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(R^(2)>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
Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI present...Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.展开更多
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
In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above ...In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above function,are obtained.Especially,we associate the multi-parameters Mittag-Leffler function with two special functions which are the generalized Wright hypergeometric and the Fox’s-H functions.Meanwhile,some interesting integral operators and derivative operators of this function,are also discussed.展开更多
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
Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in...Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in time-independent Hamiltonians.Here,our work makes an effort to explore multi-parameter estimation with time-dependent Hamiltonians.In particular,we focus on the discrimination of two close frequencies of a magnetic field by using a single qubit.We optimize the quantum controls by employing both traditional optimization methods and reinforcement learning to improve the precision for estimating the frequencies of the two magnetic fields.In addition to the estimation precision,we also evaluate the robustness of the optimization schemes against the shift of the control parameters.The results demonstrate that the hybrid reinforcement learning approach achieves the highest estimation precision,and exhibits superior robustness.Moreover,a fundamental challenge in multi-parameter quantum estimation stems from the incompatibility of the optimal control strategies for different parameters.We demonstrate that the hybrid control strategies derived through numerical optimization remain effective in enhancing the precision of multi-parameter estimation in spite of the incompatibilities,thereby mitigating incompatibilities between control strategies on the estimation precision.Finally,we investigate the trade-offs in estimation precision among different parameters for different scenarios,revealing the inherent challenges in balancing the optimization of multiple parameters simultaneously and providing insights into the fundamental distinction between quantum single-parameter estimation and multi-parameter estimation.展开更多
Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).C...Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).Conventional battery monitoring technologies struggle to track multiple physicochemical parameters in real time,hindering early hazard detection.Embedded optical fiber sensors have gained prominence as a transformative solution for next-generation smart battery sensing,owing to their micrometer size,multiplexing capability,and electromagnetic immunity.However,comprehensive reviews focusing on their advancements in operando multi-parameter monitoring remain scarce,despite their critical importance for ensuring battery safety.To address this gap,this review first introduces a classification and the fundamental principles of advanced battery-oriented optical fiber sensors.Subsequently,it summarizes recent developments in single-parameter battery monitoring using optical fiber sensors.Building on this foundation,this review presents the first comprehensive analysis of multifunctional optical fiber sensing platforms capable of simultaneously tracking temperature,strain,pressure,refractive index,and monitoring battery aging.Targeted strategies are proposed to facilitate the practical development of this technology,including optimization of sensor integration techniques,minimizing sensor invasiveness,resolving the cross-sensitivity of fiber Bragg grating(FBG)through structural innovation,enhancing techno-economics,and combining with artificial intelligence(AI).By aligning academic research with industry requirements,this review provides a methodological roadmap for developing robust optical sensing systems to ensure battery safety in decarbonization-driven applications.展开更多
Generative adversarial network(GAN)models are widely used in mechanical designs.The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance,and conditional GAN is used fo...Generative adversarial network(GAN)models are widely used in mechanical designs.The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance,and conditional GAN is used for that aim.However,the output of GAN contains uncertainties.Additionally,the uncertainties of labels have not been quantified.This paper proposes an uncertainty quantification method to estimate the uncertainty of labels using Monte Carlo dropout.In addition,an uncertainty reduction method is proposed based on imbalanced training.The proposed method was evaluated for the airfoil generation task.The results indicated that the uncertainty was appropriately quantified and successfully reduced.展开更多
