The technology in modern society is very useful.China has a lot of new advanced technology.Let me introduce some to you.The first one is Artificial Intelligence(AI).China has many AI companies,such as Baidu,Alibaba an...The technology in modern society is very useful.China has a lot of new advanced technology.Let me introduce some to you.The first one is Artificial Intelligence(AI).China has many AI companies,such as Baidu,Alibaba and Tencent.They have made many things such as machine learning,natural language processing and computer vision.展开更多
Artificial intelligence(AI)is a sophisticated technology that investigates and formulates theories,methods,techniques,and application systems designed to emulate,expand,and enhance human intelligence[1].In recent year...Artificial intelligence(AI)is a sophisticated technology that investigates and formulates theories,methods,techniques,and application systems designed to emulate,expand,and enhance human intelligence[1].In recent years,the rapid advancement of key AI technologies,including image recognition,machine learning,neural networks and robotics,has significantly propelled multidisciplinary integration and development within the medical field[2].The considerable potential of AI in the field of medicine,as evidenced by its formidable data processing and analytical capabilities,has been demonstrated in a number of ways.展开更多
Artificial intelligence(AI) is almost everywhere due to the rapid development of modern technology and popularity of intelligent devices.While control theory and machine learning techniques as two enabling technologie...Artificial intelligence(AI) is almost everywhere due to the rapid development of modern technology and popularity of intelligent devices.While control theory and machine learning techniques as two enabling technologies have shown enormous power in their own right,a rapprochement of them is required to handle nonlinearity,uncertainty and scalability induced by high complexity of modern systems,huge quantity of real-time data,and large scale of agent networks.展开更多
Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation facto...Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation factors due to regional environmental variations.Then,a single machine learning model can easily become overfitting,thus reducing the accuracy and robustness of the evaluation model.This paper proposes a combined machine-learning model to address the issues.The landslide susceptibility in mountain roads were mapped by using factor analysis to normalize and reduce the dimensionality of the initial condition factor and generating six new combination factors as evaluation indexes.The mountain roads in the Youxi County,Fujian Province,China were used for the landslide susceptibility mapping.Three most frequently used machine learning techniques,support vector machine(SVM),random forest(RF),and artificial neural network(ANN)models,were used to model the landslide susceptibility of the study area and validate the accuracy of this evaluation index system.The global minimum variance portfolio was utilized to construct a machine learning combined model.5-fold cross-validation,statistical indexes,and AUC(Area Under Curve)values were implemented to evaluate the predictive accuracy of the landslide susceptibility model.The mean AUC values for the SVM,RF,and ANN models in the training stage were 89.2%,88.5%,and 87.9%,respectively,and 78.0%,73.7%,and 76.7%,respectively,in the validating stage.In the training and validation stages,the mean AUC values of the combined model were 92.4% and 87.1%,respectively.The combined model provides greater prediction accuracy and model robustness than one single model.展开更多
With the rapid development of computer techniques,atomistic modeling is playing an increasingly important role in understanding the structure-activity relationship of materials.Molecular dynamics (MD) is a computation...With the rapid development of computer techniques,atomistic modeling is playing an increasingly important role in understanding the structure-activity relationship of materials.Molecular dynamics (MD) is a computational simulation approach to predicting the structural evolution of an atomic system over time,widely used to understand physical and chemical phenomena including phase transition,diffusion,crystallization,and reaction [1].展开更多
This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by ear...This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent screening.Mammography has been the most utilized breast ima-ging technique to date.Radiologists have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)sets.Decision Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.展开更多
Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensio...Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensional features,data heterogeneity,and data noise due to the diversity of data dimensions,formats and noise of sensors,controllers and actuators.Hence,a novel unsupervised learn-ing autoencoder model is proposed for ICS data in this paper.Although traditional methods only capture the linear correlations of ICS features,our deep industrial representation learning model(DIRL)based on a convolutional neural network can mine high-order features,thus solving the problem of high-dimensional and heterogeneous ICS data.In addition,an unsupervised denoising autoencoder is introduced for noisy ICS data in DIRL.Training the denoising autoencoder allows the model to better mitigate the sensor noise problem.In this way,the represen-tative features learned by DIRL could help to evaluate the safety state of ICSs more effectively.We tested our model with absolute and relative accuracy experi-ments on two large-scale ICS datasets.Compared with other popular methods,DIRL showed advantages in four common indicators of AI algorithms:accuracy,precision,recall,and F1-score.This study contributes to the effective analysis of large-scale ICS data,which promotes the stable operation of ICSs.展开更多
