The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychologi...The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychological perceptions(i.e.,mental space),and objective identifiability,which is based on social media data(i.e.,information space).This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique.The findings,based on data from Weibo,a Chinese social media platform,indicate that the top seven features and values for predicting social media identifiability include blog pictures(0.21),blog location(0.14),birthdate(0.12),location(0.10),blog interaction(0.10),school(0.08),and interests and hobbies(0.07).The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants.Based on the degree of deviation between the two,users can be divided into four categories—normal,conservative,active,and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure.This study provides insights into the development of privacy protection strategies based on social media data classification.展开更多
Many performance indices for parallel mechanism are put forward in the phase of dimensional synthesis,except for identifiability index,which determines the difficulty of kinematical calibration.If the dimensional para...Many performance indices for parallel mechanism are put forward in the phase of dimensional synthesis,except for identifiability index,which determines the difficulty of kinematical calibration.If the dimensional parameters are inappropriately selected,the existing methods for optimizing identifiability will not effectively work.Thus,with the aim of studying identifiability optimization in dimensional synthesis for 3-PRS mechanism,kinematics with structural errors is analyzed to provide theoretical bases for kinematical model.Then through a comparison of two 3-PRS mechanisms with different dimensional parameters,identifiability performance is proved to be necessary and feasible for optimization in the phase of dimensional design.Finally,an index δ is proposed to scale the identifiability performance.With the index,identifiability analysis and dimensional synthesis simulation in the whole workspace is completed.The index is verified to be correct and feasible,and based on the index,a procedure of dimensional synthesis,as well as an example set of non-dimensional parameters of 3-PRS mechanism,is proposed.The proposed identifiability index and design method can effectively introduce identifiability optimization into dimensional synthesis,and will obviously benefit later kinematical calibration.展开更多
In this paper an original method based on the link between a piecewise identifiability analysis and a piecewise numerical estimation is presented for estimating parameters of a phenomenological diesel engine combustio...In this paper an original method based on the link between a piecewise identifiability analysis and a piecewise numerical estimation is presented for estimating parameters of a phenomenological diesel engine combustion model. This model is used for design, validation and pre-tuning of engine control laws. A cascade algebro-differential elimination method is used for studying identifiability. This investigation is done by using input-output-parameter relationship. Then these relations are transformed by using iterated integration. They are combined with an original numerical derivative estimation based on distribution theory which gives explicit point-wise derivative?estimation formulas for each given order. Then new approximate relations, linking block of parameters and outputs (without derivative) are obtained. These relations are linear relatively to the blocks of parameters and yield a first estimation of parameters which is used as initial guess for a local optimization method (least square method and a local search genetic algorithm).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
Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,g...Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.展开更多
The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and control.In practical situations,the accuracy of ...The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and control.In practical situations,the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy,which leads to the problem of identifiability.In this paper,an identifiability analysis methodology of load model parameters,by estimating the confidential intervals(CIs)of the parameters,is proposed.The load model structure and the combined optimization and regression method to identify the parameters are first introduced.Then,the definition and analysis method of identifiability are discussed.The CIs of the parameters are estimated through the profile likelihood method,based on which a practical identifiability index(PII)is defined to quantitatively evaluate identifiability.Finally,the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power grid.The results show that the impact of various disturbance magnitudes,measurement errors and data length can all be reflected by the proposed PII.Furthermore,the proposed PII can provide guidance in data length selection in practical load model identification situations.展开更多
