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Predictive Hegemony and the Interpretive Gap:On the Philosophical Premises and Ethical Boundaries of AI-Assisted Clinical Decision-Making 认领 引用
1
作者 Yubo Wang 《Journal of Electronic Research and Application》 2026年第1期124-129,共6页
When debating the application boundaries of artificial intelligence(AI)predictive models in clinical medicine,it is clear that high predictive accuracy is desirable,but on its own,does not provide a sufficient conditi... When debating the application boundaries of artificial intelligence(AI)predictive models in clinical medicine,it is clear that high predictive accuracy is desirable,but on its own,does not provide a sufficient condition for clinical application.Drawing on three example,AlphaFold’s prediction of protein structure,radiomics’prediction of disease diagnosis and prognosis,and clinical risk scoring models’prediction of morbidity,and engaging with David Hume’s empiricist skepticism towards causality,argue that interpretability is an indispensable condition in a discipline that values mechanistic explanation.In order for AI to evolve from a capable recommender to a decision-making machine that begins to develop a sense of individual self,several preconditions need to be fulfilled.Predictions must be falsifiable,minimally grounded in mechanistic knowledge,accompanied by partially explicable decision logics,designed to be fair to populations,and embedded in an error-tolerant architecture that enables correction and rollback.The utility of AI today lies in its ability to dramatically reduce the costs of human trial and error,but should not diminish the doctor’s right to make,learn from,and reflect on mistakes as the final accountable link in the chain. 展开更多
关键词 AI interpretability Clinical decision-making Hume Machine learning Predictive models
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Prioritizing policy issues for knowledge translation:a critical interpretive synthesis 认领 引用
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作者 Racha Fadlallah Fadi El-Jardali +8 位作者 Tanja Kuchenmüller Kaelan Moat Marge Reinap Mehrnaz Kheirandish Lama Bou Karroum Najla Daher Nour Kalach Lama Hishi Gladys Honein-AbouHaidar 《Global Health Research and Policy》 CSCD 2025年第1期344-362,共19页
Background While calls for promoting evidence-informed policymaking(EIP)have become stronger in recent years,there is a paucity of methods to prioritize issues for knowledge translation(KT)and EIP.As requested by WHO ... Background While calls for promoting evidence-informed policymaking(EIP)have become stronger in recent years,there is a paucity of methods to prioritize issues for knowledge translation(KT)and EIP.As requested by WHO and as part of efforts to address this gap,we conducted a critical interpretive synthesis(CIS)to develop a conceptual framework that outlines the features of priority-setting processes and contextual factors influencing the prioritization of issues for KT efforts.Methods We systematically reviewed the literature and used an interpretive analytic approach-the CIS-to synthesize the results and develop the conceptual framework.We used a"compass"question to create a detailed search strategy and conducted electronic searches to identify papers based on their potential relevance to priority-setting for KT efforts and EIP.Results We identified 161 eligible papers.Our findings on key features of the priority-setting process unpacked three 3 levels of constructs:‘pathways'for identifying and prioritizing policy issues for knowledge translation efforts;‘phases'within each pathway;and‘steps'for each phase.There are three main pathways:(1)explicit and systemic priority-setting processes involving policymakers and stakeholders to determine priority topics(collaborative);(2)a policymaker or stakeholder brings an issue forward or asks for evidence on a particular topic(demand-driven);and(3)a need or policy gap is identified by a knowledge translation platform(supply-driven).Within each pathway,four phases emerged:“Preparatory”,“prioritization”,“knowledge translation”and“scale-up and sustainability”.Across these phases,the following steps were identified:establishing a core team,defining a scope,confirming a timeline,sensitizing stakeholders,generating potential issues,gathering contextual information,setting guiding principles,selecting prioritization criteria,applying the method for prioritization,documenting and communicating priorities,validating and revising priorities,selecting venue for decision-making,implementing priorities,monitoring and evaluation,promoting institutionalization,and engaging in peer learning and exchange of experience.We identified engaging stakeholders and strengthening capacity as cross-cutting elements.Our findings on contextual factors unpacked four categories:(1)institutions;(2)ideas;(3)interests;and(4)external factors.Conclusions This CIS generated a multi-level conceptual framework for prioritizing issues for KT efforts and laid the foundation for a WHO tool that supports prioritization in practice.The study contributes meaningfully to both the literature and the operationalization of KT and EIP. 展开更多
