BACKGROUND Severe esophagogastric varices(EGVs)significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage.Endoscopy is the gold standard for EGV detection but it is ...BACKGROUND Severe esophagogastric varices(EGVs)significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage.Endoscopy is the gold standard for EGV detection but it is invasive,costly and carries risks.Noninvasive predictive models using ultrasound and serological markers are essential for identifying high-risk patients and optimizing endoscopy utilization.Machine learning(ML)offers a powerful approach to analyze complex clinical data and improve predictive accuracy.This study hypothesized that ML models,utilizing noninvasive ultrasound and serological markers,can accurately predict the risk of EGVs in hepatitis B patients,thereby improving clinical decisionmaking.AIM To construct and validate a noninvasive predictive model using ML for EGVs in hepatitis B patients.METHODS We retrospectively collected ultrasound and serological data from 310 eligible cases,randomly dividing them into training(80%)and validation(20%)groups.Eleven ML algorithms were used to build predictive models.The performance of the models was evaluated using the area under the curve and decision curve analysis.The best-performing model was further analyzed using SHapley Additive exPlanation to interpret feature importance.RESULTS Among the 310 patients,124 were identified as high-risk for EGVs.The extreme gradient boosting model demonstrated the best performance,achieving an area under the curve of 0.96 in the validation set.The model also exhibited high sensitivity(78%),specificity(94%),positive predictive value(84%),negative predictive value(88%),F1 score(83%),and overall accuracy(86%).The top four predictive variables were albumin,prothrombin time,portal vein flow velocity and spleen stiffness.A web-based version of the model was developed for clinical use,providing real-time predictions for high-risk patients.CONCLUSION We identified an efficient noninvasive predictive model using extreme gradient boosting for EGVs among hepatitis B patients.The model,presented as a web application,has potential for screening high-risk EGV patients and can aid clinicians in optimizing the use of endoscopy.展开更多
This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging...This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.展开更多
BACKGROUND Diabetic foot ulcer(DFU)is a serious and destructive complication of diabetes,which has a high amputation rate and carries a huge social burden.Early detection of risk factors and intervention are essential...BACKGROUND Diabetic foot ulcer(DFU)is a serious and destructive complication of diabetes,which has a high amputation rate and carries a huge social burden.Early detection of risk factors and intervention are essential to reduce amputation rates.With the development of artificial intelligence technology,efficient interpretable predictive models can be generated in clinical practice to improve DFU care.AIM To develop and validate an interpretable model for predicting amputation risk in DFU patients.METHODS This retrospective study collected basic data from 599 patients with DFU in Beijing Shijitan Hospital between January 2015 and June 2024.The data set was randomly divided into a training set and test set with fivefold cross-validation.Three binary variable models were built with the eXtreme Gradient Boosting(XGBoost)algorithm to input risk factors that predict amputation probability.The model performance was optimized by adjusting the super parameters.The pre-dictive performance of the three models was expressed by sensitivity,specificity,positive predictive value,negative predictive value and area under the curve(AUC).Visualization of the prediction results was realized through SHapley Additive exPlanation(SHAP).RESULTS A total of 157(26.2%)patients underwent minor amputation during hospitalization and 50(8.3%)had major amputation.All three XGBoost models demonstrated good discriminative ability,with AUC values>0.7.The model for predicting major amputation achieved the highest performance[AUC=0.977,95%confidence interval(CI):0.956-0.998],followed by the minor amputation model(AUC=0.800,95%CI:0.762-0.838)and the non-amputation model(AUC=0.772,95%CI:0.730-0.814).Feature importance ranking of the three models revealed the risk factors for minor and major amputation.Wagner grade 4/5,osteomyelitis,and high C-reactive protein were all considered important predictive variables.CONCLUSION XGBoost effectively predicts diabetic foot amputation risk and provides interpretable insights to support person-alized treatment decisions.展开更多
