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An anisotropy-dynamic ant colony optimization with probabilistic fracture uncertainty quantification for sub-seismic fault detection 认领 引用
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作者 Shi-Chang Li Yang Zhao +5 位作者 Cheng-Gang Xian Qi-Ya Qiao Fu-Yu Zhu Xing Liang Jie-Hui Zhang Lan-Lan Yan 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2527-2544,共18页
The precise characterization of subsurface fracture systems,especially sub-seismicfractures below seismic resolution,is critical for developing complex hydrocarbon reservoirs.While antcolony optimization(Aco)introduce... The precise characterization of subsurface fracture systems,especially sub-seismicfractures below seismic resolution,is critical for developing complex hydrocarbon reservoirs.While antcolony optimization(Aco)introduced"ant tracking"for seismicfracture detection,traditional methods rely on isotropic post-stack attributes,ignoring azimuthal anisotropy—a key indicator of fracture orientation and density.The azimuth-aware anisotropic bayes ACO(Ani-Bayes ACO)integrated pre-stack anisotropy via Bayesian priors but suffered from deterministic constraints and staticheuristics,limiting its ability to model conjugate fracture systems or parameter uncertainty.To resolve these limitations,we propose the anisotropy-dynamic ACO(ADACO)algorithm.ADACO replaces deterministic constraints with probabilistic,dynamically evolving fracture parameter distributions:von Mises for orientation and lognormal for density,both parameterized by elliptical fitting credibility.During optimization,a Hidden Markov Model(HMM)globally evaluates path consistency,while elite-path feedback iteratively focuses the distributions.Thisenablesuncertainty-quantified fracture prediction,multi-settracking,and autonomous adaptation to fracture clustering.Validation in a complex shale gas reservoir showed 85%consistency with drilling data-a significant improvement over Ani-Bayes AcO(46%).ADAcO thus provides a robust tool for sub-seismic fracture characterization. 展开更多
关键词 Ant colony optimization Fracture detection Azimuthal anisotropy Hidden Markov Model(HMM) Uncertainty quantification
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Absolute Quantification of Aging-Associated Glycans in IgG for Biological Age Prediction:Insights from Glycomics and Transcriptomics 认领 引用
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作者 Huijuan Zhao Jiteng Fan +7 位作者 Jing Han Wenjun Qin Jichen Sha Weilong Zhang Yong Gu Xiaonan Ma Jianxin Gu Shifang Ren 《Engineering》 SCIE EI CSCD 2026年第2期113-125,共13页
Immunoglobulin G(IgG)N-glycans are associated with aging.In this study,we introduce a novel strategy for discovering aging-associated IgG glycans and establish a prediction model on the basis of their absolute concent... Immunoglobulin G(IgG)N-glycans are associated with aging.In this study,we introduce a novel strategy for discovering aging-associated IgG glycans and establish a prediction model on the basis of their absolute concentration alterations.We employed glycomic quantification technology to identify alterations in the amount of IgG glycan in natural aging and antiaging(caloric restriction(CR))models and discovered aging-related glycans.The glycomic analysis revealed key features:downregulation of the bisected glycan GP3(F(6)A2B)and upregulation of the digalactosylated glycan GP8(F(6)A2G2).These glycan changes showed significant fold changes from an early stage.Using external standards of these two glycans,we subsequently measured their absolute concentrations,allowing for us to establish a predictive model,abGlycoAge,for biological aging.The abGlycoAge index suggested a younger state under CR,with an average age reduction of 3.9–14.0 weeks.Additionally,RNA sequencing of splenic B cells revealed that Derl3,Smarcb1,Ankrd55,Tbkbp1,and Slc38a10 may contribute to alterations in GP3 and GP8 during the aging process.In a preliminary therapeutic study,we tested IgG modified with young signature Nglycans(IgG-Ny).High-dose IgG-Ny showed promising results,alleviating aging-related physiological declines,including reductions in inflammatory markers and improvements in organ senescence,particularly in the brain,kidney,and lungs.This research provides new insights into glycan changes during aging and lays the groundwork for potential antiaging therapies.GP3 and GP8 may serve as biomarkers for aging,offering new perspectives on aging mechanisms and therapeutic approaches. 展开更多
关键词 Absolute quantification Immunoglobulin G N-glycome Aging Glycan biomarker
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Precise quantification of skeletal muscle fibers reveals the physiological basis for growth rate discrepancies in broilers 认领 引用
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作者 Shuang Gu Chaoyi Wang +5 位作者 Qiang Huang Qiulian Wang Junying Li Congjiao Sun Chaoliang Wen Ning Yang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第8期3368-3378,共11页