关键词 Critical interpretive synthesis Priority setting Knowledge translation Evidence-informed policymaking Prioritization of issues Conceptual framework
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Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale 认领 引用 被引量：9
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作者 高慧慧 徐圆 +2 位作者 顾祥柏 林晓勇 朱群雄 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第12期1987-1996,共10页
Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalizati... Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance. 展开更多
关键词 Alarm rationalization Root-cause analysis Alarm priority Interpretive structural modeling Likert scale Tennessee Eastman process
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Knowledge translation for public health in low-and middle-income countries:a critical interpretive synthesis 认领 引用 被引量：5
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作者 Catherine Malla Paul Aylward Paul Ward 《Global Health Research and Policy》 2018年第1期77-88,共12页
Background:Effective knowledge translation allows the optimisation of access to and utilisation of research knowledge in order to inform and enhance public health policy and practice.In low-and middle-income countries... Background:Effective knowledge translation allows the optimisation of access to and utilisation of research knowledge in order to inform and enhance public health policy and practice.In low-and middle-income countries,there are substantial complexities that affect the way in which research can be utilised for public health action.This review attempts to draw out concepts in the literature that contribute to defining some of the complexities and contextual factors that influence knowledge translation for public health in low-and middle-income countries.Methods:A Critical Interpretive Synthesis was undertaken,a method of analysis which allows a critical review of a wide range of heterogeneous evidence,through incorporating systematic review methods with qualitative enquiry techniques.A search for peer-reviewed articles published between 2000 and 2016 on the topic of knowledge translation for public health in low-and middle-income countries was carried out,and 85 articles were reviewed and analysed using this method.Results:Four main concepts were identified:1)tension between‘global’and‘local’health research,2)complexities in creating and accessing evidence,3)contextualising knowledge translation strategies for low-and middle-income countries,and 4)the unique role of non-government organisations in the knowledge translation process.Conclusion:This method of review has enabled the identification of key concepts that may inform practice or further research in the field of knowledge translation in low-and middle-income countries. 展开更多
关键词 Critical interpretive synthesis Knowledge translation Low-and middle-income countries Public health
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Developing a framework to inform scale-up success for population health interventions:a critical interpretive synthesis of the literature 认领 引用 被引量：1
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作者 Duyen Thi Kim Nguyen Lindsay McLaren +1 位作者 Nelly D.Oelke Lynn McIntyre 《Global Health Research and Policy》 2020年第1期279-289,共11页
Background:Population health interventions(PHIs)have the potential to improve the health of large populations by systematically addressing underlying conditions of poor health outcomes(i.e.,social determinants of heal... Background:Population health interventions(PHIs)have the potential to improve the health of large populations by systematically addressing underlying conditions of poor health outcomes(i.e.,social determinants of health)and reducing health inequities.Scaling-up may be one means of enhancing the impact of effective PHIs.However,not all scale-up attempts have been successful.In an attempt to help guide the process of successful scale-up of a PHI,we look to the organizational readiness for change theory for a new perspective on how we may better understand the scale-up pathway.Using the change theory,our goal was to develop the foundations of an evidence-based,theory-informed framework for a PHI,through a critical examination of various PHI scale-up experiences documented in the literature.Methods:We conducted a multi-step,critical interpretive synthesis(CIS)to gather and examine insights from scale-up experiences detailed in peer-reviewed and grey literatures,with a focus on PHIs from a variety of global settings.The CIS included iterative cycles of systematic searching,sampling,data extraction,critiquing,interpreting,coding,reflecting,and synthesizing.Theories relevant to innovations,complexity,and organizational readiness guided our analysis and synthesis.Results:We retained and examined twenty different PHI scale-up experiences,which were extracted from 77 documents(47 peer-reviewed,30 grey literature)published between 1995 and 2013.Overall,we identified three phases(i.e.,Groundwork,Implementing Scale-up,and Sustaining Scale-up),11 actions,and four key components(i.e.,PHI,context,capacity,stakeholders)pertinent to the scale-up process.Our guiding theories provided explanatory power to various aspects of the scale-up process and to scale-up success,and an alternative perspective to the assessment of scale-up readiness for a PHI.Conclusion:Our synthesis provided the foundations of the Scale-up Readiness Assessment Framework.Our theoreticallyinformed and rigorous synthesis methodology permitted identification of disparate processes involved in the successful scaleup of a PHI.Our findings complement the guidance and resources currently available,and offer an added perspective to assessing scale-up readiness for a PHI. 展开更多