During high-speed forward flight,helicopter rotor blades operate across a wide range of Reynolds and Mach numbers.Under such conditions,their aerodynamic performance is significantly influenced by dynamic stall—a com...During high-speed forward flight,helicopter rotor blades operate across a wide range of Reynolds and Mach numbers.Under such conditions,their aerodynamic performance is significantly influenced by dynamic stall—a complex,unsteady flow phenomenon highly sensitive to inlet conditions such asMach and Reynolds numbers.The key features of three-dimensional blade stall can be effectively represented by the dynamic stall behavior of a pitching airfoil.In this study,we conduct an uncertainty quantification analysis of dynamic stall aerodynamics in high-Mach-number flows over pitching airfoils,accounting for uncertainties in inlet parameters.A computational fluid dynamics(CFD)model based on the compressible unsteady Reynolds-averagedNavier–Stokes(URANS)equations,coupledwith sliding mesh techniques,is developed to simulate the unsteady aerodynamic behavior and associated flow fields.To efficiently capture the aerodynamic responses while maintaining high accuracy,a multi-fidelity Co-Kriging surrogate model is constructed.This model integrates the precision of high-fidelity wind tunnel experiments with the computational efficiency of lower-fidelity URANS simulations.Its accuracy is validated through direct comparison with experimental data.Building upon this surrogate model,we employ interval analysis and the Sobol sensitivity method to quantify the uncertainty and parameter sensitivity of the unsteady aerodynamic forces resulting frominlet condition variability.Both the inlet Mach number and Reynolds number are treated as uncertain inputs,modeled using interval representations.Our results demonstrate that variations inMach number contribute far more significantly to aerodynamic uncertainty than those in Reynolds number.Moreover,the presence of dynamic stall vortices markedly amplifies the aerodynamic sensitivity to Mach number fluctuations.展开更多
In the data transaction process within a data asset trading platform,quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures.To address this issue,a ...In the data transaction process within a data asset trading platform,quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures.To address this issue,a distributed data source trust assessment management framework,a trust quantification model,and a dynamic adjustment mechanism are proposed.Themodel integrates the Analytic Hierarchy Process(AHP)and Dempster-Shafer(D-S)evidence theory to determine attribute weights and calculate direct trust values,while the PageRank algorithm is employed to derive indirect trust values.Thedirect and indirect trust values are then combined to compute the comprehensive trust value of the data source.Furthermore,a dynamic adjustment mechanism is introduced to continuously update the comprehensive trust value based on historical assessment data.By leveraging the collaborative efforts of multiple nodes in the distributed network,the proposed framework enables a comprehensive,dynamic,and objective evaluation of data source trustworthiness.Extensive experimental analyses demonstrate that the trust quantification model effectively handles large-scale data source trust assessments,exhibiting both strong trust differentiation capability and high robustness.展开更多
BACKGROUND The Streptococcus salivarius(S.salivarius)group,which produces the enzyme urease has been identified as a potential contributor to ammonia production in the gut.Researchers have reported that patients with ...BACKGROUND The Streptococcus salivarius(S.salivarius)group,which produces the enzyme urease has been identified as a potential contributor to ammonia production in the gut.Researchers have reported that patients with minimal HE had an increased abundance of the S.salivarius group,which is a specific change in the gut microbiota that distinguishes them from healthy individuals.The correlation between the aggregation of specific bacterial species and fibrosis progression in chronic liver disease(CLD)is yet to be fully elucidated.AIM To quantify S.salivarius using digital PCR(dPCR)as a liver fibrosis marker of CLD.METHODS This study retrospectively analysed 52 patients with CLD.To quantify S.salivarius in patients with CLD using dPCR,we evaluated the specificity and sensitivity of S.salivarius bacterial load using dPCR for a type strain.Next,we evaluated the clinical usefulness of dPCR for S.salivarius load quantification for detecting liver fibrosis in patients with CLD.The liver fibrosis stage was categorized into mild and advanced fibrosis based on pathological findings.RESULTS The dPCR assay revealed that S.salivarius was highly positive for the tnpA gene.The lower limit of quantification for dPCR using the tnpA gene with a 1μL template comprising 1.28×102 CFU/mL was 4.3 copies.After considering the detection range in dPCR,we adjusted the extracted DNA concentration to 5.0×10-4 ng/μL from 200 mg stool samples.The median bacterial loads of S.salivarius in stool sample from patients with mild and advanced fibrosis were 1.9 and 7.4 copies/μL,respectively.The quantification of S.salivarius load was observed more frequently in patients with advanced fibrosis than in those with mild fibrosis(P=0.032).CONCLUSION Quantifying of S.salivarius load using digital PCR is a useful biomarker for liver fibrosis in patients with CLD.展开更多