Skeletal muscle is composed of multinucleated muscle fibers,which play a crucial role in determining the quality of meat products in livestock.Quantifying the total number of muscle fibers(TNM)is essential for underst... Skeletal muscle is composed of multinucleated muscle fibers,which play a crucial role in determining the quality of meat products in livestock.Quantifying the total number of muscle fibers(TNM)is essential for understanding muscle composition,but this remains challenging in poultry,particularly since the considerable number of livestock complicates the preparation of tissue sections for analysis and makes the counting process laborious.Our previous study developed an automatic muscle fiber quantification tool powered by deep learning,named MyoV,which has addressed this bottleneck.This study employed the MyoV tool for accurately quantifying TNM in the pectoral muscles of slow-growing(SL),mediumgrowing(ML),and fast-growing(FL)broilers.The results showed that FL group exhibited higher growth performance compared to ML and SL groups from the embryonic to rearing stages.Processing of whole slide images of pectoral muscle revealed significantly higher TNM in FL and ML groups than in SL group(P<0.01).The TNM values of FL,ML and SL groups were 693,568.00±54,169.80,652,122.00±65,822.60,and 539,778.57±40,722.94 at 7 days of age(D7),respectively;and 663,014.93±58,801.11,645,784.76±80,204.34 and 507,280.29±98,092.16 at D35 for FL,ML and SL groups,respectively.Differences in the cross-sectional area(CSA)of muscle fibers among the three groups were consistent with the TNM results.A correlation analysis showed correlation coefficients of 0.73–0.89 between body weight(BW)and TNM and 0.78–0.87 between BW and CSA.These findings directly indicate that the number of muscle fibers in broilers is an important foundation for their rapid growth and development.This study precisely quantified the muscle fiber number of an important skeletal muscle in poultry for the first time,which provides direct evidence for the physiological basis of rapid development in broilers and offers important data support for further in-depth studies on muscle fiber development. 展开更多
关键词 broiler growth performance precision quantification total number of muscle fibers
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Uncertainty quantification for the ascent phase of launch vehicles using Bayesian inference 认领 引用
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作者 CHAO Tao LI Xiaonan +2 位作者 SHANG Xiaobing MA Ping YANG Ming 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期485-503,共19页
The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters,including air density,aerodynamic parameters,and engine thrust,which often exhibit deviation.Predicting the trajec... The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters,including air density,aerodynamic parameters,and engine thrust,which often exhibit deviation.Predicting the trajectory range of the launch vehicle under the influence of uncertainty is essential before launch,and uncertainty quantification serves as a crucial method to address this challenge.In traditional uncertainty quantification for launch vehicles,unknown parameters are often assigned specific distributions based on prior knowledge.However,prior knowledge is sometimes subjective,and unknown parameters are often assigned conservative ranges to meet safety margins.In addition,the flight data of the past launch is precious,especially in quantifying the uncertainty of reusable or same-type launch vehicles.This paper utilizes flight data to estimate parameters base on Bayesian methods and integrates the estimation results with prior knowledge,which can more objectively set the distribution of uncertain parameters.Reasonable distribution has a positive impact on uncertainty quantification,which can avoid control strategies that are not robust enough or overly redundant.Therefore,the uncertainty quantification for launch vehicles is discussed under different information sources.In addition,the algorithm is accelerated based on Gaussian process regression and polynomial chaos expansions. 展开更多
关键词 launch vehicle uncertainty quantification Bayesian inference launch experience Gaussian process regression polynomial chaos expansions
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Hybridndiff-UQ:Uncertainty quantification for hybrid neural differentiable modeling 认领 引用
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作者 Deepak Akhare Tengfei Luo Jian-Xun Wang 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第2期1-24,共24页
The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning.These models,integrating numerical representations of known physics into deep neural networks,offer en... The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning.These models,integrating numerical representations of known physics into deep neural networks,offer enhanced predictive capabilities and show great potential for data-driven modeling of complex physical systems.However,a critical and yet unaddressed challenge lies in the quantification of inherent uncertainties stemming from multiple sources.Addressing this gap,we introduce a novel method,uncertainty quantification for hybrid neural differentiable modeling,for effective and efficient uncertainty propagation and estimation in hybrid neural differentiable models,leveraging the strengths of deep ensemble Bayesian learning and nonlinear transformations.Specifically,our approach effectively discerns and quantifies both aleatoric uncertainties,arising from data noise,and epistemic uncertainties,resulting from model-form discrepancies and data sparsity.This