Considering the structural analysis problem of systems properties with Bouc-Wen hysteresis (BWH), various approaches are proposed for the identification of BWH parameters. The applied methods and algorithms are based ...Considering the structural analysis problem of systems properties with Bouc-Wen hysteresis (BWH), various approaches are proposed for the identification of BWH parameters. The applied methods and algorithms are based on the design of parametric models and consider a priori information and the results of data analysis. Structural changes in the BWH form a priori. Methods for the Bouc-Wen model (BWM) identification and its structure estimation are not considered under uncertainty. The study’s purpose is the analysis the structural problems of the Bouc-Wen hysteresis identification. The analysis base is the application of geometric frameworks (GF) under uncertainty. Methods for adaptive estimation parameters and structural of BWM were proposed. The adaptive system stability is proved based on vector Lyapunov functions. An approach is proposed to estimate the identifiability and structure of the system with BWH. The method for estimating the identifiability degree based on the analysis of GF is considered. BWM modifications are proposed to guarantee the system’s stability and simplify its description.展开更多
Modal analysis,which provides modal parameters including frequencies,damping ratios,and mode shapes,is essential for assessing structural safety in structural health monitoring.Automated operational modal analysis(AOM...Modal analysis,which provides modal parameters including frequencies,damping ratios,and mode shapes,is essential for assessing structural safety in structural health monitoring.Automated operational modal analysis(AOMA)offers a promising alternative to traditional methods that depend heavily on human intervention and engineering judgment.However,estimating structural dynamic properties and managing spurious modes remain challenging due to uncertainties in practical application conditions.To address this issue,we propose an automated modal identification approach comprising three key aspects:(1)identification of modal parameters using covariance-driven stochastic subspace identification;(2)automated interpretation of the stabilization diagram;(3)an improved self-adaptive algorithm for grouping physical modes based on ordering points to identify the clustering structure(OPTICS)combined with k-nearest neighbors(KNN).The proposed approach can play a crucial role in enabling real-time structural health monitoring without human intervention.A simulated 10-story shear frame was used to verify the methodology.Identification results from a cable-stayed bridge demonstrate the practicality of the proposed method for conducting AOMA in engineering practice.The proposed approach can automatically identify modal parameters with high accuracy,making it suitable for a real-time structural health monitoring framework.展开更多
The present work studies the chemical constituents from marine-derived streptomyces 3320^# and their antitumor activities. The n-BuOH extract of the ferment broth of 3320^# was chromatographed on silica gel, Sephadex ...The present work studies the chemical constituents from marine-derived streptomyces 3320^# and their antitumor activities. The n-BuOH extract of the ferment broth of 3320^# was chromatographed on silica gel, Sephadex LH-20, ODS columns and HPLC to separate the compounds with antitoumor activities. Their structures were identified using IR, UV, NMR, MS spectroscopic techniques and compared with published data. The antitumor activities of the isolates were assayed using SRB method and flow cytometry assay, accompanied with the morphological observation of the cells under light micro- scope against mammalian tsFT210 cells. Ten compounds, cyclo-（Ala-Leu） 1, cyclo-（Ala-Ile） 2, cyclo-（Ala-Val） 3, cyclo- （Phe-Pro） 4, cyclo-（Phe-Gly）5, cyclo-（Leu-Pro）6, 1-methyl-1, 2, 3, 4-tetrahydro-β-carboline-3-carboxylic acid 7, N-（4- hydroxyphenethyl） acetamide 8, 4-methyoxy-l-（2-hydroxy） ethylbenzene 9 and uridine 10, were isolated from the ferment broth of streptomyces 3320^# . Among them, compounds 6, 7, 8 and 10 showed potent cytotoxicity against the tsFT210 cell with the IC50 values of 3.6, 7.2, 5.2 and 1.6 mmol L-1, respectively. Compounds 8, 10 also exhibited apoptosis inducing activity under 2.0 mmol L-1. Compounds 6, 7, 8 and 10 are the principle bioactive constituents responsible for the antitumor activities of marine streptomyces 3320^#. Compound 7 was isolated from this species for the first time.展开更多
