The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on th...The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on the Dagangshan high-arch dam during its normal water storage operating period to assess potential damage.The study analyzes the MS characteristics of the dam during the Luding earthquake(Ms=6.8).A framework for constructing a damage driven DT model of a high-arch dam is proposed.The DT model is capable of self-updating its mechanical parameters based on MS data.Seismic response calculations are conducted utilizing cloud computing,allowing for the direct presentation of results within the DT model.The results indicate a high-risk area of the Dagangshan arch dam,characterized by significantMS deformation,primarily centered on the arch crown beam.This zone encompasses dam sections Nos.5-6,10-11,13-16,and 19-20,all located above 1030 m elevation.Under seismic loading,the arch dam exhibits a back-and-forth movement along the river,ultimately reaching a stable state.Following the earthquake,the stress state of the dam does not experience substantial changes.The average relative error between numerical results and measured peak ground acceleration values is 17%when considering the cumulative effect of damage,compared to 36%when neglecting this effect.This study presents a more reliable approach for assessing the state of dams.展开更多
The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable pr...The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.展开更多
Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze we...Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.展开更多
Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery conditi...Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.展开更多
BACKGROUND Syngeneic orthotopic tumor models offer an optimal functional tumor–immune interface for hepatocellular carcinoma research.Yet,unpredictable growth kinetics and spontaneous regression pose major obstacles....BACKGROUND Syngeneic orthotopic tumor models offer an optimal functional tumor–immune interface for hepatocellular carcinoma research.Yet,unpredictable growth kinetics and spontaneous regression pose major obstacles.Efficient induction protocols and continuous monitoring are therefore essential.Routine exploratory surgeries are ethically untenable,making non-invasive imaging modalities attractive alternatives.High-resolution magnetic resonance imaging and microcomputed tomography deliver detailed insights but incur substantial equipment costs,radiation risks,time demands,and require specialized expertise—challenges that limit their routine use.In contrast,ultrasound(US)imaging emerges as a cost-effective,radiation-free,and rapid approach,facilitating practical and ethical longitudinal assessment of tumor progression in preclinical studies.AIM To optimize the orthotopic hepatocellular carcinoma model and evaluate the potential of US imaging for accurate and cost-effective tumor monitoring.METHODS Hepatocellular carcinoma was induced in 28 Sprague Dawley rats by implanting 5×10^(6) N1S1 cells into the left lateral hepatic lobe.Tumor progression was monitored weekly via US.Upon reaching 100-150 mm^(3),an experimental group(n=14)received Sorafenib(40 mg/kg)orally on alternate days for 28 days;efficacy was compared to untreated controls.US accuracy was validated against micro-computed tomography,gross caliper measurements and histopathological analysis.Reliability and operator proficiency in US assessment were also evaluated.RESULTS US images procured 7-day post-surgery revealed a well-defined hypoechoic nodule at the left liver lobe tip,confirming successful tumor induction(mean volume 130±39 mm^(3)).Only three animals exhibited spontaneous regression by week 2,underscoring the model’s stability.Sorafenib treatment elicited a marked tumor reduction(678±103 mm^(3))vs untreated control(6005±1760 mm^(3)).US assessment demonstrated robust intra and interobserver reproducibility with high sensitivity and specificity for tumor detection.Moreover,US derived volumes correlated strongly with gross caliper measurements,histopathological analysis,and microcomputed tomography imaging,validating its reliability as a non-invasive monitoring tool in preclinical hepatocellular carcinoma studies.CONCLUSION The results demonstrate that US imaging is a reliable,cost-effective,and animal sparing approach with an easy tomaster protocol,enabling monitoring of tumor progression and therapeutic response in orthotopic liver tumor models.展开更多
