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A Model-Data Driven Approach for Calibration of a 5-DOF Hybrid Machining Robot 认领 引用
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作者 Haitao Liu Zhibiao Yan +1 位作者 Conglin Wu Tian Huang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第4期248-265,共18页
Current research on robot calibration can be roughly classified into two categories,and both of them have certain inherent limitations.Model-based methods are difficult to model and compensate the pose errors arising ... Current research on robot calibration can be roughly classified into two categories,and both of them have certain inherent limitations.Model-based methods are difficult to model and compensate the pose errors arising from configuration-dependent geometric and non-geometric source errors,whereas the accuracy of data-driven methods depends on a large amount of measurement data.Using a 5-DOF(degrees of freedom)hybrid machining robot as an exemplar,this study presents a model data-driven approach for the calibration of robotic manipulators.An f-DOF realistic robot containing various source errors is visualized as a 6-DOF fictitious robot having error-free parameters,but erroneous actuated/virtual joint motions.The calibration process essentially involves four steps:(1)formulating the linear map relating the pose error twist to the joint motion errors,(2)parameterizing the joint motion errors using second-order polynomials in terms of nominal actuated joint variables,(3)identifying the polynomial coefficients using the weighted least squares plus principal component analysis,and(4)compensating the compensable pose errors by updating the nominal actuated joint variables.The merit of this approach is that it enables compensation of the pose errors caused by configuration-dependent geometric and non-geometric source errors using finite measurement configurations.Experimental studies on a prototype machine illustrate the effectiveness of the proposed approach. 展开更多
关键词 Hybrid machining robot Calibration Model-data driven approach
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AI-driven integration of multi-omics and multimodal data for precision medicine 认领 引用 被引量：1
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作者 Heng-Rui Liu 《Medical Data Mining》 CAS 2026年第1期1-2,共2页
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ... High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1). 展开更多
关键词 high throughput transcriptomics multi omics single cell multimodal learning frameworks foundation models omics data modalitiesemerging ai driven precision medicine
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Erratum to“False Data Injection Attacks on Data-Driven Algorithms in Smart Grids Utilizing Distributed Power Supplies”[Engineering 51(2025)62-74] 认领 引用
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作者 Zengji Liu Mengge Liu +1 位作者 Qi Wang Yi Tang 《Engineering》 SCIE EI CSCD 2026年第5期362-362,共1页
The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualizati... The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualization,Validation,Supervision,Software,Resources,Project administration,Methodology,Investigation,Funding acquisition,Formal analysis,Data curation,Conceptualization.Mengge Liu:Writing-review&editing,Writing-original draft,Investigation.Qi Wang:Writing-review&editing,Writing-original draft.Yi Tang:Writing-review&editing,Writing-original draft. 展开更多
关键词 data driven algorithms credit authorship contribution statement false data injection attacks smart grids distributed power supplies
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Electrochemical Corrosion Assists Dendrite-Driven Fracture in Solid Electrolytes 认领 引用
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作者 Haofei Sun Hongrui Yue 《Rare Metals》 SCIE EI CAS CSCD 2026年第7期19-21,共3页
Solid-state lithium batteries are being pursued as next-generation energy-storage systems since they promise improved safety and higher energy density by pairing nonflammable inorganic solid electrolytes with lithium-... Solid-state lithium batteries are being pursued as next-generation energy-storage systems since they promise improved safety and higher energy density by pairing nonflammable inorganic solid electrolytes with lithium-metal anodes[1-3].Among various solid electrolytes,inorganic ceramics are particularly attractive because they combine high Li+conductivity,a wide electrochemical stability window,and sufficient mechanical rigidity.This rigidity would be expected to resist lithium penetration during electrodeposition[4].However,lithium filaments can still penetrate dense ceramic electrolytes,causing internal short circuits.This apparent contradiction raises a fundamental question:how can soft lithium propagate through a hard ceramic electrolyte? 展开更多