The prediction of crop yield is one of the important factor and also challenging,to predict the future crop yield based on various criteria’s.Many advanced technologies are incorporated in the agricultural processes,...The prediction of crop yield is one of the important factor and also challenging,to predict the future crop yield based on various criteria’s.Many advanced technologies are incorporated in the agricultural processes,which enhances the crop yield production efficiency.The process of predicting the crop yield can be done by taking agriculture data,which helps to analyze and make important decisions before and during cultivation.This paper focuses on the prediction of crop yield,where two models of machine learning are developed for this work.One is Modified Convolutional Neural Network(MCNN),and the other model is TLBO(Teacher Learning Based Optimization)-a Genetic algorithm which reduces the input size of data.In this work,some spatial information used for analysis is the Normalized Difference Vegetation Index,Standard Precipitation Index and Vegetation Condition Index.TLBO finds some best feature value set in the data that represents the specific yield of the crop.So,these selected feature valued set is passed in the Error Back Propagation Neural Network for learning.Here,the training was done in such a way that all set of features were utilized in pair with their yield value as output.For increasing the reliability of the work whole experiment was done on a real dataset from Madhya Pradesh region of country India.The result shows that the proposed model has overcome various evaluation parameters on different scales as compared to previous approaches adopted by researchers.展开更多
We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system,wh...We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system,which is suitable for repeated measurements in mass screening.Sixty-three optical tomographic images were collected from women with dense breasts,and a dataset of 12602D gray scale images sliced from these 3D images was built.After image preprocessing and normalization,we tested the network on this dataset and obtained 0.80 specificity,0.95 sensitivity,90.2%accuracy,and 0.94 area under the receiver operating characteristic curve(AUC).Furthermore,a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset.The sensitivity,specificity,accuracy,and AUC of the classification on the augmented dataset were 0.88,0.96,93.3%,and 0.95,respectively.展开更多
"Artificial intelligence (AI) for Science"is,at an unprecedented speed and scale,shaping the paradigm of scientific research,accelerating innovation in technology and industry.Research on modern or contempor..."Artificial intelligence (AI) for Science"is,at an unprecedented speed and scale,shaping the paradigm of scientific research,accelerating innovation in technology and industry.Research on modern or contemporary mechanics,rooted in Galileo's"experiments-mathematics"paradigm,has embraced AI in recent years.Machine learning (ML) of data becomes crucial in the face of complex mechanical problems ranging from turbulence of fluids,failure of solids,to multiscale structural optimization.展开更多
The complex relationship between environmental exposure and human health constitutes a major global challenge requiring innovative solutions.Artificial Intelligence(AI)and Machine Learning(ML)show exceptional strength...The complex relationship between environmental exposure and human health constitutes a major global challenge requiring innovative solutions.Artificial Intelligence(AI)and Machine Learning(ML)show exceptional strength for data analysis and pattern recognition.Applying these technologies to environmental health provides new insights to improve and advance environmental exposure assessment,health risk assessment,and related policy development.It is with great pleasure that we present this Special Issue of Environment&Health on Machine Learning and Artificial Intelligence for Environmental Health.This collection of research highlights the latest advancements and broad potential of ML and AI to empower our response to pressing and future environmental health issues.展开更多
Glass formation is frequently observed in metallic alloys.Machine learning has been applied to discover new metallic glasses.However,the incomplete understanding of glass formation hinders descriptor selection and mat...Glass formation is frequently observed in metallic alloys.Machine learning has been applied to discover new metallic glasses.However,the incomplete understanding of glass formation hinders descriptor selection and material property representation.Here,we use X-ray diffraction spectra,the essential tool for identifying amorphous structure,as an intermediate link.By representing spectra as images,we train generative models to produce high-fidelity spectra for all alloys in multicomponent alloy systems.Training with spectra from a tiny fraction of the total alloys is sufficient for accurate spectra generation,enabling the identification of compositional regions with a high probability of glass formation.The shift from numerical to image-based representation unlocks the potential of machine learning in the design of glass-forming alloys.Furthermore,our approach is applicable to a wide range of materials and spectroscopic techniques.We anticipate that this strategy will accelerate materials discovery across previously unexplored compositional and processing spaces.展开更多