The structure and moisture retention of Tremella polysaccharide fermented from GCMCC5.39(FTP)were evaluated.After UV,infrared spectrum analysis,HPAEC-PAD,HPSEC and 1 D NMR analysis,the composition of the purifi ed FTP...The structure and moisture retention of Tremella polysaccharide fermented from GCMCC5.39(FTP)were evaluated.After UV,infrared spectrum analysis,HPAEC-PAD,HPSEC and 1 D NMR analysis,the composition of the purifi ed FTP was determined.Purifi ed components of fermented Tremella polysaccharide(FTPS)was made of galactose,mannose,glucose,galactosmine,glucosamine,and contain a large amount of hydroxyl,carbonyl and amino groups.FTPS wasα-neutral pyranose without uronic acid.FTPS-1 and FTPS-2 were obtained after purifi cation by DEAE-Sepharose Fast Flow Column.The molecular weights of FTPS-1 and FTPS-2 were 25722 and 177263 Da.FTPS-2 had a better ability to prevent moisture loss,and the optimal moisture retention period was 0–4 h.FTPS-2 could signifi cantly increase the moisture content of the skin epidermis and showed a dose-concentration relationship.The effect of FTPS-2 on the expression of different moisturizing genes was evaluated in a human skin keratinocyte model.The results showed that FTPS-2 has no cytotoxicity,and could signifi cantly promote AQP3,TGM1,CASP14,HYAL2,FLG gene expression level in HaCaT cells.It has the most signifi cant infl uence at HYAL2 protein expression on 50μg/mL.展开更多
The purpose of this review is to apply geometric frameworks in identification problems. In contrast to the qualitative theory of dynamical systems (DSQT), the chaos and catastrophes, researches on the application of g...The purpose of this review is to apply geometric frameworks in identification problems. In contrast to the qualitative theory of dynamical systems (DSQT), the chaos and catastrophes, researches on the application of geometric frameworks have not </span><span style="font-family:Verdana;">been </span><span style="font-family:Verdana;">performed in identification problems. The direct transfer of DSQT ideas is inefficient through the peculiarities of identification systems. In this paper, the attempt </span><span style="font-family:Verdana;">is </span><span style="font-family:Verdana;">made based on the latest researches in this field. A methodology for the synthesis of geometric frameworks (GF) </span><span style="font-family:Verdana;">is </span><span style="font-family:Verdana;">propose</span><span style="font-family:Verdana;">d</span><span style="font-family:Verdana;">, which reflects features of nonlinear systems. Methods based on GF analysis </span><span style="font-family:Verdana;">are </span><span style="font-family:Verdana;">developed for the decision-making on properties and structure of nonlinear systems. The problem solution of structural identifiability </span><span style="font-family:Verdana;">is </span><span style="font-family:Verdana;">obtain</span><span style="font-family:Verdana;">ed</span><span style="font-family:Verdana;"> for nonlinear systems under uncertainty.展开更多
Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.The specific physical properties linked to a threedimensional atomi...Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.The specific physical properties linked to a threedimensional atomic arrangement make this an essential task in the development of new materials.We present a method that efficiently uses active learning of neural network force fields for structure relaxation,minimizing the required number of steps in the process.This is achieved by neural network force fields equipped with uncertainty estimation,which iteratively guide a pool of randomly generated candidates toward their respective local minima.Using this approach,we are able to effectively identify themost promising candidates for further evaluation using density functional theory(DFT).Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systemsSi_(16),Na_(8)Cl_(8),Ga_(8)As_(8)and Al_(4)O_(6)but also excels in finding themost stable minimum for the unseen,more complex systems Si46 and Al16O24.Moreover,we demonstrate at the example of Si_(16)that our method can find multiple relevant local minima while only adding minor computational effort.展开更多
Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion.The recent development of generative models and machine learning(ML)holds great promise for predicting n...Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion.The recent development of generative models and machine learning(ML)holds great promise for predicting new functional materials.However,these data-driven methods are not tailored to identifying energetically stable structures and accurately predicting their thermal properties,as they lack physical constraints and information about the complexity of atomic many-body interactions.Here,we show how combining deep generative models of crystal structures with quantum-accurate,fast ML interatomic potentials can accelerate the prediction of materials with ultrahigh lattice thermal conductivity while ensuring energy optimality.We exploit structural symmetry and similarity metrics derived from atomic coordination environments to enable fast exploration of the structural space produced by the generative model.Additionally,we propose an active-learning-based protocol for the on-the-fly training of ML potentials to achieve high-fidelity predictions of stability and lattice thermal conductivity in prospective materials.Applying this method to carbon materials,we screen 100,000 candidates and identify 34 carbon polymorphs,approximately a quarter of which had not been previously predicted,to have lattice thermal conductivity above 800 W m^(−1)K^(−1),reaching up to 2,400 W m^(−1)K^(−1)aside from diamond.These findings provide a viable pathway toward the ML-assisted prediction of periodic materials with exceptional thermal properties.展开更多