is achieved within a Bayesian model averaging framework,where aleatoric uncertainties are modeled through hybrid neural models.The unscented transformation plays a pivotal role in enabling the flow of these uncertainties through the nonlinear functions within the hybrid model.In contrast,epistemic uncertainties are estimated using an ensemble of stochastic gradient descent trajectories.This approach offers a practical approximation to the posterior distribution of both the network parameters and the physical parameters.Notably,our framework is designed for simplicity in implementation and high scalability,making it suitable for parallel computing environments.The merits of the proposed method have been demonstrated through problems governed by both ordinary and partial differentiable equations. 展开更多
关键词 Differentiable programming Scientific machine learning Grey box modeling Uncertainty quantification Scalable bayesian learning
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Deep Learning-Based Structural Displacement Identification and Quantification under Target Feature Loss 认领 引用
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作者 Lishuai Zhu Guangcai Zhang +4 位作者 Qun Xie Zhen Peng Li Ai Ruijun Liang Taochun Yang 《Structural Durability & Health Monitoring》 EI 2026年第2期57-77,共21页
Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accur... Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accuracy and robustness.Therefore,this study proposes a structural displacement identification and quantification method that integrates YOLOv8n with an improved edge-orientation gradient-based template matching algorithm.By combining deep learning techniques with traditional template matching methods,the accuracy and robustness of monitoring are enhanced under adverse conditions such as noise and extremely low illumination.Specifically,in the edge-orientation gradient matching stage,the Canny-Devernay sub-pixel edge detection technique and an improved ellipse-fitting method are employed for sub-pixel edge extraction,and a five-level Gaussian pyramid structure is introduced to accelerate the matching speed.Experimental results show that the proposed method achieves high-precision displacement monitoring under sufficient illumination,and it maintains stable target localization and displacement quantification performance under conditions of noise interference and extremely low illumination.Notably,under salt-and-pepper noise interference,although YOLOv8n maintains a high level of localization confidence,the accuracy of gradient matching deteriorates,resulting in a root-mean-square error(RMSE)of 0.035 mm.This finding reveals the differential impact of various noise types on different stages of the algorithm.The proposed method offers a novel technological approach for precise structural displacement monitoring in complex environments. 展开更多
关键词 Structural displacement quantification complex environments edge detection ellipse fitting template matching
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Home-built LC-MiniMS system for quantification of tacrolimus in whole blood 认领 引用 被引量：1
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作者 Wenke Liu Di Zhang +7 位作者 Ziyu Qu Keke Yi Shumin Wan Zihong Ye Xinhua Dai Jie Xie You Jiang Xiang Fang 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第6期808-815,共8页
Precise assessment of tacrolimus(TAC)concentrations is critical in clinical diagnostics,and liquid chromatography–mass spectrometry(LC-MS/MS)is the preferred approach due to its high specificity and sensitivity.Howev... Precise assessment of tacrolimus(TAC)concentrations is critical in clinical diagnostics,and liquid chromatography–mass spectrometry(LC-MS/MS)is the preferred approach due to its high specificity and sensitivity.However,classic LC-MS/MS systems are frequently enormous,costly,and need expert operation,which restricts its applicability in numerous industries.In this paper,a liquid chromatography–miniature mass spectrometry(LC-MiniMS)system was designed and developed.The miniature linear ion trap spectrometer had a footprint of 59×38×27 cm3,which substantially reduced the instrument size and cost while maintaining quantitative performance.The LC-MiniMS system’s circuit boards were integrated and the software automation was optimized,so it was more convenient to use and maintain.Results demonstrated excellent linearity over the range of 0.5–50 ng/mL with R2>0.99.The limit of detection and limit of quantification were 0.1 and 0.3 ng/mL,respectively.The accuracy ranged from 99.67%to 106.10%,intraday precision was between 0.70%and 2.61%,and interday precision was between 0.90%and 2.90%,all within acceptable limits,and matrix effects were negligible.The method was successfully applied to quantify TAC in 32 clinical whole-blood samples,and the results strongly agreed with those from a conventional LC-MS/MS system(QTRAP 6500+).The LC-MiniMS system can efficiently quantify TAC in whole blood and provide a tiny,cost-effective,and uncomplicated option for therapeutic drug monitoring in clinical settings,especially in decentralized or resource-limited scenarios. 展开更多
关键词 LC-MiniMS system Tacrolimus Therapeutic drug monitoring Immunosuppressant quantification Clinical diagnostics