To explore the MAX phase with experimental value over a wider range,a data-driven machine learning(ML)model was trained to rapidly predict the stability of MAX phases via a random forest classifier(RFC),support vector...To explore the MAX phase with experimental value over a wider range,a data-driven machine learning(ML)model was trained to rapidly predict the stability of MAX phases via a random forest classifier(RFC),support vector machine(SVM),and gradient boosting tree(GBT),where the deemed significant descriptors were compiled from the literature and the stability of 1804 combinations of MAX phases was collected.Using this well-trained model,190 new MAX phases were screened from 4347 MAX phases,150 of which met the criteria for thermodynamic and intrinsic stability on the basis of first-principles calculations.Additionally,with the help of the ML model,the mean number of valence electrons and the valence electron deviation are the two most critical factors influencing stability.Additionally,one of these predicted MAX phases,Ti_(2)SnN,was experimentally synthesized through Lewis acid substitution reactions at 750℃,with interesting A-site deintercalation and self-extrusion.First-principles calculations revealed that Ti_(2)SnN has lower elastic properties,higher damage tolerance and fracture toughness,and a higher coefficient of thermal expansion(CTE).展开更多
Two-dimensional(2D)materials attract considerable attention due to their remarkable electronic,mechanical and optical properties.Despite their use in combination with substrates in practical applications,computational...Two-dimensional(2D)materials attract considerable attention due to their remarkable electronic,mechanical and optical properties.Despite their use in combination with substrates in practical applications,computational studies often neglect the effects of substrate interactions for simplicity.This study presents a novel method for predicting the atomic structure of 2D materials on substrates by combining an evolutionary algorithm,a lattice-matching technique,an automated machinelearning interatomic potentials training protocol,and the ab initio thermodynamics approach.Using the molybdenum-sulfur system on a sapphire substrate as a case study,we reveal several new stable and metastable structures,including previously known 1H-MoS_(2)and newly found Pmma Mo_(3)S_(2),P1^(-)Mo_(2)S,P2_(1)m Mo_(5)S_(3),and P_(4)mm Mo_(4)S,where the Mo_(4)S structure is specifically stabilized by interaction with the substrate.Finally,we use the ab initio thermodynamics approach to predict the synthesis conditions of the discovered structures in the parameter space of the commonly used chemical vapor deposition technique.展开更多
Convective cooling by wind is crucial for large-scale photovoltaic(PV)systems,as power generation inversely correlates with panel temperature.Therefore,accurately determining the convective heat transfer coefficient f...Convective cooling by wind is crucial for large-scale photovoltaic(PV)systems,as power generation inversely correlates with panel temperature.Therefore,accurately determining the convective heat transfer coefficient for PV arrays with various geometric configurations is essential to optimize array design.Traditional methods to quantify the effects of configuration utilize either Computational Fluid Dynamics(CFD)simulations or empirical methods.These approaches often face challenges due to high computational demands or limited accuracy,particularly with complex array configurations.Machine learning approaches,especially hybrid learning models,have emerged as effective tools to address challenges in heat transfer design optimization.This study introduces a method that combines Physics-Informed Machine Learning with a Deep Convolutional Neural Network(PIML-DCNN)to predict convective heat transfer rates with high accuracy and computational efficiency.Additionally,an innovative loss function,termed the“Pocket Loss”,is developed to enhance the interpretability and robustness of the PIML-DCNN model.The proposed model achieves relative estimation errors of 2.5%and 2.7%on the validation and test datasets,respectively,when benchmarked against comprehensive CFD simulations.These results highlight the potential of the proposed model to efficiently guide the configuration design of PV arrays,thereby enhancing power generation in real-world operations.展开更多
The integration of solar greenhouses into smart energy systems(SESs)remains largely unexplored,despite their potential to enhance energy sharing and hydrogen production.This review investigates the role of solar green...The integration of solar greenhouses into smart energy systems(SESs)remains largely unexplored,despite their potential to enhance energy sharing and hydrogen production.This review investigates the role of solar greenhouses as active energy contributors within SESs,emphasizing their biomass waste gasification for hydrogen production and their integration into district heating and cooling(DHC)networks.A structured classification of machine learning(ML)and deep learning(DL)techniques applied in forecasting and optimizing these processes is provided.Additionally,the evolution of DHC systems is analyzed,with a focus on fifth-generation DHC(5GDHC)networks,which facilitate bidirectional energy exchange at near-ambient temperatures.The review highlights that existing studies have predominantly addressed SES advancements and ML-driven energy management without considering the contributions of solar greenhouses.A novel framework is proposed,illustrating their role as prosumers capable of exchanging electricity,hydrogen,and thermal energy within SESs.Key findings reveal that integrating solar greenhouses with SESs can enhance energy efficiency,reduce carbon emissions,and improve system resilience.Furthermore,ML-driven predictive control strategies,particularly model predictive control(MPC),are identified as essential for optimizing real-time energy flows and biomass gasification processes.This study provides a foundation for future research on the technical,economic,and environmental feasibility of integrating greenhouses into SESs.The insights presented offer a pathway toward more sustainable,AI-driven energy-sharing networks,supporting policymakers and industry stakeholders in the transition toward low-carbon energy solutions.展开更多