Parameter identification involves the estimation of undisclosed parameters within a system based on observed data and mathematical models.In this investigation,we employ DAISY to meticulously examine the structural id...Parameter identification involves the estimation of undisclosed parameters within a system based on observed data and mathematical models.In this investigation,we employ DAISY to meticulously examine the structural identifiability of parameters of a within-host SARS-CoV-2 epidemic model,taking into account an array of observable datasets.Furthermore,Monte Carlo simulations are performed to offer a comprehensive practical analysis of model parameters.Lastly,sensitivity analysis is employed to ascertain that decreasing the replication rate of the SARS-CoV-2 virus and curbing the infectious period are the most efficacious measures in alleviating the dissemination of COVID-19 amongst hosts.展开更多
Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are ...Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are used to localize only a single fault.However,existing clustering algorithms cannot always obtain completely correct clustering results,which is a potential threat for bug isolation based MFL approaches.To address this issue,we first analyze the influence of the accuracy of the clustering on the performance of MFL,and the results of a controlled study indicate that using the clustering algorithm with the highest accuracy can achieve the best performance of MFL.Moreover,previous studies on clustering algorithms also show that the elements in a higher density cluster have a higher similarity.Based on the above motivation,we propose a novel approach FATOC(One-Fault-at-a-Time via OPTICS Clustering).In particular,FATOC first leverages the OPTICS(Ordering Points to Identify the Clustering Structure)clustering algorithm to group failed test cases,and then identifies a cluster with the highest density.OPTICS clustering is a density-based clustering algorithm,which can reduce the misgrouping and calculate a density value for each cluster.Such a density value of each cluster is helpful for finding a cluster with the highest clustering effectiveness.FATOC then combines the failed test cases in this cluster with all passed test cases to localize a single-fault through the traditional spectrum-based fault localization(SBFL)formula.After this fault is localized and fixed,FATOC will use the same method to localize the next single-fault,until all the test cases are passed.Our evaluation results show that FATOC can significantly outperform the traditional SBFL technique and a state-of-the-art MFL approach MSeer on 804 multi-faulty versions from nine real-world programs.Specifically,FATOC’s performance is 10.32%higher than that of traditional SBFL when using Ochiai formula in terms of metric A-EXAM.Besides,the results also indicate that,when checking 1%,3%and 5%statements of all subject programs,FATOC can locate 36.91%,48.50%and 66.93%of all faults respectively,which is also better than the traditional SBFL and the MFL approach MSeer.展开更多
文摘Considering the structural analysis problem of systems properties with Bouc-Wen hysteresis (BWH), various approaches are proposed for the identification of BWH parameters. The applied methods and algorithms are based on the design of parametric models and consider a priori information and the results of data analysis. Structural changes in the BWH form a priori. Methods for the Bouc-Wen model (BWM) identification and its structure estimation are not considered under uncertainty. The study’s purpose is the analysis the structural problems of the Bouc-Wen hysteresis identification. The analysis base is the application of geometric frameworks (GF) under uncertainty. Methods for adaptive estimation parameters and structural of BWM were proposed. The adaptive system stability is proved based on vector Lyapunov functions. An approach is proposed to estimate the identifiability and structure of the system with BWH. The method for estimating the identifiability degree based on the analysis of GF is considered. BWM modifications are proposed to guarantee the system’s stability and simplify its description.
