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
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
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
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
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
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
This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer.Firstly,the dynamic model of projectile motion is establi...This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer.Firstly,the dynamic model of projectile motion is established considering the flexible deformation of the barrel and the interaction between the projectile and the barrel.Subsequently,the accuracy of the dynamic model is verified based on the external ballistic projectile attitude test platform.Furthermore,the probability density evolution method(PDEM)is developed to high-dimensional uncertainty quantification of projectile motion.The engineering example highlights the results of the proposed method are consistent with the results obtained by the Monte Carlo Simulation(MCS).Finally,the influence of parameter uncertainty on the projectile disturbance at muzzle under different working conditions is analyzed.The results show that the disturbance of the pitch angular,pitch angular velocity and pitch angular of velocity decreases with the increase of launching angle,and the random parameter ranges of both the projectile and coupling model have similar influence on the disturbance of projectile angular motion at muzzle.展开更多
In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to pro...In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method,in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system.The advantages of this include establishing intuitive coupling between the mechanism and motor,facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism,and enabling dynamic simulation with controller to take the randomness of the electric load into account.Dynamic simulation considering feedback control of ammunition delivery system is carried out,and the feasibility of the model is verified experimentally.Based on probability density evolution theory,we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization.Our work can not only provide guidance for engineering design of ammunition delivery mechanism,but also provide theoretical support for modeling and uncertainty quantification research of mechatronics system.展开更多
The reasonable quantification of the concrete freezing environment on the Qinghai-Tibet Plateau(QTP)is the primary issue in frost resistant concrete design,which is one of the challenges that the QTP engineering manag...The reasonable quantification of the concrete freezing environment on the Qinghai-Tibet Plateau(QTP)is the primary issue in frost resistant concrete design,which is one of the challenges that the QTP engineering managers should take into account.In this paper,we propose a more realistic method to calculate the number of concrete freeze-thaw cycles(NFTCs)on the QTP.The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7.Four machine learning methods,i.e.,the random forest(RF)model,generalized boosting method(GBM),generalized linear model(GLM),and generalized additive model(GAM),are used to fit the NFTCs.The root mean square error(RMSE)values of the RF,GBM,GLM,and GAM are 32.3,4.3,247.9,and 161.3,respectively.The R^(2)values of the RF,GBM,GLM,and GAM are 0.93,0.99,0.48,and 0.66,respectively.The GBM method performs the best compared to the other three methods,which was shown by the results of RMSE and R^(2)values.The quantitative results from the GBM method indicate that the lowest,medium,and highest NFTC values are distributed in the northern,central,and southern parts of the QTP,respectively.The annual NFTCs in the QTP region are mainly concentrated at 160 and above,and the average NFTCs is 200 across the QTP.Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.展开更多
Short-chain fatty acids (SCFA) play an important role in human biochemistry. They originate primarily from the digestive system through carbohydrates microbial fermentation. Most SCFA produced in the colon are absorbe...Short-chain fatty acids (SCFA) play an important role in human biochemistry. They originate primarily from the digestive system through carbohydrates microbial fermentation. Most SCFA produced in the colon are absorbed by the intestinal wall and enter the bloodstream to be distributed throughout the body for multiple purposes. At the intestinal level, SCFA play a role in controlling fat storage and fatty acid metabolism. The effects of these beneficial compounds therefore concern overall health. They facilitate energy expenditure and are valuable allies in the fight against obesity and diabetes. SCFA are also involved in the regulation of the levels of several neurotransmitters such as GABA (γ-aminobutyric acid), glutamate, serotonin, dopamine, and norepinephrine. Their role is also highlighted in many inflammatory and neurodegenerative diseases such as Alzheimer’s disease (AD) or Parkinson’s disease (PD). To have a realistic picture of the distribution of SCFA in different biological compartments of the human body, we propose to study SCFA simultaneously in five human biological samples: feces, saliva, serum, cerebrospinal fluid (CSF), and urine, as well as in Dried Blood Spot (DBS). To evaluate their concentration and repeatability, we used 10 aliquots from pooled samples, analyzed by 3-nitrophenylhydrazine (3-NPH) derivation and liquid chromatography coupled with high sensitivity mass spectrometry (LC-QqQ-MS). We also evaluated the SCFA assay on Dried Blood Spot (DBS). In this work, we adapted the pre-analytical parts for each sample to be able to use a common calibration curve, thus facilitating multi-assay quantification studies and so being less time-consuming. Moreover, we proposed new daughter ions from the same neutral loss (43 Da) to quantify SCFAs, thus improving the sensitivity. In conclusion, our methodology, based on a unique calibration curve for all samples for each SCFA, is well-suited to quantified them in a clinical context.展开更多