To get large dissymmetric factor(g_(lum))of organic circularly polarized luminescence(CPL)materials is still a great challenge.Although helical chirality and planar chirality are usual efficient access to enhancement ...To get large dissymmetric factor(g_(lum))of organic circularly polarized luminescence(CPL)materials is still a great challenge.Although helical chirality and planar chirality are usual efficient access to enhancement of CPL,they are not combined together to boost CPL.Here,a new tetraphenylethylene(TPE)tetracycle acid helicate bearing both helical chirality and planar chirality was designed and synthesized.Uniquely,synergy of the helical chirality and planar chirality was used to boost CPL signals both in solution and in helical self-assemblies.In the presence of octadecylamine,the TPE helicate could form helical nanofibers that emitted strong CPL signals with an absolute g_(lum)value up to 0.237.Exceptionally,followed by addition of para-phenylenediamine,the g_(lum)value was successively increased to 0.387 due to formation of bigger helical nanofibers.Compared with that of TPE helicate itself,the CPL signal of the self-assemblies was not only magnified by 104-fold but also inversed,which was very rare result for CPL-active materials.Surprisingly,the interaction of TPE helicate with xylylenediamine even gave a gel,which was transformed into suspension by shaking.Unexpectedly,the suspension showed 40-fold stronger CPL signals than the gel with signal direction inversion each other.Using synergy of the helical chirality and planar chirality to significantly boost CPL intensity provides a new strategy in preparation of organic CPL materials having very large g_(lum)value.展开更多
In a video that has mesmerized audiences worldwide,a humanoid robot displays a magical move of self-defense,executing a flawless 720-degree spinning kick to knock out a baton held in a human hand.This is Chinese compa...In a video that has mesmerized audiences worldwide,a humanoid robot displays a magical move of self-defense,executing a flawless 720-degree spinning kick to knock out a baton held in a human hand.This is Chinese company Unitree Robotics’G1 robot,embodying the innovation that has propelled China forward as the world’s second largest economy.展开更多
The methods of network attacks have become increasingly sophisticated,rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively.In recent years,artificial int...The methods of network attacks have become increasingly sophisticated,rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively.In recent years,artificial intelligence has achieved significant progress in the field of network security.However,many challenges and issues remain,particularly regarding the interpretability of deep learning and ensemble learning algorithms.To address the challenge of enhancing the interpretability of network attack prediction models,this paper proposes a method that combines Light Gradient Boosting Machine(LGBM)and SHapley Additive exPlanations(SHAP).LGBM is employed to model anomalous fluctuations in various network indicators,enabling the rapid and accurate identification and prediction of potential network attack types,thereby facilitating the implementation of timely defense measures,the model achieved an accuracy of 0.977,precision of 0.985,recall of 0.975,and an F1 score of 0.979,demonstrating better performance compared to other models in the domain of network attack prediction.SHAP is utilized to analyze the black-box decision-making process of the model,providing interpretability by quantifying the contribution of each feature to the prediction results and elucidating the relationships between features.The experimental results demonstrate that the network attack predictionmodel based on LGBM exhibits superior accuracy and outstanding predictive capabilities.Moreover,the SHAP-based interpretability analysis significantly improves the model’s transparency and interpretability.展开更多
The first 2^(+)excited states of the nucleus directly reflect the interaction between the shell structure and the nucleus,providing insights into the validity of the shell model and nuclear structure characteristics.A...The first 2^(+)excited states of the nucleus directly reflect the interaction between the shell structure and the nucleus,providing insights into the validity of the shell model and nuclear structure characteristics.Although the features of the first 2^(+)excited states can be measured for stable nuclei and calculated using nuclear models,significant uncertainty remains.This study employs a machine learning model based on a light gradient boosting machine(LightGBM)to investigate the first 2^(+)excited states.Specifically,the training of the LightGBM algorithm and the prediction of the first 2^(+)properties of 642 nuclei are presented.Furthermore,detailed comparisons of the LightGBM predictions were performed with available experimental data,shell model calculations,and Bayesian neural network predictions.The results revealed that the average difference between the LightGBM predictions and the experimental data was 18 times smaller than that obtained by the shell model and only 70%of the BNN prediction results.Considering Mg,Ca,Kr,Sm,and Pb isotopes as examples,it was also observed that LightGBM can effectively reproduce the magic number mutation caused by shell effects,with the energy being as low as 0.04 MeV due to shape coexistence.Therefore,we believe that leveraging LightGBM-based machine learning can profoundly enhance our insights into nuclear structures and provide new avenues for nuclear physics research.展开更多
To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the accele...To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.展开更多
Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for st...Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.展开更多
Glial cells have often been referred to as the support cells of the brain.While they do have numerous supportive functions,there is emerging research showing they play an active role in shaping the brain and behaviour...Glial cells have often been referred to as the support cells of the brain.While they do have numerous supportive functions,there is emerging research showing they play an active role in shaping the brain and behaviour.Studying the cellular and molecular crosstalk between brain cell types is immensely valuable as this research topic continues to demonstrate that many brain functions are a result of a system of cells working together,rather than any cell type independently.展开更多
Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challen...Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM model.To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average rainfall.The XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM model.Furthermore,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM model.The boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer precision.Additionally,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer performance.The HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test set.The model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study area.This study offers a scientific reference for LSM and disaster prevention research.This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District.It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models.However,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.展开更多
Conical picks are important tools for rock mechanical excavation.Mean cutting force(MCF)of conical pick determines the suitability of the target rock for mechanical excavation.Accurate evaluation of MCF is important f...Conical picks are important tools for rock mechanical excavation.Mean cutting force(MCF)of conical pick determines the suitability of the target rock for mechanical excavation.Accurate evaluation of MCF is important for pick design and rock cutting.This study proposed hybrid methods composed of boosting trees and Bayesian optimization(BO)for accurate evaluation of MCF.220 datasets including uniaxial compression strength,tensile strength,tip angle(θ),attack angle,and cutting depth,were collected.Four boosting trees were developed based on the database to predict MCF.BO optimized the hyper-parameters of these boosting trees.Model evaluation suggested that the proposed hybrid models outperformed many commonly utilized machine learning models.The hybrid model composed of BO and categorical boosting(BO-CatBoost)was the best.Its outstanding performance was attributed to its advantages in dealing with categorical features(θincluded 6 types of angles and could be considered as categorical features).A graphical user interface was developed to facilitate the application of BO-CatBoost for the estimation of MCF.Moreover,the influences of the input parameters on the model and their relationship with MCF were analyzed.Whenθincreased from 80°to 90°,it had a significant contribution to the increase of MCF.展开更多
BACKGROUND Development of distant metastasis(DM)is a major concern during treatment of nasopharyngeal carcinoma(NPC).However,studies have demonstrated im-proved distant control and survival in patients with advanced N...BACKGROUND Development of distant metastasis(DM)is a major concern during treatment of nasopharyngeal carcinoma(NPC).However,studies have demonstrated im-proved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy.Therefore,precise prediction of metastasis in patients with NPC is crucial.AIM To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging(MRI)reports.METHODS This retrospective study included 792 patients with non-distant metastatic NPC.A total of 469 imaging variables were obtained from detailed MRI reports.Data were stratified and randomly split into training(50%)and testing sets.Gradient boosting tree(GBT)models were built and used to select variables for predicting DM.A full model comprising all variables and a reduced model with the top-five variables were built.Model performance was assessed by area under the curve(AUC).RESULTS Among the 792 patients,94 developed DM during follow-up.The number of metastatic cervical nodes(30.9%),tumor invasion in the posterior half of the nasal cavity(9.7%),two sides of the pharyngeal recess(6.2%),tubal torus(3.3%),and single side of the parapharyngeal space(2.7%)were the top-five contributors for predicting DM,based on their relative importance in GBT models.The testing AUC of the full model was 0.75(95%confidence interval[CI]:0.69-0.82).The testing AUC of the reduced model was 0.75(95%CI:0.68-0.82).For the whole dataset,the full(AUC=0.76,95%CI:0.72-0.82)and reduced models(AUC=0.76,95%CI:0.71-0.81)outperformed the tumor node-staging system(AUC=0.67,95%CI:0.61-0.73).CONCLUSION The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC.The number of metastatic cervical nodes was identified as the principal contributing variable.展开更多
针对代价敏感学习问题,研究boosting算法的代价敏感扩展。提出一种基于代价敏感采样的代价敏感boosting学习方法,通过在原始boosting每轮迭代中引入代价敏感采样,最小化代价敏感损失期望。基于上述学习框架,推导出两种代价敏感boosting...针对代价敏感学习问题,研究boosting算法的代价敏感扩展。提出一种基于代价敏感采样的代价敏感boosting学习方法,通过在原始boosting每轮迭代中引入代价敏感采样,最小化代价敏感损失期望。基于上述学习框架,推导出两种代价敏感boosting算法,同时,揭示并解释已有算法的不稳定本质。在加州大学欧文分校(University of California,Irvine,UCI)数据集和麻省理工学院生物和计算学习中心(Center for Biological&Computational Learning,CBCL)人脸数据集上的实验结果表明,对于代价敏感分类问题,代价敏感采样boosting算法优于原始boosting和已有代价敏感boosting算法。展开更多
Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. ...Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R<sup>2</sup> values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.展开更多
基金Supported by the Agency Natural Science Foundation of Fujian Province,China,No.2022J011285 and No.2023J011480.