基金supported by the National Natural Science Foundation of China(No.52408200)the Natural Science Foundation of Jiangsu Province(No.BK20240996)+1 种基金China,the Suzhou Science and Technology Plan(Basic Research)Project(No.SJC2023002)China,and the Natural Science Research Projects of Colleges and Universities in Jiangsu Province(No.24KJB560022),China.
文摘Modal analysis,which provides modal parameters including frequencies,damping ratios,and mode shapes,is essential for assessing structural safety in structural health monitoring.Automated operational modal analysis(AOMA)offers a promising alternative to traditional methods that depend heavily on human intervention and engineering judgment.However,estimating structural dynamic properties and managing spurious modes remain challenging due to uncertainties in practical application conditions.To address this issue,we propose an automated modal identification approach comprising three key aspects:(1)identification of modal parameters using covariance-driven stochastic subspace identification;(2)automated interpretation of the stabilization diagram;(3)an improved self-adaptive algorithm for grouping physical modes based on ordering points to identify the clustering structure(OPTICS)combined with k-nearest neighbors(KNN).The proposed approach can play a crucial role in enabling real-time structural health monitoring without human intervention.A simulated 10-story shear frame was used to verify the methodology.Identification results from a cable-stayed bridge demonstrate the practicality of the proposed method for conducting AOMA in engineering practice.The proposed approach can automatically identify modal parameters with high accuracy,making it suitable for a real-time structural health monitoring framework.
基金supported by the Project of Chinese National Programs for High Technology Research and Development(No.2003AA624020)the National Natural Science Foundation of China(Nos.30472136 and 30470196)Shandong Province and Qingdao Natural Science Foundation(Nos.Z2001C01 and 04-2-JZ-81).
文摘The present work studies the chemical constituents from marine-derived streptomyces 3320^# and their antitumor activities. The n-BuOH extract of the ferment broth of 3320^# was chromatographed on silica gel, Sephadex LH-20, ODS columns and HPLC to separate the compounds with antitoumor activities. Their structures were identified using IR, UV, NMR, MS spectroscopic techniques and compared with published data. The antitumor activities of the isolates were assayed using SRB method and flow cytometry assay, accompanied with the morphological observation of the cells under light micro- scope against mammalian tsFT210 cells. Ten compounds, cyclo-（Ala-Leu） 1, cyclo-（Ala-Ile） 2, cyclo-（Ala-Val） 3, cyclo- （Phe-Pro） 4, cyclo-（Phe-Gly）5, cyclo-（Leu-Pro）6, 1-methyl-1, 2, 3, 4-tetrahydro-β-carboline-3-carboxylic acid 7, N-（4- hydroxyphenethyl） acetamide 8, 4-methyoxy-l-（2-hydroxy） ethylbenzene 9 and uridine 10, were isolated from the ferment broth of streptomyces 3320^# . Among them, compounds 6, 7, 8 and 10 showed potent cytotoxicity against the tsFT210 cell with the IC50 values of 3.6, 7.2, 5.2 and 1.6 mmol L-1, respectively. Compounds 8, 10 also exhibited apoptosis inducing activity under 2.0 mmol L-1. Compounds 6, 7, 8 and 10 are the principle bioactive constituents responsible for the antitumor activities of marine streptomyces 3320^#. Compound 7 was isolated from this species for the first time.
基金We gratefully acknowledge the financial support from the Open Project Program of National R&D Center for Edible Fungus Processing Technology(20200110)Shanghai Science and Technology Commission Project(18495810900).
