Developing machine learning frameworks with predictive power,interpretability,and transferability is crucial,yet it faces challenges in the field of electrocatalysis.To achieve this,we employed rigorous feature engine...Developing machine learning frameworks with predictive power,interpretability,and transferability is crucial,yet it faces challenges in the field of electrocatalysis.To achieve this,we employed rigorous feature engineering to establish a finely tuned gradient boosting regressor(GBR)model,which adeptly captures the physical complexity from feature space to target variables.We demonstrated that environmental electron effects and atomic number significantly govern the success of the mapping process via global and local explanations.The finely tuned GBR model exhibits exceptional robustness in predicting CO adsorption energies(R_(ave)^(2)=0.937,RMSE=0.153 eV).Moreover,the model demonstrated remarkable transfer learning ability,showing excellent predictive power for OH,NO,and N_(2) adsorption.Importantly,the GBR model exhibits exceptional predictive capability across an extensive search space,thereby demonstrating profound adaptability and versatility.Our research framework significantly enhances the interpretability and transferability of machine learning in electrocatalysis,offering vital insights for further advancements.展开更多
The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectrosc...The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.展开更多
Soil desiccation cracking is ubiquitous in nature and has significantpotential impacts on the engineering geological properties of soils.Previous studies have extensively examined various factors affecting soil cracki...Soil desiccation cracking is ubiquitous in nature and has significantpotential impacts on the engineering geological properties of soils.Previous studies have extensively examined various factors affecting soil cracking behavior through a numerous small-sample experiments.However,experimental studies alone cannot accurately describe soil cracking behavior.In this study,we firstly propose a modeling framework for predicting the surface crack ratio of soil desiccation cracking based on machine learning and interpretable analysis.The framework utilizes 1040 sets of soil cracking experimental data and employs random forest(RF),extreme gradient boosting(XGBoost),and artificialneural network(ANN)models to predict the surface crack ratio of soil desiccation cracking.To clarify the influenceof input features on soil cracking behavior,feature importance and Shapley additive explanations(SHAP)are applied for interpretability analysis.The results reveal that ensemble methods(RF and XGBoost)provide better predictive performance than the deep learning model(ANN).The feature importance analysis shows that soil desiccation cracking is primarily influencedby initial water content,plasticity index,finalwater content,liquid limit,sand content,clay content and thickness.Moreover,SHAP-based interpretability analysis further explores how soil cracking responds to various input variables.This study provides new insight into the evolution of soil cracking behavior,enhancing the understanding of its physical mechanisms and facilitating the assessment of potential regional development of soil desiccation cracking.展开更多
Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular puls...Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse rates.Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection difficult.In this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable insights.Compared to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in healthcare.The framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular dataset.Additionally,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable information.Our experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,respectively.The innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing approaches.This framework paves the way for more explainable and transparent decision-making in healthcare,ensuring that the model is not only accurate but also trustworthy and actionable for clinicians.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.展开更多
Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of po...Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.展开更多
Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only f...Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.展开更多
With the development of information technology,radio communication technology has made rapid progress.Many radio signals that have appeared in space are difficult to classify without manually labeling.Unsupervised rad...With the development of information technology,radio communication technology has made rapid progress.Many radio signals that have appeared in space are difficult to classify without manually labeling.Unsupervised radio signal clustering methods have recently become an urgent need for this situation.Meanwhile,the high complexity of deep learning makes it difficult to understand the decision results of the clustering models,making it essential to conduct interpretable analysis.This paper proposed a combined loss function for unsupervised clustering based on autoencoder.The combined loss function includes reconstruction loss and deep clustering loss.Deep clustering loss is added based on reconstruction loss,which makes similar deep features converge more in feature space.In addition,a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency Map.Extensive experiments have been conducted on a modulated signal dataset,and the results indicate the superior performance of our proposed method over other clustering algorithms.In particular,for the simulated dataset containing six modulation modes,when the SNR is 20dB,the clustering accuracy of the proposed method is greater than 78%.The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.展开更多
Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are desig...Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clinically interpretable and highly accurate.Methods We collected medical records from 261 patients diagnosed with HBV-ACLF,including three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaundice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly analyzed.Furthermore,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublingual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accuracy on clinical data characterized by small sample sizes and a class imbalance.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
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
Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
Deep Learning(DL)model has been widely used in the field of Synthetic Aperture Radar Automatic Target Recognition(SAR-ATR)and has achieved excellent performance.However,the black-box nature of DL models has been the f...Deep Learning(DL)model has been widely used in the field of Synthetic Aperture Radar Automatic Target Recognition(SAR-ATR)and has achieved excellent performance.However,the black-box nature of DL models has been the focus of criticism,especially in the application of SARATR,which is closely associated with the national defense and security domain.To address these issues,a new interpretable recognition model Physics-Guided BagNet(PGBN)is proposed in this article.The model adopts an interpretable convolutional neural network framework and uses time–frequency analysis to extract physical scattering features in SAR images.Based on the physical scattering features,an unsupervised segmentation method is proposed to distinguish targets from the background in SAR images.On the basis of the segmentation result,a structure is designed,which constrains the model's spatial attention to focus more on the targets themselves rather than the background,thereby making the model's decision-making more in line with physical principles.In contrast to previous interpretable research methods,this model combines interpretable structure with physical interpretability,further reducing the model's risk of error recognition.Experiments on the MSTAR dataset verify that the PGBN model exhibits excellent interpretability and recognition performance,and comparative experiments with heatmaps indicate that the physical feature guidance module presented in this article can constrain the model to focus more on the target itself rather than the background.展开更多
Based on 1,003 articles about empirical research on interpreting teaching from 2002 to 2022 retrieved from China National Knowledge Internet,this paper extracts three main research methods,uncovering common problems i...Based on 1,003 articles about empirical research on interpreting teaching from 2002 to 2022 retrieved from China National Knowledge Internet,this paper extracts three main research methods,uncovering common problems in interpreting education and practical teaching suggestions:(1)Corpus-based researches collect numerous audios to study typical mistakes made by interpreting learners,particularly pause and self-repair,and suggest interpreting teaching improve learners’ability to use language chunks and encourage students to interpret smoothly;(2)Questionnaire surveys help understand requirements for professional interpreters and how interpreting teaching meets market demands;(3)Teaching experiments last for one to two semesters,addressing issues like outdated teaching materials and modes,and show how teaching materials and modes integrate modern technology.But empirical researches need to build new corpora,professional interpreters’corpora and address problems that haven’t been adequately discussed.This paper is helpful for improving interpreting education in China and other countries and for making clear tasks to be fulfilled in empirical research on interpreting education.展开更多
The“First Multidisciplinary Forum on COVID-19,”as one of the pivotal medical forums organized by China during the initial outbreak of the pandemic,garnered significant attention from numerous countries worldwide.Ens...The“First Multidisciplinary Forum on COVID-19,”as one of the pivotal medical forums organized by China during the initial outbreak of the pandemic,garnered significant attention from numerous countries worldwide.Ensuring the seamless execution of the forum’s translation necessitated exceptionally high standards for simultaneous interpreters.This paper,through the lens of the Translation as Adaptation and Selection theory within Eco-Translatology,conducts an analytical study of the live simultaneous interpretation at the First Multidisciplinary Forum on COVID-19.It examines the interpreters’adaptations and selections across multiple dimensions-namely,linguistic,cultural,and communicative-with the aim of elucidating the guiding role and recommendations that Eco-Translatology can offer to simultaneous interpretation.Furthermore,it seeks to provide insights that may enhance the quality of interpreters’oral translations.展开更多
