The advanced driver assistance system(ADAS)primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision,which leads to fewer fatal accidents and ensures higher safety.In...The advanced driver assistance system(ADAS)primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision,which leads to fewer fatal accidents and ensures higher safety.In the artificial Intelligence domain,machine learning(ML)was developed to make inferences with a degree of accuracy similar to that of humans;however,enormous amounts of data are required.Machine learning enhances the accuracy of the decisions taken by ADAS,by evaluating all the data received from various vehicle sensors.This study summarizes all the critical algorithms used in ADAS technologies and presents the evolution of ADAS technology.Initially,ADAS technology is introduced,along with its evolution,to understand the objectives of developing this technology.Subsequently,the critical algorithms used in ADAS technology,which include face detection,head-pose estimation,gaze estimation,and link detection are discussed.A further discussion follows on the impact of ML on each algorithm in different environments,leading to increased accuracy at the expense of additional computing,to increase efficiency.The aim of this study was to evaluate all the methods with or without ML for each algorithm.展开更多
Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a part...Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.展开更多
Together,the heart and lung sound comprise the thoracic cavity sound,which provides informative details that reflect patient conditions,particularly heart failure(HF)patients.However,due to the limitations of human he...Together,the heart and lung sound comprise the thoracic cavity sound,which provides informative details that reflect patient conditions,particularly heart failure(HF)patients.However,due to the limitations of human hearing,a limited amount of information can be auscultated from thoracic cavity sounds.With the aid of artificial intelligence–machine learning,these features can be analyzed and aid in the care of HF patients.Machine learning of thoracic cavity sound data involves sound data pre-processing by denoising,resampling,segmentation,and normalization.Afterwards,the most crucial step is feature extraction and se-lection where relevant features are selected to train the model.The next step is classification and model performance evaluation.This review summarizes the currently available studies that utilized different machine learning models,different feature extraction and selection methods,and different classifiers to generate the desired output.Most studies have analyzed the heart sound component of thoracic cavity sound to distinguish between normal and HF patients.Additionally,some studies have aimed to classify HF patients based on thoracic cavity sounds in their entirety,while others have focused on risk strati-fication and prognostic evaluation of HF patients using thoracic cavity sounds.Overall,the results from these studies demonstrate a promisingly high level of accuracy.Therefore,future prospective studies should incorporate these machine learning models to expedite their integration into daily clinical practice for managing HF patients.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this...During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this paper,the electrochemical dissolution behavior of Ti-6.5Al-2Zr-1Mo-1V(TA15)titanium alloy at without particle impact,low(15°)and high(90°)angle particle impact was investigated,and the influence of Al_(2)O_(3)particles on ECM was systematically expounded.It was found that under the condition of no particle erosion,the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film,and the surface roughness(Sa)of the local area reached 10.088μm.Under the condition of a high-impact angle(90°),due to the existence of strain hardening and particle embedding,only the edge of the surface is dissolved,while the central area is almost insoluble,with the surface roughness(S_(a))reaching 16.086μm.On the contrary,under the condition of a low-impact angle(15°),the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion,and the surface roughness(S_(a))reached 2.823μm.Based on these findings,the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.展开更多
Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the ...Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML...Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML)has emerged as a powerful data analysis tool,widely applied in the prediction,diagnosis,and mechanistic study of kidney transplant rejection.This mini-review systematically summarizes the recent applications of ML techniques in post-kidney transplant rejection,covering areas such as the construction of predictive models,identification of biomarkers,analysis of pathological images,assessment of immune cell infiltration,and formulation of personalized treatment strategies.By integrating multi-omics data and clinical information,ML has significantly enhanced the accuracy of early rejection diagnosis and the capability for prognostic evaluation,driving the development of precision medicine in the field of kidney transplantation.Furthermore,this article discusses the challenges faced in existing research and potential future directions,providing a theoretical basis and technical references for related studies.展开更多