Introduction:Conceptual hydrological models are useful tools to support catchment water management.However,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to...Introduction:Conceptual hydrological models are useful tools to support catchment water management.However,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to be a difficult task.Here,we aim to evaluate the performance of a conceptual semi-distributed rainfall-runoff model,HBV-light,with emphasis on parameter identifiability,uncertainty,and model structural validity.Results:The results of a regional sensitivity analysis(RSA)show that most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used.Results based on the generalized likelihood uncertainty estimation(GLUE)method further show that most of the model parameters are well constrained,showing higher parameter identifiability and lower model uncertainty when runoff signatures or combined objective functions are used.Finally,the dynamic identifiability analysis(DYNIA)shows different types of parameter behavior and reveals that model parameters have a higher identifiability in periods where they play a crucial role in representing the predicted runoff.Conclusions:The HBV-light model is generally able to simulate the runoff in the Pailugou catchment with an acceptable accuracy.Model parameter sensitivity is largely dependent upon the objective function used for the model evaluation in the sensitivity analysis.More frequent runoff observations would substantially increase the knowledge on the rainfall-runoff transformation in the catchment and,specifically,improve the distinction of fast surface-near runoff and interflow components in their contribution to the total catchment runoff.Our results highlight the importance of identifying the periods when intensive monitoring is critical for deriving parameter values of reduced uncertainty.展开更多
In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is studied.Given the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by co...In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is studied.Given the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.展开更多
This paper gives a definition of identifiability for multidimensional linear input-output systems and presents a necessary and sufficient condition for its satisfaction.For a class of identifiable systems it is also s...This paper gives a definition of identifiability for multidimensional linear input-output systems and presents a necessary and sufficient condition for its satisfaction.For a class of identifiable systems it is also shown that the unknown coeffcients of the system can consistently be estimated by a recursive algorithm.展开更多
We discuss the discovery of causal mechanisms and identifiability of intermediate variables on a causal path. Different from variable selection, we try to distinguish intermediate variables on the causal path from oth...We discuss the discovery of causal mechanisms and identifiability of intermediate variables on a causal path. Different from variable selection, we try to distinguish intermediate variables on the causal path from other variables. It is also different from ordinary model selection approaches which do not concern the causal relationships and do not contain unobserved variables. We propose an approach for selecting a causal mechanism depicted by a directed acyclic graph （DAG） with an unobserved variable. We consider several causal networks, and discuss their identifiability by observed data. We show that causal mechanisms of linear structural equation models are not identifiable. Furthermore, we present that causal mechanisms of nonlinear models are identifiable, and we demonstrate the identifiability of causal mechanisms of quadratic equation models. Sensitivity analysis is conducted for the identifiability.展开更多
CR Dhan 310(CRD310),a biofortified rice variety,contains a significantly higher level of grain protein compared with its recurrent parent Naveen(NV),as well as most adapted high-yielding rice varieties in India.Althou...CR Dhan 310(CRD310),a biofortified rice variety,contains a significantly higher level of grain protein compared with its recurrent parent Naveen(NV),as well as most adapted high-yielding rice varieties in India.Although a limited investigation depicted that CRD310 contained higher levels of glutelin and some essential amino acids,detailed biochemical,molecular,and cellular mechanisms remain to be studied.As one of the means to identify the proteins and understand the underlying mechanism of higher proteins accumulation in grains of CRD310,the comparative proteomics was undertaken on grains of CRD310 and NV at the yellow ripening stage.展开更多
Brown spot(BS)of rice,caused by Bipolaris oryzae,is a serious concern that not only causes quantitative losses but also affects grain quality.To manage this disease,the use of resistant genetic sources and QTLs is an ...Brown spot(BS)of rice,caused by Bipolaris oryzae,is a serious concern that not only causes quantitative losses but also affects grain quality.To manage this disease,the use of resistant genetic sources and QTLs is an eco-friendly and economical option.In the current study,F_(3) progenies derived from a cross of susceptible parent PMS-18-B(PAU 10845-1-1-1-1)×resistant parent RP Path 77(RP patho-17)were used to identify potential QTLs linked to BS resistance and to associate this resistance with a temporal spike in defense-related enzymes.展开更多