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Developing a standardized procedure for SPE-enzyme-linked immunosorbent assay to provide high-quality quantification of ambient antibiotics 认领 引用
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作者 Yun Yang Lulu Li +7 位作者 Jinxin Wang Jiahao Ouyang Yu Quan Sijie Chen Chunzhao Chen Wei Ouyang Gang Yu Li Ling 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第5期670-676,共7页
The trade-off between quality and difficulty is a challenge when quantifying ambient antibiotics at trace levels.Compared with the precise yet complicate methods such as mass spectrometry(MS)techniques,the enzyme-link... The trade-off between quality and difficulty is a challenge when quantifying ambient antibiotics at trace levels.Compared with the precise yet complicate methods such as mass spectrometry(MS)techniques,the enzyme-linked immunosorbent assay(ELISA)offer a simple alternative.While some studies applied it on quantifying environmental pollutants,diverse optimization procedures were employed and matrix effects were not well-addressed.Here,the quantification capability of solid-phase extraction(SPE)coupled with ELISA on ambient antibiotics was evaluated using a newly developed standardized procedure.SPE-ELISA first underwent more rigorous optimization using an overall performance index and three-dimensional recovery response surface.A series of quantitative indicators including precision(relative standard deviation reached 0.3%),sensitivity(a minimal of 3.8 ng/L variation can be distinguished),limit of detection(0.3µg/L without pretreatment),and recoveries(>90%)of SPE-ELISA were achieved and the corresponding conditions were revealed.To eliminate matrix effects,the standard addition method was adopted.This approach,coupled with the linearization of the nonlinear calibration curve,yielded highly accurate(errors of 9%and 5.2%)and reliable(standard deviation of 0.49 and 0.61)results on measuring simulated surface and wastewaters with 5 ng/L and 10 ng/L sulfamethoxazole,which were highly comparable to those of MS methods(P>0.05).Overall,with more rigorous optimization and matrix effect eliminated,the standardized procedure in this study enabled SPE-ELISA to achieve high-quality quantification results.Considering the high throughputs,simple procedure,and low installation costs of SPE-ELISA,it could be a promising alternative for quantifying ambient antibiotics. 展开更多
关键词 Antibiotics ELISA Quantification Solid phase extraction Mass spectrometry
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Continental-scale mapping of forest tree density in North America using remote sensing and deep learning with uncertainty quantification 认领 引用
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作者 Mustak Ahmad Yun Tang +34 位作者 Andrew J.Lister Javier G.P.Gamarra William G.Powell Nathan R.Beane Wook Jin Choi Ankita Mitra Amit Kumar Anibal Cuchietti Alain Paquette Eric Searle Jiaxin Chen Han Y.H.Chen Frans Bongers Jorge A.Meave Mario Guevara Aylin Barreras Jose Armando Alanís de la Rosa Rafael Mayorga Saucedo Rubi Angélica Cuenca Lara César Moreno García Carlos Isaías Godínez Valdivia Carina Edith Delgado Caballero María de los Angeles Soriano Luna Metzli Ileana Aldrete Leal Sandra Liliana Medina Casillas Johny Romero Correa Sergio Armando Villela Gaytán J.Javier Corral Rivas Jose Daniel Vega-Nieva Jaime Briseño-Reyes Pablito Marcelo López-Serrano Tom M.Fayle Jan Altman Daniel J.Johnson Jingjing Liang 《Forest Ecosystems》 SCIE CAS CSCD 2026年第3期757-776,共20页
Accurate,spatially consistent estimates of tree density remain elusive at continental scales,limiting our ability to assess forest structure,carbon stocks,and biodiversity.Existing global assessments have relied on si... Accurate,spatially consistent estimates of tree density remain elusive at continental scales,limiting our ability to assess forest structure,carbon stocks,and biodiversity.Existing global assessments have relied on simplified statistical models and sparse,heterogeneous ground data that are insufficient to capture nonlinear ecological interactions and spatial variability.To address these limitations,we integrated more than 600,000 harmonized ground-based forest inventory plots with satellite-derived vegetation indices,climate surfaces,soil properties,and topographic covariates to develop a deep learning framework for high-resolution mapping of tree density across North America.We evaluated four modeling approaches-generalized linear models(GLMs),ridge regression(RR),random forest(RF),and a feedforward neural network(FFNN).Among all models tested,the FFNN achieved the highest predictive accuracy(RMSE=344.8;R 2=39.53%),and was used to produce a wall-to-wall tree density map at 3 km resolution for the continent.We estimated that the total number of forest trees with diameter at breast height(DBH)≥10 cm across North America ranges from 339 to 514 billion,substantially lower than the widely cited estimate of 603 billion trees reported by Crowther et al.(2015).When smaller stems were included(no DBH threshold),totals more than doubled,reaching 738 billion to 1.12 trillion trees.We quantified uncertainty using Monte Carlo(MC)Dropout,generating pixel-level error estimates and confidence intervals.Spatial patterns reveal high tree densities in boreal and temperate forests,intermediate densities in mixed broadleaf regions,and relatively low densities in deserts,Mediterranean systems,and tundra.Compared to the global GLM-based benchmark by Crowther et al.(2015),our deep learning framework achieves markedly higher predictive accuracy,aligns more closely with national forest inventory statistics,and provides explicit uncertainty quantification,supporting applications in carbon accounting,biodiversity modeling,and ecosystem monitoring at scales through region specific calibration and validation. 展开更多