Design patterns are often used in the development of object-oriented software. It offers reusable abstract information that is helpful in solving recurring design problems. Detecting design patterns is beneficial to t...Design patterns are often used in the development of object-oriented software. It offers reusable abstract information that is helpful in solving recurring design problems. Detecting design patterns is beneficial to the comprehension and maintenance of object-oriented software systems. Several pattern detection techniques based on static analysis often encounter problems when detecting design patterns for identical structures of patterns. In this study, we attempt to detect software design patterns by using software metrics and classification-based techniques. Our study is conducted in two phases： creation of metrics-oriented dataset and detection of software design patterns. The datasets are prepared by using software metrics for the learning of classifiers. Then, pattern detection is performed by using classification-based techniques. To evaluate the proposed method, experiments are conducted using three open source software programs, JHotDraw, QuickUML, and JUnit, and the results are analyzed.展开更多
Behavior-based malware analysis is an important technique for automatically analyzing and detecting malware, and it has received considerable attention from both academic and industrial communities. By considering how...Behavior-based malware analysis is an important technique for automatically analyzing and detecting malware, and it has received considerable attention from both academic and industrial communities. By considering how malware behaves, we can tackle the malware obfuscation problem, which cannot be processed by traditional static analysis approaches, and we can also derive the as-built behavior specifications and cover the entire behavior space of the malware samples. Although there have been several works focusing on malware behavior analysis, such research is far from mature, and no overviews have been put forward to date to investigate current developments and challenges. In this paper, we conduct a survey on malware behavior description and analysis considering three aspects： malware behavior description, behavior analysis methods, and visualization techniques. First, existing behavior data types and emerging techniques for malware behavior description are explored, especially the goals, prin- ciples, characteristics, and classifications of behavior analysis techniques proposed in the existing approaches. Second, the in- adequacies and challenges in malware behavior analysis are summarized from different perspectives. Finally, several possible directions are discussed for future research.展开更多
Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence.Organoids,three-dimensional miniature organ-like structures cultivated fro...Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence.Organoids,three-dimensional miniature organ-like structures cultivated from stem cells,offer an unparalleled opportunity to simulate complex human organ systems in vitro.Through the convergence of organoid technology and AI,researchers gain the means to accelerate discoveries and insights across various disciplines.Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors,intricate cellular interactions,and dynamic responses to stimuli.This synergy empowers the development of predictive models,precise disease simulations,and personalized medicine approaches,revolutionizing our understanding of human development,disease mechanisms,and therapeutic interventions.Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling,propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.展开更多
The most widely used method of identification of microbial morphology and structure is microscopy,but it can be difficult to distinguish between pathogens with a similar appearance.Existing fluorescent staining method...The most widely used method of identification of microbial morphology and structure is microscopy,but it can be difficult to distinguish between pathogens with a similar appearance.Existing fluorescent staining methods require a combination of a variety of fluorescent materials to meet this demand.In this study,unique concentration-dependent fluorescent carbon dots(CDs)were synthesized for the identification and quantification of pathogens.The emission wavelength of the CDs could be tuned spanning the full visible region by virtue of aggregation-induced narrowing of bandgaps.This tunable emission wavelength of the specific concentration response to diverse microbes can be used to distinguish microorganisms with a similar appearance,even in a same genus.A hyperspectral microscopy system was demonstrated to distinguish Aspergillus flavus and A.fumigatus based on the results above.The identification accuracy of the two similar-looking pathogens can be close to 100%,and the relative proportions and spatial distributions can also be profiled from the mixture of the pathogens.This technique can provide a solution to the fast detection of microorganisms and is potentially applicable to a wide range of problems in areas such as healthcare,food preparation,biotechnology,and health emergency.展开更多
文摘The technology in modern society is very useful.China has a lot of new advanced technology.Let me introduce some to you.The first one is Artificial Intelligence(AI).China has many AI companies,such as Baidu,Alibaba and Tencent.They have made many things such as machine learning,natural language processing and computer vision.