Variations in herb dosage due to species adulteration and dosing inaccuracies can substantially affect clinical safety and efficacy.Accurate species quantification remains challenging,as current methods often yield in...Variations in herb dosage due to species adulteration and dosing inaccuracies can substantially affect clinical safety and efficacy.Accurate species quantification remains challenging,as current methods often yield inconsistent results.This study introduces a novel pyrosequencing-based technique,termed herb molecular quantification(Herb-Q),designed to precisely quantify herbal products.We evaluated its effectiveness using Pinellia ternata and five of its adulterants.Initially,we assessed commonly used DNA barcodes with sequences from a public database,identifying two candidate regions,Maturase K(matK)and internal transcribed spacer 2(ITS2),for screening specific single nucleotide polymorphism(SNP)loci,allowing for species-specific identification.These loci were validated by amplifying and sequencing genomic material from collected samples.Our validation studies showed that Herb-Q demonstrated excellent linearity,accuracy,repeatability,and detection limits.We established quantitative standard curves with high R^(2)values(>0.99)to enable precise species quantification,which were combined with external standards to provide clear and accurate visual quantification results.The average bias in quantifying the tuber of P.ternata was 2.38%,confirming that Herb-Q can accurately identify and quantify herbal product constituents.Moreover,the entire quantification process took less than 4 h.This study presents a novel,rapid method for accurately quantifying species in herbal products and advances the application of DNA barcoding from species identification to quantitative detection.展开更多
3D printing is widely adopted to quickly produce rock mass models with complex structures in batches,improving the consistency and repeatability of physical modeling.It is necessary to regulate the mechanical properti...3D printing is widely adopted to quickly produce rock mass models with complex structures in batches,improving the consistency and repeatability of physical modeling.It is necessary to regulate the mechanical properties of 3D-printed specimens to make them proportionally similar to natural rocks.This study investigates mechanical properties of 3D-printed rock analogues prepared by furan resin-bonded silica sand particles.The mechanical property regulation of 3D-printed specimens is realized through quantifying its similarity to sandstone,so that analogous deformation characteristics and failure mode are acquired.Considering similarity conversion,uniaxial compressive strength,cohesion and stress–strain relationship curve of 3D-printed specimen are similar to those of sandstone.In the study ranges,the strength of 3D-printed specimen is positively correlated with the additive content,negatively correlated with the sand particle size,and first increases then decreases with the increase of curing temperature.The regulation scheme with optimal similarity quantification index,that is the sand type of 70/140,additive content of 2.5‰and curing temperature of 81.6℃,is determined for preparing 3D-printed sandstone analogues and models.The effectiveness of mechanical property regulation is proved through uniaxial compression contrast tests.This study provides a reference for preparing rock-like specimens and engineering models using 3D printing technology.展开更多
Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis.Nevertheless,the approximations inherent in surrogate models necessitate...Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis.Nevertheless,the approximations inherent in surrogate models necessitate the consideration of surrogate model uncertainty in estimating failure probabilities.This paper proposes a new reliability analysis method in which the uncertainty from the Kriging surrogate model is quantified simultaneously.This method treats surrogate model uncertainty as an independent entity,characterizing the estimation error of failure probabilities.Building upon the probabilistic classification function,a failure probability uncertainty is proposed by integrating the difference between the traditional indicator function and the probabilistic classification function to quantify the impact of surrogate model uncertainty on failure probability estimation.Furthermore,the proposed uncertainty quantification method is applied to a newly designed reliability analysis approach termed SUQ-MCS,incorporating a proposed median approximation function for active learning.The proposed failure probability uncertainty serves as the stopping criterion of this framework.Through benchmarking,the effectiveness of the proposed uncertainty quantification method is validated.The empirical results present the competitive performance of the SUQ-MCS method relative to alternative approaches.展开更多
Artificial intelligence(AI),particularly machine learning(ML)and deep learning(DL)techniques,such as convolutional neural networks(CNNs),have emerged as transformative technologies with vast potential in healthcare.Bo...Artificial intelligence(AI),particularly machine learning(ML)and deep learning(DL)techniques,such as convolutional neural networks(CNNs),have emerged as transformative technologies with vast potential in healthcare.Body iron load is usually assessed using slightly invasive blood tests(serum ferritin,serum iron,and serum transferrin).Serum ferritin is widely used to assess body iron and drive medical management;however,it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients.Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron.The ML and DL algorithms can be used to enhance the detection of minor changes.However,a lack of open-access datasets may delay the advancement of medical research in this field.In this letter,we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging.Despite the current limitations,embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.展开更多