Under sensorless control, the position estimation error in interior permanent magnet(PM) synchronous machines will lead to parameter identification errors and a rank-deficiency issue. This paper proposes a parameter i...Under sensorless control, the position estimation error in interior permanent magnet(PM) synchronous machines will lead to parameter identification errors and a rank-deficiency issue. This paper proposes a parameter identification model that is independent of position error by combining the dq-axis voltage equations. Then, a novel dual signal alternate injection method is proposed to address the rank-deficiency issue, i.e., during one injection period, a zero, positive, and negative d-axis current injection together with a rotor position offset injection, to simultaneously identify the multi-parameters, including stator resistance, dq-axis inductances, and PM flux linkage. The proposed method is verified by experiments at different dq-axis current conditions.展开更多
Excessive Fe^(3+) ion concentrations in wastewater pose a long-standing threat to human health.Achieving low-cost,high-efficiency quantification of Fe^(3+) ion concentration in unknown solutions can guide environmenta...Excessive Fe^(3+) ion concentrations in wastewater pose a long-standing threat to human health.Achieving low-cost,high-efficiency quantification of Fe^(3+) 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 Fe^(3+),a linear relationship between the ion current and Fe^(3+) ion concentration was established.Utilizing this linear relationship,quantification of Fe^(3+) ion concentration in unknown solutions was achieved.Furthermore,ethylenediaminetetraacetic acid disodium salt was employed to displace Fe^(3+) from the nanopores,allowing them to be restored to their initial conditions and reused for Fe^(3+) 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 Fe^(3+) concentration using nanopores,with potential applications in environmental assessment,health monitoring,and so forth.展开更多
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 mm^(2),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
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^(-1)of 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
To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-r...To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.展开更多
Abstract： In the present study, we established an ultra performance liquid chromatography coupled with time-of-flight mass spectrometry （UPLC-QTOF-MSE） method to simultaneously quantify 33 components in Ginkgo bilo...Abstract： In the present study, we established an ultra performance liquid chromatography coupled with time-of-flight mass spectrometry （UPLC-QTOF-MSE） method to simultaneously quantify 33 components in Ginkgo biloba leaf extracts （GBEs）, including 17 flavonol glycosides, five terpene trilactones （TTLs）, four polyphenols and seven carboxylic acids. This optimized method was successfully applied to analyze the explicit compositions of GBE samples collected from different places. Furthermore, the data were processed through unsupervised principal component analysis （PCA） and supervised orthogonal partial least squared discrimination analysis （OPLS-DA） to evaluate the quality and compare the differences between the samples according to the contents of the 33 chemical constituents. Bilobalide, protocatechuic acid, shikimic acid, quinic acid, ginkgolide B, ginkgolide J, kaempferol-3-O-rutinoside, isorhamnetin-3-O-rutinoside, quercetin-3-O-ct-L-rhamnopyranocyl-2＂-（6＇＂-p-coumaroyl）-β-D-glucoside and rutin were recognized as characteristic chemical markers that contributed most to control the quality of GBEs. Based on the fact that GBEs should be standardized with the characteristic components as quality control chemical markers, it is most important to maintain the quality of GBEs stable and reliable, and this method also provided a good strategy to further rectify and standardize the GBEs market.展开更多
In the present study, we developed and validated a high-performance liquid chromatography method for the simultaneous determination of seven phenylpropanoid compounds （2-hydroxyl cinnamaldehyde, coumarin, cinnamyl al...In the present study, we developed and validated a high-performance liquid chromatography method for the simultaneous determination of seven phenylpropanoid compounds （2-hydroxyl cinnamaldehyde, coumarin, cinnamyl alcohol, cinnamic acid, 2-methoxy cinnamic acid, cinnamaldehyde and 2-methoxy cinnamaldehyde） in Cinnamomi Cortex and Cinnamomi Ramulus. The levels of seven phenylpropanoid compounds in Cinnamomi Cortex and Cinnamomi Ramulus were compared using this method. A total of 48 samples （27 Cinnamomi Cortex and 21 Cinnamomi Ramulus） were purchased in China and analyzed. Quantities of seven phenylpropanoid compounds ranged from 17.5 to 61.6 mg/g in Cinnamomi Cortex and ranged from 9.91 to 23.4 mg/g in Ciunamomi Ramulus. The level of 2-methoxy cinnamic acid in the Cinnamomi Cortex samples was below the LOD, whereas it ranged from 0 to 0.119 mg/g in the Cinnamomi Ramulus samples. The （cinnamyl alcohol＋cinnamic acid）/cinnamaldehyde ratios （R346） of Ciunamomi Cortex and Cinnamomi Ramulus ranged from 0.0121 to 0.0467 and 0.0598 to 0.182, respectively. This ratio could be used to discriminate Cinnamomi Cortex （〈0.05） and Cinnamomi Ramulus （〉0.05）. The extraction rates （Dn） of seven compounds in boiling water were different, with the lowest dissolution for cinnamaldehyde （〈3%） and the highest for cinnamic acid （about 60%）.展开更多