采用UML分析与设计的业务信息系统,业务流程经过层层的抽象迭代,缺乏一种透明的业务流程实现。WF提供了可视化的业务过程编程模型,便于实现业务流程自动化,在对比分析WF State Machine和UML状态图的基础上,研究从UML状态图到WF State Ma...采用UML分析与设计的业务信息系统,业务流程经过层层的抽象迭代,缺乏一种透明的业务流程实现。WF提供了可视化的业务过程编程模型,便于实现业务流程自动化,在对比分析WF State Machine和UML状态图的基础上,研究从UML状态图到WF State Machine业务流程映射关系,选取UML中典型状态图,依据一定的命名转换规则,实现了从UML状态图分析设计到WF状态机业务过程可视化的构建,完成了动态测试。展开更多
Perovskite solar cells have drawn extensive attention in the photovoltaic(PV)field due to their rapidly increasing efficiency.Recently,additives have become necessary for the fabrication of highly efficient perovskite...Perovskite solar cells have drawn extensive attention in the photovoltaic(PV)field due to their rapidly increasing efficiency.Recently,additives have become necessary for the fabrication of highly efficient perovskite solar cells(PSCs).Additionally,alkali metal doping has been an effective method to decrease the defect density in the perovskite film.However,the traditional trial-and-error method to find the optimal doping concentration is timeconsuming and needs a significant amount of raw materials.In this work,in order to explore new ways of facilitating the process of finding the optimal doping concentration in perovskite solar cells,we applied a machine learning(ML)approach to assist the optimization of KI doping in MAPbI_(3) solar cells.With the aid of ML technique,we quickly found that 3%KI doping could further improve the efficiency of MAPbI_(3) solar cells.As a result,a highest efficiency of 20.91%has been obtained for MAPbI_(3) solar cells.展开更多
The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper ...The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.展开更多
DEAR EDITOR,Somatic mutations are a large category of genetic variations,which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants(SNVs) could facilitate downstream analysis of tum...DEAR EDITOR,Somatic mutations are a large category of genetic variations,which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants(SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have been developed to detect SNVs, but most require normal matched samples to differentiate somatic SNVs from the normal state, which can be difficult to obtain.展开更多
Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper pr...Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.展开更多
Photovoltaic (PV) technologies have attracted great interest due to their capability of generating electricity directly from sunlight. Machine learning(ML) is a technique for computer to learn how to perform a specifi...Photovoltaic (PV) technologies have attracted great interest due to their capability of generating electricity directly from sunlight. Machine learning(ML) is a technique for computer to learn how to perform a specific task using known data. It can be used in many areas and has become a hot research topic recently due to the rapid accumulation of data and advancement of computer hardware. The application of ML techniques in the design and fabrication of solar cells started slowly but has recently gained tremendous momentum. An exhaustive compilation of the literatures indicates that all the major aspects in the research and development of solar cells can be effectively assisted by ML techniques. If combined with other tools and fed with additional theoretical and experimental data, more accurate and robust results can be achieved from ML techniques. The aspects can be grouped into four categories:prediction of material properties,optimization of device structures, optimization of fabrication processes, and reconstruction of measurement data. A statistical analysis of the literatures shows that artificial neural network (ANN) and genetic algorithm (GA) are the two most applied ML techniques and the topics in the optimization of device structures and optimization of fabrication processes are more popular.This article can be used as a reference by all PV researchers who are interested in ML techniques.展开更多
为保证复杂仿真系统达到可信度要求和缩短开发周期,应在构建复杂仿真系统之初确定各个仿真子系统的可信度。为此,提出一种复杂仿真系统可信度智能分配方法,在明确复杂仿真系统总体可信度的情况下获取各仿真子系统的可信度分配结果。根...为保证复杂仿真系统达到可信度要求和缩短开发周期,应在构建复杂仿真系统之初确定各个仿真子系统的可信度。为此,提出一种复杂仿真系统可信度智能分配方法,在明确复杂仿真系统总体可信度的情况下获取各仿真子系统的可信度分配结果。根据复杂仿真系统的组成和结构,提出基于多层成对马尔可夫随机场(multi-layer pairwise Markov random field,ML-PMRF)的复杂仿真系统可信度分配模型构建方法。基于最大后验推理和离散萤火虫群优化,提出一种面向ML-PMRF的智能推理方法。通过实例应用及对比实验,验证了所提方法的有效性和合理性。展开更多
