Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them...Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them.An association measurement between two variables and may be changed dramatically from positive to negative by omitting a third variable,which is called Yule-Simpson paradox.We shall discuss how to evaluate the causal effect of a treatment or exposure on an outcome to avoid the phenomena of Yule-Simpson paradox. Surrogates and intermediate variables are often used to reduce measurement costs or duration when measurement of endpoint variables is expensive,inconvenient,infeasible or unobservable in practice.There have been many criteria for surrogates.However,it is possible that for a surrogate satisfying these criteria,a treatment has a positive effect on the surrogate,which in turn has a positive effect on the outcome,but the treatment has a negative effect on the outcome,which is called the surrogate paradox.We shall discuss criteria for surrogates to avoid the phenomena of the surrogate paradox. Causal networks which describe the causal relationships among a large number of variables have been applied to many research fields.It is important to discover structures of causal networks from observed data.We propose a recursive approach for discovering a causal network in which a structural learning of a large network is decomposed recursively into learning of small networks.Further to discover causal relationships,we present an active learning approach in terms of external interventions on some variables.When we focus on the causes of an interest outcome, instead of discovering a whole network,we propose a local learning approach to discover these causes that affect the outcome.展开更多
We perform a time-resolved statistical study of GRB 221009A’s X-ray emission using Swift XRT Photon Counting and Windowed Timing data.After standard reduction(barycentric correction,pile-up,background subtraction via...We perform a time-resolved statistical study of GRB 221009A’s X-ray emission using Swift XRT Photon Counting and Windowed Timing data.After standard reduction(barycentric correction,pile-up,background subtraction via HEASOFT),we extracted light curves for each observational ID and for their aggregation.Countrate histograms were fitted using various statistical distributions;fit quality was assessed by chi-squared and the Bayesian Information Criterion.The first observational segment is best described by a Gaussian distribution(χ^(2)=68.4;BIC=7651.2),and the second by a Poisson distribution(χ^(2)=33.5;BIC=4413.3).When all segments are combined,the lognormal model provides the superior fit(χ^(2)=541.9;BIC=34365.5),indicating that the full data set’s count rates exhibit the skewness expected from a multiplicative process.These findings demonstrate that while individual time intervals conform to discrete or symmetric statistics,the collective emission profile across multiple observations is better captured by a lognormal distribution,consistent with complex,compounded variability in GRB afterglows.展开更多
Background: The dried roots of Inula helenium L.(IH) and Inula racemosa Hook f.(IR) are used commonly as folk medicine under the name of "tumuxiang(TMX)". Phenolic acid compounds and their derivatives, as ma...Background: The dried roots of Inula helenium L.(IH) and Inula racemosa Hook f.(IR) are used commonly as folk medicine under the name of "tumuxiang(TMX)". Phenolic acid compounds and their derivatives, as main active constituents in IH and IR, exhibit prominent anti-inflammation effect.Objective: To develop a holistic method based on chemical characteristic and anti-inflammation effect for systematically evaluating the quality of twenty-seven TMX samples(including 18 IH samples and 9 IR samples) from different origins.Methods: HPLC fingerprints data of AL(Aucklandia lappa Decne.) whose dried root was similar with HR was added for classification analysis. The HPLC fingerprints of twenty-seven TMX samples and four AL samples were evaluated using hierarchical clustering analysis(HCA) and principle component analysis(PCA). The spectrum-efficacy model between HPLC fingerprints and anti-inflammatory activities was investigated by principal component regression(PCR) and partial least squares(PLS).Results: All samples were successfully divided into three main clusters and peaks 7, 9, 11, 22, 24 and 26 had a primary contribution to classify these medicinal herbs. The results were in accord with the appraisal results of herbs. The spectrum-efficacy relationship results indicated that citric acid, quinic acid, caffeic acid-β-D-glucopyranoside, chlorogenic acid, caffeic acid, 1,3-O-dicaffeoyl quinic acid, tianshic acid and 3β-Hydroxypterondontic acid had main contribution to anti-inflammatory activities.Conclusion: This comprehensive strategy was successfully used for identification of IH, IR and AL, which provided a reliable and adequate theoretical basis for the bioactivity relevant quality standards and studying the material basis of anti-inflammatory effect of TMX.展开更多