关键词 Tree density estimation Feedforward neural network(FFNN) Remote sensing Deep learning Uncertainty quantification Monte Carlo(MC)dropout
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Deterministic modeling and uncertainty quantification of wind waves in Ilha Solteira Reservoir,Brazil 认领 引用
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作者 Germano de Oliveira Mattosinho Fabiana de Oliveira Ferreira Geraldo de Freitas Maciel 《Water Science and Engineering》 EI CAS CSCD 2026年第2期291-301,共11页
Wind waves in reservoirs represent a key hydrodynamic process influencing shoreline stability,navigation safety,and the design of hydraulic infrastructure.Despite their practical relevance,wave prediction in inland wa... Wind waves in reservoirs represent a key hydrodynamic process influencing shoreline stability,navigation safety,and the design of hydraulic infrastructure.Despite their practical relevance,wave prediction in inland waters remains subject to significant uncertainties,particularly related to wind forcing and empirical model parameters.This study integrated deterministic and probabilistic approaches for predicting wind waves in reservoirs.Using a deterministic approach,the Simulating Waves Nearshore(SWAN)model was applied to estimate wave height and period.Key variables analyzed included wind velocity,wind direction,the Joint North Sea Wave Project(JONSWAP)bottom friction coefficient,the whitecapping coefficient,and the depth-induced breaking index.Through a probabilistic approach,uncertainties were quantified using polynomial chaos expansion(PCE),and sensitivity analysis was performed via Sobol indices.This framework was applied to a case study of the Tiete—Parana Waterway in the Ilha Solteira Reservoir,Sao Paulo,Brazil.Simulations using the Janssen formulation yielded the most accurate wave height estimates.Sensitivity analysis based on Sobol indices identified wind velocity and the whitecapping coefficient as the most influential factors governing wave behavior.This integrated approach enables the generation of contour maps for wave height and period,offering valuable insights for project planning.Thus,the combination of deterministic and probabilistic analyses enhances the understanding of wind wave dynamics in inland waters. 展开更多
关键词 Uncertainty quantification Wave modeling Wind waves SWAN model Metamodeling Sobol index
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Probabilistic framework for uncertainty quantification in the seismic response of buildings 认领 引用
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作者 Moussa Leblouba Samer Barakat Raghad Awad 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2026年第2期393-410,共18页
This paper introduces a probabilistic framework for enhancing the seismic design of structures by incorporating uncertainty quantification(UQ)in response analysis.Traditional design codes,often deterministic,can lead ... This paper introduces a probabilistic framework for enhancing the seismic design of structures by incorporating uncertainty quantification(UQ)in response analysis.Traditional design codes,often deterministic,can lead to either overly conservative or unreliable designs.The proposed method integrates uncertainties in vibration periods and damping ratios as random variables,using elastic response spectra and the ASCE 7-16 design response spectrum for a more accurate seismic risk assessment.The framework effectively identifies discrepancies between measured and predicted vibration periods and damping ratios through numerical examples and case studies,highlighting the risk of non-conservative designs with nominal values.It emphasizes the need to account for biases in vibration period approximations as per ASCE 7 to prevent under-conservative designs.This approach allows engineers and researchers to estimate building responses more realistically,which is crucial for appropriate seismic design and performance evaluation. 展开更多
关键词 uncertainty quantification seismic design response spectra probabilistic approach
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Multiparameter Bayesian full-waveform inversion with uncertainty quantification based on regularized inverse scattering theory for elastic transversely isotropic media 认领 引用
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作者 Wen-Rui Ye Xing-Guo Huang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3180-3212,共33页