文摘The advanced driver assistance system(ADAS)primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision,which leads to fewer fatal accidents and ensures higher safety.In the artificial Intelligence domain,machine learning(ML)was developed to make inferences with a degree of accuracy similar to that of humans;however,enormous amounts of data are required.Machine learning enhances the accuracy of the decisions taken by ADAS,by evaluating all the data received from various vehicle sensors.This study summarizes all the critical algorithms used in ADAS technologies and presents the evolution of ADAS technology.Initially,ADAS technology is introduced,along with its evolution,to understand the objectives of developing this technology.Subsequently,the critical algorithms used in ADAS technology,which include face detection,head-pose estimation,gaze estimation,and link detection are discussed.A further discussion follows on the impact of ML on each algorithm in different environments,leading to increased accuracy at the expense of additional computing,to increase efficiency.The aim of this study was to evaluate all the methods with or without ML for each algorithm.
文摘Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.
文摘Together,the heart and lung sound comprise the thoracic cavity sound,which provides informative details that reflect patient conditions,particularly heart failure(HF)patients.However,due to the limitations of human hearing,a limited amount of information can be auscultated from thoracic cavity sounds.With the aid of artificial intelligence–machine learning,these features can be analyzed and aid in the care of HF patients.Machine learning of thoracic cavity sound data involves sound data pre-processing by denoising,resampling,segmentation,and normalization.Afterwards,the most crucial step is feature extraction and se-lection where relevant features are selected to train the model.The next step is classification and model performance evaluation.This review summarizes the currently available studies that utilized different machine learning models,different feature extraction and selection methods,and different classifiers to generate the desired output.Most studies have analyzed the heart sound component of thoracic cavity sound to distinguish between normal and HF patients.Additionally,some studies have aimed to classify HF patients based on thoracic cavity sounds in their entirety,while others have focused on risk strati-fication and prognostic evaluation of HF patients using thoracic cavity sounds.Overall,the results from these studies demonstrate a promisingly high level of accuracy.Therefore,future prospective studies should incorporate these machine learning models to expedite their integration into daily clinical practice for managing HF patients.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金supported by the National Natural Science Foundation of China(No.52175414)the Natural Science Foundation of Jiangsu Province of China(No.BK20220134)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.NE2023002)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(No.KYCX24_0559)。
文摘During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this paper,the electrochemical dissolution behavior of Ti-6.5Al-2Zr-1Mo-1V(TA15)titanium alloy at without particle impact,low(15°)and high(90°)angle particle impact was investigated,and the influence of Al_(2)O_(3)particles on ECM was systematically expounded.It was found that under the condition of no particle erosion,the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film,and the surface roughness(Sa)of the local area reached 10.088μm.Under the condition of a high-impact angle(90°),due to the existence of strain hardening and particle embedding,only the edge of the surface is dissolved,while the central area is almost insoluble,with the surface roughness(S_(a))reaching 16.086μm.On the contrary,under the condition of a low-impact angle(15°),the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion,and the surface roughness(S_(a))reached 2.823μm.Based on these findings,the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.
文摘Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
文摘Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML)has emerged as a powerful data analysis tool,widely applied in the prediction,diagnosis,and mechanistic study of kidney transplant rejection.This mini-review systematically summarizes the recent applications of ML techniques in post-kidney transplant rejection,covering areas such as the construction of predictive models,identification of biomarkers,analysis of pathological images,assessment of immune cell infiltration,and formulation of personalized treatment strategies.By integrating multi-omics data and clinical information,ML has significantly enhanced the accuracy of early rejection diagnosis and the capability for prognostic evaluation,driving the development of precision medicine in the field of kidney transplantation.Furthermore,this article discusses the challenges faced in existing research and potential future directions,providing a theoretical basis and technical references for related studies.