Avian wings are central to their remarkable flight ability and diverse life history strategies,including behaviors such as fighting and mating.These multifaceted functions are intricately tied to wing shape,which vari...Avian wings are central to their remarkable flight ability and diverse life history strategies,including behaviors such as fighting and mating.These multifaceted functions are intricately tied to wing shape,which varies significantly across species because of the complex interplay of evolutionary and ecological pressures.Many indices have been developed to quantify wing characteristics to facilitate the study and comparison of avian wing morphology across species.This study provides a comprehensive overview of existing quantitative methods for analyzing avian wing shapes.We then constructed a new quantification framework through the beta distribution,which can generate indices reflecting the shape of avian wings(center,dispersion,skewness,and kurtosis).Next,we used the flight feathers of 613 bird species to perform different quantitative analyses and explore the relationships between various wing shape quantification methods and life history traits,which serve as proxies for the selective forces shaping wing morphology.We find that the wing shape indices are more strongly associated with ecological variables than with morphological variables,especially for migration,habitat and territoriality.This research guides the selection of appropriate methods for wing shape analysis,contributing to a deeper understanding of avian morphology and its evolutionary drivers.展开更多
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.
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.
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.
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.
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the National Natural Science Foundation of China(Grant No.11472137).
文摘This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer.Firstly,the dynamic model of projectile motion is established considering the flexible deformation of the barrel and the interaction between the projectile and the barrel.Subsequently,the accuracy of the dynamic model is verified based on the external ballistic projectile attitude test platform.Furthermore,the probability density evolution method(PDEM)is developed to high-dimensional uncertainty quantification of projectile motion.The engineering example highlights the results of the proposed method are consistent with the results obtained by the Monte Carlo Simulation(MCS).Finally,the influence of parameter uncertainty on the projectile disturbance at muzzle under different working conditions is analyzed.The results show that the disturbance of the pitch angular,pitch angular velocity and pitch angular of velocity decreases with the increase of launching angle,and the random parameter ranges of both the projectile and coupling model have similar influence on the disturbance of projectile angular motion at muzzle.
基金supported by the National Natural Science Foundation of China(Grant Nos.11472137 and U2141246)。
文摘In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method,in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system.The advantages of this include establishing intuitive coupling between the mechanism and motor,facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism,and enabling dynamic simulation with controller to take the randomness of the electric load into account.Dynamic simulation considering feedback control of ammunition delivery system is carried out,and the feasibility of the model is verified experimentally.Based on probability density evolution theory,we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization.Our work can not only provide guidance for engineering design of ammunition delivery mechanism,but also provide theoretical support for modeling and uncertainty quantification research of mechatronics system.
基金supported by Shandong Provincial Natural Science Foundation(grant number:ZR2023MD036)Key Research and Development Project in Shandong Province(grant number:2019GGX101064)project for excellent youth foundation of the innovation teacher team,Shandong(grant number:2022KJ310)。
文摘The reasonable quantification of the concrete freezing environment on the Qinghai-Tibet Plateau(QTP)is the primary issue in frost resistant concrete design,which is one of the challenges that the QTP engineering managers should take into account.In this paper,we propose a more realistic method to calculate the number of concrete freeze-thaw cycles(NFTCs)on the QTP.The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7.Four machine learning methods,i.e.,the random forest(RF)model,generalized boosting method(GBM),generalized linear model(GLM),and generalized additive model(GAM),are used to fit the NFTCs.The root mean square error(RMSE)values of the RF,GBM,GLM,and GAM are 32.3,4.3,247.9,and 161.3,respectively.The R^(2)values of the RF,GBM,GLM,and GAM are 0.93,0.99,0.48,and 0.66,respectively.The GBM method performs the best compared to the other three methods,which was shown by the results of RMSE and R^(2)values.The quantitative results from the GBM method indicate that the lowest,medium,and highest NFTC values are distributed in the northern,central,and southern parts of the QTP,respectively.The annual NFTCs in the QTP region are mainly concentrated at 160 and above,and the average NFTCs is 200 across the QTP.Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.