关键词 solid electrolytesinorganic ceramics dendrite driven fracture nonflammable inorganic solid electrolytes solid electrolytes inorganic solid electrolytes solid state lithium batteries electrochemical corrosion Li conductivity
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Comparison of data driven and data-mechanism hybrid driven methods for key variables prediction based on data sets with different sample sizes and noises 认领 引用
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作者 Qihang Tan Chao Wang +2 位作者 Wange Li Jinghao Sun Jun Zhao 《ENGINEERING Chemical Engineering》 SCIE EI CAS CSCD 2026年第2期57-68,共12页
Soft measurement based on data-driven models is an important method to predict key variables in process industry due to low latency demand and economics costs.However,data-driven models cannot provide accurate predict... Soft measurement based on data-driven models is an important method to predict key variables in process industry due to low latency demand and economics costs.However,data-driven models cannot provide accurate prediction on a noisy data set with a small number of samples.In response to the challenge of noisy data and lack of samples,several data-mechanism hybrid driven methods are proposed to improve key variables prediction performances on the basis of three data-driven models including random forest,extreme gradient boosting,and artificial neural network.Simultaneously,the effectiveness of hybrid driven methods proposed is validated via two cases including benzene-toluene-xylene distillation and steam methane reforming process,where data sets feature different sample sizes and noise intensity.The comparison results show that the hybrid driven methods can improve the prediction accuracy to a certain extent.The degree of improvement depends on the noise intensity,sample size,and data-driven model selected.Under conditions of noise intensity at 10%–20%and sample size ranging from 100 to 400 in this work,after adopting the hybrid driven methods,the coefficient of determination for random forest,extreme gradient boosting,and artificial neural network can be improved by 0.3%–5.2%,0.6%–17.7%,and 0.1%–36.2%compared to corresponding data driven models. 展开更多
关键词 data-mechanism hybrid driven methods different sample sizes noise dataset machine learning process industry
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Multi-interface-induced radiant heat activation strategy:Achieving solar-driven hydrogen production from formic acid 认领 引用
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作者 Kun Liu Rui Wang +3 位作者 Zhengjun Tu Liang Zhao Fengnian Wang Yinshi Li 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第4期76-84,I0004,共9页
Forced convection between the reactants and the catalyst in solar-driven hydrogen production systems increases heat loss,thereby constraining the hydrogen evolution rate.To address these challenges,we proposed a multi... Forced convection between the reactants and the catalyst in solar-driven hydrogen production systems increases heat loss,thereby constraining the hydrogen evolution rate.To address these challenges,we proposed a multi-interface-induced radiant heat activation strategy that utilizes photothermally generated radiant heat to pre-activate reactants.This process enables the rapid interfacial vaporization of reactants and significantly enhances mass transfer.The resulting multi-interface heating system(MIH)developed achieves gradient heat utilization,combining broadband solar absorption with low thermal emittance,while ensuring precise spatiotemporal coordination between reactant supply and catalytic activity.As a result,a high hydrogen evolution rate of 242 mmol g-1h-1is achieved under 1 sun illumination at room temperature,using formic acid(HCOOH) as a liquid hydrogen carrier.This work demonstrates an efficient,low-energy pathway for hydrogen generation and offers a promising platform for practical solar-to-hydrogen conversion under ambient conditions. 展开更多
关键词 Multi‑interface heating system Hydrogen evolution Solar‑driven energy conversion Formic acid
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An efficient multi‑objective optimization framework based on data‑driven identification and adaptive directed correction for complex distillation processes 认领 引用
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作者 Fucheng Xu Lu Yang +6 位作者 Zihao Wang Tao Shi Rongsheng Lin Bohong Wang Hengcong Tao Zhiliang Cheng Weifeng Shen 《ENGINEERING Chemical Engineering》 SCIE EI CAS CSCD 2026年第7期25-35,共11页