The Neogene Shawan Formation in the Chepaizi Uplift of the Junggar Basin(NW China)has obtained high oil flow,demonstrating a good potential for oil and gas exploration.The multi-source hydrocarbon generation backgroun...The Neogene Shawan Formation in the Chepaizi Uplift of the Junggar Basin(NW China)has obtained high oil flow,demonstrating a good potential for oil and gas exploration.The multi-source hydrocarbon generation background and strong tectonic activity have led to the simultaneous production of heavy oil and light oil from multi-layer in the area,which makes it very difficult to identify oil origins,presently,the hot debate on the oil origins needs to be clarified.In this paper,due to the selective consumption of different types of compounds in crude oils by severe and intense biodegradation,the commonly used oilsource correlation tools are ineffective or may produce misleading results,this study adopted a biomarker recovery method based on the principle of mass conservation that uses the sum of the mass of the residual biomarkers and their corresponding biodegradation products to obtain the mass of the original biomarkers,improving the reliability of oil origins determination.Based on the nature and occurrence of crude oils,the investigated oils are subdivided into three types,Group A,Group B and Group C.Group A,light oils occurred mainly in lower structure Neogene Shawan Formation in the western Chepaizi Uplift,while Group B,heavy oils occurred mainly in higher structure Neogene Shawan Formation in the western Chepaizi Uplift.The two types of crude oils may come from the mixed source of Jurassic Badaowan Formation source rocks(J_(1)b)and Paleogene Anjihaihe Formation source rocks(E_(2-3)a)in the Sikeshu Sag,and Jurassic Badaowan Formation source rocks(J_(1)b)are the main source of crude oils.Group C,heavy oils occurred mainly in Neogene Shawan Formation in the eastern Chepaizi Uplift,showing good correlation with the Permian(P_(1)f and P_(2)w)source rocks in the Shawan Sag.At the same time,by combining stable carbon isotope and parameters related to triaromatic steroids,the accuracy of the oilsource correlation results by biomarker recovery method was further verified.展开更多
Array configuration of multiple-input multiple-output （MIMO） radar with non-uniform linear array （NLA） is proposed. Unlike a standard phased-array radar where NLA is used to generate thinner beam patterns, in MIMO...Array configuration of multiple-input multiple-output （MIMO） radar with non-uniform linear array （NLA） is proposed. Unlike a standard phased-array radar where NLA is used to generate thinner beam patterns, in MIMO radar the property of NLA is exploited to get more distinct virtual array elements so as to improve pa- rameter identifiability, which means the maximum number of targets that can be uniquely identified by the radar. A class of NLA called minimum redundancy linear array （MRLA） is employed and a new method to construct large MRLAs is descrihed. The numerical results verify that compared to uniform linear array （ULA） MIMO radars, NLA MIMO radars can retain the same parameter identifiability with fewer physical antennas and achieve larger aperture length and lower Cramer-Rao bound with the same number of the physical antennas.展开更多
In order to identify the tilt direction of the self-mixing signals under weak feedback regime interfered by noise,a deep learning method is proposed.The one-dimensional U-Net(1D U-Net)neural network can identify the d...In order to identify the tilt direction of the self-mixing signals under weak feedback regime interfered by noise,a deep learning method is proposed.The one-dimensional U-Net(1D U-Net)neural network can identify the direction of the self-mixing fringes accurately and quickly.In the process of measurement,the measurement signal can be normalized and then the neural network can be used to discriminate the direction.Simulation and experimental results show that the proposed method is suitable for self-mixing interference signals with noise in the whole weak feedback regime,and can maintain a high discrimination accuracy for signals interfered by 5 dB large noise.Combined with fringe counting method,accurate and rapid displacement reconstruction can be realized.展开更多
Patients with cheiro-oral syndrome(COS) often present with minor perioral and upper extremity sensory disturbances,which can be easily overlooked in busy emergency departments(EDs).^([1]) COS,a rare spectrum of stroke...Patients with cheiro-oral syndrome(COS) often present with minor perioral and upper extremity sensory disturbances,which can be easily overlooked in busy emergency departments(EDs).^([1]) COS,a rare spectrum of stroke syndromes,necessitates expeditious and aggressive modification of risk factors.展开更多
Purpose:To describe the characteristics of research outputs using persistent identifiers generated by ResearchGate to gain insight into what publications are shared and disseminated through this functionality,revealin...Purpose:To describe the characteristics of research outputs using persistent identifiers generated by ResearchGate to gain insight into what publications are shared and disseminated through this functionality,revealing their academic and non-academic impact.Design/methodology/approach:A total of 1,092,934 RG-DOIs were collected,using the DataCite API,along with bibliographic metadata for the associated registered output(RG-DOI publications).The subsequent analysis evaluated the publication date,document type,and language.These values were crossreferenced against the full text of a random sample of 666 records to verify accuracy.Findings:RG-DOIs have served primarily to identify and make accessible scholarly gray literature,including posters,presentations,conference papers,and theses,with notable emphasis on publications in Spanish and Portuguese.Around 41,000 citations from Web of Science indexed publications to RG publications are evidence of their infrequent but perceptible use in scholarly discourse.The declining number of registrations of RG-DOIs observed may indicate a shift in researcher preferences to alternative platforms for DOI generation.Research limitations:The study uncovered substantial inconsistencies in DataCite metadata,which can be attributed to the automated DOI registration process and internal changes in the available document types on ResearchGate.Practical implications:The study encountered challenges in conducting a quantitative analysis due to inconsistencies in the metadata.These have potential implications for researchers,practitioners,and librarians relying on RG-DOIs to conduct bibliometric or bibliographic analysis.Originality/value:This study is the first comprehensive analysis of RG-DOIs and,as such,provides a unique perspective into academic gray literature.It also sheds light on the quality of ResearchGate data transmitted to DataCite when registering DOIs.展开更多