关键词 Population health intervention Scale-up Framework Critical interpretive synthesis Readiness
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The Application of the Interpretive Theory of Translation 认领 引用
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作者 TAN Ning 《海外英语》 2014年第17期158-159,共2页
The interpretive theory of translation(ITT) is a school of theory originated in the late 1960 s in France,focusing on the discussion of the theory and teaching of interpreting and non-literary translation. ITT believe... The interpretive theory of translation(ITT) is a school of theory originated in the late 1960 s in France,focusing on the discussion of the theory and teaching of interpreting and non-literary translation. ITT believes that what the translator should convey is not the meaning of linguistic notation,but the non-verbal sense. In this paper,the author is going to briefly introduce ITT and analyze several examples to show different situations where ITT is either useful or unsuitable. 展开更多
关键词 the interpretive theory of translation interpretin
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Application of Interpretive Theory to Business Interpretation 认领 引用
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作者 刘杰 《海外英语》 2014年第18期301-302,共2页
Interpretive theory brings forward three phases of interpretation: understanding, deverberlization and re-expression. It needs linguistic knowledge and non-linguistic knowledge. This essay discusses application of int... Interpretive theory brings forward three phases of interpretation: understanding, deverberlization and re-expression. It needs linguistic knowledge and non-linguistic knowledge. This essay discusses application of interpretive theory to business interpretation from the perspective of theory and practice. 展开更多
关键词 interpretive theory interpretation business interp
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Structural Analysis of the Factors Influencing the Financing of Forestry Enterprises Based on Interpretive Structural Modeling(ISM) 认领 引用 被引量：1
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作者 Zhen WANG Weiping LIU Xiaomin JIANG 《Asian Agricultural Research》 2015年第2期8-10,共3页
Through the collection of related literature,we point out the six major factors influencing China's forestry enterprises' financing: insufficient national support; regulations and institutional environmental f... Through the collection of related literature,we point out the six major factors influencing China's forestry enterprises' financing: insufficient national support; regulations and institutional environmental factors; narrow channels of financing; inappropriate existing mortgagebacked approach; forestry production characteristics; forestry enterprises' defects. Then,we use interpretive structural modeling( ISM) from System Engineering to analyze the structure of the six factors and set up ladder-type structure. We put three factors including forestry production characteristics,shortcomings of forestry enterprises and regulatory,institutional and environmental factors as basic factors and put other three factors as important factors. From the perspective of the government and enterprises,we put forward some personal advices and ideas based on the basic factors and important factors to ease the financing difficulties of forestry enterprises. 展开更多
关键词 Forestry enterprises Financing Interpretive struct
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Research on SAP Business One Implementation Risk Factors with Interpretive Structural Model 认领 引用
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作者 Jiangping Wan Jiajun Hou 《Journal of Software Engineering and Applications》 2012年第3期147-155,共9页
The possible risk factors during SAP Business One implementation were studied with depth interview. The results are then adjusted by experts. 20 categories of risk factors that are totally 49 factors were found. Based... The possible risk factors during SAP Business One implementation were studied with depth interview. The results are then adjusted by experts. 20 categories of risk factors that are totally 49 factors were found. Based on the risk factors during the SAP Business One implementation, questionnaire was used to study the key risk factors of SAP Business One implementation. Results illustrate ten key risk factors, these are risk of senior managers leadership, risk of project management, risk of process improvement, risk of implementation team organization, risk of process analysis, risk of based data, risk of personnel coordination, risk of change management, risk of secondary development, and risk of data import. Focus on the key risks of SAP Business One implementation, the interpretative structural modeling approach is used to study the relationship between these factors and establish a seven-level hierarchical structure. The study illustrates that the structure is olive-like, in which the risk of data import is on the top, and the risk of senior managers is on the bottom. They are the most important risk factors. 展开更多