Monitoring of the mechanical behavior of underwater shield tunnels is vital for ensuring their long-term structural stability.Typically determined by empirical or semi-empirical methods,the limited number of monitorin...Monitoring of the mechanical behavior of underwater shield tunnels is vital for ensuring their long-term structural stability.Typically determined by empirical or semi-empirical methods,the limited number of monitoring points and coarse monitoring schemes pose huge challenges in terms of capturing the complete mechanical state of the entire structure.Therefore,with the aim of optimizing the monitoring scheme,this study introduces a spatial deduction model for the stress distribution of the overall structure using a machine learning algorithm.Initially,clustering experiments were performed on a numerical data set to determine the typical positions of structural mechanical responses.Subsequently,supervised learning methods were applied to derive the data information across the entire surface by using the data from these typical positions,which allows flexibility in the number and combinations of these points.According to the evaluation results of the model under various conditions,the optimized number of monitoring points and their locations are determined.Experimental findings suggest that an excessive number of monitoring points results in information redundancy,thus diminishing the deduction capability.The primary positions for monitoring points are determined as the spandrel and hance of the tunnel structure,with the arch crown and inch arch serving as additional positions to enhance the monitoring network.Compared with common methods,the proposed model shows significantly improved characterization abilities,establishing its reliability for optimizing the monitoring scheme.展开更多
This paper investigates the use of explainable artificial intelligence(XAI)and trustworthy artificial intelligence(TAI)methods for condition monitoring on a laser cutting machine.The focus is on the analysis of the ra...This paper investigates the use of explainable artificial intelligence(XAI)and trustworthy artificial intelligence(TAI)methods for condition monitoring on a laser cutting machine.The focus is on the analysis of the rack and pinion contact with wear being predicted by four differently derived adaptive-network-based fuzzy inference system(s)(ANFIS)models.Using both model-agnostic and model-specific parameters integrated in a weighted evaluation framework,the models are evaluated with respect to the effectiveness of explanations.This framework is based on the observation of the outputs of the individual layers of ANFIS,also focusing on aspects of two multivalued logics,namely fuzzy logic and support logic.The results show that the introduced weighted evaluation framework makes it possible to quantify the explainability of the individual models in terms of XAI and TAI.Finally,a preselection of a model for predicting the wear of the rack and pinion contact can be made.展开更多
The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warni...The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.展开更多
Ensuring the stability of the surrounding rock mass is of great importance during the construction of a large underground powerhouse.The presence of unfavorable structural planes within the rock mass,such as faults,ca...Ensuring the stability of the surrounding rock mass is of great importance during the construction of a large underground powerhouse.The presence of unfavorable structural planes within the rock mass,such as faults,can lead to substantial deformation and subsequent collapse.A series of in situ experiments and discrete element numerical simulations have been conducted to gain insight into the progressive failure behavior and deformation response of rocks in relation to controlled collapse scenarios involving gently inclined faults.First,the unloading damage evolution process of the surrounding rock mass is characterized by microscopic analysis using microseismic(MS)data.Second,the moment tensor inversion method is used to elucidate the temporal distribution of MS event fracture types in the surrounding rock mass.During the development stage of the collapse,numerous tensile fracture events occur,while a few shear fractures corresponding to structural plane dislocation precede their occurrence.The use of the digital panoramic borehole camera,acoustic wave test,and numerical simulation revealed that gently inclined faults and deep cracks at a certain depth from the cavern periphery are the primary factors contributing to rock collapse.These results provide a valuable case study that can help anticipate and mitigate fault-slip collapse incidents while providing practical insights for underground cave excavation.展开更多
Compressor surge is a major aerodynamic instability that constrains the performance and reliability of industrial gas turbines.To address this challenge,this paper provides a comprehensive review of recent progress in...Compressor surge is a major aerodynamic instability that constrains the performance and reliability of industrial gas turbines.To address this challenge,this paper provides a comprehensive review of recent progress in surge monitoring,modeling,and control strategies.Key difficulties in early surge detection are identified,including ambiguous precursor signals,strongly coupled system dynamics,and sensor-actuator time delays.The review categorizes existing modeling approaches into high-fidelity computational fluid dynamics(CFD),reducedorder physical models,and data-driven techniques,evaluating each in terms of accuracy,adaptability,and realtime feasibility.In terms of control strategies,both passive and active methods are analyzed,with a particular focus on closed-loop feedback,model predictive control,robust control,and intelligent data-driven approaches.The review concludes by outlining future directions that prioritize model integration,control reliability,and systemlevel coordination for enhanced compressor stability.展开更多