文摘Short-chain fatty acids (SCFA) play an important role in human biochemistry. They originate primarily from the digestive system through carbohydrates microbial fermentation. Most SCFA produced in the colon are absorbed by the intestinal wall and enter the bloodstream to be distributed throughout the body for multiple purposes. At the intestinal level, SCFA play a role in controlling fat storage and fatty acid metabolism. The effects of these beneficial compounds therefore concern overall health. They facilitate energy expenditure and are valuable allies in the fight against obesity and diabetes. SCFA are also involved in the regulation of the levels of several neurotransmitters such as GABA (γ-aminobutyric acid), glutamate, serotonin, dopamine, and norepinephrine. Their role is also highlighted in many inflammatory and neurodegenerative diseases such as Alzheimer’s disease (AD) or Parkinson’s disease (PD). To have a realistic picture of the distribution of SCFA in different biological compartments of the human body, we propose to study SCFA simultaneously in five human biological samples: feces, saliva, serum, cerebrospinal fluid (CSF), and urine, as well as in Dried Blood Spot (DBS). To evaluate their concentration and repeatability, we used 10 aliquots from pooled samples, analyzed by 3-nitrophenylhydrazine (3-NPH) derivation and liquid chromatography coupled with high sensitivity mass spectrometry (LC-QqQ-MS). We also evaluated the SCFA assay on Dried Blood Spot (DBS). In this work, we adapted the pre-analytical parts for each sample to be able to use a common calibration curve, thus facilitating multi-assay quantification studies and so being less time-consuming. Moreover, we proposed new daughter ions from the same neutral loss (43 Da) to quantify SCFAs, thus improving the sensitivity. In conclusion, our methodology, based on a unique calibration curve for all samples for each SCFA, is well-suited to quantified them in a clinical context.
基金supported by the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences(Nos.CI2021A04106 and CI2021A03910)the National Key Research and Development Program of China(No.2019YFC1710601)+3 种基金the Fundamental Research Funds for the Central Public Welfare Research Institutes of China(Nos.ZZ15-YQ-033,ZXKT21026 and ZXKT23004)the Major Special Project of Scientific and Technological Cooperation of Bijie(No.2021-02)the Advantageous Chinese Medicinal Materials R&D Talent Base Project of Bijie,Guizhou Province(No.RCJD2020-21)Bijie Technology Innovation Platform and Talent Team(Bikehe[2023]No.66-BJZDSYS 2024-05).
文摘Variations in herb dosage due to species adulteration and dosing inaccuracies can substantially affect clinical safety and efficacy.Accurate species quantification remains challenging,as current methods often yield inconsistent results.This study introduces a novel pyrosequencing-based technique,termed herb molecular quantification(Herb-Q),designed to precisely quantify herbal products.We evaluated its effectiveness using Pinellia ternata and five of its adulterants.Initially,we assessed commonly used DNA barcodes with sequences from a public database,identifying two candidate regions,Maturase K(matK)and internal transcribed spacer 2(ITS2),for screening specific single nucleotide polymorphism(SNP)loci,allowing for species-specific identification.These loci were validated by amplifying and sequencing genomic material from collected samples.Our validation studies showed that Herb-Q demonstrated excellent linearity,accuracy,repeatability,and detection limits.We established quantitative standard curves with high R^(2)values(>0.99)to enable precise species quantification,which were combined with external standards to provide clear and accurate visual quantification results.The average bias in quantifying the tuber of P.ternata was 2.38%,confirming that Herb-Q can accurately identify and quantify herbal product constituents.Moreover,the entire quantification process took less than 4 h.This study presents a novel,rapid method for accurately quantifying species in herbal products and advances the application of DNA barcoding from species identification to quantitative detection.