关键词 Enterprise Resource Planning SAP Business One Risk Interpretive Structural Model Project Management
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A Study of Subtitle Translation Under the Guidance of the Interpretive Theory—A Case Study of Casablanca 认领 引用
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作者 ZHANG Xiaoyi NI Jincheng 《US-China Foreign Language》 2020年第10期302-307,共6页
The interpretive theory,also named as the theory of sense,was born at the Ecole Superieure des Interpreters and Translators(ESIT).By breaking free from the sentence patterns of the original text,the translation can be... The interpretive theory,also named as the theory of sense,was born at the Ecole Superieure des Interpreters and Translators(ESIT).By breaking free from the sentence patterns of the original text,the translation can be faithful to the original meaning and reproduce the expression style of the original text.It is an interpretation of the original meanings by the translator through language signs and cognitive supplements of the translator’s experiences.What translator should produce is not the imitation of linguistic units,but the equivalence of the original meaning.The subtitle translation is different from the traditional translation practice.As a special kind of translation field,its task is to translate the lines spoken by the roles into a written form,which combines characteristics of both interpreting and translation.Therefore,this paper chooses the interpretive theory as the guiding theory of subtitle translation,the subtitles in the classic movie Casablanca as a study case,aiming to conclude that some useful translation strategies are to be used to improve the quality of subtitle translation. 展开更多
关键词 subtitle translation the interpretive theory Casablanca
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Application of STEEP and Interpretive Structural Modeling in the Design Imagery of Taiwan Public Ceramic Relief Murals 认领 引用
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作者 Chuan-Chin Chen Jiann-Sheng Jiang Shaolei Zhou 《Journal of Contemporary Educational Research》 2024年第5期117-127,共11页
Ceramic relief mural is a contemporary landscape art that is carefully designed based on human nature,culture,and architectural wall space,combined with social customs,visual sensibility,and art.It may also become the... Ceramic relief mural is a contemporary landscape art that is carefully designed based on human nature,culture,and architectural wall space,combined with social customs,visual sensibility,and art.It may also become the main axis of ceramic art in the future.Taiwan public ceramic relief murals(PCRM)are most distinctive with the PCRM pioneered by Pan-Hsiung Chu of Meinong Kiln in 1987.In addition to breaking through the limitations of traditional public ceramic murals,Chu leveraged local culture and sensibility.The theme of art gives PCRM its unique style and innovative value throughout the Taiwan region.This study mainly analyzes and understands the design image of public ceramic murals,taking Taiwan PCRM’s design and creation as the scope,and applies STEEP analysis,that is,the social,technological,economic,ecological,and political-legal environments are analyzed as core factors;eight main important factors in the artistic design image of ceramic murals are evaluated.Then,interpretive structural modeling(ISM)is used to establish five levels,analyze the four main problems in the main core factor area and the four main target results in the affected factor area;and analyze the problem points and target points as well as their causal relationships.It is expected to sort out the relationship between these factors,obtain the hierarchical relationship of each factor,and provide a reference basis and research methods. 展开更多
关键词 Interpretive structural modeling(ISM) STEEP analysis Public ceramic relief murals(PCRM)
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On Teaching of Interpreting from Interpretive Theory 认领 引用
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作者 栗蔷薇 赵保成 《海外英语》 2013年第11X期148-149,共2页
This paper aims to explore teaching of interpreting nowadays by starting from the interpretive theory and its characteristics. The author believes that the theory is mainly based on the study of interpretation practic... This paper aims to explore teaching of interpreting nowadays by starting from the interpretive theory and its characteristics. The author believes that the theory is mainly based on the study of interpretation practice, whose core content, namely,"deverbalization"has made great strides and breakthroughs in the theory of translation; when we examine translation, or rather interpretation once again from the bi-perspective of language and culture, we will have come across new thoughts in terms of translation as well as teaching of interpreting. 展开更多