Complex subsurface structures exhibit significant anisotropic characteristics,making multi-parameter imaging techniques important for achieving a more comprehensive geological interpretation.Fullwaveform inversion(FWI... Complex subsurface structures exhibit significant anisotropic characteristics,making multi-parameter imaging techniques important for achieving a more comprehensive geological interpretation.Fullwaveform inversion(FWI)as a state-of-the-art method for reconstructing subsurface properties based on seismic wavefield modeling and data misfit minimization has been widely applied to isotropic media in both synthetic and field datasets.However,challenges such as crosstalk correlation and inaccuracy of the initial model indicate that further advancements are required to enhance resolution and computational efficiency.We propose an elastic FWI in the frequency domain for two-dimensional(2D)TI media to characterize their physical properties appropriately,as they are common in sedimentary basin environments.Different from traditional inversion schemes,our approach is formulated based on Bayesian inference,which automatically facilitates uncertainty analysis of the inversion results.Seismic data are acquired via the integral equation(IE)method grounded in scattering theory,where the sensitivity kernel is explicitly constructed using Green's functions,hence facilitating the calculation of gradient and Hessian.A Krylov subspace iterative method provides the approximated solution of the Lippmann-Schwinger(L-S)equation without sacrificing the accuracy.Furthermore,we incorporate the minimum support(MS)stabilizing functional as a model misfit term to regularize the objective function.A randomized singular value decomposition(SVD)approach is used to approximate and decompose the prior preconditioned Hessian.Both the model and covariance are updated through the iterative extended Kalman filter(IEKF)that implemented in the form of the Levenberg-Marquardt(LM)algorithm,thereby enabling practical uncertainty quantification.Numerical tests are conducted on two synthetic TI models with vertical and tilted symmetry axes,respectively,illustrating the precision and robustness of our method. 展开更多
关键词 Inverse scattering theory Full waveform inversion Anisotropy Multi-parameter inversion Uncertainty quantification Regularization term Randomized singular value decomposition
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Toward Reliable Battery Life Prediction:A Hybrid Data-Driven Framework with Uncertainty Quantification 认领 引用
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作者 Mingqi Liu Ying Wang +2 位作者 Wujiang Li Juyong Cao Fuyong Yang 《Energy Engineering》 EI 2026年第8期297-312,共16页
Accurately predicting battery life is essential for performance management and system safety.Due to the complexity and diversity of internal mechanisms in lithium-ion batteries,their nonlinear characteristics directly... Accurately predicting battery life is essential for performance management and system safety.Due to the complexity and diversity of internal mechanisms in lithium-ion batteries,their nonlinear characteristics directly give rise to uncertainty in the battery degradation process.However,most existing prediction methods do not fully account for the uncertainty caused by various factors and only provide a point estimate finally.To address this issue,this paper proposes a new framework that combines Random Forest and Conformal Prediction to predict battery life and quantify the uncertainty of the results.This approach leverages the efficiency of Random Forest while enhancing computational robustness and reliability through conformal prediction.The method utilizes early degradation data to select relevant features.Based on this,high-importance feature combinations are selected,and a Random Forest model is used to obtain point estimates.Then,the Conformal Prediction method is introduced to quantify uncertainty and generate prediction intervals with confidence levels and sample-specific bounds.Furthermore,the proposed method is compared against existing uncertainty quantification approaches,with coverage evaluation conducted to enhance the credibility of the prediction results.This method offers a new perspective for the practical application of battery lifetime prediction.Integrating uncertainty quantification into lithium-ion battery research can improve the reliability of the results and support decision-making in practical applications. 展开更多
关键词 Lithium-ion battery uncertainty quantification conformal prediction random forest
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Preliminary study on a quantification method and standardization for aquatic microbial loads based on microbial diversity absolute quantitative sequencing 认领 引用 被引量：1
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作者 Wen Li Jing Libin +4 位作者 Li Xiawei Lu Jing Jin Haowei Yang Yongqi Li Xueling 《China Standardization》 2026年第1期68-73,共6页
This study establishes and validates a method for the precise quantification of aquatic microbial loads using microbial diversity absolute quantitative sequencing.By adding synthetic spike-in DNA to water samples from... This study establishes and validates a method for the precise quantification of aquatic microbial loads using microbial diversity absolute quantitative sequencing.By adding synthetic spike-in DNA to water samples from the Dahei River prior to DNA extraction and 16S rRNA gene sequencing,it generates standard curves to convert sequencing data into absolute microbial copy numbers.The method,which is proved highly accurate(R2>0.99),reveals a clear contrast between the river sites:the upstream community has not only a significantly higher total microbial load but also a completely different makeup of species compared to the downstream site.This approach effectively overcomes the limitations of relative abundance analysis,providing a powerful tool for environmental monitoring,and proposes key steps for future standardization to ensure data comparability and integration. 展开更多
