the model's generalization ability and prediction accuracy,with a coefficient of determination(R^(2))of 95.9%for the ultimate tensile strength(UTS)model and a R^(2)of 95.3%for the elongation-to-failure(EL)model.The correlation coefficient assisted screening(CCAS)method is proposed to filter low-alloyed target alloys.A new Mg-2.2Mn-0.4Zn-0.2Al-0.2Ca(MZAX2000,wt%)alloy is designed and extruded into bar at given processing parameters,achieving room-temperature strength-ductility synergy showing an excellent UTS of 395 MPa and a high EL of 17.9%.This is closely related to its hetero-structured characteristic in the as-extruded MZAX2000 alloy consisting of coarse grains(16%),fine grains(75%),and fiber regions(9%).Therefore,this work offers new insights into optimizing alloy compositions and processing parameters for attaining new high strong and ductile low-alloyed Mg alloys.