文摘BACKGROUND Severe esophagogastric varices(EGVs)significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage.Endoscopy is the gold standard for EGV detection but it is invasive,costly and carries risks.Noninvasive predictive models using ultrasound and serological markers are essential for identifying high-risk patients and optimizing endoscopy utilization.Machine learning(ML)offers a powerful approach to analyze complex clinical data and improve predictive accuracy.This study hypothesized that ML models,utilizing noninvasive ultrasound and serological markers,can accurately predict the risk of EGVs in hepatitis B patients,thereby improving clinical decisionmaking.AIM To construct and validate a noninvasive predictive model using ML for EGVs in hepatitis B patients.METHODS We retrospectively collected ultrasound and serological data from 310 eligible cases,randomly dividing them into training(80%)and validation(20%)groups.Eleven ML algorithms were used to build predictive models.The performance of the models was evaluated using the area under the curve and decision curve analysis.The best-performing model was further analyzed using SHapley Additive exPlanation to interpret feature importance.RESULTS Among the 310 patients,124 were identified as high-risk for EGVs.The extreme gradient boosting model demonstrated the best performance,achieving an area under the curve of 0.96 in the validation set.The model also exhibited high sensitivity(78%),specificity(94%),positive predictive value(84%),negative predictive value(88%),F1 score(83%),and overall accuracy(86%).The top four predictive variables were albumin,prothrombin time,portal vein flow velocity and spleen stiffness.A web-based version of the model was developed for clinical use,providing real-time predictions for high-risk patients.CONCLUSION We identified an efficient noninvasive predictive model using extreme gradient boosting for EGVs among hepatitis B patients.The model,presented as a web application,has potential for screening high-risk EGV patients and can aid clinicians in optimizing the use of endoscopy.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00222536).
文摘This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.
文摘BACKGROUND Diabetic foot ulcer(DFU)is a serious and destructive complication of diabetes,which has a high amputation rate and carries a huge social burden.Early detection of risk factors and intervention are essential to reduce amputation rates.With the development of artificial intelligence technology,efficient interpretable predictive models can be generated in clinical practice to improve DFU care.AIM To develop and validate an interpretable model for predicting amputation risk in DFU patients.METHODS This retrospective study collected basic data from 599 patients with DFU in Beijing Shijitan Hospital between January 2015 and June 2024.The data set was randomly divided into a training set and test set with fivefold cross-validation.Three binary variable models were built with the eXtreme Gradient Boosting(XGBoost)algorithm to input risk factors that predict amputation probability.The model performance was optimized by adjusting the super parameters.The pre-dictive performance of the three models was expressed by sensitivity,specificity,positive predictive value,negative predictive value and area under the curve(AUC).Visualization of the prediction results was realized through SHapley Additive exPlanation(SHAP).RESULTS A total of 157(26.2%)patients underwent minor amputation during hospitalization and 50(8.3%)had major amputation.All three XGBoost models demonstrated good discriminative ability,with AUC values>0.7.The model for predicting major amputation achieved the highest performance[AUC=0.977,95%confidence interval(CI):0.956-0.998],followed by the minor amputation model(AUC=0.800,95%CI:0.762-0.838)and the non-amputation model(AUC=0.772,95%CI:0.730-0.814).Feature importance ranking of the three models revealed the risk factors for minor and major amputation.Wagner grade 4/5,osteomyelitis,and high C-reactive protein were all considered important predictive variables.CONCLUSION XGBoost effectively predicts diabetic foot amputation risk and provides interpretable insights to support person-alized treatment decisions.
基金National Natural Science Foundation of China(Nos.22072050,22372066 and 22301090)the Open Research Fund(No.2024JYBKF05)of Key Laboratory of Material Chemistry for Energy Conversion and Storage(HUST)Ministry of Educationthe China Postdoctoral Science Foundation(No.2023M731189)for financial support,and thank the Analytical and Testing Centre at Huazhong University of Science and Technology for measurement.