文摘The structure and moisture retention of Tremella polysaccharide fermented from GCMCC5.39(FTP)were evaluated.After UV,infrared spectrum analysis,HPAEC-PAD,HPSEC and 1 D NMR analysis,the composition of the purifi ed FTP was determined.Purifi ed components of fermented Tremella polysaccharide(FTPS)was made of galactose,mannose,glucose,galactosmine,glucosamine,and contain a large amount of hydroxyl,carbonyl and amino groups.FTPS wasα-neutral pyranose without uronic acid.FTPS-1 and FTPS-2 were obtained after purifi cation by DEAE-Sepharose Fast Flow Column.The molecular weights of FTPS-1 and FTPS-2 were 25722 and 177263 Da.FTPS-2 had a better ability to prevent moisture loss,and the optimal moisture retention period was 0–4 h.FTPS-2 could signifi cantly increase the moisture content of the skin epidermis and showed a dose-concentration relationship.The effect of FTPS-2 on the expression of different moisturizing genes was evaluated in a human skin keratinocyte model.The results showed that FTPS-2 has no cytotoxicity,and could signifi cantly promote AQP3,TGM1,CASP14,HYAL2,FLG gene expression level in HaCaT cells.It has the most signifi cant infl uence at HYAL2 protein expression on 50μg/mL.
文摘The purpose of this review is to apply geometric frameworks in identification problems. In contrast to the qualitative theory of dynamical systems (DSQT), the chaos and catastrophes, researches on the application of geometric frameworks have not </span><span style="font-family:Verdana;">been </span><span style="font-family:Verdana;">performed in identification problems. The direct transfer of DSQT ideas is inefficient through the peculiarities of identification systems. In this paper, the attempt </span><span style="font-family:Verdana;">is </span><span style="font-family:Verdana;">made based on the latest researches in this field. A methodology for the synthesis of geometric frameworks (GF) </span><span style="font-family:Verdana;">is </span><span style="font-family:Verdana;">propose</span><span style="font-family:Verdana;">d</span><span style="font-family:Verdana;">, which reflects features of nonlinear systems. Methods based on GF analysis </span><span style="font-family:Verdana;">are </span><span style="font-family:Verdana;">developed for the decision-making on properties and structure of nonlinear systems. The problem solution of structural identifiability </span><span style="font-family:Verdana;">is </span><span style="font-family:Verdana;">obtain</span><span style="font-family:Verdana;">ed</span><span style="font-family:Verdana;"> for nonlinear systems under uncertainty.
基金N.W.A.G.and M.G.contributed to this research while working at the BASLEARN-TU Berlin/BASF Joint Lab for Machine Learning,co-financed by TU Berlin and BASF SE.K.T.S.contributed to this research while working at TU Berlin and BIFOLD with grant number 01IS18037Asupported by JSPS KAKENHI Grant Number JP23H05457 and by JST-CREST Grant Number JPMJCR22O2.We thank Jonas Lederer and Klaus-Robert Müller for insightful discussions and feedback.
文摘Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.The specific physical properties linked to a threedimensional atomic arrangement make this an essential task in the development of new materials.We present a method that efficiently uses active learning of neural network force fields for structure relaxation,minimizing the required number of steps in the process.This is achieved by neural network force fields equipped with uncertainty estimation,which iteratively guide a pool of randomly generated candidates toward their respective local minima.Using this approach,we are able to effectively identify themost promising candidates for further evaluation using density functional theory(DFT).Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systemsSi_(16),Na_(8)Cl_(8),Ga_(8)As_(8)and Al_(4)O_(6)but also excels in finding themost stable minimum for the unseen,more complex systems Si46 and Al16O24.Moreover,we demonstrate at the example of Si_(16)that our method can find multiple relevant local minima while only adding minor computational effort.
基金supported by the National Natural Science Foundation of China(Nos.52425601,52250273,52327809,U20A20301,and 82361138571)National Key Research and Development Program of China(No.2023YFB4404104)Beijing Natural Science Foundation(No.L233022).