基金supported by the National Natural Science Foundation of China(No.82205271)the Chinese Medicine Research Project supported by the Hubei Administration of Traditional Chinese Medicine(No.ZY2025L183)the Graduate Innovation Projects of Hebei University of Chinese Medicine(No.XCXZZBS2024005).
文摘Artificial intelligence(AI)is a sophisticated technology that investigates and formulates theories,methods,techniques,and application systems designed to emulate,expand,and enhance human intelligence[1].In recent years,the rapid advancement of key AI technologies,including image recognition,machine learning,neural networks and robotics,has significantly propelled multidisciplinary integration and development within the medical field[2].The considerable potential of AI in the field of medicine,as evidenced by its formidable data processing and analytical capabilities,has been demonstrated in a number of ways.
文摘Artificial intelligence(AI) is almost everywhere due to the rapid development of modern technology and popularity of intelligent devices.While control theory and machine learning techniques as two enabling technologies have shown enormous power in their own right,a rapprochement of them is required to handle nonlinearity,uncertainty and scalability induced by high complexity of modern systems,huge quantity of real-time data,and large scale of agent networks.
基金the financial support from the National Natural Science Foundation of China(No.U2005205,No.42007235,No.41972268)the Science and Technology Innovation Platform Project of Fuzhou Science and Technology Bureau(No.2021-P-032)。
文摘Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation factors due to regional environmental variations.Then,a single machine learning model can easily become overfitting,thus reducing the accuracy and robustness of the evaluation model.This paper proposes a combined machine-learning model to address the issues.The landslide susceptibility in mountain roads were mapped by using factor analysis to normalize and reduce the dimensionality of the initial condition factor and generating six new combination factors as evaluation indexes.The mountain roads in the Youxi County,Fujian Province,China were used for the landslide susceptibility mapping.Three most frequently used machine learning techniques,support vector machine(SVM),random forest(RF),and artificial neural network(ANN)models,were used to model the landslide susceptibility of the study area and validate the accuracy of this evaluation index system.The global minimum variance portfolio was utilized to construct a machine learning combined model.5-fold cross-validation,statistical indexes,and AUC(Area Under Curve)values were implemented to evaluate the predictive accuracy of the landslide susceptibility model.The mean AUC values for the SVM,RF,and ANN models in the training stage were 89.2%,88.5%,and 87.9%,respectively,and 78.0%,73.7%,and 76.7%,respectively,in the validating stage.In the training and validation stages,the mean AUC values of the combined model were 92.4% and 87.1%,respectively.The combined model provides greater prediction accuracy and model robustness than one single model.
基金supported by the National Natural Science Foundation of China (22209094)。
文摘With the rapid development of computer techniques,atomistic modeling is playing an increasingly important role in understanding the structure-activity relationship of materials.Molecular dynamics (MD) is a computational simulation approach to predicting the structural evolution of an atomic system over time,widely used to understand physical and chemical phenomena including phase transition,diffusion,crystallization,and reaction [1].
基金N.I.R.R.and K.I.M.have received a grant from the Malaysian Ministry of Higher Education.Grant number:203/PKOMP/6712025,http://portal.mygrants.gov.my/main.php.
文摘This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent screening.Mammography has been the most utilized breast ima-ging technique to date.Radiologists have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)sets.Decision Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.
基金This study is supported by The National Key Research and Development Program of China:“Key measurement and control equipment with built-in information security functions”(Grant No.2018YFB2004200)Independent Subject of State Key Laboratory of Robotics“Research on security industry network construction technology for 5G communication”(No.2022-Z13).
文摘Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensional features,data heterogeneity,and data noise due to the diversity of data dimensions,formats and noise of sensors,controllers and actuators.Hence,a novel unsupervised learn-ing autoencoder model is proposed for ICS data in this paper.Although traditional methods only capture the linear correlations of ICS features,our deep industrial representation learning model(DIRL)based on a convolutional neural network can mine high-order features,thus solving the problem of high-dimensional and heterogeneous ICS data.In addition,an unsupervised denoising autoencoder is introduced for noisy ICS data in DIRL.Training the denoising autoencoder allows the model to better mitigate the sensor noise problem.In this way,the represen-tative features learned by DIRL could help to evaluate the safety state of ICSs more effectively.We tested our model with absolute and relative accuracy experi-ments on two large-scale ICS datasets.Compared with other popular methods,DIRL showed advantages in four common indicators of AI algorithms:accuracy,precision,recall,and F1-score.This study contributes to the effective analysis of large-scale ICS data,which promotes the stable operation of ICSs.