For the first time, we have utilized high-performance liquid chromatography （HPLC） to simultaneously quantify the eugenol and bancroffione in Caryophylli Fructus. The optimized parameters included： Inertsil ODS-4 c...For the first time, we have utilized high-performance liquid chromatography （HPLC） to simultaneously quantify the eugenol and bancroffione in Caryophylli Fructus. The optimized parameters included： Inertsil ODS-4 column （150 mm×4.6 mm, 5 μm）; column temperature： 35 ℃; mobile phase： methanol water （65：35, v/v）; flow rate： 1.0 mL/min; detection wavelength： 280 nm. Eugenol and bancroftione showed good linear relationships with peak areas within the range of （0.0998 0.8982） mg/mL （r = 0.9999） and （0.1474-1.3266） mg/mL （r = 0.9999）, respectively. The average recoveries were 102.52% and 100.96% for eugenol and bancroftione, respectively. Our results showed that the established method is simple, rapid, and accurate with good reproducibility to evaluate the quality of Caryophylli Fructus.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.32160172)the Key Science-Technology Project of Inner Mongolia(2023KYPT0010)+1 种基金the Natural Science Foundation of Inner Mongolia Autonomous Region of China(Grant No.2025QN03006)the 2023 Inner Mongolia Public Institution High-level Talent Introduction Scientific Research Support Project.
文摘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(R^(2)>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.
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant No.2021QNLM020001)the National Key R&D Program of China(Grant No.2019YFC0605503C)+2 种基金the Major Scientific and Technological Projects of China National Petroleum Corporation(CNPC)(Grant No.ZD2019-183-003)the National Outstanding Youth Science Foundation(Grant No.41922028)the National Innovation Group Project(Grant No.41821002).
文摘Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.
基金co-supported by the National Natural Science Foundation of China(Nos.51875014,U2233212 and 51875015)the Natural Science Foundation of Beijing Municipality,China(No.L221008)+1 种基金Science,Technology Innovation 2025 Major Project of Ningbo of China(No.2022Z005)the Tianmushan Laboratory Project,China(No.TK2023-B-001)。
文摘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.
基金Supported by The National Undergraduate Innovation Training Program(Grant No.202310290069Z).
文摘In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above function,are obtained.Especially,we associate the multi-parameters Mittag-Leffler function with two special functions which are the generalized Wright hypergeometric and the Fox’s-H functions.Meanwhile,some interesting integral operators and derivative operators of this function,are also discussed.
基金Funding from Natural Sciences and Engineering Research Council of Canada award number RGPIN/4002-2020.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.12075323)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0300702).
文摘Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in time-independent Hamiltonians.Here,our work makes an effort to explore multi-parameter estimation with time-dependent Hamiltonians.In particular,we focus on the discrimination of two close frequencies of a magnetic field by using a single qubit.We optimize the quantum controls by employing both traditional optimization methods and reinforcement learning to improve the precision for estimating the frequencies of the two magnetic fields.In addition to the estimation precision,we also evaluate the robustness of the optimization schemes against the shift of the control parameters.The results demonstrate that the hybrid reinforcement learning approach achieves the highest estimation precision,and exhibits superior robustness.Moreover,a fundamental challenge in multi-parameter quantum estimation stems from the incompatibility of the optimal control strategies for different parameters.We demonstrate that the hybrid control strategies derived through numerical optimization remain effective in enhancing the precision of multi-parameter estimation in spite of the incompatibilities,thereby mitigating incompatibilities between control strategies on the estimation precision.Finally,we investigate the trade-offs in estimation precision among different parameters for different scenarios,revealing the inherent challenges in balancing the optimization of multiple parameters simultaneously and providing insights into the fundamental distinction between quantum single-parameter estimation and multi-parameter estimation.