关键词 interpreting interpretive theory deverbalization culture
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A Deep Learning Framework for Heart Disease Prediction with Explainable Artificial Intelligence 认领 引用 被引量：2
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作者 Muhammad Adil Nadeem Javaid +2 位作者 Imran Ahmed Abrar Ahmed Nabil Alrajeh 《Computers, Materials & Continua》 SCIE EI 2026年第1期1944-1963,共20页
Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learni... Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction. 展开更多
关键词 Heart disease deep learning localized random affine shadowsampling local interpretable modelagnostic explanations shapley additive explanations 10-fold cross-validation
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An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling 认领 引用 被引量：1
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作者 Lei Liu Wei Li +7 位作者 Jian Gao Da-Li Yue De-Gang Wu Wu-Rong Wang Jin Lin Zhi-Bo Li Qian Zhong Jia-Gen Hou 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1754-1772,共19页
Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we... Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we innovatively propose an interpretable attention-guided generative adversarial network framework with dual-domain learning,which achieves precise sedimentary facies modeling under the constraints of well facies and soft probability data.Specifically,we first effectively extract and preserve prior information of sedimentary facies models from both spatial and frequency domain perspectives.Then,during simulation,to enhance the capability of the network model for finely characterizing complex heterogeneous models,cross-spatial attention mechanisms are designed to effectively capture short-range and long-range dependencies between multi-scale pattern features.Additionally,through systematic feature map visualization analysis,we elucidate the processes of conditional fitting and complex sedimentary facies model reconstruction,intuitively demonstrating the functional mechanisms of each module.Finally,systematic experiments are conducted on multiple datasets to validate the effectiveness of the proposed method.The results demonstrate that the generated sedimentary facies models exhibit high consistency with training datasets in terms of visual realism and statistical indicators.Quantitative comparisons reveal remarkable performance of the method,achieving low Wasserstein distance(0.09),Kernel Inception Distance(0.0017)and Kernel Maximum Mean Discrepancy(0.21).These findings further confirm the high realism of the generated realizations regarding pattern features.This study offers a reliable and practical method for geological reservoir modeling,thereby advancing quantitative,precise geological research with broad application prospects. 展开更多
关键词 Sedimentary facies models Attention-guided generative adversarial network Interpretable framework Sedimentary patterns Multi-condition modeling
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Tunnel ahead prospecting methods and intelligent interpretation of adverse geology:A review 认领 引用 被引量：1
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作者 Shucai Li Bin Liu +4 位作者 Lei Chen Huaifeng Sun Lichao Nie Zhengyu Liu Yuxiao Ren 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期1-19,共19页
Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects exte... Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects extend into deeper and more mountainous terrains,engineers face increasingly complex geological conditions,including high water pressure,intense geo-stress,elevated geothermal gradients,and active fault zones.These conditions pose substantial risks such as high-pressure water inrush,largescale collapses,and tunnel boring machine(TBM)blockages.Addressing these challenges requires advanced detection technologies capable of long-distance,high-precision,and intelligent assessments of adverse geology.This paper presents a comprehensive review of recent advancements in tunnel geological ahead prospecting methods.It summarizes the fundamental principles,technical maturity,key challenges,development trends,and real-world applications of various detection techniques.Airborne and semi-airborne geophysical methods enable large-scale reconnaissance for initial surveys in complex terrain.Tunnel-and borehole-based approaches offer high-resolution detection during excavation,including seismic ahead prospecting(SAP),TBM rock-breaking source seismic methods,fulltime-domain tunnel induced polarization(TIP),borehole electrical resistivity,and ground penetrating radar(GPR).To address scenarios involving multiple,coexisting adverse geologies,intelligent inversion and geological identification methods have been developed based on multi-source data fusion and artificial intelligence(AI)techniques.Overall,these advances significantly improve detection range,resolution,and geological characterization capabilities.The methods demonstrate strong adaptability to complex environments and provide reliable subsurface information,supporting safer and more efficient tunnel construction. 展开更多