文摘The prediction of crop yield is one of the important factor and also challenging,to predict the future crop yield based on various criteria’s.Many advanced technologies are incorporated in the agricultural processes,which enhances the crop yield production efficiency.The process of predicting the crop yield can be done by taking agriculture data,which helps to analyze and make important decisions before and during cultivation.This paper focuses on the prediction of crop yield,where two models of machine learning are developed for this work.One is Modified Convolutional Neural Network(MCNN),and the other model is TLBO(Teacher Learning Based Optimization)-a Genetic algorithm which reduces the input size of data.In this work,some spatial information used for analysis is the Normalized Difference Vegetation Index,Standard Precipitation Index and Vegetation Condition Index.TLBO finds some best feature value set in the data that represents the specific yield of the crop.So,these selected feature valued set is passed in the Error Back Propagation Neural Network for learning.Here,the training was done in such a way that all set of features were utilized in pair with their yield value as output.For increasing the reliability of the work whole experiment was done on a real dataset from Madhya Pradesh region of country India.The result shows that the proposed model has overcome various evaluation parameters on different scales as compared to previous approaches adopted by researchers.
基金This research was supported by the University of Electronic Science and Technology of ChinaChina Postdoctoral Science Foundation(No.2018M633347).
文摘We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system,which is suitable for repeated measurements in mass screening.Sixty-three optical tomographic images were collected from women with dense breasts,and a dataset of 12602D gray scale images sliced from these 3D images was built.After image preprocessing and normalization,we tested the network on this dataset and obtained 0.80 specificity,0.95 sensitivity,90.2%accuracy,and 0.94 area under the receiver operating characteristic curve(AUC).Furthermore,a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset.The sensitivity,specificity,accuracy,and AUC of the classification on the augmented dataset were 0.88,0.96,93.3%,and 0.95,respectively.
文摘"Artificial intelligence (AI) for Science"is,at an unprecedented speed and scale,shaping the paradigm of scientific research,accelerating innovation in technology and industry.Research on modern or contemporary mechanics,rooted in Galileo's"experiments-mathematics"paradigm,has embraced AI in recent years.Machine learning (ML) of data becomes crucial in the face of complex mechanical problems ranging from turbulence of fluids,failure of solids,to multiscale structural optimization.
文摘The complex relationship between environmental exposure and human health constitutes a major global challenge requiring innovative solutions.Artificial Intelligence(AI)and Machine Learning(ML)show exceptional strength for data analysis and pattern recognition.Applying these technologies to environmental health provides new insights to improve and advance environmental exposure assessment,health risk assessment,and related policy development.It is with great pleasure that we present this Special Issue of Environment&Health on Machine Learning and Artificial Intelligence for Environmental Health.This collection of research highlights the latest advancements and broad potential of ML and AI to empower our response to pressing and future environmental health issues.
基金supported by the National Natural Science Foundation of China(grant nos.52331007,52192602,T2222028,52471189).The AI-driven experiments,simulations and model trainingwere performed on the robotic AI-Scientist platform of Chinese Academy of Sciences.
文摘Glass formation is frequently observed in metallic alloys.Machine learning has been applied to discover new metallic glasses.However,the incomplete understanding of glass formation hinders descriptor selection and material property representation.Here,we use X-ray diffraction spectra,the essential tool for identifying amorphous structure,as an intermediate link.By representing spectra as images,we train generative models to produce high-fidelity spectra for all alloys in multicomponent alloy systems.Training with spectra from a tiny fraction of the total alloys is sufficient for accurate spectra generation,enabling the identification of compositional regions with a high probability of glass formation.The shift from numerical to image-based representation unlocks the potential of machine learning in the design of glass-forming alloys.Furthermore,our approach is applicable to a wide range of materials and spectroscopic techniques.We anticipate that this strategy will accelerate materials discovery across previously unexplored compositional and processing spaces.
基金supported by the National Natural Science Foundation of China(No.51972080).
文摘To explore the MAX phase with experimental value over a wider range,a data-driven machine learning(ML)model was trained to rapidly predict the stability of MAX phases via a random forest classifier(RFC),support vector machine(SVM),and gradient boosting tree(GBT),where the deemed significant descriptors were compiled from the literature and the stability of 1804 combinations of MAX phases was collected.Using this well-trained model,190 new MAX phases were screened from 4347 MAX phases,150 of which met the criteria for thermodynamic and intrinsic stability on the basis of first-principles calculations.Additionally,with the help of the ML model,the mean number of valence electrons and the valence electron deviation are the two most critical factors influencing stability.Additionally,one of these predicted MAX phases,Ti_(2)SnN,was experimentally synthesized through Lewis acid substitution reactions at 750℃,with interesting A-site deintercalation and self-extrusion.First-principles calculations revealed that Ti_(2)SnN has lower elastic properties,higher damage tolerance and fracture toughness,and a higher coefficient of thermal expansion(CTE).