基金the financial supports of the National Natural Science Foundation of China(No.52372200)a project supported by the State Key Laboratory of Mechanics and Control for Aerospace Structures(No.MCAS-S-0324G01)。
文摘Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).Conventional battery monitoring technologies struggle to track multiple physicochemical parameters in real time,hindering early hazard detection.Embedded optical fiber sensors have gained prominence as a transformative solution for next-generation smart battery sensing,owing to their micrometer size,multiplexing capability,and electromagnetic immunity.However,comprehensive reviews focusing on their advancements in operando multi-parameter monitoring remain scarce,despite their critical importance for ensuring battery safety.To address this gap,this review first introduces a classification and the fundamental principles of advanced battery-oriented optical fiber sensors.Subsequently,it summarizes recent developments in single-parameter battery monitoring using optical fiber sensors.Building on this foundation,this review presents the first comprehensive analysis of multifunctional optical fiber sensing platforms capable of simultaneously tracking temperature,strain,pressure,refractive index,and monitoring battery aging.Targeted strategies are proposed to facilitate the practical development of this technology,including optimization of sensor integration techniques,minimizing sensor invasiveness,resolving the cross-sensitivity of fiber Bragg grating(FBG)through structural innovation,enhancing techno-economics,and combining with artificial intelligence(AI).By aligning academic research with industry requirements,this review provides a methodological roadmap for developing robust optical sensing systems to ensure battery safety in decarbonization-driven applications.
基金supported by the Japan Society for the Promotion of Science and Grants-in-Aid for Scientific Research(Grant Nos.JP21K14064 and JP23K13239).
文摘Generative adversarial network(GAN)models are widely used in mechanical designs.The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance,and conditional GAN is used for that aim.However,the output of GAN contains uncertainties.Additionally,the uncertainties of labels have not been quantified.This paper proposes an uncertainty quantification method to estimate the uncertainty of labels using Monte Carlo dropout.In addition,an uncertainty reduction method is proposed based on imbalanced training.The proposed method was evaluated for the airfoil generation task.The results indicated that the uncertainty was appropriately quantified and successfully reduced.
文摘During high-speed forward flight,helicopter rotor blades operate across a wide range of Reynolds and Mach numbers.Under such conditions,their aerodynamic performance is significantly influenced by dynamic stall—a complex,unsteady flow phenomenon highly sensitive to inlet conditions such asMach and Reynolds numbers.The key features of three-dimensional blade stall can be effectively represented by the dynamic stall behavior of a pitching airfoil.In this study,we conduct an uncertainty quantification analysis of dynamic stall aerodynamics in high-Mach-number flows over pitching airfoils,accounting for uncertainties in inlet parameters.A computational fluid dynamics(CFD)model based on the compressible unsteady Reynolds-averagedNavier–Stokes(URANS)equations,coupledwith sliding mesh techniques,is developed to simulate the unsteady aerodynamic behavior and associated flow fields.To efficiently capture the aerodynamic responses while maintaining high accuracy,a multi-fidelity Co-Kriging surrogate model is constructed.This model integrates the precision of high-fidelity wind tunnel experiments with the computational efficiency of lower-fidelity URANS simulations.Its accuracy is validated through direct comparison with experimental data.Building upon this surrogate model,we employ interval analysis and the Sobol sensitivity method to quantify the uncertainty and parameter sensitivity of the unsteady aerodynamic forces resulting frominlet condition variability.Both the inlet Mach number and Reynolds number are treated as uncertain inputs,modeled using interval representations.Our results demonstrate that variations inMach number contribute far more significantly to aerodynamic uncertainty than those in Reynolds number.Moreover,the presence of dynamic stall vortices markedly amplifies the aerodynamic sensitivity to Mach number fluctuations.
基金funded by Haikou Science and Technology Plan Project(2022-007),in part by key Laboratory of PK System Technologies Research of Hainan,China.