The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm ...The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm fertilizing capacity.Among these,sperm concentration and motility are the first parameters to be evaluated through an estimation carried out by expert examiners.展开更多
TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing.Due to its remarkable guessing performance,the model has drawn considerable attention in password secur...TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing.Due to its remarkable guessing performance,the model has drawn considerable attention in password security research.However,through an analysis of the vulnerable behavior of users when constructing passwords by combining popular passwords with their Personally Identifiable Information,we identified that the model fails to consider popular passwords and frequent substrings,and it uses overly broad personal information categories,with extensive duplicate statistics.To address these issues,we propose an improved password guessing model,TGI-FPR,which incorporates three semantic methods:(1)identification of popular passwords by generating top 300 lists from similar websites,(2)use of frequent substrings as new grammatical labels to capture finer-grained password structures,and(3)further subdivision of the six major categories of personal information.To evaluate the performance of the proposed model,we conducted experiments on six large-scale real-world password leak datasets and compared its accuracy within the first 100 guesses to that of TarGuess-I.The results indicate a 2.65%improvement in guessing accuracy.展开更多
Metal-organic frameworks(MOFs)have garnered widespread attention due to their designability and diversity[1].Customization has always been a pursuit of chemists and materials scientists[2].Topology provides a means of...Metal-organic frameworks(MOFs)have garnered widespread attention due to their designability and diversity[1].Customization has always been a pursuit of chemists and materials scientists[2].Topology provides a means of abstracting the complex structures of MOFs by identifying and classifying the fundamental building units and connection patterns,simplifying the understanding of MOF structures[3].展开更多
基金supported by the National Social Science Funds of China(Grant No.21BSH050)Major Project of National Social Science Funds of China(Grant No.20&ZD013).
文摘The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychological perceptions(i.e.,mental space),and objective identifiability,which is based on social media data(i.e.,information space).This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique.The findings,based on data from Weibo,a Chinese social media platform,indicate that the top seven features and values for predicting social media identifiability include blog pictures(0.21),blog location(0.14),birthdate(0.12),location(0.10),blog interaction(0.10),school(0.08),and interests and hobbies(0.07).The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants.Based on the degree of deviation between the two,users can be divided into four categories—normal,conservative,active,and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure.This study provides insights into the development of privacy protection strategies based on social media data classification.
基金supported by National Natural Science Foundation of China (Grant No. 50775125)National Hi-tech Research and Development Program of China (863 Program,Grant No. 2007AA042003,No. 2007AA041901)
文摘Many performance indices for parallel mechanism are put forward in the phase of dimensional synthesis,except for identifiability index,which determines the difficulty of kinematical calibration.If the dimensional parameters are inappropriately selected,the existing methods for optimizing identifiability will not effectively work.Thus,with the aim of studying identifiability optimization in dimensional synthesis for 3-PRS mechanism,kinematics with structural errors is analyzed to provide theoretical bases for kinematical model.Then through a comparison of two 3-PRS mechanisms with different dimensional parameters,identifiability performance is proved to be necessary and feasible for optimization in the phase of dimensional design.Finally,an index δ is proposed to scale the identifiability performance.With the index,identifiability analysis and dimensional synthesis simulation in the whole workspace is completed.The index is verified to be correct and feasible,and based on the index,a procedure of dimensional synthesis,as well as an example set of non-dimensional parameters of 3-PRS mechanism,is proposed.The proposed identifiability index and design method can effectively introduce identifiability optimization into dimensional synthesis,and will obviously benefit later kinematical calibration.