Trade deficit has provided international trade protection forces with a grand excuse. e US and EU sides are prone to either imposing anti-dumping on China, or demanding revaluation of the Renminbi (RMB) under the pret...Trade deficit has provided international trade protection forces with a grand excuse. e US and EU sides are prone to either imposing anti-dumping on China, or demanding revaluation of the Renminbi (RMB) under the pretext of trade deficit. Many businessmen claimed that they have suffered great losses whenever trade deficit is mentioned.展开更多
Objective To facilitate the quality evaluation suitable for the unique characteristics of Chinese materia medica(CMM)by developing and implementing a novel approach known as the matching frequency statistical moment(M...Objective To facilitate the quality evaluation suitable for the unique characteristics of Chinese materia medica(CMM)by developing and implementing a novel approach known as the matching frequency statistical moment(MFSM)method.Methods This study established the MFSM method.To demonstrate its effectiveness,we applied this novel approach to analyze Danxi Granules(丹膝颗粒,DXG)and its constituent herbal materials.To begin with,the ultra-performance liquid chromatography(UPLC)was applied to obtain the chromatographic fingerprints of DXG and its constituent herbal materi-als.Next,the MFSM was leveraged to compress and integrate them into a new fingerprint with fewer analytical units.Then,we characterized the properties and variability of both the original and integrated fingerprints by calculating total quantum statistical moment(TQSM)parameters,information entropy and information amount,along with their relative standard deviation(RSD).Finally,we compared the TQSM parameters,information entropy and infor-mation amount,and their RSD between the traditional and novel fingerprints to validate the new analytical method.Results The chromatographic peaks of DXG and its 12 raw herbal materials were divided and integrated into peak families by the MFSM method.Before integration,the ranges of the peak number,three TQSM parameters,information entropy and information amount for each peak or peak family of UPLC fingerprints of DXG and its 12 raw herbal materials were 95.07−209.73,9390−183064μv·s,5.928−21.33 min,22.62−106.69 min^(2),4.230−6.539,and 50530−974186μv·s,respectively.After integration,the ranges of these parameters were 10.00−88.00,9390−183064μv·s,5.951−22.02 min,22.27−104.73 min^(2),2.223−5.277,and 38159−807200μv·s,respectively.Correspondingly,the RSD of all the aforementioned pa-rameters before integration were 2.12%−9.15%,6.04%−49.78%,1.15%−23.10%,3.97%−25.79%,1.49%−19.86%,and 6.64%−51.20%,respectively.However,after integration,they changed to 0.00%,6.04%−49.87%,1.73%−23.02%,3.84%−26.85%,1.17%−16.54%,and 6.40%−48.59%,respectively.The results demonstrated that in the newly integrated fingerprint,the analytical units of constituent herbal materials,information entropy and information amount were significantly reduced(P<0.05),while the TQSM parameters remained unchanged(P>0.05).Additionally,the RSD of the TQSM parameters,information entropy,and information amount didn’t show significant difference before and after integration(P>0.05),but the RSD of the number and area of the integrated analytical units significantly decreased(P<0.05).Conclusion The MFSM method could reduce the analytical units of constituent herbal mate-rials while maintain the properties and variability from their original fingerprint.Thus,it could serve as a feasible and reliable tool to reduce difficulties in analyzing multi-compo-nents within CMMs and facilitating the evaluation of their quality.展开更多
Aim To improve the efficiency of fatigue material tests and relevant statistical treatment of test data. Methods\ Least square approach and other special treatments were used. Results and Conclusion\ The concepts...Aim To improve the efficiency of fatigue material tests and relevant statistical treatment of test data. Methods\ Least square approach and other special treatments were used. Results and Conclusion\ The concepts of each phase in fatigue tests and statistical treatment are clarified. The method proposed leads to three important properties. Reduced number of specimens brings to the advantage of lowering test expenditures. The whole test procedure has more flexibility for there is no need to conduct many tests at the same stress level as in traditional cases.展开更多
Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A la...Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A large population also uses local groundwater for drinking purposes.Therefore,in this study,this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis(CA),Discriminant Analysis(DA),and Principal Component Analysis(PCA).Water quality data was monitored at 22 different wells,for five years(2009-2014)with 10 water quality parameters.By using cluster analysis,the sampling wells were grouped into two clusters with distinct water qualities at different locations.The Lasso Discriminant Analysis(LDA)technique was used to assess the spatial variability of water quality.Based on the results,all of the variables except sodium absorption ratio(SAR)are effective in the LDA model with all variables affording 92.80%correct assignation to discriminate between the clusters from the primary 10 variables.Principal component(PC)analysis and factor analysis reduced the complex data matrix into two main components,accounting for more than 95.93%of the total variance.The first PC contained the parameters of TH,Ca2+,and Mg2+.Therefore,the first dominant factor was hardness.In the second PC,Cl-,SAR,and Na+were the dominant parameters,which may indicate salinity.The originally acquired factors illustrate natural(existence of geological formations)and anthropogenic(improper disposal of domestic and agricultural wastes)factors which affect the groundwater quality.展开更多