文摘In the data transaction process within a data asset trading platform,quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures.To address this issue,a distributed data source trust assessment management framework,a trust quantification model,and a dynamic adjustment mechanism are proposed.Themodel integrates the Analytic Hierarchy Process(AHP)and Dempster-Shafer(D-S)evidence theory to determine attribute weights and calculate direct trust values,while the PageRank algorithm is employed to derive indirect trust values.Thedirect and indirect trust values are then combined to compute the comprehensive trust value of the data source.Furthermore,a dynamic adjustment mechanism is introduced to continuously update the comprehensive trust value based on historical assessment data.By leveraging the collaborative efforts of multiple nodes in the distributed network,the proposed framework enables a comprehensive,dynamic,and objective evaluation of data source trustworthiness.Extensive experimental analyses demonstrate that the trust quantification model effectively handles large-scale data source trust assessments,exhibiting both strong trust differentiation capability and high robustness.
文摘BACKGROUND The Streptococcus salivarius(S.salivarius)group,which produces the enzyme urease has been identified as a potential contributor to ammonia production in the gut.Researchers have reported that patients with minimal HE had an increased abundance of the S.salivarius group,which is a specific change in the gut microbiota that distinguishes them from healthy individuals.The correlation between the aggregation of specific bacterial species and fibrosis progression in chronic liver disease(CLD)is yet to be fully elucidated.AIM To quantify S.salivarius using digital PCR(dPCR)as a liver fibrosis marker of CLD.METHODS This study retrospectively analysed 52 patients with CLD.To quantify S.salivarius in patients with CLD using dPCR,we evaluated the specificity and sensitivity of S.salivarius bacterial load using dPCR for a type strain.Next,we evaluated the clinical usefulness of dPCR for S.salivarius load quantification for detecting liver fibrosis in patients with CLD.The liver fibrosis stage was categorized into mild and advanced fibrosis based on pathological findings.RESULTS The dPCR assay revealed that S.salivarius was highly positive for the tnpA gene.The lower limit of quantification for dPCR using the tnpA gene with a 1μL template comprising 1.28×102 CFU/mL was 4.3 copies.After considering the detection range in dPCR,we adjusted the extracted DNA concentration to 5.0×10-4 ng/μL from 200 mg stool samples.The median bacterial loads of S.salivarius in stool sample from patients with mild and advanced fibrosis were 1.9 and 7.4 copies/μL,respectively.The quantification of S.salivarius load was observed more frequently in patients with advanced fibrosis than in those with mild fibrosis(P=0.032).CONCLUSION Quantifying of S.salivarius load using digital PCR is a useful biomarker for liver fibrosis in patients with CLD.
文摘Under sensorless control, the position estimation error in interior permanent magnet(PM) synchronous machines will lead to parameter identification errors and a rank-deficiency issue. This paper proposes a parameter identification model that is independent of position error by combining the dq-axis voltage equations. Then, a novel dual signal alternate injection method is proposed to address the rank-deficiency issue, i.e., during one injection period, a zero, positive, and negative d-axis current injection together with a rotor position offset injection, to simultaneously identify the multi-parameters, including stator resistance, dq-axis inductances, and PM flux linkage. The proposed method is verified by experiments at different dq-axis current conditions.
基金supported by the National Natural Science Foundation of China(Nos.52303380,52025132,52273305,22205185,21621091,22021001,and 22121001)Fundamental Research Funds for the Central Universities(No.20720240041)+3 种基金the 111 Project(Nos.B17027 and B16029)the National Science Foundation of Fujian Province of China(No.2022J02059)the Science and Technology Projects of Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(No.RD2022070601)the New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘Excessive Fe^(3+) ion concentrations in wastewater pose a long-standing threat to human health.Achieving low-cost,high-efficiency quantification of Fe^(3+) 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 Fe^(3+),a linear relationship between the ion current and Fe^(3+) ion concentration was established.Utilizing this linear relationship,quantification of Fe^(3+) ion concentration in unknown solutions was achieved.Furthermore,ethylenediaminetetraacetic acid disodium salt was employed to displace Fe^(3+) from the nanopores,allowing them to be restored to their initial conditions and reused for Fe^(3+) 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 Fe^(3+) concentration using nanopores,with potential applications in environmental assessment,health monitoring,and so forth.
基金Hefei Municipal Natural Science Foundation,Grant/Award Number:2022009Suqian Guiding Program Project,Grant/Award Number:Z202309Suqian Traditional Chinese Medicine Science and Technology Plan,Grant/Award Number:MS202301。
文摘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 mm^(2),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.