The 2016–2022 monitoring data from three ecological buoys in the Wenzhou coastal region of Zhejiang Province and the dataset European Centre for Medium-Range Weather Forecasts were examined to clarify the elaborate r...The 2016–2022 monitoring data from three ecological buoys in the Wenzhou coastal region of Zhejiang Province and the dataset European Centre for Medium-Range Weather Forecasts were examined to clarify the elaborate relationship between variations in ecological parameters during spring algal bloom incidents and the associated changes in temperature and wind fields in this study.A long short-term memory recurrent neural network was employed,and a predictive model for spring algal bloom in this region was developed.This model integrated various inputs,including temperature,wind speed,and other pertinent variables,and chlorophyll concentration served as the primary output indicator.The model training used chlorophyll concentration data,which were supplemented by reanalysis and forecast temperature and wind field data.The model demonstrated proficiency in forecasting next-day chlorophyll concentrations and assessing the likelihood of spring algal bloom occurrences using a defined chlorophyll concentration threshold.The historical validation from 2016 to 2019 corroborated the model's accuracy with an 81.71%probability of correct prediction,which was further proven by its precise prediction of two spring algal bloom incidents in late April 2023 and early May 2023.An interpretable machine learning-based model for spring algal bloom prediction,displaying effective forecasting with limited data,was established through the detailed analysis of the spring algal bloom mechanism and the careful selection of input variables.The insights gained from this study offer valuable contributions to the development of early warning systems for spring algal bloom in the Wenzhou coastal area of Zhejiang Province.展开更多
The potential toxicity of ionic liquids(ILs)affects their applications;how to control the toxicity is one of the key issues in their applications.To understand its toxicity structure relationship and promote its green...The potential toxicity of ionic liquids(ILs)affects their applications;how to control the toxicity is one of the key issues in their applications.To understand its toxicity structure relationship and promote its greener application,six different machine learning algorithms,including Bagging,Adaptive Boosting(AdaBoost),Gradient Boosting(GBoost),Stacking,Voting and Categorical Boosting(CatBoost),are established to model the toxicity of ILs on four distinct datasets including Leukemia rat cell line IPC-81(IPC-81),Acetylcholinesterase(AChE),Escherichia coli(E.coli)and Vibrio fischeri.Molecular descriptors obtained from the simplified molecular input line entry system(SMILES)are used to characterize ILs.All models are assessed by the mean square error(MSE),root mean square error(RMSE),mean absolute error(MAE)and correlation coefficient(R^(2)).Additionally,an interpretation model based on SHapley Additive exPlanations(SHAP)is built to determine the positive and negative effects of each molecular feature on toxicity.With additional parameters and complexity,the Catboost model outperforms the other models,making it a more reliable model for ILs'toxicity prediction.The results of the model's interpretation indicate that the most significant positive features,SMR_VSA5,PEOE_VSA8,Kappa2,PEOE_VSA6,SMR_VSA5,PEOE_VSA6 and EState_VSA1,can increase the toxicity of ILs as their levels rise,while the most significant negative features,VSA_EState7,EState_VSA8,PEOE_VSA9 and FpDensityMorgan1,can decrease the toxicity as their levels rise.Also,an IL's toxicity will grow as its average molecular weight and number of pyridine rings increase,whereas its toxicity will decrease as its hydrogen bond acceptors increase.This finding offers a theoretical foundation for rapid screening and synthesis of environmentally-benign ILs.展开更多
The Husimi function(Q-function)of a quantum state is the distribution function of the density operator in the coherent state representation.It is widely used in theoretical research,such as in quantum optics.The Wehrl...The Husimi function(Q-function)of a quantum state is the distribution function of the density operator in the coherent state representation.It is widely used in theoretical research,such as in quantum optics.The Wehrl entropy is the Shannon entropy of the Husimi function,and is nonzero even for pure states.This entropy has been extensively studied in mathematical physics.Recent research also suggests a significant connection between the Wehrl entropy and manybody quantum entanglement in spin systems.We investigate the statistical interpretation of the Husimi function and the Wehrl entropy,taking the system of N spin-1/2 particles as an example.Due to the completeness of coherent states,the Husimi function and Wehrl entropy can be explained via the positive operator-valued measurement(POVM)theory,although the coherent states are not a set of orthonormal basis.Here,with the help of the Bayes’theorem,we provide an alternative probabilistic interpretation for the Husimi function and the Wehrl entropy.This interpretation is based on direct measurements of the system,and thus does not require the introduction of an ancillary system as in the POVM theory.Moreover,under this interpretation the classical correspondences of the Husimi function and the Wehrl entropy are just phase-space probability distribution function of N classical tops,and its associated entropy,respectively.Therefore,this explanation contributes to a better understanding of the relationship between the Husimi function,Wehrl entropy,and classical-quantum correspondence.The generalization of this statistical interpretation to continuous-variable systems is also discussed.展开更多