文摘In this paper an original method based on the link between a piecewise identifiability analysis and a piecewise numerical estimation is presented for estimating parameters of a phenomenological diesel engine combustion model. This model is used for design, validation and pre-tuning of engine control laws. A cascade algebro-differential elimination method is used for studying identifiability. This investigation is done by using input-output-parameter relationship. Then these relations are transformed by using iterated integration. They are combined with an original numerical derivative estimation based on distribution theory which gives explicit point-wise derivative?estimation formulas for each given order. Then new approximate relations, linking block of parameters and outputs (without derivative) are obtained. These relations are linear relatively to the blocks of parameters and yield a first estimation of parameters which is used as initial guess for a local optimization method (least square method and a local search genetic algorithm).
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Authors acknowledge financial support from the NSF grant 1610429 and the NSF grant 1414374 as part of the joint NSFNIH-USDA Ecology and Evolution of Infectious Diseases programUK BiotechnologyBiological Sciences Research Council grant BB/M008894/1 and the Division of International Epidemiology and Population Studies,National Institutes of Health.
文摘Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.
基金supported by National Natural Science Foundation of China under Grant No.52107066 and 5210071352.
文摘The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and control.In practical situations,the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy,which leads to the problem of identifiability.In this paper,an identifiability analysis methodology of load model parameters,by estimating the confidential intervals(CIs)of the parameters,is proposed.The load model structure and the combined optimization and regression method to identify the parameters are first introduced.Then,the definition and analysis method of identifiability are discussed.The CIs of the parameters are estimated through the profile likelihood method,based on which a practical identifiability index(PII)is defined to quantitatively evaluate identifiability.Finally,the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power grid.The results show that the impact of various disturbance magnitudes,measurement errors and data length can all be reflected by the proposed PII.Furthermore,the proposed PII can provide guidance in data length selection in practical load model identification situations.
基金This research was jointly funded by Robert Bosch Foundation and Beijing Municipal Commission of Education(Key Laboratory for Silviculture and Conservation).
文摘Introduction:Conceptual hydrological models are useful tools to support catchment water management.However,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to be a difficult task.Here,we aim to evaluate the performance of a conceptual semi-distributed rainfall-runoff model,HBV-light,with emphasis on parameter identifiability,uncertainty,and model structural validity.Results:The results of a regional sensitivity analysis(RSA)show that most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used.Results based on the generalized likelihood uncertainty estimation(GLUE)method further show that most of the model parameters are well constrained,showing higher parameter identifiability and lower model uncertainty when runoff signatures or combined objective functions are used.Finally,the dynamic identifiability analysis(DYNIA)shows different types of parameter behavior and reveals that model parameters have a higher identifiability in periods where they play a crucial role in representing the predicted runoff.Conclusions:The HBV-light model is generally able to simulate the runoff in the Pailugou catchment with an acceptable accuracy.Model parameter sensitivity is largely dependent upon the objective function used for the model evaluation in the sensitivity analysis.More frequent runoff observations would substantially increase the knowledge on the rainfall-runoff transformation in the catchment and,specifically,improve the distinction of fast surface-near runoff and interflow components in their contribution to the total catchment runoff.Our results highlight the importance of identifying the periods when intensive monitoring is critical for deriving parameter values of reduced uncertainty.
文摘In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is studied.Given the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.
基金This project is supported by the National Natural Science Foundation of Chinathe TWAS RG MP898-117
文摘This paper gives a definition of identifiability for multidimensional linear input-output systems and presents a necessary and sufficient condition for its satisfaction.For a class of identifiable systems it is also shown that the unknown coeffcients of the system can consistently be estimated by a recursive algorithm.
文摘We discuss the discovery of causal mechanisms and identifiability of intermediate variables on a causal path. Different from variable selection, we try to distinguish intermediate variables on the causal path from other variables. It is also different from ordinary model selection approaches which do not concern the causal relationships and do not contain unobserved variables. We propose an approach for selecting a causal mechanism depicted by a directed acyclic graph （DAG） with an unobserved variable. We consider several causal networks, and discuss their identifiability by observed data. We show that causal mechanisms of linear structural equation models are not identifiable. Furthermore, we present that causal mechanisms of nonlinear models are identifiable, and we demonstrate the identifiability of causal mechanisms of quadratic equation models. Sensitivity analysis is conducted for the identifiability.