基金supported by the funding from the National Natural Science Foundation of China(32072359)。
文摘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^(-1)of 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.
基金supported by the National Natural Science Foundation of China(Grant No.52339001).
文摘To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.
文摘Abstract： In the present study, we established an ultra performance liquid chromatography coupled with time-of-flight mass spectrometry （UPLC-QTOF-MSE） method to simultaneously quantify 33 components in Ginkgo biloba leaf extracts （GBEs）, including 17 flavonol glycosides, five terpene trilactones （TTLs）, four polyphenols and seven carboxylic acids. This optimized method was successfully applied to analyze the explicit compositions of GBE samples collected from different places. Furthermore, the data were processed through unsupervised principal component analysis （PCA） and supervised orthogonal partial least squared discrimination analysis （OPLS-DA） to evaluate the quality and compare the differences between the samples according to the contents of the 33 chemical constituents. Bilobalide, protocatechuic acid, shikimic acid, quinic acid, ginkgolide B, ginkgolide J, kaempferol-3-O-rutinoside, isorhamnetin-3-O-rutinoside, quercetin-3-O-ct-L-rhamnopyranocyl-2＂-（6＇＂-p-coumaroyl）-β-D-glucoside and rutin were recognized as characteristic chemical markers that contributed most to control the quality of GBEs. Based on the fact that GBEs should be standardized with the characteristic components as quality control chemical markers, it is most important to maintain the quality of GBEs stable and reliable, and this method also provided a good strategy to further rectify and standardize the GBEs market.
基金National Natural Science Foundation of China(Grant No.30873416)
文摘In the present study, we developed and validated a high-performance liquid chromatography method for the simultaneous determination of seven phenylpropanoid compounds （2-hydroxyl cinnamaldehyde, coumarin, cinnamyl alcohol, cinnamic acid, 2-methoxy cinnamic acid, cinnamaldehyde and 2-methoxy cinnamaldehyde） in Cinnamomi Cortex and Cinnamomi Ramulus. The levels of seven phenylpropanoid compounds in Cinnamomi Cortex and Cinnamomi Ramulus were compared using this method. A total of 48 samples （27 Cinnamomi Cortex and 21 Cinnamomi Ramulus） were purchased in China and analyzed. Quantities of seven phenylpropanoid compounds ranged from 17.5 to 61.6 mg/g in Cinnamomi Cortex and ranged from 9.91 to 23.4 mg/g in Ciunamomi Ramulus. The level of 2-methoxy cinnamic acid in the Cinnamomi Cortex samples was below the LOD, whereas it ranged from 0 to 0.119 mg/g in the Cinnamomi Ramulus samples. The （cinnamyl alcohol＋cinnamic acid）/cinnamaldehyde ratios （R346） of Ciunamomi Cortex and Cinnamomi Ramulus ranged from 0.0121 to 0.0467 and 0.0598 to 0.182, respectively. This ratio could be used to discriminate Cinnamomi Cortex （〈0.05） and Cinnamomi Ramulus （〉0.05）. The extraction rates （Dn） of seven compounds in boiling water were different, with the lowest dissolution for cinnamaldehyde （〈3%） and the highest for cinnamic acid （about 60%）.
基金973 Project-Medical Characteristics of Orthodox Herbs(Grant No.2006CB504707)
文摘For the first time, we have utilized high-performance liquid chromatography （HPLC） to simultaneously quantify the eugenol and bancroffione in Caryophylli Fructus. The optimized parameters included： Inertsil ODS-4 column （150 mm×4.6 mm, 5 μm）; column temperature： 35 ℃; mobile phase： methanol water （65：35, v/v）; flow rate： 1.0 mL/min; detection wavelength： 280 nm. Eugenol and bancroftione showed good linear relationships with peak areas within the range of （0.0998 0.8982） mg/mL （r = 0.9999） and （0.1474-1.3266） mg/mL （r = 0.9999）, respectively. The average recoveries were 102.52% and 100.96% for eugenol and bancroftione, respectively. Our results showed that the established method is simple, rapid, and accurate with good reproducibility to evaluate the quality of Caryophylli Fructus.