关键词 absolute quantification microbial load 16S rRNA sequencing spike-in standardization aquatic microbes
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A preliminary study on microbial load quantification method and standardization in fermented foods based on microbial diversity absolute quantitative sequencing:A case study of Inner Mongolia traditional fermented vegetables(lanyancai) 认领 引用
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作者 Xiawei Duan Bin +1 位作者 Lu Jing Yang Yongqi 《China Standardization》 2026年第3期60-65,共6页
Traditional fermented vegetables(lanyancai)in Inner Mongolia are culturally significant fermented foods characterized by intricate microbial communities.However,the empirical traditional production methodologies frequ... Traditional fermented vegetables(lanyancai)in Inner Mongolia are culturally significant fermented foods characterized by intricate microbial communities.However,the empirical traditional production methodologies frequently result in inconsistent product quality.Conventional high-throughput sequencing approaches,which generate relative abundance data,are inherently limited in their capacity to reflect absolute microbial biomass dynamics.This limitation obscures the distinction between quality deterioration attributable to“microbial community succession”and that driven by“total biomass over-accumulation.”To address this methodological gap,this study implemented the Absolute Quantitative Microbiome Profiling(aQMP),utilizing a spike-in normalization strategy to establish a metrological framework for microbial load quantification within this high-salt and high-acid fermented matrix.The data demonstrated the robust stability of this method,enabling precise quantification of total microbial load.Notably,while lactic acid bacteria maintained a dominant relative abundance throughout the process,samples exhibiting quality defects displayed a significant escalation in total microbial load-increasing approximately tenfold compared to samples at the standard fermentation stage.These findings suggest that product quality decline is primarily due to the uncontrolled proliferation of the total microbial biomass rather than the dominance of specific spoilage organisms.This study provides a scientific foundation for the standardized production and quality control of traditional fermented foods through absolute microbial quantification. 展开更多
关键词 absolute quantification microbial load spike-in normalization traditional fermented vegetables quality control standardization
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Sequential search-based Latin hypercube sampling scheme for digital twin uncertainty quantification with application in EHA 认领 引用 被引量：1
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作者 Dong LIU Shaoping WANG +1 位作者 Jian SHI Di LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第4期176-192,共17页
For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube samplin... For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes. 展开更多
关键词 Digital Twin(DT) Genetic algorithms(GA) Optimal Latin Hypercube Design(Opt LHD) Sequential test Uncertainty Quantification(UQ) EHA
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Fe3+ ion quantification with reusable bioinspired nanopores 认领 引用 被引量：1
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作者 Yanqiong Wang Yaqi Hou +1 位作者 Fengwei Huo Xu Hou 《Chinese Chemical Letters》 SCIE CAS CSCD 2025年第2期179-184,共6页
Excessive Fe3+ ion concentrations in wastewater pose a long-standing threat to human health.Achieving low-cost,high-efficiency quantification of Fe3+ ion concentration in unknown solutions can guide environmenta... Excessive Fe3+ ion concentrations in wastewater pose a long-standing threat to human health.Achieving low-cost,high-efficiency quantification of Fe3+ ion concentration in unknown solutions can guide environmental management decisions and optimize water treatment processes.In this study,by leveraging the rapid,real-time detection capabilities of nanopores and the specific chemical binding affinity of tannic acid to Fe3+,a linear relationship between the ion current and Fe3+ ion concentration was established.Utilizing this linear relationship,quantification of Fe3+ ion concentration in unknown solutions was achieved.Furthermore,ethylenediaminetetraacetic acid disodium salt was employed to displace Fe3+ from the nanopores,allowing them to be restored to their initial conditions and reused for Fe3+ ion quantification.The reusable bioinspired nanopores remain functional over 330 days of storage.This recycling capability and the long-term stability of the nanopores contribute to a significant reduction in costs.This study provides a strategy for the quantification of unknown Fe3+ concentration using nanopores,with potential applications in environmental assessment,health monitoring,and so forth. 展开更多
关键词 Bioinspired nanopores Fe3+ion quantification Chemical binding affinity Tannic acid Reusability
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A Gel-Free Budget-Friendly Approach to GFP-Tagged Viruses Quantification in Plant Samples 认领 引用