基金supported by the director of Indian Council of Agricultural Research and International Rice Research Institute (ICAR-CRRI), Cuttack, Indiathe coordinator of the ICAR-sponsored project ‘C-reactive protein (CRP) in Biofortification in Selected Crops’, India
文摘CR Dhan 310(CRD310),a biofortified rice variety,contains a significantly higher level of grain protein compared with its recurrent parent Naveen(NV),as well as most adapted high-yielding rice varieties in India.Although a limited investigation depicted that CRD310 contained higher levels of glutelin and some essential amino acids,detailed biochemical,molecular,and cellular mechanisms remain to be studied.As one of the means to identify the proteins and understand the underlying mechanism of higher proteins accumulation in grains of CRD310,the comparative proteomics was undertaken on grains of CRD310 and NV at the yellow ripening stage.
基金supported by Punjab Agricultural University,Ludhiana,India,for providing the infrastructure and other facilities for conducting experiments.All other forms of support and financial assistance are duly acknowledged.
文摘Brown spot(BS)of rice,caused by Bipolaris oryzae,is a serious concern that not only causes quantitative losses but also affects grain quality.To manage this disease,the use of resistant genetic sources and QTLs is an eco-friendly and economical option.In the current study,F_(3) progenies derived from a cross of susceptible parent PMS-18-B(PAU 10845-1-1-1-1)×resistant parent RP Path 77(RP patho-17)were used to identify potential QTLs linked to BS resistance and to associate this resistance with a temporal spike in defense-related enzymes.
基金co-funded by the National Natural Science Foundation of China(42372160,42072172)。
文摘The Neogene Shawan Formation in the Chepaizi Uplift of the Junggar Basin(NW China)has obtained high oil flow,demonstrating a good potential for oil and gas exploration.The multi-source hydrocarbon generation background and strong tectonic activity have led to the simultaneous production of heavy oil and light oil from multi-layer in the area,which makes it very difficult to identify oil origins,presently,the hot debate on the oil origins needs to be clarified.In this paper,due to the selective consumption of different types of compounds in crude oils by severe and intense biodegradation,the commonly used oilsource correlation tools are ineffective or may produce misleading results,this study adopted a biomarker recovery method based on the principle of mass conservation that uses the sum of the mass of the residual biomarkers and their corresponding biodegradation products to obtain the mass of the original biomarkers,improving the reliability of oil origins determination.Based on the nature and occurrence of crude oils,the investigated oils are subdivided into three types,Group A,Group B and Group C.Group A,light oils occurred mainly in lower structure Neogene Shawan Formation in the western Chepaizi Uplift,while Group B,heavy oils occurred mainly in higher structure Neogene Shawan Formation in the western Chepaizi Uplift.The two types of crude oils may come from the mixed source of Jurassic Badaowan Formation source rocks(J_(1)b)and Paleogene Anjihaihe Formation source rocks(E_(2-3)a)in the Sikeshu Sag,and Jurassic Badaowan Formation source rocks(J_(1)b)are the main source of crude oils.Group C,heavy oils occurred mainly in Neogene Shawan Formation in the eastern Chepaizi Uplift,showing good correlation with the Permian(P_(1)f and P_(2)w)source rocks in the Shawan Sag.At the same time,by combining stable carbon isotope and parameters related to triaromatic steroids,the accuracy of the oilsource correlation results by biomarker recovery method was further verified.
基金Supported by the Aeronautic Science Foundation of China(2008ZC52026)the Innovation Foundation of Nanjing University of Aeronautics and Astronautics~~
文摘Array configuration of multiple-input multiple-output （MIMO） radar with non-uniform linear array （NLA） is proposed. Unlike a standard phased-array radar where NLA is used to generate thinner beam patterns, in MIMO radar the property of NLA is exploited to get more distinct virtual array elements so as to improve pa- rameter identifiability, which means the maximum number of targets that can be uniquely identified by the radar. A class of NLA called minimum redundancy linear array （MRLA） is employed and a new method to construct large MRLAs is descrihed. The numerical results verify that compared to uniform linear array （ULA） MIMO radars, NLA MIMO radars can retain the same parameter identifiability with fewer physical antennas and achieve larger aperture length and lower Cramer-Rao bound with the same number of the physical antennas.