Complex distillation processes can often be effectively optimized using meta‑heuristic algorithms.However,during optimization procedure,a large number of infeasible solutions are generated,hindering efficient explorat... Complex distillation processes can often be effectively optimized using meta‑heuristic algorithms.However,during optimization procedure,a large number of infeasible solutions are generated,hindering efficient exploration of feasible,high‑performance regions of the search space.In this study,we propose a data‑driven identification and adaptive directed correction strategy for handling infeasible solutions,and on this basis,develop an efficient multi‑objective optimization framework(MO‑DIDC)for complex distillation processes.By identifying infeasible solutions that closely resemble high‑performance ones,the framework leverages them to accelerate convergence to optimal designs.A surrogate model is trained to distinguish high‑and low‑performance solutions and is then used to identify potentially high‑performance candidates within the infeasible set.Through similarity analysis,the most influential variable is selected for correction to generate new promising solutions.This strategy reduces unnecessary exploration of infeasible regions and concentrates computational effort on feasible,high‑quality solutions.Demonstrated on a side‑stream double‑column extractive distillation system and a four‑column extractive distillation system,the proposed optimization framework outperforms a widely used genetic algorithm while substantially improving computational efficiency,achieving optimization time reductions of 35.3%and 20.8%,respectively.Overall,the proposed MO‑DIDC framework provides an effective and computationally efficient tool for the optimization of complex distillation processes. 展开更多
关键词 data‑driven identification adaptive directed correction infeasible solutions handling multi‑objective optimization complex distillation process
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Erratum:Data-Driven Prediction of Thermal Conductivity from Short MD Trajectories:A GCN-LSTM Approach [Chin.Phys.Lett.43 020801 (2026)] 认领 引用
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作者 Shihao Feng Haifeng Chen +2 位作者 Jian Zhang Meng An Gang Zhang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第3期380-380,共1页
In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structu... In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted. 展开更多
关键词 typesetting error production processfigure short MD trajectories GCN LSTM molecular dynamics simulation thermal conductivity graph structured representations data driven prediction
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Diversity-Driven Contrastive Value Ensembles With Categorical Constraints for Goal-Conditioned Robotic Control 认领 引用
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作者 Zhiyi Shi Ruihao Zhu +3 位作者 Shuai Wu Wei Tong Guangyu Zhu Edmond Q.Wu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期1001-1003,共3页
Dear Editor,This letter presents a contrastive reinforcement learning(Contrastive RL)-based framework,addressing challenging goal-conditioned problems in robotic control.While Contrastive RL offers promise in learning... Dear Editor,This letter presents a contrastive reinforcement learning(Contrastive RL)-based framework,addressing challenging goal-conditioned problems in robotic control.While Contrastive RL offers promise in learning state-action-goal relationships,it suffers from a critical limitation:Insufficient discriminability between positive and negative samples attributed to inefficient value exploration and model overfitting. 展开更多
关键词 categorical constraints value exploration contrastive contrastive rl value ensembles contrastive reinforcement learning contrastive rl based diversity driven goal conditioned
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Pressure-Driven Electronic Reconstruction and Anomalous Hall Transport Evolution in Monoclinic FeNbTe2 认领 引用
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作者 Pengyu Su Zhaoyu Zhu +5 位作者 Jingyi He Jing Wang Weiwei Wang Chenchao Xu Haiyang Yang Jie Wu 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第7期106-110,I0055-I0062,共5页