To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail stee...To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge.An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns.XGBoost(eXtreme Gradient Boosting),a gradient-boosting machine learning(ML)algorithm,was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution.Unlike traditional numerical models that rely on extensive assumptions and idealizations,XGBoost effectively captures nonlinear and time-varying relationships between stress states and operational/environmental factors,such as temperature,traffic load,and structural geometry.This approach allows for the identification of critical periods and conditions under which stress redistribution becomes significant.Results indicate a clear shift of stress concentrations frombeamends toward mid-span regions following the commencement of metro operations,reflecting both structural adaptation and localized overstress near arch ribs.Furthermore,the model generates robust predictions of stress evolution,demonstrating potential applications in early warning systems and fatigue risk assessment.This work represents the first application of interpretable gradient-boosting techniques to stress redistribution modeling in double-deck bridges.In addition,a Stress Redistribution Index(SRI)is proposed,derived from this monitoring study and finite-element-based transverse load distributions,to quantify temporal stress shifts between midspan and edge beams.The results provide both theoretical contributions and practical guidance for the design,inspection,and maintenance of complex bridge structures.展开更多
In the version of the article originally published in the volume 68,issue 12,2025 of Sci China Mater(pages 4413-4422,https://doi.org/10.1007/s40843-025-3667-7),the Chinese name of the co-first author(肖天孝)was incorr...In the version of the article originally published in the volume 68,issue 12,2025 of Sci China Mater(pages 4413-4422,https://doi.org/10.1007/s40843-025-3667-7),the Chinese name of the co-first author(肖天孝)was incorrect.The corrected Chinese name is:肖天笑.展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as dela...With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as delayed retraining,inconsistent version management,insufficient drift monitoring,and limited data security still hinder efficient and reliable model operations.To address these issues,this paper proposes the Intelligent Model Lifecycle Management Algorithm(IMLMA).The algorithm employs a dual-trigger mechanism based on both data volume thresholds and time intervals to automate retraining,and applies Bayesian optimization for adaptive hyperparameter tuning to improve performance.A multi-metric replacement strategy,incorporating MSE,MAE,and R2,ensures that new models replace existing ones only when performance improvements are guaranteed.A versioning and traceability database supports comparison and visualization,while real-time monitoring with stability analysis enables early warnings of latency and drift.Finally,hash-based integrity checks secure both model files and datasets.Experimental validation in a power metering operation scenario demonstrates that IMLMA reduces model update delays,enhances predictive accuracy and stability,and maintains low latency under high concurrency.This work provides a practical,reusable,and scalable solution for intelligent model lifecycle management,with broad applicability to complex systems such as smart grids.展开更多
It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typica...It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge gen...To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge generated during the deformation and failure of igneous rocks.The charge originates mainly from a combination of electrical polarization and triboelectric effects.Through laboratory experiments,we analyzed the time-frequency evolution of induced electric charge signals and identified relevant monitoring parameters.An online downhole electric charge induction monitoring system was developed and validated in the field.Experimental results show that the dominant frequency range of induced electric charge signals generated during igneous rock deformation and failure lies between 0 and 23 Hz,and a low-pass finite impulse response(FIR)filter effectively suppresses noise.Optimal sensor distances for monitoring cubic and cylindrical specimens were determined to be 17 mm and 13 mm,respectively.We proposed early warning indicators,including the maximum absolute value of the induced electric charge,the arithmetic mean value,the distribution dispersion coefficient,and the cumulative sum value.In field application,time-domain curves and spatial distribution charts of these warning indicators correspond well with changes in abutment stress ahead of the mining face,offering indirect insights into local stress evolution.This research provides technical and equipment support for the application of electric charge induction technology to monitoring and early warning of coal bursts.展开更多
A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without in...A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52379098 and 42122052)the Liaoning XingLiao Talent Program(Grant No.XLYC2203008).