文摘To get large dissymmetric factor(g_(lum))of organic circularly polarized luminescence(CPL)materials is still a great challenge.Although helical chirality and planar chirality are usual efficient access to enhancement of CPL,they are not combined together to boost CPL.Here,a new tetraphenylethylene(TPE)tetracycle acid helicate bearing both helical chirality and planar chirality was designed and synthesized.Uniquely,synergy of the helical chirality and planar chirality was used to boost CPL signals both in solution and in helical self-assemblies.In the presence of octadecylamine,the TPE helicate could form helical nanofibers that emitted strong CPL signals with an absolute g_(lum)value up to 0.237.Exceptionally,followed by addition of para-phenylenediamine,the g_(lum)value was successively increased to 0.387 due to formation of bigger helical nanofibers.Compared with that of TPE helicate itself,the CPL signal of the self-assemblies was not only magnified by 104-fold but also inversed,which was very rare result for CPL-active materials.Surprisingly,the interaction of TPE helicate with xylylenediamine even gave a gel,which was transformed into suspension by shaking.Unexpectedly,the suspension showed 40-fold stronger CPL signals than the gel with signal direction inversion each other.Using synergy of the helical chirality and planar chirality to significantly boost CPL intensity provides a new strategy in preparation of organic CPL materials having very large g_(lum)value.
文摘In a video that has mesmerized audiences worldwide,a humanoid robot displays a magical move of self-defense,executing a flawless 720-degree spinning kick to knock out a baton held in a human hand.This is Chinese company Unitree Robotics’G1 robot,embodying the innovation that has propelled China forward as the world’s second largest economy.
基金supported by the National Natural Science Foundation of China Project(No.62302540)please visit their website at https://www.nsfc.gov.cn/(accessed on 18 June 2024).
文摘The methods of network attacks have become increasingly sophisticated,rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively.In recent years,artificial intelligence has achieved significant progress in the field of network security.However,many challenges and issues remain,particularly regarding the interpretability of deep learning and ensemble learning algorithms.To address the challenge of enhancing the interpretability of network attack prediction models,this paper proposes a method that combines Light Gradient Boosting Machine(LGBM)and SHapley Additive exPlanations(SHAP).LGBM is employed to model anomalous fluctuations in various network indicators,enabling the rapid and accurate identification and prediction of potential network attack types,thereby facilitating the implementation of timely defense measures,the model achieved an accuracy of 0.977,precision of 0.985,recall of 0.975,and an F1 score of 0.979,demonstrating better performance compared to other models in the domain of network attack prediction.SHAP is utilized to analyze the black-box decision-making process of the model,providing interpretability by quantifying the contribution of each feature to the prediction results and elucidating the relationships between features.The experimental results demonstrate that the network attack predictionmodel based on LGBM exhibits superior accuracy and outstanding predictive capabilities.Moreover,the SHAP-based interpretability analysis significantly improves the model’s transparency and interpretability.
基金supported by the National Key R&D Program of China (No. 2022YFA1603300)the Romanian Ministry of Research,Innovation and Digitalization under Contract PN 23.21.01.06+1 种基金The ELI-RO project with Contract ELI-RORDI-2024-008 (AMAP)a grant from the Romanian Ministry of Research,Innovation and Digitization,CNCS-UEFIS-CDI,with project numbers PN-Ⅲ-P4-PCE-2021-1014, PN-Ⅲ-P4-PCE-2021-0595, and PN-Ⅲ-P1-1.1-TE2021-1464 within PNCDI Ⅲ
文摘The first 2^(+)excited states of the nucleus directly reflect the interaction between the shell structure and the nucleus,providing insights into the validity of the shell model and nuclear structure characteristics.Although the features of the first 2^(+)excited states can be measured for stable nuclei and calculated using nuclear models,significant uncertainty remains.This study employs a machine learning model based on a light gradient boosting machine(LightGBM)to investigate the first 2^(+)excited states.Specifically,the training of the LightGBM algorithm and the prediction of the first 2^(+)properties of 642 nuclei are presented.Furthermore,detailed comparisons of the LightGBM predictions were performed with available experimental data,shell model calculations,and Bayesian neural network predictions.The results revealed that the average difference between the LightGBM predictions and the experimental data was 18 times smaller than that obtained by the shell model and only 70%of the BNN prediction results.Considering Mg,Ca,Kr,Sm,and Pb isotopes as examples,it was also observed that LightGBM can effectively reproduce the magic number mutation caused by shell effects,with the energy being as low as 0.04 MeV due to shape coexistence.Therefore,we believe that leveraging LightGBM-based machine learning can profoundly enhance our insights into nuclear structures and provide new avenues for nuclear physics research.
基金The National Natural Science Foundation of China(No.52361165658,52378318,52078459).
文摘To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.
基金supported by the National Nat-ural Science Foundation of China(No.52203376)the National Key Research and Development Program of China(No.2023YFB3813200).