Pressure provides a clean route to uncover hidden electronic and magnetic instabilities in layered tellurides by continuously tuning bandwidths and Fermi-surface topology without introducing chemical disorder.Here we ... Pressure provides a clean route to uncover hidden electronic and magnetic instabilities in layered tellurides by continuously tuning bandwidths and Fermi-surface topology without introducing chemical disorder.Here we report high-pressure electrical transport,magne-toresistance,Hall effect,and synchrotron x-ray diffraction measurements on single-crystalline monoclinic FeNbTe2.At ambient pressure,FeNbTe2 displays a low-temperature resistive upturn together with negative magnetoresistance and an anomalous Hall effect.Upon compression across Pc~5.5 GPa,the resistive upturn is fully suppressed,the anomalous Hall response collapses,and the Hall coeffi-cient reverses sign.Room-temperature high-pressure synchrotron x-ray diffraction reveals that the monoclinic structure remains stable throughout the investigated pressure range,with no evidence for a symmetry-changing structural transition.These combined results suggest a pressure-driven electronic reconstruction near Pc within the preserved monoclinic framework,accompanied by a marked modi-fication of the magnetic transport response.Our work establishes monoclinic FeNbTe2 as a useful platform for exploring pressure-con-trolled electronic reconstruction and anomalous transverse transport in layered magnetic tellurides. 展开更多
关键词 pressure driven electronic reconstruction layered tellurides anomalous Hall transport chemical disorderhere electronic magnetic instabilities ambient pressurefenbte monoclinic FenBTe high pressure electrical transport
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Data-Driven Prediction of Thermal Conductivity from Short MD Trajectories:A GCN-LSTM Approach 认领 引用
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作者 Shihao Feng Haifeng Chen +2 位作者 Jian Zhang Meng An Gang Zhang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第2期282-299,共18页
We propose a data-driven framework for rapid prediction of thermal conductivity in solids based on shorttime molecular dynamics(MD)simulations.By converting atomic configurations into graph representations,a graph con... We propose a data-driven framework for rapid prediction of thermal conductivity in solids based on shorttime molecular dynamics(MD)simulations.By converting atomic configurations into graph representations,a graph convolutional network(GCN)is used to extract spatial features,which are then processed by a long short-term memory(LSTM)network to capture the temporal evolution of physical properties.The framework is validated using equilibrium MD simulations of germanium at 1000 K across various system sizes.With sizespecific normalization and optimized hyperparameters,the model accurately predicts the converged thermal conductivity,achieving results consistent with experimental data.Notably,the proposed method significantly reduces computational time by up to 800-fold at large system sizes,which demonstrates its potential to accelerate thermal transport simulations in solid-state systems. 展开更多
关键词 equilibrium md simulations molecular dynamics long short term memory data driven prediction graph representationsa graph convolutional network gcn thermal conductivity extract spatial featureswhich
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Lactate‑driven lysine lactylation in tumor progression,therapy resistance,and prognosis 认领 引用
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作者 Dan Huang Lan Jiang +1 位作者 Qihui Zhao Zhe Chen 《MedScience》 SCIE CAS CSCD 2026年第2期392-395,共4页
Lactylation is a post‑translational modification(PTM)of proteins involved in epigenetic regulation.The large amounts of lactate produced in tumors via the Warburg effect drive lysine lactylation.Lysine lactylation is ... Lactylation is a post‑translational modification(PTM)of proteins involved in epigenetic regulation.The large amounts of lactate produced in tumors via the Warburg effect drive lysine lactylation.Lysine lactylation is a key link between the Warburg effect and the malignant phenotype of cancer that enables tumors to adapt to their microenvironment and resist therapeutic interventions through regulating gene expression and metabolism[1].In a seminal study,Chen et al.[2]demonstrated that a large amount of the lactate produced becomes an“accomplice”of tumor cells during tumor cell metabolism,helping them to evade the killing effect of radiotherapy and chemotherapy in the journal Nature.And they found that lactate is primarily involved in DNA repair mediated by homologous recombination(HR). 展开更多
关键词 malignant phenotype lysine lactylationlysine lactylation epigenetic regulationthe warburg effect regulating gene expression metabolism therapy resistance lactate driven lysine lactylation tumor progression