The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitori...The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitoring,complication observation and management,positioning and mobility away from the bed,and quality assurance.These Guidelines encompass all the phases of intravenous thrombolysis care for patients experiencing acute ischemic stroke.This article aims to elucidate the Guidelines by discussing their developmental background,the designation process,usage recommendations,and the interpretation of evolving perspectives,thereby providing valuable insights for clinical practice.展开更多
Low-temperature hydrogenation of silicon tetrachloride(STC)is an essential step in polysilicon production.The addition of CuCl to silicon powder is currently a commonly used catalytic method and the silicon powder act...Low-temperature hydrogenation of silicon tetrachloride(STC)is an essential step in polysilicon production.The addition of CuCl to silicon powder is currently a commonly used catalytic method and the silicon powder acts as both a reactant and a catalyst.However,the reaction mechanism and the structure-activity relationship of this process have not been fully elucidated.In this work,a comprehensive study of the reaction mechanism in the presence of Si and Cu_(3)Si was carried out using density functional theory(DFT)combined with experiments,respectively.The results indicated that the ratedetermining step(RDS)in the presence of Si is the phase transition of Si atom,meanwhile,the RDS in the presence of Cu_(3)Si is the TCS-generation process.The activation barrier of the latter is smaller,highlighting that the interaction of Si with the bulk phase is the pivotal factor influencing the catalytic activity.The feasibility of transition metal doping to facilitate this step was further investigated.The Si disengage energy(E_(d))was used as a quantitative parameter to assess the catalytic activity of the catalysts,and the optimal descriptor was determined through interpretable machine learning.It was demonstrated that d-band center and electron transfer play a crucial role in regulating the level of Ed.This work reveals the mechanism and structure-activity relationship for the low-temperature hydrogenation reaction of STC,and provides a basis for the rational design of catalysts.展开更多
基金supported by the Research Grants Council of Hong Kong(CityU 11305919 and 11308620)and NSFC/RGC Joint Research Scheme N_CityU104/19Hong Kong Research Grant Council Collaborative Research Fund:C1002-21G and C1017-22Gsupported by the Hong Kong Research Grant Council Collaborative Research Fund:C6021-19E.
文摘Developing machine learning frameworks with predictive power,interpretability,and transferability is crucial,yet it faces challenges in the field of electrocatalysis.To achieve this,we employed rigorous feature engineering to establish a finely tuned gradient boosting regressor(GBR)model,which adeptly captures the physical complexity from feature space to target variables.We demonstrated that environmental electron effects and atomic number significantly govern the success of the mapping process via global and local explanations.The finely tuned GBR model exhibits exceptional robustness in predicting CO adsorption energies(R_(ave)^(2)=0.937,RMSE=0.153 eV).Moreover,the model demonstrated remarkable transfer learning ability,showing excellent predictive power for OH,NO,and N_(2) adsorption.Importantly,the GBR model exhibits exceptional predictive capability across an extensive search space,thereby demonstrating profound adaptability and versatility.Our research framework significantly enhances the interpretability and transferability of machine learning in electrocatalysis,offering vital insights for further advancements.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korean government(MSIT)(Grant number:RS-2025-02316700,and RS-2025-00522430)the China Scholarship Council Program。
文摘The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.
基金supported by the National Key Research and Development Program of China(Grant Nos.2023YFC3707900 and 2024YFC3012700)the National Natural Science Foundation of China(Grant No.42230710).
文摘Soil desiccation cracking is ubiquitous in nature and has significantpotential impacts on the engineering geological properties of soils.Previous studies have extensively examined various factors affecting soil cracking behavior through a numerous small-sample experiments.However,experimental studies alone cannot accurately describe soil cracking behavior.In this study,we firstly propose a modeling framework for predicting the surface crack ratio of soil desiccation cracking based on machine learning and interpretable analysis.The framework utilizes 1040 sets of soil cracking experimental data and employs random forest(RF),extreme gradient boosting(XGBoost),and artificialneural network(ANN)models to predict the surface crack ratio of soil desiccation cracking.To clarify the influenceof input features on soil cracking behavior,feature importance and Shapley additive explanations(SHAP)are applied for interpretability analysis.The results reveal that ensemble methods(RF and XGBoost)provide better predictive performance than the deep learning model(ANN).The feature importance analysis shows that soil desiccation cracking is primarily influencedby initial water content,plasticity index,finalwater content,liquid limit,sand content,clay content and thickness.Moreover,SHAP-based interpretability analysis further explores how soil cracking responds to various input variables.This study provides new insight into the evolution of soil cracking behavior,enhancing the understanding of its physical mechanisms and facilitating the assessment of potential regional development of soil desiccation cracking.