The flow of novel coronavirus(COVID-19)has affected almost every aspect of human life around the globe.Being the emerging ground and early sufferer of the virus,Wuhan city-data remains a case of multifold significance...The flow of novel coronavirus(COVID-19)has affected almost every aspect of human life around the globe.Being the emerging ground and early sufferer of the virus,Wuhan city-data remains a case of multifold significance.Further,it is of notable importance to explore the impact of unique and unprecedented public health response of Chinese authorities—the extreme lockdown of the city.In this research,we investigate the statistical nature of the viral transmission concerning social distancing,extreme quarantine,and robust lockdown interventions.We observed highly convincing and statistically significant evidences in favor of quarantine and social distancing approaches.These findings might help countries,now facing,or likely to face the wave of the virus.We analyzed Wuhan-based data of“number of deaths”and“confirmed cases,”extracted from China CDC weekly database,dated from February 13,2020,to March 24,2020.To estimate the underlying group structure,the assembled data is further subdivided into three blocks,each consists of two weeks.Thus,the complete data set is studied in three phases,such as,phase 1(Ph 1)=February 13,2020,to February 26,2020;phase 2(Ph 2)=February 27,2020 to March 11,2020;and phase 3(Ph 3)=March 12,2020 to March 24,2020.We observed the overall median proportion of deaths in those six weeks remained 0.0127.This estimate is highly influenced by Ph1,when the early flaws of weak health response were still prevalent.Over the time,we witnessed a median decline of 92.12%in the death proportions.Moreover,a non-parametric version of the variability analysis of death data,estimated that the average rank of reported proportions in Ph 3 remained 7,which was 20.5 in Ph 2,and stayed 34.5 in the first phase.Similar patterns were observed,when studying the confirmed cases data.We estimated the overall median of the proportion of confirmed cases in Wuhan as 0.0041,which again,is highly inclined towards Ph 1 and Ph 2.We also witnessed minimum average rank proportions for Ph 3,such as 7,which was noticeably lower than Ph 2,21.71,and Ph 1, 32.29. Moreover, the varying degree of clustering indicates that the effectivenessof quarantine based policies is time-dependent. In general, the declinein coronavirus transmission in Wuhan significantly coincides with the lockdown.展开更多
Solar magnetic field measurements mainly use the Zeeman effect,but this method has two problems,namely,low accuracy of the transverse magnetic field components and a 180°ambiguity.Multi-perspective observations c...Solar magnetic field measurements mainly use the Zeeman effect,but this method has two problems,namely,low accuracy of the transverse magnetic field components and a 180°ambiguity.Multi-perspective observations can increase the measurement accuracy and resolve the ambiguity.This study investigates how combined observations from the Sun-Earth L5 point,Sun-Earth line,and solar polar-orbiting satellites improve the accuracy of the transverse solar magnetic field under different satellite positional configurations.A three-satellite model is developed using spherical trigonometry to establish coordinate relationships,and the error propagation formulas are applied to correc transverse field measurement errors.The magnetic field measurement error distribution of the Helioseismic and Magnetic Imager is analyzed,and the magnetograms from the three satellites are simulated.The improvement to the transverse field accuracy under various satellite configurations is then assessed based on simulation results.The results show that multi-perspective measurements can reduce transverse component errorsΔB_(x)to approximately 10%andΔB_(y)to about 15%compared to the error from a single satellite.An optimally designed polar orbit can decrease the transverse field error by nearly an order of magnitude for 80%of its operation time.展开更多