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Thermally Driven Soliton Tuning and State Transition in Bi2TeSe2-Based Ultrafast Fiber Lasers for Encoding Applications 认领 引用
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作者 Bin Shen Rui Diao +4 位作者 Chong-Zhou Zhao Xin Guo Xiao-Bo Ma Chao-Qing Dai Yue-Yue Wang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第2期99-110,共12页
We demonstrate a thermally tunable mode-locked fiber laser integrating a polarization-sensitive SMF-PMFSMF modulator and a Bi2TeSe2 saturable absorber(SMF:single-mode fiber;PMF:polarization-maintaining fiber).By... We demonstrate a thermally tunable mode-locked fiber laser integrating a polarization-sensitive SMF-PMFSMF modulator and a Bi2TeSe2 saturable absorber(SMF:single-mode fiber;PMF:polarization-maintaining fiber).By controlling the PMF temperature,reversible switching among conventional,dissipative,and boundstate solitons is achieved.The wavelength tuning ranges are about 5 nm and 2.8 nm for conventional and dissipative solitons,respectively,with a tuning efficiency of 0.35 nm/℃.Numerical simulations based on temperatureinduced birefringence variation reproduce the observed dynamics.Furthermore,a wavelength-encoding scheme utilizing thermally driven soliton shifts is proposed,providing a feasible approach for soliton-state-controlled optical communication. 展开更多
关键词 polarization sensitive smf pmfsmf modulator wavelength tuning bi tese based ultrafast fiber lasers soliton shifts birefringence variation thermally driven soliton tuning state transition encoding applications
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Symmetry-Driven Giant Magneto–Optical Kerr Effects in Altermagnetic Insulator 认领 引用
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作者 Jiaxin Luo Xiaodong Zhou +7 位作者 Jinxuan Liang Ledong Wang Qiuyun Zhou Yong Jiang Wenhong Wang Yugui Yao Luyi Yang Wanjun Jiang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第2期232-253,共22页
Altermagnets have attracted tremendous interest for revealing intriguing physics and promising spintronics applications.In contrast to conventional antiferromagnets,altermagnets break both PT and Tτsymmetries,and sim... Altermagnets have attracted tremendous interest for revealing intriguing physics and promising spintronics applications.In contrast to conventional antiferromagnets,altermagnets break both PT and Tτsymmetries,and simultaneously exhibit spin-split band structures with a vanishing net magnetization.To quantify altermagnetic insulators without conduction electrons,we propose to use the magneto–optical Kerr effect(MOKE).In particular,we demonstrate not only the giant MOKE responses,but also their connection with the orientations of Néel vectors at room temperature in the altermagnetic insulator hematite(α-Fe2O3).Specifically,under the Néel vector along the[1100]axis,we find a giant polar Kerr rotation angle of 103.7 mdeg in the(1120)plane,which is allowed by the magnetic space group C2′/c′.Under the Néeel vector along the[1120]axis,we find a longitudinal Kerr angle of 9.6 mdeg in the(0001)plane,which is allowed by the magnetic space group C2/c.Further,we show that such pronounced MOKE effects directly enable optical imaging of altermagnetic domains,together with their reversible domain wall(DW)motion.Our studies not only suggest that MOKE can be used to identify altermagnetic candidates,but also signify the feasibility of exploring altermagnetic optical and DW spintronics,which could largely expand the current research paradigm of altermagnetism. 展开更多
关键词 optical imaging magneto optical kerr effect moke symmetry driven magneto optical Kerr effect altermagnetic insulators hematite n el vectors giant magneto optical Kerr effects
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Small-molecule TREM2 agonists:From artificial intelligence driven discovery to therapeutic application in Alzheimer's disease 认领 引用
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作者 Sungwoo Cho Moustafa Gabr 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第10期4904-4905,共2页
The Alzheimer's disease(AD)therapeutic landscape is evolving rapidly.While anti-amyloid antibodies have achieved regulatory approval,their incremental clinical benefits have intensified interest in neuroinflammati... The Alzheimer's disease(AD)therapeutic landscape is evolving rapidly.While anti-amyloid antibodies have achieved regulatory approval,their incremental clinical benefits have intensified interest in neuroinflammation as a complementary therapeutic axis.Triggering receptor expressed on myeloid cells 2(TREM2)represents a particularly attractive microglial target,given that loss-offunction variants confer a three-fold elevation in AD risk. 展开更多