关键词 Tunnel geological ahead prospecting Complex geological and environmental conditions Airborne geophysical methods Tunnel geophysical detection Borehole geophysical prospecting Intelligent geological interpretation
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Prediction model for indoor rock compression failure time based on ensemble learning and optimization algorithms 认领 引用 被引量：1
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作者 Boyang Zhang Xiancheng Wang +5 位作者 Fei Ding Liyuan Yu Luyuan Wu Shuai Zhao Yingkang Weng Zhaoyang Feng 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第7期2437-2453,共17页
The prediction of rock failure,a key fundamental research for addressing mining safety issues(such as mine slope stability and rockburst),faces challenges with traditional methods due to their complex generalization a... The prediction of rock failure,a key fundamental research for addressing mining safety issues(such as mine slope stability and rockburst),faces challenges with traditional methods due to their complex generalization and computational processes that struggle to describe the entire failure process.Consequently,12 prediction models integrating ensemble learning and optimization algorithms were established to predict rock peak stress and failure time using strain,elastic modulus,density,mass,and confining pressure as inputs.Fivefold cross-validation was used to optimize hyperparameters,significantly improving the model's generalization ability,robustness,and stability.Dataset was established through rock mechanics experiments,with strain increments configured at 0.008‰,0.01‰,and 0.012‰ in the test set.The cross-validation optimized particle swarm optimization eXtreme gradient boosting(CV-PSO-XGBoost)model performed best under a strain increment of 0.01‰,and its stress prediction achieved coefficient of determination R2=0.904,mean absolute error(MAE)=4.315,and root mean square error(RMSE)=5.435;while the failure time prediction demonstrated R2=0.811,mean absolute percentage error(MAPE)=7.842%,and MAE=30.343.Finally,SHapley Additive explanations(SHAP)analysis showed strain and stress significantly impact the model,with strain positively predicting failure time,aligning with traditional rock validating reliability.This study provides insights into the research on rock strata stability in mining. 展开更多
关键词 rock failure strain increment failure time ensemble learning SHapley Additive exPlanations interpretation
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Analytical equations for thermal and electrical conductivity prediction in as-cast magnesium alloys:A symbolic regression approach 认领 引用
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作者 Junwei Chen Jun Luan +3 位作者 Shuai Jiang Zhigang Yu Yunying Fan Kuochih Chou 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2026年第1期490-504,共15页
The thermal and electrical conductivities of magnesium alloys are highly sensitive to composition and microstructure,with thermal conductivity varying by up to 20-fold across different as-cast alloy systems,making rap... The thermal and electrical conductivities of magnesium alloys are highly sensitive to composition and microstructure,with thermal conductivity varying by up to 20-fold across different as-cast alloy systems,making rapid and accurate prediction crucial for high-throughput screening and development of high-performance alloys.This study introduces a physics-informed symbolic regression approach that addresses the limitations of traditional methods,including the high computational cost of first-principles calculations and the poor interpretability of machine learning models.Comprehensive datasets comprising 1512 data points from 60 literature sources were analyzed,including thermal conductivity measurements from 52 alloy systems and electrical conductivity measurements from 36 systems.The derived symbolic regression model achieved Mean Absolute Percentage Errors(MAPEs)of 11.2%and 11.4%for thermal conductivity in low and high-component systems,respectively.When integrated with the Smith-Palmer equation,electrical conductivity predictions reached MAPEs of 15.6%and 16.4%.Independent validation on an entirely separate dataset of 554 data points from 53 additional literature sources,including 37 previously unseen alloy systems,confirmed model generalizability with MAPEs of 10.7%-15.2%.Shapley Additive Explanations(SHAP)analysis was employed to evaluate the relative importance of different features affecting conductivity,while equation decomposition quantified the contribution of individual functional terms.This methodology bridges data-driven prediction with mechanistic understanding,establishing a foundation for knowledge-based design of magnesium alloys with tailored transport properties. 展开更多
关键词 Electrical conductivity Interpretable modeling Magnesium alloys Symbolic regression Thermal conductivity
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Machine learning-assisted design of lightweight refractory high-entropy alloys: A comprehensive review 认领 引用