文摘In order to identify the tilt direction of the self-mixing signals under weak feedback regime interfered by noise,a deep learning method is proposed.The one-dimensional U-Net(1D U-Net)neural network can identify the direction of the self-mixing fringes accurately and quickly.In the process of measurement,the measurement signal can be normalized and then the neural network can be used to discriminate the direction.Simulation and experimental results show that the proposed method is suitable for self-mixing interference signals with noise in the whole weak feedback regime,and can maintain a high discrimination accuracy for signals interfered by 5 dB large noise.Combined with fringe counting method,accurate and rapid displacement reconstruction can be realized.
文摘Patients with cheiro-oral syndrome(COS) often present with minor perioral and upper extremity sensory disturbances,which can be easily overlooked in busy emergency departments(EDs).^([1]) COS,a rare spectrum of stroke syndromes,necessitates expeditious and aggressive modification of risk factors.
基金supported by Grant PID2022-142569NA-I00,funded by MCIN/AEI/10.13039/501100011033 and by the European Union through“ERDF A way of making Europe.”。
文摘Purpose:To describe the characteristics of research outputs using persistent identifiers generated by ResearchGate to gain insight into what publications are shared and disseminated through this functionality,revealing their academic and non-academic impact.Design/methodology/approach:A total of 1,092,934 RG-DOIs were collected,using the DataCite API,along with bibliographic metadata for the associated registered output(RG-DOI publications).The subsequent analysis evaluated the publication date,document type,and language.These values were crossreferenced against the full text of a random sample of 666 records to verify accuracy.Findings:RG-DOIs have served primarily to identify and make accessible scholarly gray literature,including posters,presentations,conference papers,and theses,with notable emphasis on publications in Spanish and Portuguese.Around 41,000 citations from Web of Science indexed publications to RG publications are evidence of their infrequent but perceptible use in scholarly discourse.The declining number of registrations of RG-DOIs observed may indicate a shift in researcher preferences to alternative platforms for DOI generation.Research limitations:The study uncovered substantial inconsistencies in DataCite metadata,which can be attributed to the automated DOI registration process and internal changes in the available document types on ResearchGate.Practical implications:The study encountered challenges in conducting a quantitative analysis due to inconsistencies in the metadata.These have potential implications for researchers,practitioners,and librarians relying on RG-DOIs to conduct bibliometric or bibliographic analysis.Originality/value:This study is the first comprehensive analysis of RG-DOIs and,as such,provides a unique perspective into academic gray literature.It also sheds light on the quality of ResearchGate data transmitted to DataCite when registering DOIs.
文摘The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm fertilizing capacity.Among these,sperm concentration and motility are the first parameters to be evaluated through an estimation carried out by expert examiners.
基金supported by the Joint Funds of National Natural Science Foundation of China(Grant No.U23A20304)the Fund of Laboratory for Advanced Computing and Intelligence Engineering(No.2023-LYJJ-01-033)+1 种基金the Special Funds of Jiangsu Province Science and Technology Plan(Key R&D ProgramIndustryOutlook and Core Technologies)(No.BE2023005-4)the Science Project of Hainan University(KYQD(ZR)-21075).
文摘TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing.Due to its remarkable guessing performance,the model has drawn considerable attention in password security research.However,through an analysis of the vulnerable behavior of users when constructing passwords by combining popular passwords with their Personally Identifiable Information,we identified that the model fails to consider popular passwords and frequent substrings,and it uses overly broad personal information categories,with extensive duplicate statistics.To address these issues,we propose an improved password guessing model,TGI-FPR,which incorporates three semantic methods:(1)identification of popular passwords by generating top 300 lists from similar websites,(2)use of frequent substrings as new grammatical labels to capture finer-grained password structures,and(3)further subdivision of the six major categories of personal information.To evaluate the performance of the proposed model,we conducted experiments on six large-scale real-world password leak datasets and compared its accuracy within the first 100 guesses to that of TarGuess-I.The results indicate a 2.65%improvement in guessing accuracy.
基金supported by the National Natural Science Foundation of China(22101039,22471027,22311530679)the Science Fund for Creative Research Groups of the National Natural Science Foundation of China(22021005)the Fundamental Research Funds for the Central Universities(DUT24LK004).
文摘Metal-organic frameworks(MOFs)have garnered widespread attention due to their designability and diversity[1].Customization has always been a pursuit of chemists and materials scientists[2].Topology provides a means of abstracting the complex structures of MOFs by identifying and classifying the fundamental building units and connection patterns,simplifying the understanding of MOF structures[3].