文摘Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion.The recent development of generative models and machine learning(ML)holds great promise for predicting new functional materials.However,these data-driven methods are not tailored to identifying energetically stable structures and accurately predicting their thermal properties,as they lack physical constraints and information about the complexity of atomic many-body interactions.Here,we show how combining deep generative models of crystal structures with quantum-accurate,fast ML interatomic potentials can accelerate the prediction of materials with ultrahigh lattice thermal conductivity while ensuring energy optimality.We exploit structural symmetry and similarity metrics derived from atomic coordination environments to enable fast exploration of the structural space produced by the generative model.Additionally,we propose an active-learning-based protocol for the on-the-fly training of ML potentials to achieve high-fidelity predictions of stability and lattice thermal conductivity in prospective materials.Applying this method to carbon materials,we screen 100,000 candidates and identify 34 carbon polymorphs,approximately a quarter of which had not been previously predicted,to have lattice thermal conductivity above 800 W m^(−1)K^(−1),reaching up to 2,400 W m^(−1)K^(−1)aside from diamond.These findings provide a viable pathway toward the ML-assisted prediction of periodic materials with exceptional thermal properties.
基金This work is partially supported by Humanities and Social Foundation of Ministry of Education of China(22YJAZH129)the National Natural Science Foundation of China(No.12271143,No.61573016)+1 种基金the Shanxi Province Science Foundation(No.20210302123454)Shanxi Scholarship Council of China(2023–024).
文摘Parameter identification involves the estimation of undisclosed parameters within a system based on observed data and mathematical models.In this investigation,we employ DAISY to meticulously examine the structural identifiability of parameters of a within-host SARS-CoV-2 epidemic model,taking into account an array of observable datasets.Furthermore,Monte Carlo simulations are performed to offer a comprehensive practical analysis of model parameters.Lastly,sensitivity analysis is employed to ascertain that decreasing the replication rate of the SARS-CoV-2 virus and curbing the infectious period are the most efficacious measures in alleviating the dissemination of COVID-19 amongst hosts.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61902015,61872026,and 61672085the Nantong Application Research Plan under Grant No:JC2019106the Open Project of State Key Laboratory of Information Security(Institute of Information Engineering,Chinese Academy of Sciences)under Grant No.2020-MS-07.
文摘Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are used to localize only a single fault.However,existing clustering algorithms cannot always obtain completely correct clustering results,which is a potential threat for bug isolation based MFL approaches.To address this issue,we first analyze the influence of the accuracy of the clustering on the performance of MFL,and the results of a controlled study indicate that using the clustering algorithm with the highest accuracy can achieve the best performance of MFL.Moreover,previous studies on clustering algorithms also show that the elements in a higher density cluster have a higher similarity.Based on the above motivation,we propose a novel approach FATOC(One-Fault-at-a-Time via OPTICS Clustering).In particular,FATOC first leverages the OPTICS(Ordering Points to Identify the Clustering Structure)clustering algorithm to group failed test cases,and then identifies a cluster with the highest density.OPTICS clustering is a density-based clustering algorithm,which can reduce the misgrouping and calculate a density value for each cluster.Such a density value of each cluster is helpful for finding a cluster with the highest clustering effectiveness.FATOC then combines the failed test cases in this cluster with all passed test cases to localize a single-fault through the traditional spectrum-based fault localization(SBFL)formula.After this fault is localized and fixed,FATOC will use the same method to localize the next single-fault,until all the test cases are passed.Our evaluation results show that FATOC can significantly outperform the traditional SBFL technique and a state-of-the-art MFL approach MSeer on 804 multi-faulty versions from nine real-world programs.Specifically,FATOC’s performance is 10.32%higher than that of traditional SBFL when using Ochiai formula in terms of metric A-EXAM.Besides,the results also indicate that,when checking 1%,3%and 5%statements of all subject programs,FATOC can locate 36.91%,48.50%and 66.93%of all faults respectively,which is also better than the traditional SBFL and the MFL approach MSeer.