关键词 loss function variants microglial targetgiven neuroinflammation triggering receptor expressed myeloid cells small molecule trem agonists alzheimers disease artificial intelligence driven discovery therapeutic application
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Multi-objective ANN-driven genetic algorithm optimization of energy efficiency measures in an NZEB multi-family house building in Greece 认领 引用
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《建筑节能（中英文）》 CAS 2026年第2期62-62,共1页
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu... The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%. 展开更多
关键词 energy efficiency measures gas boilerssplit units building envelope components energy efficiency economic performance artificial neural network ann driven multi objective optimization economic performance optimization ANN driven GA methods
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Data-Driven Research Drives Earth System Science 认领 引用
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作者 Xing Yu Shufeng Yang 《Journal of Earth Science》 SCIE CAS CSCD 2026年第1期361-367,共7页
0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has... 0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system. 展开更多
关键词 natural science data interpretation earth system science field investigationsdata earth science composition study individual components earth system data driven research
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Made with Minds Integration of manufacturing and services signals a structural shift toward intelligence-driven growth in which value creation increasingly depends on deep cross-sector fusion 认领 引用
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作者 Wang Jinchen 《China Report ASEAN》 2026年第7期23-25,共3页
At the intersection of a new wave of technological revolution and industrial transformation,the boundaries between manufacturing and services are blurring at an unprecedented pace.This is not a simple process of addin... At the intersection of a new wave of technological revolution and industrial transformation,the boundaries between manufacturing and services are blurring at an unprecedented pace.This is not a simple process of adding one sector to another,but a deeper structural shift.As manufacturing and services become increasingly integrated,a broader transformation is underway—from labor-intensive production to more intelligence-driven development. 展开更多
关键词 minds value creation manufacturing industrial transformationthe intelligence driven growth cross sector fusion new wave technological revolution structural shift
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Rewriting the Rules of Cancer Therapy Using Radioactivity-Driven Chemistry 认领 引用
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《Bulletin of the Chinese Academy of Sciences》 2026年第2期101-103,共3页
The Tan Kah Kee Young Scientist Award in Chemistry goes to Prof.LIU Zhibo from Peking University,recognizing his pioneering contributions to radioactivity-driven chemistry.Operating at the intersection of life science... The Tan Kah Kee Young Scientist Award in Chemistry goes to Prof.LIU Zhibo from Peking University,recognizing his pioneering contributions to radioactivity-driven chemistry.Operating at the intersection of life sciences and urgent clinical needs,his innovative covalent radiopharmaceutical technology shatters the historical bottleneck of traditional radiopharmaceuticals:the persistent trade-off between therapeutic efficacy and safety.Furthermore,LIU has leveraged the unique properties of radioactivity to uncover the anti-tumor immune activity of pyroptosis. 展开更多
关键词 cancer therapy tan kah kee young scientist award radioactivity driven chemistry therapeutic efficacy pyroptosis safety life sciences covalent radiopharmaceutical technology
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Knowledge Driven Machine Learning Towards Interpretable Intelligent Prognostics and Health Management:Review and Case Study 认领 引用 被引量：4
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作者 Ruqiang Yan Zheng Zhou +6 位作者 Zuogang Shang Zhiying Wang Chenye Hu Yasong Li Yuangui Yang Xuefeng Chen Robert X.Gao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第1期31-61,共31页
Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpret... Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM. 展开更多
关键词 PHM Knowledge driven machine learning Signal processing Physics informed Interpretability
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