基金the National Natural Science Foundation of China(Nos.51988101 and 42007262).
文摘3D printing is widely adopted to quickly produce rock mass models with complex structures in batches,improving the consistency and repeatability of physical modeling.It is necessary to regulate the mechanical properties of 3D-printed specimens to make them proportionally similar to natural rocks.This study investigates mechanical properties of 3D-printed rock analogues prepared by furan resin-bonded silica sand particles.The mechanical property regulation of 3D-printed specimens is realized through quantifying its similarity to sandstone,so that analogous deformation characteristics and failure mode are acquired.Considering similarity conversion,uniaxial compressive strength,cohesion and stress–strain relationship curve of 3D-printed specimen are similar to those of sandstone.In the study ranges,the strength of 3D-printed specimen is positively correlated with the additive content,negatively correlated with the sand particle size,and first increases then decreases with the increase of curing temperature.The regulation scheme with optimal similarity quantification index,that is the sand type of 70/140,additive content of 2.5‰and curing temperature of 81.6℃,is determined for preparing 3D-printed sandstone analogues and models.The effectiveness of mechanical property regulation is proved through uniaxial compression contrast tests.This study provides a reference for preparing rock-like specimens and engineering models using 3D printing technology.
基金supported by the National Key Research and Development Program of China(No.2023YFB3406900)the National Natural Science Foundation of China(No.52075068).
文摘Surrogate models offer an efficient approach to tackle the computationally intensive evaluation of performance functions in reliability analysis.Nevertheless,the approximations inherent in surrogate models necessitate the consideration of surrogate model uncertainty in estimating failure probabilities.This paper proposes a new reliability analysis method in which the uncertainty from the Kriging surrogate model is quantified simultaneously.This method treats surrogate model uncertainty as an independent entity,characterizing the estimation error of failure probabilities.Building upon the probabilistic classification function,a failure probability uncertainty is proposed by integrating the difference between the traditional indicator function and the probabilistic classification function to quantify the impact of surrogate model uncertainty on failure probability estimation.Furthermore,the proposed uncertainty quantification method is applied to a newly designed reliability analysis approach termed SUQ-MCS,incorporating a proposed median approximation function for active learning.The proposed failure probability uncertainty serves as the stopping criterion of this framework.Through benchmarking,the effectiveness of the proposed uncertainty quantification method is validated.The empirical results present the competitive performance of the SUQ-MCS method relative to alternative approaches.
文摘Artificial intelligence(AI),particularly machine learning(ML)and deep learning(DL)techniques,such as convolutional neural networks(CNNs),have emerged as transformative technologies with vast potential in healthcare.Body iron load is usually assessed using slightly invasive blood tests(serum ferritin,serum iron,and serum transferrin).Serum ferritin is widely used to assess body iron and drive medical management;however,it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients.Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron.The ML and DL algorithms can be used to enhance the detection of minor changes.However,a lack of open-access datasets may delay the advancement of medical research in this field.In this letter,we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging.Despite the current limitations,embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.
基金supported by the National Natural Science Foundation of China(No.32170491)the Scientific Research Team Project of the College of Life Sciences,Beijing Normal University in 2024。
文摘Avian wings are central to their remarkable flight ability and diverse life history strategies,including behaviors such as fighting and mating.These multifaceted functions are intricately tied to wing shape,which varies significantly across species because of the complex interplay of evolutionary and ecological pressures.Many indices have been developed to quantify wing characteristics to facilitate the study and comparison of avian wing morphology across species.This study provides a comprehensive overview of existing quantitative methods for analyzing avian wing shapes.We then constructed a new quantification framework through the beta distribution,which can generate indices reflecting the shape of avian wings(center,dispersion,skewness,and kurtosis).Next,we used the flight feathers of 613 bird species to perform different quantitative analyses and explore the relationships between various wing shape quantification methods and life history traits,which serve as proxies for the selective forces shaping wing morphology.We find that the wing shape indices are more strongly associated with ecological variables than with morphological variables,especially for migration,habitat and territoriality.This research guides the selection of appropriate methods for wing shape analysis,contributing to a deeper understanding of avian morphology and its evolutionary drivers.