In this study,we investigate the potential of mark-weighted angular correlation functions,which integrateβ-cosmic-web classification with angular correlation function analysis to improve cosmological constraints.Usin...In this study,we investigate the potential of mark-weighted angular correlation functions,which integrateβ-cosmic-web classification with angular correlation function analysis to improve cosmological constraints.Using SDSS DR12 CMASS-NGC galaxies and mock catalogs withΩ_(m)varying from 0.25 to 0.40,we assess the discriminative power of different statistics via the average improvement in chi-squared,ΔX^(2),across six redshift bins.This metric quantifies how effectively each statistic distinguishes between different cosmological models.Incorporating cosmic-web weights leads to substantial improvements.Using statistics weighted by the mean neighbor distance(Dnei)increasesΔX^(2)by approximately 40%–130%,while applying inverse mean neighbor distance weighting(1/Dnei)yields even larger gains,boostingΔX^(2)by a factor of 2–3 compared to traditional unweighted angular statistics.These enhancements are consistent with previous 3D clustering results,demonstrating the superior sensitivity of theβ-weighted approaches.Our method,based on thin redshift slices,is particularly suitable for slitless surveys(e.g.,Euclid,CSST)where redshift uncertainties limit 3D analyses.This study also offers a framework for applying marked statistics to 2D angular clustering.展开更多
With the development of science and technology, the relationship between information and data and our life and production is getting closer and closer. Statistics play a very important role in promoting social and eco...With the development of science and technology, the relationship between information and data and our life and production is getting closer and closer. Statistics play a very important role in promoting social and economic development. High-quality statistical data can intuitively reflect the current situation of social environment, market environment and political environment, and can also predict the future development trend. Based on this, this paper makes a comprehensive analysis of the value of statistical data and the current situation of statistical data work, and also summarizes the main factors affecting the quality of statistical data, so as to formulate more perfect countermeasures.展开更多
Identification of modal parameters of a linear structure with output-only measurements has received much attention over the past decades. In the paper, the Natural Excitation Technique (NExT) is used for acquisition o...Identification of modal parameters of a linear structure with output-only measurements has received much attention over the past decades. In the paper, the Natural Excitation Technique (NExT) is used for acquisition of the impulse signals from the structural responses. Then Eigensystem Realization Algorithm (ERA) is utilized for modal identification. For disregarding the fictitious ‘computational modes', a procedure, Statistically Averaging Modal Frequency Method (SAMFM), is developed to distinguish the true modes from noise modes, and to improve the precision of the identified modal frequencies of the structure. An offshore platform is modeled with the finite element method. The theoretical modal parameters are obtained for a comparison with the identified values. The dynamic responses of the platform under random wave loading are computed for providing the output signals used for identification with ERA. Results of simulation demonstrate that the proposed method can determine the system modal frequency with high precision.展开更多
A novel damage detection method is applied to a 3-story frame structure, to obtain statistical quantification control criterion of the existence, location and identification of damage. The mean, standard deviation, an...A novel damage detection method is applied to a 3-story frame structure, to obtain statistical quantification control criterion of the existence, location and identification of damage. The mean, standard deviation, and exponentially weighted moving average （EWMA） are applied to detect damage information according to statistical process control （SPC） theory. It is concluded that the detection is insignificant with the mean and EWMA because the structural response is not independent and is not a normal distribution. On the other hand, the damage information is detected well with the standard deviation because the influence of the data distribution is not pronounced with this parameter. A suitable moderate confidence level is explored for more significant damage location and quantification detection, and the impact of noise is investigated to illustrate the robustness of the method.展开更多
In this study,geochemical anomaly separation was carried out with methods based on the distribution model,which includes probability diagram(MPD),fractal(concentration-area technique),and U-statistic methods.The main ...In this study,geochemical anomaly separation was carried out with methods based on the distribution model,which includes probability diagram(MPD),fractal(concentration-area technique),and U-statistic methods.The main objective is to evaluate the efficiency and accuracy of the methods in separation of anomalies on the shear zone gold mineralization.For this purpose,samples were taken from the secondary lithogeochemical environment(stream sediment samples)on the gold mineralization in Saqqez,NW of Iran.Interpretation of the histograms and diagrams showed that the MPD is capable of identifying two phases