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作者 Lei Chen Gang Qin +4 位作者 Yao Chen Qi Wang Liang Wang Yanqing Su Ruirun Chen 《Metals Advances》 SCIE EI CAS CSCD 2026年第2期26-47,共22页
Lightweight refractory high-entropy alloys(LRHEAs)represent an emerging class of structural materials that integrate low density with exceptional strength and outstanding high-temperature stability,positioning them as... Lightweight refractory high-entropy alloys(LRHEAs)represent an emerging class of structural materials that integrate low density with exceptional strength and outstanding high-temperature stability,positioning them as promising candidates for aerospace and advanced industrial applications.Nevertheless,the design of LRHEAs is challenged by their vast compositional space,complex multi-objective performance trade-offs,and the inefficiency of conventional trial-and-error experimental approaches.In recent years,machine learning(ML)has emerged as a transformative tool in this domain,offering the capacity to analyze high-dimensional datasets and uncover hidden correlations between composition,processing,microstructure,and properties.This review systematically examines both conventional design strategies-including empirical parameters,phase diagram calculations,and first-principles simulations-and the emerging ML-aided design framework,with a focus on bridging traditional knowledge and data-driven methodologies.We critically survey recent advances in ML applications across three key areas:compositional optimization,mechanistic interpretation,and atomic-scale simulation.Target-driven ML models facilitate efficient navigation of the alloy design space,while interpretable algorithms integrated with atomic simulations provide fundamental insights into strengthening and toughening mechanisms.The review concludes by summarizing current achievements and identifying persistent challenges related to data scarcity,model transferability,and physical interpretability.Looking forward,we envision that a deeper integration of high-throughput experiments,multi-scale simulations,and artificial intelligence will establish a robust,systematic,and accelerated design paradigm for next-generation LRHEAs. 展开更多
关键词 Lightweight refractory high-entropy alloys Machine learning Interpretability analysis Properties optimization Alloy design
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A Deep Learning–Based Bias Correction Model for Tropical Cyclone Track and Intensity towards Forecasting of the TianXing Large Weather Model 认领 引用
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作者 Shijin YUAN Xingzhou WANG +3 位作者 Bin MU Guansong WANG Zeyi NIU Hao LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第3期612-630,共19页
Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,i... Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s-1,and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction. 展开更多
关键词 tropical cyclone TianXing large weather model bias correction interpretability analysis deep learning-based model
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Harnessing speckle images:efficient extraction of hidden information 认领 引用
20
作者 Weiru Fan Xiaobin Tang +5 位作者 Xingqi Xu Huizhu Hu Vladislav V.Yakovlev Shi-Yao Zhu Da-Wei Wang Delong Zhang 《Advanced Photonics Nexus》 CSCD 2026年第1期211-223,共13页
Scattering obscures information carried by waves by producing speckle patterns,posing a fundamental challenge across diverse fields,from microscopy to astronomy.Although machine learning has recently shown promise in ... Scattering obscures information carried by waves by producing speckle patterns,posing a fundamental challenge across diverse fields,from microscopy to astronomy.Although machine learning has recently shown promise in speckle analysis,existing approaches are hindered by their dependence on large,labeled datasets—a significant bottleneck in many real-world applications.Here,we introduce speckle unsupervised recognition and evaluation(SURE),a groundbreaking unsupervised learning strategy for speckle recognition that eliminates the need for labeled training data.SURE's distinctive feature lies in its ability to extract invariant features through advanced clustering algorithms to enable direct classification of high-level information from speckle patterns without prior knowledge.We demonstrate the transformative potential of this approach in two key applications:(1)a noninvasive glucose monitoring system that accurately tracks glucose concentrations over time without extensive calibration and(2)a high-throughput communication system using multimode fibers,achieving improved performance in dynamic environments.In addition,we showcase SURE's unprecedented capability to classify objects hidden behind obstacles using scattered light,further broadening its scope.This versatile approach opens new frontiers in biomedical diagnostics,quantum network decoupling,and remote sensing,unlocking a transformative new paradigm for extracting information from seemingly random optical patterns. 展开更多
关键词 scattering unsupervised learning speckle interpretation pattern recognition image sensing
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