文摘采用UML分析与设计的业务信息系统,业务流程经过层层的抽象迭代,缺乏一种透明的业务流程实现。WF提供了可视化的业务过程编程模型,便于实现业务流程自动化,在对比分析WF State Machine和UML状态图的基础上,研究从UML状态图到WF State Machine业务流程映射关系,选取UML中典型状态图,依据一定的命名转换规则,实现了从UML状态图分析设计到WF状态机业务过程可视化的构建,完成了动态测试。
基金Nanchang University High Talent Project(No.9166-2701010119)the National Key R&D Program of China(No.2016YFB0401003)+1 种基金the National Natural Science Foundation of China(Nos.61935016,61775004 and U1605244)。
文摘Perovskite solar cells have drawn extensive attention in the photovoltaic(PV)field due to their rapidly increasing efficiency.Recently,additives have become necessary for the fabrication of highly efficient perovskite solar cells(PSCs).Additionally,alkali metal doping has been an effective method to decrease the defect density in the perovskite film.However,the traditional trial-and-error method to find the optimal doping concentration is timeconsuming and needs a significant amount of raw materials.In this work,in order to explore new ways of facilitating the process of finding the optimal doping concentration in perovskite solar cells,we applied a machine learning(ML)approach to assist the optimization of KI doping in MAPbI_(3) solar cells.With the aid of ML technique,we quickly found that 3%KI doping could further improve the efficiency of MAPbI_(3) solar cells.As a result,a highest efficiency of 20.91%has been obtained for MAPbI_(3) solar cells.
文摘The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.
基金supported by the CAS Pioneer Hundred Talents Program and National Natural Science Foundation of China (32070683) to Y.P.C。
文摘DEAR EDITOR,Somatic mutations are a large category of genetic variations,which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants(SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have been developed to detect SNVs, but most require normal matched samples to differentiate somatic SNVs from the normal state, which can be difficult to obtain.
文摘Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.
基金partialy supported by Nanchang University, under Research Grant 9166-2701010119
文摘Photovoltaic (PV) technologies have attracted great interest due to their capability of generating electricity directly from sunlight. Machine learning(ML) is a technique for computer to learn how to perform a specific task using known data. It can be used in many areas and has become a hot research topic recently due to the rapid accumulation of data and advancement of computer hardware. The application of ML techniques in the design and fabrication of solar cells started slowly but has recently gained tremendous momentum. An exhaustive compilation of the literatures indicates that all the major aspects in the research and development of solar cells can be effectively assisted by ML techniques. If combined with other tools and fed with additional theoretical and experimental data, more accurate and robust results can be achieved from ML techniques. The aspects can be grouped into four categories:prediction of material properties,optimization of device structures, optimization of fabrication processes, and reconstruction of measurement data. A statistical analysis of the literatures shows that artificial neural network (ANN) and genetic algorithm (GA) are the two most applied ML techniques and the topics in the optimization of device structures and optimization of fabrication processes are more popular.This article can be used as a reference by all PV researchers who are interested in ML techniques.
文摘为保证复杂仿真系统达到可信度要求和缩短开发周期,应在构建复杂仿真系统之初确定各个仿真子系统的可信度。为此,提出一种复杂仿真系统可信度智能分配方法,在明确复杂仿真系统总体可信度的情况下获取各仿真子系统的可信度分配结果。根据复杂仿真系统的组成和结构,提出基于多层成对马尔可夫随机场(multi-layer pairwise Markov random field,ML-PMRF)的复杂仿真系统可信度分配模型构建方法。基于最大后验推理和离散萤火虫群优化,提出一种面向ML-PMRF的智能推理方法。通过实例应用及对比实验,验证了所提方法的有效性和合理性。