of mineralization.The fractal method could separate only one phase of change based on the fractal dimension with high concentration areas of the Au element.The spatial analysis showed two mixed subpopulations after U=0 and another subpopulation with very high U values.The MPD analysis followed spatial analysis,which shows the detail of the variations.Six mineralized zones detected from local geochemical exploration results were used for validating the methods mentioned above.The MPD method was able to identify the anomalous areas higher than 90%,whereas the two other methods identified 60%(maximum)of the anomalous areas.The raw data without any estimation for the concentration was used by the MPD method using aminimum of calculations to determine the threshold values.Therefore,the MPD method is more robust than the other methods.The spatial analysis identified the detail soft hegeological and mineralization events that were affected in the study area.MPD is recommended as the best,and the spatial U-analysis is the next reliable method to be used.The fractal method could show more detail of the events and variations in the area with asymmetrical grid net and a higher density of sampling or at the detailed exploration stage.展开更多
Making use of the 2MASS Data Release, we have searched for nearinfrared （JHK） counterparts to 268 blazars from Donato et al. and obtained 238 counterparts within 5＇＇ in the area covered by 2MASS. It provides us a ...Making use of the 2MASS Data Release, we have searched for nearinfrared （JHK） counterparts to 268 blazars from Donato et al. and obtained 238 counterparts within 5＇＇ in the area covered by 2MASS. It provides us a sample with infrared data several times larger than the previous one of the same kind. Based on our sample and the sample by Donato et al., we have compared in detail the properties of HBLs, LBLs and FSRQs from five aspects and found that HBLs are significantly different from LBLs and FSRQs while LBLs are not obviously different from FSRQs. Our results strongly support the division of BL Lac objects into the high-frequency peaked （HBL） and low-frequency peaked （LBL） objects introduced by Padovani ＆ Giommi and show that HBLs and LBLs are two kinds of blazar having different physical properties.展开更多
Predicting seeing of astronomical observations can provide hints of the quality of optical imaging in the near future,and facilitate flexible scheduling of observation tasks to maximize the use of astronomical observa...Predicting seeing of astronomical observations can provide hints of the quality of optical imaging in the near future,and facilitate flexible scheduling of observation tasks to maximize the use of astronomical observatories.Traditional approaches to seeing prediction mostly rely on regional weather models to capture the in-dome optical turbulence patterns.Thanks to the developing of data gathering and aggregation facilities of astronomical observatories in recent years,data-driven approaches are becoming increasingly feasible and attractive to predict astronomical seeing.This paper systematically investigates data-driven approaches to seeing prediction by leveraging various big data techniques,from traditional statistical modeling,machine learning to new emerging deep learning methods,on the monitoring data of the Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST).The raw monitoring data are preprocessed to allow for big data modeling.Then we formulate the seeing prediction task under each type of modeling framework and develop seeing prediction models through using representative big data techniques,including ARIMA and Prophet for statistical modeling,MLP and XGBoost for machine learning,and LSTM,GRU and Transformer for deep learning.We perform empirical studies on the developed models with a variety of feature configurations,yielding notable insights into the applicability of big data techniques to the seeing prediction task.展开更多
We present an analysis of the timing observations on 27 radio pulsars, collected at Hartebeesthoek Radio Astronomy Observatory （HartRAO）, with time spans ranging between - 9 and 14yr. Our results show that the measu...We present an analysis of the timing observations on 27 radio pulsars, collected at Hartebeesthoek Radio Astronomy Observatory （HartRAO）, with time spans ranging between - 9 and 14yr. Our results show that the measured pulsar frequency second derivatives are non-stationary. Both the magnitude and the sign of the v values depend upon the choice of epoch and data span. A simple statistical analysis of the observed second time derivative of the pulse frequency （v obs） of a large sample of 391 （25 HartRAO and 366 Jodrell Bank Observatory）. pulsars reveals that v is only marginally correlated with both the pulsar spindown rate （P） and the characteristic age （τc）. We find correlation coefficients of ,- 0.20 and -0.30 between the measured braking indices and, respectively,P and τc. This result reaffirms earlier conclusions that the braking indices of most radio pulsars, obtained through the standard timing technique, are strongly dominated by sustained random fluctuations in the observed pulse phase.展开更多