文摘The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on the Dagangshan high-arch dam during its normal water storage operating period to assess potential damage.The study analyzes the MS characteristics of the dam during the Luding earthquake(Ms=6.8).A framework for constructing a damage driven DT model of a high-arch dam is proposed.The DT model is capable of self-updating its mechanical parameters based on MS data.Seismic response calculations are conducted utilizing cloud computing,allowing for the direct presentation of results within the DT model.The results indicate a high-risk area of the Dagangshan arch dam,characterized by significantMS deformation,primarily centered on the arch crown beam.This zone encompasses dam sections Nos.5-6,10-11,13-16,and 19-20,all located above 1030 m elevation.Under seismic loading,the arch dam exhibits a back-and-forth movement along the river,ultimately reaching a stable state.Following the earthquake,the stress state of the dam does not experience substantial changes.The average relative error between numerical results and measured peak ground acceleration values is 17%when considering the cumulative effect of damage,compared to 36%when neglecting this effect.This study presents a more reliable approach for assessing the state of dams.
基金supported by the National Science and Technology Council,Taiwan,with grant numbers NSTC 114-2622-8-992-007-TD1 and 112-2811-E-992-003-MY3.
文摘The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.
基金supported by the National Natural Science Foundation of China(No.51605054).
文摘Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.
基金supported by the National Natural Science Foundation of China under Grant Nos.523B2043 and 52475112.
文摘Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.
基金Supported by Amrita Vishwa Vidyapeetham Seed Grant,No.K-PHAR-24-722DST INSPIRE Fellowship,No.IF190226.
文摘BACKGROUND Syngeneic orthotopic tumor models offer an optimal functional tumor–immune interface for hepatocellular carcinoma research.Yet,unpredictable growth kinetics and spontaneous regression pose major obstacles.Efficient induction protocols and continuous monitoring are therefore essential.Routine exploratory surgeries are ethically untenable,making non-invasive imaging modalities attractive alternatives.High-resolution magnetic resonance imaging and microcomputed tomography deliver detailed insights but incur substantial equipment costs,radiation risks,time demands,and require specialized expertise—challenges that limit their routine use.In contrast,ultrasound(US)imaging emerges as a cost-effective,radiation-free,and rapid approach,facilitating practical and ethical longitudinal assessment of tumor progression in preclinical studies.AIM To optimize the orthotopic hepatocellular carcinoma model and evaluate the potential of US imaging for accurate and cost-effective tumor monitoring.METHODS Hepatocellular carcinoma was induced in 28 Sprague Dawley rats by implanting 5×10^(6) N1S1 cells into the left lateral hepatic lobe.Tumor progression was monitored weekly via US.Upon reaching 100-150 mm^(3),an experimental group(n=14)received Sorafenib(40 mg/kg)orally on alternate days for 28 days;efficacy was compared to untreated controls.US accuracy was validated against micro-computed tomography,gross caliper measurements and histopathological analysis.Reliability and operator proficiency in US assessment were also evaluated.RESULTS US images procured 7-day post-surgery revealed a well-defined hypoechoic nodule at the left liver lobe tip,confirming successful tumor induction(mean volume 130±39 mm^(3)).Only three animals exhibited spontaneous regression by week 2,underscoring the model’s stability.Sorafenib treatment elicited a marked tumor reduction(678±103 mm^(3))vs untreated control(6005±1760 mm^(3)).US assessment demonstrated robust intra and interobserver reproducibility with high sensitivity and specificity for tumor detection.Moreover,US derived volumes correlated strongly with gross caliper measurements,histopathological analysis,and microcomputed tomography imaging,validating its reliability as a non-invasive monitoring tool in preclinical hepatocellular carcinoma studies.CONCLUSION The results demonstrate that US imaging is a reliable,cost-effective,and animal sparing approach with an easy tomaster protocol,enabling monitoring of tumor progression and therapeutic response in orthotopic liver tumor models.
基金Key project in Hubei Province,Grant/Award Number:2023BCB048National Key R&D Program of China,Grant/Award Number:2021YFC3100805+1 种基金National Natural Science Foundation of China,Grant/Award Numbers:42293355,51991392Project for Research Assistant of Chinese Academy of Sciences。
文摘Monitoring of the mechanical behavior of underwater shield tunnels is vital for ensuring their long-term structural stability.Typically determined by empirical or semi-empirical methods,the limited number of monitoring points and coarse monitoring schemes pose huge challenges in terms of capturing the complete mechanical state of the entire structure.Therefore,with the aim of optimizing the monitoring scheme,this study introduces a spatial deduction model for the stress distribution of the overall structure using a machine learning algorithm.Initially,clustering experiments were performed on a numerical data set to determine the typical positions of structural mechanical responses.Subsequently,supervised learning methods were applied to derive the data information across the entire surface by using the data from these typical positions,which allows flexibility in the number and combinations of these points.According to the evaluation results of the model under various conditions,the optimized number of monitoring points and their locations are determined.Experimental findings suggest that an excessive number of monitoring points results in information redundancy,thus diminishing the deduction capability.The primary positions for monitoring points are determined as the spandrel and hance of the tunnel structure,with the arch crown and inch arch serving as additional positions to enhance the monitoring network.Compared with common methods,the proposed model shows significantly improved characterization abilities,establishing its reliability for optimizing the monitoring scheme.