基金funded by Researchers Supporting Project Number(RSPD2025R947),King Saud University,Riyadh,Saudi Arabia.
文摘Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse rates.Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection difficult.In this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable insights.Compared to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in healthcare.The framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular dataset.Additionally,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable information.Our experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,respectively.The innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing approaches.This framework paves the way for more explainable and transparent decision-making in healthcare,ensuring that the model is not only accurate but also trustworthy and actionable for clinicians.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
基金European Commission,Joint Research Center,Grant/Award Number:HUMAINTMinisterio de Ciencia e Innovación,Grant/Award Number:PID2020‐114924RB‐I00Comunidad de Madrid,Grant/Award Number:S2018/EMT‐4362 SEGVAUTO 4.0‐CM。
文摘Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.
文摘Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.
基金supported in part by the National Natural Science Foundation of China(No.62276206)the Key Research and Development Program of Shaanxi under Grant S2022-YF-YBGY-0921+2 种基金the State Key Program of National Natural Science of China(No.62231027)supported by the Science and Technology on Communication Information Security Control Laboratory。
文摘With the development of information technology,radio communication technology has made rapid progress.Many radio signals that have appeared in space are difficult to classify without manually labeling.Unsupervised radio signal clustering methods have recently become an urgent need for this situation.Meanwhile,the high complexity of deep learning makes it difficult to understand the decision results of the clustering models,making it essential to conduct interpretable analysis.This paper proposed a combined loss function for unsupervised clustering based on autoencoder.The combined loss function includes reconstruction loss and deep clustering loss.Deep clustering loss is added based on reconstruction loss,which makes similar deep features converge more in feature space.In addition,a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency Map.Extensive experiments have been conducted on a modulated signal dataset,and the results indicate the superior performance of our proposed method over other clustering algorithms.In particular,for the simulated dataset containing six modulation modes,when the SNR is 20dB,the clustering accuracy of the proposed method is greater than 78%.The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.
基金Key research project of Hunan Provincial Administration of Traditional Chinese Medicine(A2023048)Key Research Foundation of Education Bureau of Hunan Province,China(23A0273).
文摘Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clinically interpretable and highly accurate.Methods We collected medical records from 261 patients diagnosed with HBV-ACLF,including three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaundice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly analyzed.Furthermore,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublingual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accuracy on clinical data characterized by small sample sizes and a class imbalance.
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.
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
基金co-supported by the National Natural Science Foundation of China(No.62001507)the Youth Talent Lifting Project of the China Association for Science and Technology(No.2021-JCJQ-QT-018)+1 种基金the Program of the Youth Innovation Team of Shaanxi Universitiesthe Natural Science Basic Research Plan in Shaanxi Province of China(No.2023-JC-YB-491)。
文摘Deep Learning(DL)model has been widely used in the field of Synthetic Aperture Radar Automatic Target Recognition(SAR-ATR)and has achieved excellent performance.However,the black-box nature of DL models has been the focus of criticism,especially in the application of SARATR,which is closely associated with the national defense and security domain.To address these issues,a new interpretable recognition model Physics-Guided BagNet(PGBN)is proposed in this article.The model adopts an interpretable convolutional neural network framework and uses time–frequency analysis to extract physical scattering features in SAR images.Based on the physical scattering features,an unsupervised segmentation method is proposed to distinguish targets from the background in SAR images.On the basis of the segmentation result,a structure is designed,which constrains the model's spatial attention to focus more on the targets themselves rather than the background,thereby making the model's decision-making more in line with physical principles.In contrast to previous interpretable research methods,this model combines interpretable structure with physical interpretability,further reducing the model's risk of error recognition.Experiments on the MSTAR dataset verify that the PGBN model exhibits excellent interpretability and recognition performance,and comparative experiments with heatmaps indicate that the physical feature guidance module presented in this article can constrain the model to focus more on the target itself rather than the background.