With the assistance of the identified/associated sources in the second Fermi Large Area Telescope （LAT） catalog, we analyze and resolve the spatial distribution and the distributions of the gamma-ray spectral and va...With the assistance of the identified/associated sources in the second Fermi Large Area Telescope （LAT） catalog, we analyze and resolve the spatial distribution and the distributions of the gamma-ray spectral and variability indices of the remaining 575 unassociated Fermi LAT sources. Consequently, it is suggested that the unassociated sources could statistically consist of Galactic supernova remnants/pulsar wind nebulae, BL Lacertae objects, fiat spectrum radio quasars and other types of active galaxies with fractions of 25%, 29%, 41% and 5%, respectively.展开更多
Ag-sheathed (Bi,Pb)(2)SoCa(2)Cu(3)O(x) tapes were prepared by the powder-in-tube method. The influences of rolling parameters on superconducting characteristics of Bi(2223)/Ag tapes were analyzed qualitatively with a ...Ag-sheathed (Bi,Pb)(2)SoCa(2)Cu(3)O(x) tapes were prepared by the powder-in-tube method. The influences of rolling parameters on superconducting characteristics of Bi(2223)/Ag tapes were analyzed qualitatively with a statistical method. The results demonstrate that roll diameter and reduction per pass significantly influence the properties of Bi(2223)/Ag superconducting tapes while roll speed does less and working friction the least. An optimized rolling process was therefore achieved according to the above results.展开更多
Identifying patterns,recognition systems,prediction methods,and detection methods is a major challenge in solving different medical issues.Few categories of devices for personal and professional assessment of body com...Identifying patterns,recognition systems,prediction methods,and detection methods is a major challenge in solving different medical issues.Few categories of devices for personal and professional assessment of body composition are available.Bioelectrical impedance analyzer is a simple,safe,affordable,mobile,non-invasive,and less expensive alternative device for body composition assessment.Identifying the body composition pattern of different groups with varying age and gender is a major challenge in defining an optimal level because of the body shape,body mass,energy requirements,physical fitness,health status,and metabolic profile.Thus,this research aims to identify the statistical medical pattern recognition of body composition data by using a bioelectrical impedance analyzer.In previous studies,a pattern was identified for four indicators that concern body composition(e.g.,body mass index(BMI),body fat,muscle mass,and total body water).The novelty of our study is the fact that we identified a recognition pattern by using medical statistical methods for a body composition that contains seven indicators(e.g.,body fat,visceral fat,BMI,muscle mass,skeletal muscle mass,sarcopenic index,and total body water).The youth that exhibited the body composition pattern identified in our study could be considered healthy.Every deviation of one or more parameters outside the margins of the pattern for body composition could be associated with health issues,and more medical investigations would be needed for a diagnosis.BIA is considered a valid and reliable device to assess body composition along with medical statistical methods to identify a pattern for body composition according to the age,gender,and other relevant parameters.展开更多
文摘Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them.An association measurement between two variables and may be changed dramatically from positive to negative by omitting a third variable,which is called Yule-Simpson paradox.We shall discuss how to evaluate the causal effect of a treatment or exposure on an outcome to avoid the phenomena of Yule-Simpson paradox. Surrogates and intermediate variables are often used to reduce measurement costs or duration when measurement of endpoint variables is expensive,inconvenient,infeasible or unobservable in practice.There have been many criteria for surrogates.However,it is possible that for a surrogate satisfying these criteria,a treatment has a positive effect on the surrogate,which in turn has a positive effect on the outcome,but the treatment has a negative effect on the outcome,which is called the surrogate paradox.We shall discuss criteria for surrogates to avoid the phenomena of the surrogate paradox. Causal networks which describe the causal relationships among a large number of variables have been applied to many research fields.It is important to discover structures of causal networks from observed data.We propose a recursive approach for discovering a causal network in which a structural learning of a large network is decomposed recursively into learning of small networks.Further to discover causal relationships,we present an active learning approach in terms of external interventions on some variables.When we focus on the causes of an interest outcome, instead of discovering a whole network,we propose a local learning approach to discover these causes that affect the outcome.