基金This project(ProKInect N°02P20A090)was funded by the German Federal Ministry of Education and Research(BMBF)within the“The Future of Value Creation-Research on Production,Services and Work”program and managed by the Project Management Agency Karlsruhe(PTKA).The support is greatly acknowledged.
文摘This paper investigates the use of explainable artificial intelligence(XAI)and trustworthy artificial intelligence(TAI)methods for condition monitoring on a laser cutting machine.The focus is on the analysis of the rack and pinion contact with wear being predicted by four differently derived adaptive-network-based fuzzy inference system(s)(ANFIS)models.Using both model-agnostic and model-specific parameters integrated in a weighted evaluation framework,the models are evaluated with respect to the effectiveness of explanations.This framework is based on the observation of the outputs of the individual layers of ANFIS,also focusing on aspects of two multivalued logics,namely fuzzy logic and support logic.The results show that the introduced weighted evaluation framework makes it possible to quantify the explainability of the individual models in terms of XAI and TAI.Finally,a preselection of a model for predicting the wear of the rack and pinion contact can be made.
基金support from the Sichuan Science and Technology Program(Grant No.2023NSFSC0812).
文摘The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U23A2060,42177143,and 42277461).
文摘Ensuring the stability of the surrounding rock mass is of great importance during the construction of a large underground powerhouse.The presence of unfavorable structural planes within the rock mass,such as faults,can lead to substantial deformation and subsequent collapse.A series of in situ experiments and discrete element numerical simulations have been conducted to gain insight into the progressive failure behavior and deformation response of rocks in relation to controlled collapse scenarios involving gently inclined faults.First,the unloading damage evolution process of the surrounding rock mass is characterized by microscopic analysis using microseismic(MS)data.Second,the moment tensor inversion method is used to elucidate the temporal distribution of MS event fracture types in the surrounding rock mass.During the development stage of the collapse,numerous tensile fracture events occur,while a few shear fractures corresponding to structural plane dislocation precede their occurrence.The use of the digital panoramic borehole camera,acoustic wave test,and numerical simulation revealed that gently inclined faults and deep cracks at a certain depth from the cavern periphery are the primary factors contributing to rock collapse.These results provide a valuable case study that can help anticipate and mitigate fault-slip collapse incidents while providing practical insights for underground cave excavation.
文摘Compressor surge is a major aerodynamic instability that constrains the performance and reliability of industrial gas turbines.To address this challenge,this paper provides a comprehensive review of recent progress in surge monitoring,modeling,and control strategies.Key difficulties in early surge detection are identified,including ambiguous precursor signals,strongly coupled system dynamics,and sensor-actuator time delays.The review categorizes existing modeling approaches into high-fidelity computational fluid dynamics(CFD),reducedorder physical models,and data-driven techniques,evaluating each in terms of accuracy,adaptability,and realtime feasibility.In terms of control strategies,both passive and active methods are analyzed,with a particular focus on closed-loop feedback,model predictive control,robust control,and intelligent data-driven approaches.The review concludes by outlining future directions that prioritize model integration,control reliability,and systemlevel coordination for enhanced compressor stability.
基金supported by the Key Technologies Research and Development Program under Grant 2021YFB1600300.