基金USST Construction Project of English-taught Courses for International Students in 2024Key Course Construction Project in Universities of Shanghai in 2024USST Teaching Achievement Award(postgraduate)Cultivation Project in 2024。
文摘Based on 1,003 articles about empirical research on interpreting teaching from 2002 to 2022 retrieved from China National Knowledge Internet,this paper extracts three main research methods,uncovering common problems in interpreting education and practical teaching suggestions:(1)Corpus-based researches collect numerous audios to study typical mistakes made by interpreting learners,particularly pause and self-repair,and suggest interpreting teaching improve learners’ability to use language chunks and encourage students to interpret smoothly;(2)Questionnaire surveys help understand requirements for professional interpreters and how interpreting teaching meets market demands;(3)Teaching experiments last for one to two semesters,addressing issues like outdated teaching materials and modes,and show how teaching materials and modes integrate modern technology.But empirical researches need to build new corpora,professional interpreters’corpora and address problems that haven’t been adequately discussed.This paper is helpful for improving interpreting education in China and other countries and for making clear tasks to be fulfilled in empirical research on interpreting education.
文摘The“First Multidisciplinary Forum on COVID-19,”as one of the pivotal medical forums organized by China during the initial outbreak of the pandemic,garnered significant attention from numerous countries worldwide.Ensuring the seamless execution of the forum’s translation necessitated exceptionally high standards for simultaneous interpreters.This paper,through the lens of the Translation as Adaptation and Selection theory within Eco-Translatology,conducts an analytical study of the live simultaneous interpretation at the First Multidisciplinary Forum on COVID-19.It examines the interpreters’adaptations and selections across multiple dimensions-namely,linguistic,cultural,and communicative-with the aim of elucidating the guiding role and recommendations that Eco-Translatology can offer to simultaneous interpretation.Furthermore,it seeks to provide insights that may enhance the quality of interpreters’oral translations.
基金the Zhejiang Provincial Natural Science Foundation of China(No.LY21D 060003)the Project of State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Ocean-ography,MNR(No.SOEDZZ2103)+1 种基金the National Natural Science Foundation of China(No.42076216)the Open Research Fund of the Key Laboratory of Marine Ecological Monitoring and Restoration Technologies,MNR(No.MEMRT202210)。
文摘The 2016–2022 monitoring data from three ecological buoys in the Wenzhou coastal region of Zhejiang Province and the dataset European Centre for Medium-Range Weather Forecasts were examined to clarify the elaborate relationship between variations in ecological parameters during spring algal bloom incidents and the associated changes in temperature and wind fields in this study.A long short-term memory recurrent neural network was employed,and a predictive model for spring algal bloom in this region was developed.This model integrated various inputs,including temperature,wind speed,and other pertinent variables,and chlorophyll concentration served as the primary output indicator.The model training used chlorophyll concentration data,which were supplemented by reanalysis and forecast temperature and wind field data.The model demonstrated proficiency in forecasting next-day chlorophyll concentrations and assessing the likelihood of spring algal bloom occurrences using a defined chlorophyll concentration threshold.The historical validation from 2016 to 2019 corroborated the model's accuracy with an 81.71%probability of correct prediction,which was further proven by its precise prediction of two spring algal bloom incidents in late April 2023 and early May 2023.An interpretable machine learning-based model for spring algal bloom prediction,displaying effective forecasting with limited data,was established through the detailed analysis of the spring algal bloom mechanism and the careful selection of input variables.The insights gained from this study offer valuable contributions to the development of early warning systems for spring algal bloom in the Wenzhou coastal area of Zhejiang Province.
基金funded by Research Platforms and Projects for Higher Education Institutions of Department of Education of Guangdong Province in 2024(2024KTSCX256)2023 Guangdong Province Higher Vocational Education Teaching Quality and Teaching Reform Project(2023JG080).