文摘Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.
基金supported by Canadian Institutes of Health Research (CIHR)grants awarded to MET.
文摘Glial cells have often been referred to as the support cells of the brain.While they do have numerous supportive functions,there is emerging research showing they play an active role in shaping the brain and behaviour.Studying the cellular and molecular crosstalk between brain cell types is immensely valuable as this research topic continues to demonstrate that many brain functions are a result of a system of cells working together,rather than any cell type independently.
基金funded by the Natural Science Foundation of Chongqing(Grants No.CSTB2022NSCQ-MSX0594)the Humanities and Social Sciences Research Project of the Ministry of Education(Grants No.16YJCZH061).
文摘Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM model.To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average rainfall.The XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM model.Furthermore,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM model.The boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer precision.Additionally,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer performance.The HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test set.The model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study area.This study offers a scientific reference for LSM and disaster prevention research.This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District.It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models.However,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.
基金Project(52374153)supported by the National Natural Science Foundation of ChinaProject(2023zzts0726)supported by the Fundamental Research Funds for the Central Universities of Central South University,China。
文摘Conical picks are important tools for rock mechanical excavation.Mean cutting force(MCF)of conical pick determines the suitability of the target rock for mechanical excavation.Accurate evaluation of MCF is important for pick design and rock cutting.This study proposed hybrid methods composed of boosting trees and Bayesian optimization(BO)for accurate evaluation of MCF.220 datasets including uniaxial compression strength,tensile strength,tip angle(θ),attack angle,and cutting depth,were collected.Four boosting trees were developed based on the database to predict MCF.BO optimized the hyper-parameters of these boosting trees.Model evaluation suggested that the proposed hybrid models outperformed many commonly utilized machine learning models.The hybrid model composed of BO and categorical boosting(BO-CatBoost)was the best.Its outstanding performance was attributed to its advantages in dealing with categorical features(θincluded 6 types of angles and could be considered as categorical features).A graphical user interface was developed to facilitate the application of BO-CatBoost for the estimation of MCF.Moreover,the influences of the input parameters on the model and their relationship with MCF were analyzed.Whenθincreased from 80°to 90°,it had a significant contribution to the increase of MCF.
文摘BACKGROUND Development of distant metastasis(DM)is a major concern during treatment of nasopharyngeal carcinoma(NPC).However,studies have demonstrated im-proved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy.Therefore,precise prediction of metastasis in patients with NPC is crucial.AIM To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging(MRI)reports.METHODS This retrospective study included 792 patients with non-distant metastatic NPC.A total of 469 imaging variables were obtained from detailed MRI reports.Data were stratified and randomly split into training(50%)and testing sets.Gradient boosting tree(GBT)models were built and used to select variables for predicting DM.A full model comprising all variables and a reduced model with the top-five variables were built.Model performance was assessed by area under the curve(AUC).RESULTS Among the 792 patients,94 developed DM during follow-up.The number of metastatic cervical nodes(30.9%),tumor invasion in the posterior half of the nasal cavity(9.7%),two sides of the pharyngeal recess(6.2%),tubal torus(3.3%),and single side of the parapharyngeal space(2.7%)were the top-five contributors for predicting DM,based on their relative importance in GBT models.The testing AUC of the full model was 0.75(95%confidence interval[CI]:0.69-0.82).The testing AUC of the reduced model was 0.75(95%CI:0.68-0.82).For the whole dataset,the full(AUC=0.76,95%CI:0.72-0.82)and reduced models(AUC=0.76,95%CI:0.71-0.81)outperformed the tumor node-staging system(AUC=0.67,95%CI:0.61-0.73).CONCLUSION The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC.The number of metastatic cervical nodes was identified as the principal contributing variable.
文摘针对代价敏感学习问题,研究boosting算法的代价敏感扩展。提出一种基于代价敏感采样的代价敏感boosting学习方法,通过在原始boosting每轮迭代中引入代价敏感采样,最小化代价敏感损失期望。基于上述学习框架,推导出两种代价敏感boosting算法,同时,揭示并解释已有算法的不稳定本质。在加州大学欧文分校(University of California,Irvine,UCI)数据集和麻省理工学院生物和计算学习中心(Center for Biological&Computational Learning,CBCL)人脸数据集上的实验结果表明,对于代价敏感分类问题,代价敏感采样boosting算法优于原始boosting和已有代价敏感boosting算法。
文摘Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R<sup>2</sup> values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.