文摘To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge.An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns.XGBoost(eXtreme Gradient Boosting),a gradient-boosting machine learning(ML)algorithm,was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution.Unlike traditional numerical models that rely on extensive assumptions and idealizations,XGBoost effectively captures nonlinear and time-varying relationships between stress states and operational/environmental factors,such as temperature,traffic load,and structural geometry.This approach allows for the identification of critical periods and conditions under which stress redistribution becomes significant.Results indicate a clear shift of stress concentrations frombeamends toward mid-span regions following the commencement of metro operations,reflecting both structural adaptation and localized overstress near arch ribs.Furthermore,the model generates robust predictions of stress evolution,demonstrating potential applications in early warning systems and fatigue risk assessment.This work represents the first application of interpretable gradient-boosting techniques to stress redistribution modeling in double-deck bridges.In addition,a Stress Redistribution Index(SRI)is proposed,derived from this monitoring study and finite-element-based transverse load distributions,to quantify temporal stress shifts between midspan and edge beams.The results provide both theoretical contributions and practical guidance for the design,inspection,and maintenance of complex bridge structures.
文摘In the version of the article originally published in the volume 68,issue 12,2025 of Sci China Mater(pages 4413-4422,https://doi.org/10.1007/s40843-025-3667-7),the Chinese name of the co-first author(肖天孝)was incorrect.The corrected Chinese name is:肖天笑.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
基金funded by Anhui NARI ZT Electric Co.,Ltd.,entitled“Research on the Shared Operation and Maintenance Service Model for Metering Equipment and Platform Development for the Modern Industrial Chain”(Grant No.524636250005).
文摘With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as delayed retraining,inconsistent version management,insufficient drift monitoring,and limited data security still hinder efficient and reliable model operations.To address these issues,this paper proposes the Intelligent Model Lifecycle Management Algorithm(IMLMA).The algorithm employs a dual-trigger mechanism based on both data volume thresholds and time intervals to automate retraining,and applies Bayesian optimization for adaptive hyperparameter tuning to improve performance.A multi-metric replacement strategy,incorporating MSE,MAE,and R2,ensures that new models replace existing ones only when performance improvements are guaranteed.A versioning and traceability database supports comparison and visualization,while real-time monitoring with stability analysis enables early warnings of latency and drift.Finally,hash-based integrity checks secure both model files and datasets.Experimental validation in a power metering operation scenario demonstrates that IMLMA reduces model update delays,enhances predictive accuracy and stability,and maintains low latency under high concurrency.This work provides a practical,reusable,and scalable solution for intelligent model lifecycle management,with broad applicability to complex systems such as smart grids.
基金the support from National Natural Science Foundation of China(No.52275153)the Frontier Technologies R&D Program of Jiangsu,China(No.BF2024068)+1 种基金The Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics,ChinaResearch Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics),China(Nos.MCAS-I-0425K01,MCAS-I-0423G01)。
文摘It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
基金supported by the National Key Research and Development Project of the National Natural Science Foundation of China(Grant No.2022YFC3004605)the National Natural Science Foundation of China Youth Science Fund(Grant No.52104087).
文摘To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge generated during the deformation and failure of igneous rocks.The charge originates mainly from a combination of electrical polarization and triboelectric effects.Through laboratory experiments,we analyzed the time-frequency evolution of induced electric charge signals and identified relevant monitoring parameters.An online downhole electric charge induction monitoring system was developed and validated in the field.Experimental results show that the dominant frequency range of induced electric charge signals generated during igneous rock deformation and failure lies between 0 and 23 Hz,and a low-pass finite impulse response(FIR)filter effectively suppresses noise.Optimal sensor distances for monitoring cubic and cylindrical specimens were determined to be 17 mm and 13 mm,respectively.We proposed early warning indicators,including the maximum absolute value of the induced electric charge,the arithmetic mean value,the distribution dispersion coefficient,and the cumulative sum value.In field application,time-domain curves and spatial distribution charts of these warning indicators correspond well with changes in abutment stress ahead of the mining face,offering indirect insights into local stress evolution.This research provides technical and equipment support for the application of electric charge induction technology to monitoring and early warning of coal bursts.
基金supported by the National Natural Science Foundation of China(22074072,22274083,52376199)the Shandong Provincial Natural Science Foundation(ZR2023LZY005)+1 种基金the Exploration Project of the State Key Laboratory of BioFibers and EcoTextiles of Qingdao University(TSKT202101)the Fundamental Research Funds for the Central Universities(2022BLRD13,2023BLRD01).
文摘A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