基金RSF No.24-73-10055 for financial support of the development of ML interatomic potential for Mo-S system and calculation of dynamical properties of newly found materialsA.V.A.is supported by the Ministry of Science and Higher Education(FSMG-2025-0005)+3 种基金D.G.K.acknowledges financial support from the federal budget of the Russian Ministry of Science and Higher Education(No.125020401357-4)A.R.O.gratefully acknowledges support from Russian Science Foundation(grant 24-43-00162)K.S.N acknowledges support from the Ministry of Education,Singapore under Research Centre of Excellence award to the Institute for Functional Intelligent Materials,I-FIM(project No.EDUNC-33-18-279-V12)and under the Tier 3 program(MOE-MOET32024-0001)the National Research Foundation,Singapore under its AI Singapore Programme(AISG Award No:AISG3-RP-2022-028).
文摘Two-dimensional(2D)materials attract considerable attention due to their remarkable electronic,mechanical and optical properties.Despite their use in combination with substrates in practical applications,computational studies often neglect the effects of substrate interactions for simplicity.This study presents a novel method for predicting the atomic structure of 2D materials on substrates by combining an evolutionary algorithm,a lattice-matching technique,an automated machinelearning interatomic potentials training protocol,and the ab initio thermodynamics approach.Using the molybdenum-sulfur system on a sapphire substrate as a case study,we reveal several new stable and metastable structures,including previously known 1H-MoS_(2)and newly found Pmma Mo_(3)S_(2),P1^(-)Mo_(2)S,P2_(1)m Mo_(5)S_(3),and P_(4)mm Mo_(4)S,where the Mo_(4)S structure is specifically stabilized by interaction with the substrate.Finally,we use the ab initio thermodynamics approach to predict the synthesis conditions of the discovered structures in the parameter space of the commonly used chemical vapor deposition technique.
基金supported by the Research Grants Council of the Hong Kong Special Administrative Region,China(Project No.C6003-22Y)supported by the Hong Kong Polytechnic University Undergraduate Research and Innovation Scheme(Project No.P0043659).
文摘Convective cooling by wind is crucial for large-scale photovoltaic(PV)systems,as power generation inversely correlates with panel temperature.Therefore,accurately determining the convective heat transfer coefficient for PV arrays with various geometric configurations is essential to optimize array design.Traditional methods to quantify the effects of configuration utilize either Computational Fluid Dynamics(CFD)simulations or empirical methods.These approaches often face challenges due to high computational demands or limited accuracy,particularly with complex array configurations.Machine learning approaches,especially hybrid learning models,have emerged as effective tools to address challenges in heat transfer design optimization.This study introduces a method that combines Physics-Informed Machine Learning with a Deep Convolutional Neural Network(PIML-DCNN)to predict convective heat transfer rates with high accuracy and computational efficiency.Additionally,an innovative loss function,termed the“Pocket Loss”,is developed to enhance the interpretability and robustness of the PIML-DCNN model.The proposed model achieves relative estimation errors of 2.5%and 2.7%on the validation and test datasets,respectively,when benchmarked against comprehensive CFD simulations.These results highlight the potential of the proposed model to efficiently guide the configuration design of PV arrays,thereby enhancing power generation in real-world operations.
文摘The integration of solar greenhouses into smart energy systems(SESs)remains largely unexplored,despite their potential to enhance energy sharing and hydrogen production.This review investigates the role of solar greenhouses as active energy contributors within SESs,emphasizing their biomass waste gasification for hydrogen production and their integration into district heating and cooling(DHC)networks.A structured classification of machine learning(ML)and deep learning(DL)techniques applied in forecasting and optimizing these processes is provided.Additionally,the evolution of DHC systems is analyzed,with a focus on fifth-generation DHC(5GDHC)networks,which facilitate bidirectional energy exchange at near-ambient temperatures.The review highlights that existing studies have predominantly addressed SES advancements and ML-driven energy management without considering the contributions of solar greenhouses.A novel framework is proposed,illustrating their role as prosumers capable of exchanging electricity,hydrogen,and thermal energy within SESs.Key findings reveal that integrating solar greenhouses with SESs can enhance energy efficiency,reduce carbon emissions,and improve system resilience.Furthermore,ML-driven predictive control strategies,particularly model predictive control(MPC),are identified as essential for optimizing real-time energy flows and biomass gasification processes.This study provides a foundation for future research on the technical,economic,and environmental feasibility of integrating greenhouses into SESs.The insights presented offer a pathway toward more sustainable,AI-driven energy-sharing networks,supporting policymakers and industry stakeholders in the transition toward low-carbon energy solutions.