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作者 Rohith Grandhi Mélodie B.Plourde +1 位作者 Aditi Balasubramani Hugo Germain 《Phyton-International Journal of Experimental Botany》 SCIE 2025年第5期1497-1504,共8页
Viral diseases are an important threat to crop yield,as they are responsible for losses greater than US$30 billion annually.Thus,understanding the dynamics of virus propagation within plant cells is essential for devi... Viral diseases are an important threat to crop yield,as they are responsible for losses greater than US$30 billion annually.Thus,understanding the dynamics of virus propagation within plant cells is essential for devising effective control strategies.However,viruses are complex to propagate and quantify.Existing methodologies for viral quantification tend to be expensive and time-consuming.Here,we present a rapid cost-effective approach to quantify viral propagation using an engineered virus expressing a fluorescent reporter.Using a microplate reader,we measured viral protein levels and we validated our findings through comparison by western blot analysis of viral coat protein,the most common approach to quantify viral titer.Our proposed methodology provides a practical and accessible approach to studying virus-host interactions and could contribute to enhancing our understanding of plant virology. 展开更多
关键词 Microplate reader CP-PlAMV viruses plant viral quantification green fluorescent protein western blot quantification Nicotiana benthamiana Arabidopsis thaliana Pearson’s correlation
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MultiJSQ:Direct joint segmentation and quantification of left ventricle with deep multitask-derived regression network 认领 引用
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作者 Xiuquan Du Zheng Pei +3 位作者 Ying Liu Xinzhi Cao Lei Li Shuo Li 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第1期175-192,共18页
Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high va... Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high variability among patients and the time-consuming nature of the process.In this study,the authors introduce a framework named MultiJSQ,comprising the feature presentation network(FRN)and the indicator prediction network(IEN),which is designed for simultaneous joint segmentation and quantification.The FRN is tailored for representing global image features,facilitating the direct acquisition of left ventricle(LV)contour images through pixel classification.Additionally,the IEN incorporates specifically designed modules to extract relevant clinical indices.The authors’method considers the interdependence of different tasks,demonstrating the validity of these relationships and yielding favourable results.Through extensive experiments on cardiac MR images from 145 patients,MultiJSQ achieves impressive outcomes,with low mean absolute errors of 124 mm2,1.72 mm,and 1.21 mm for areas,dimensions,and regional wall thicknesses,respectively,along with a Dice metric score of 0.908.The experimental findings underscore the excellent performance of our framework in LV segmentation and quantification,highlighting its promising clinical application prospects. 展开更多
关键词 global image features joint segmentation and quantification left ventricle(LV) multitask-derived regression network
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A rapid tool for quantification of latent infection of wheat leaves by powdery mildew 认领 引用
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作者 Aolin Wang Ru Jiang +9 位作者 Meihui Zhang Hudie Shao Fei Xu Kouhan Liu Haifeng Gao Jieru Fan Wei Liu Xiaoping Hu Yilin Zhou Xiangming Xu 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2025年第12期4690-4702,共13页
Wheat powdery mildew caused by Blumeria graminis f.sp.tritici(Bgt)is an important disease worldwide.Detection of latent infection of leaves by the pathogen in late autumn is valuable for estimating the inoculum potent... Wheat powdery mildew caused by Blumeria graminis f.sp.tritici(Bgt)is an important disease worldwide.Detection of latent infection of leaves by the pathogen in late autumn is valuable for estimating the inoculum potential to assess disease risks in the spring.We developed a new tool for rapid detection and quantification of latent infection of seedlings by the pathogen.The method was based on recombinase polymerase amplification(RPA)coupled with an end-point detection via lateral flow device(LFD).The limit of detection is 100 agμL-1of Bgt DNA,without noticeable interference from either other common wheat pathogens or wheat material(Triticum aestivum).It was evaluated on wheat seedlings for this accuracy and sensitivity in detecting latent infection of Bgt.We further extended this RPALFD assay to estimate the level of latent infection by Bgt based on imaging analysis.There was a strong correlation between the image-based and real-time PCR assay estimates of Bgt DNA.The present results suggested that this new tool can provide rapid and accurate quantification of Bgt in latently infected leaves and can be further development as an on-site monitoring tool. 展开更多
关键词 Blumeria graminis f.sp.tritici(Bgt) recombinase polymerase amplification(RPA) lateral flow device(LFD) image-based quantification disease monitoring
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