文摘The potential toxicity of ionic liquids(ILs)affects their applications;how to control the toxicity is one of the key issues in their applications.To understand its toxicity structure relationship and promote its greener application,six different machine learning algorithms,including Bagging,Adaptive Boosting(AdaBoost),Gradient Boosting(GBoost),Stacking,Voting and Categorical Boosting(CatBoost),are established to model the toxicity of ILs on four distinct datasets including Leukemia rat cell line IPC-81(IPC-81),Acetylcholinesterase(AChE),Escherichia coli(E.coli)and Vibrio fischeri.Molecular descriptors obtained from the simplified molecular input line entry system(SMILES)are used to characterize ILs.All models are assessed by the mean square error(MSE),root mean square error(RMSE),mean absolute error(MAE)and correlation coefficient(R^(2)).Additionally,an interpretation model based on SHapley Additive exPlanations(SHAP)is built to determine the positive and negative effects of each molecular feature on toxicity.With additional parameters and complexity,the Catboost model outperforms the other models,making it a more reliable model for ILs'toxicity prediction.The results of the model's interpretation indicate that the most significant positive features,SMR_VSA5,PEOE_VSA8,Kappa2,PEOE_VSA6,SMR_VSA5,PEOE_VSA6 and EState_VSA1,can increase the toxicity of ILs as their levels rise,while the most significant negative features,VSA_EState7,EState_VSA8,PEOE_VSA9 and FpDensityMorgan1,can decrease the toxicity as their levels rise.Also,an IL's toxicity will grow as its average molecular weight and number of pyridine rings increase,whereas its toxicity will decrease as its hydrogen bond acceptors increase.This finding offers a theoretical foundation for rapid screening and synthesis of environmentally-benign ILs.
基金supported by the National Key Research and Development Program of China[Grant No.2022YFA1405300(PZ)]the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300700)。
文摘The Husimi function(Q-function)of a quantum state is the distribution function of the density operator in the coherent state representation.It is widely used in theoretical research,such as in quantum optics.The Wehrl entropy is the Shannon entropy of the Husimi function,and is nonzero even for pure states.This entropy has been extensively studied in mathematical physics.Recent research also suggests a significant connection between the Wehrl entropy and manybody quantum entanglement in spin systems.We investigate the statistical interpretation of the Husimi function and the Wehrl entropy,taking the system of N spin-1/2 particles as an example.Due to the completeness of coherent states,the Husimi function and Wehrl entropy can be explained via the positive operator-valued measurement(POVM)theory,although the coherent states are not a set of orthonormal basis.Here,with the help of the Bayes’theorem,we provide an alternative probabilistic interpretation for the Husimi function and the Wehrl entropy.This interpretation is based on direct measurements of the system,and thus does not require the introduction of an ancillary system as in the POVM theory.Moreover,under this interpretation the classical correspondences of the Husimi function and the Wehrl entropy are just phase-space probability distribution function of N classical tops,and its associated entropy,respectively.Therefore,this explanation contributes to a better understanding of the relationship between the Husimi function,Wehrl entropy,and classical-quantum correspondence.The generalization of this statistical interpretation to continuous-variable systems is also discussed.
文摘The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitoring,complication observation and management,positioning and mobility away from the bed,and quality assurance.These Guidelines encompass all the phases of intravenous thrombolysis care for patients experiencing acute ischemic stroke.This article aims to elucidate the Guidelines by discussing their developmental background,the designation process,usage recommendations,and the interpretation of evolving perspectives,thereby providing valuable insights for clinical practice.
基金supported by Hubei Three Gorges Laboratory Open Innovation Fund Project(SC231002)CFD Simulation to Explore the Mass and Heat Transfer Laws of Thermal Decomposition of Mixed Salt Organic Compounds Project(2021YFC 3201404).
文摘Low-temperature hydrogenation of silicon tetrachloride(STC)is an essential step in polysilicon production.The addition of CuCl to silicon powder is currently a commonly used catalytic method and the silicon powder acts as both a reactant and a catalyst.However,the reaction mechanism and the structure-activity relationship of this process have not been fully elucidated.In this work,a comprehensive study of the reaction mechanism in the presence of Si and Cu_(3)Si was carried out using density functional theory(DFT)combined with experiments,respectively.The results indicated that the ratedetermining step(RDS)in the presence of Si is the phase transition of Si atom,meanwhile,the RDS in the presence of Cu_(3)Si is the TCS-generation process.The activation barrier of the latter is smaller,highlighting that the interaction of Si with the bulk phase is the pivotal factor influencing the catalytic activity.The feasibility of transition metal doping to facilitate this step was further investigated.The Si disengage energy(E_(d))was used as a quantitative parameter to assess the catalytic activity of the catalysts,and the optimal descriptor was determined through interpretable machine learning.It was demonstrated that d-band center and electron transfer play a crucial role in regulating the level of Ed.This work reveals the mechanism and structure-activity relationship for the low-temperature hydrogenation reaction of STC,and provides a basis for the rational design of catalysts.