文摘Design patterns are often used in the development of object-oriented software. It offers reusable abstract information that is helpful in solving recurring design problems. Detecting design patterns is beneficial to the comprehension and maintenance of object-oriented software systems. Several pattern detection techniques based on static analysis often encounter problems when detecting design patterns for identical structures of patterns. In this study, we attempt to detect software design patterns by using software metrics and classification-based techniques. Our study is conducted in two phases： creation of metrics-oriented dataset and detection of software design patterns. The datasets are prepared by using software metrics for the learning of classifiers. Then, pattern detection is performed by using classification-based techniques. To evaluate the proposed method, experiments are conducted using three open source software programs, JHotDraw, QuickUML, and JUnit, and the results are analyzed.
基金Project supported by the National Natural Science Foundation of China(No.61472437)
文摘Behavior-based malware analysis is an important technique for automatically analyzing and detecting malware, and it has received considerable attention from both academic and industrial communities. By considering how malware behaves, we can tackle the malware obfuscation problem, which cannot be processed by traditional static analysis approaches, and we can also derive the as-built behavior specifications and cover the entire behavior space of the malware samples. Although there have been several works focusing on malware behavior analysis, such research is far from mature, and no overviews have been put forward to date to investigate current developments and challenges. In this paper, we conduct a survey on malware behavior description and analysis considering three aspects： malware behavior description, behavior analysis methods, and visualization techniques. First, existing behavior data types and emerging techniques for malware behavior description are explored, especially the goals, prin- ciples, characteristics, and classifications of behavior analysis techniques proposed in the existing approaches. Second, the in- adequacies and challenges in malware behavior analysis are summarized from different perspectives. Finally, several possible directions are discussed for future research.
基金NIH[R01HD101130,R15HD108720]NSF[CMMI-2130192,CBET-1943798]Research Seed Grants(2021 and 2023)from UNT Research and Innovation Office(H.X.Y.),Syracuse University intramural CUSE grant[II-3245-2022](Z.M.).
文摘Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence.Organoids,three-dimensional miniature organ-like structures cultivated from stem cells,offer an unparalleled opportunity to simulate complex human organ systems in vitro.Through the convergence of organoid technology and AI,researchers gain the means to accelerate discoveries and insights across various disciplines.Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors,intricate cellular interactions,and dynamic responses to stimuli.This synergy empowers the development of predictive models,precise disease simulations,and personalized medicine approaches,revolutionizing our understanding of human development,disease mechanisms,and therapeutic interventions.Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling,propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.61935010,61975069,21905253,and 51973200)the China Postdoctoral Science Foundation(Nos.2018M640681 and 2019T120632)+5 种基金the Natural Science Foundation of Henan(No.202300410372)Key-Area Research and Development Program of Guangdong Province(No.2020B090922006)Guangdong Project of Science and Technology Grants(No.2018B030323017)Guangzhou science and technology project(Nos.201903010042 and 201904010294)Youth project of science and technology research program of Chongqing Education Commission of China(No.KJQN202001322)the Science and Technology Development Fund from Macao SAR(File Nos.0125/2018/A3 and 0071/2019/AMJ).
文摘The most widely used method of identification of microbial morphology and structure is microscopy,but it can be difficult to distinguish between pathogens with a similar appearance.Existing fluorescent staining methods require a combination of a variety of fluorescent materials to meet this demand.In this study,unique concentration-dependent fluorescent carbon dots(CDs)were synthesized for the identification and quantification of pathogens.The emission wavelength of the CDs could be tuned spanning the full visible region by virtue of aggregation-induced narrowing of bandgaps.This tunable emission wavelength of the specific concentration response to diverse microbes can be used to distinguish microorganisms with a similar appearance,even in a same genus.A hyperspectral microscopy system was demonstrated to distinguish Aspergillus flavus and A.fumigatus based on the results above.The identification accuracy of the two similar-looking pathogens can be close to 100%,and the relative proportions and spatial distributions can also be profiled from the mixture of the pathogens.This technique can provide a solution to the fast detection of microorganisms and is potentially applicable to a wide range of problems in areas such as healthcare,food preparation,biotechnology,and health emergency.