文摘We perform a time-resolved statistical study of GRB 221009A’s X-ray emission using Swift XRT Photon Counting and Windowed Timing data.After standard reduction(barycentric correction,pile-up,background subtraction via HEASOFT),we extracted light curves for each observational ID and for their aggregation.Countrate histograms were fitted using various statistical distributions;fit quality was assessed by chi-squared and the Bayesian Information Criterion.The first observational segment is best described by a Gaussian distribution(χ^(2)=68.4;BIC=7651.2),and the second by a Poisson distribution(χ^(2)=33.5;BIC=4413.3).When all segments are combined,the lognormal model provides the superior fit(χ^(2)=541.9;BIC=34365.5),indicating that the full data set’s count rates exhibit the skewness expected from a multiplicative process.These findings demonstrate that while individual time intervals conform to discrete or symmetric statistics,the collective emission profile across multiple observations is better captured by a lognormal distribution,consistent with complex,compounded variability in GRB afterglows.
基金the Project of Science and Technology Agency of Gansu(1208RTZA211)Science and Technology Agency of Lanzhou(2013-4-75)the item of scientific and technological researches from Gansu province administration bureau of traditional Chinese medicine(GZK-2014-13)
文摘Background: The dried roots of Inula helenium L.(IH) and Inula racemosa Hook f.(IR) are used commonly as folk medicine under the name of "tumuxiang(TMX)". Phenolic acid compounds and their derivatives, as main active constituents in IH and IR, exhibit prominent anti-inflammation effect.Objective: To develop a holistic method based on chemical characteristic and anti-inflammation effect for systematically evaluating the quality of twenty-seven TMX samples(including 18 IH samples and 9 IR samples) from different origins.Methods: HPLC fingerprints data of AL(Aucklandia lappa Decne.) whose dried root was similar with HR was added for classification analysis. The HPLC fingerprints of twenty-seven TMX samples and four AL samples were evaluated using hierarchical clustering analysis(HCA) and principle component analysis(PCA). The spectrum-efficacy model between HPLC fingerprints and anti-inflammatory activities was investigated by principal component regression(PCR) and partial least squares(PLS).Results: All samples were successfully divided into three main clusters and peaks 7, 9, 11, 22, 24 and 26 had a primary contribution to classify these medicinal herbs. The results were in accord with the appraisal results of herbs. The spectrum-efficacy relationship results indicated that citric acid, quinic acid, caffeic acid-β-D-glucopyranoside, chlorogenic acid, caffeic acid, 1,3-O-dicaffeoyl quinic acid, tianshic acid and 3β-Hydroxypterondontic acid had main contribution to anti-inflammatory activities.Conclusion: This comprehensive strategy was successfully used for identification of IH, IR and AL, which provided a reliable and adequate theoretical basis for the bioactivity relevant quality standards and studying the material basis of anti-inflammatory effect of TMX.
文摘Trade deficit has provided international trade protection forces with a grand excuse. e US and EU sides are prone to either imposing anti-dumping on China, or demanding revaluation of the Renminbi (RMB) under the pretext of trade deficit. Many businessmen claimed that they have suffered great losses whenever trade deficit is mentioned.
基金Natural Science Foundation of Hunan province(2022JJ30453 and 2024JJ6362)the Key Research and Development Program of Hunan Province(2022SK2014).
文摘Objective To facilitate the quality evaluation suitable for the unique characteristics of Chinese materia medica(CMM)by developing and implementing a novel approach known as the matching frequency statistical moment(MFSM)method.Methods This study established the MFSM method.To demonstrate its effectiveness,we applied this novel approach to analyze Danxi Granules(丹膝颗粒,DXG)and its constituent herbal materials.To begin with,the ultra-performance liquid chromatography(UPLC)was applied to obtain the chromatographic fingerprints of DXG and its constituent herbal materi-als.Next,the MFSM was leveraged to compress and integrate them into a new fingerprint with fewer analytical units.Then,we characterized the properties and variability of both the original and integrated fingerprints by calculating total quantum statistical moment(TQSM)parameters,information entropy and information amount,along with their relative standard deviation(RSD).Finally,we compared the TQSM parameters,information entropy and infor-mation amount,and their RSD between the traditional and novel fingerprints to validate the new analytical method.Results The chromatographic peaks of DXG and its 12 raw herbal materials were divided and integrated into peak families by the MFSM method.Before integration,the ranges of the peak number,three TQSM parameters,information entropy and information amount for each peak or peak family of UPLC fingerprints of DXG and its 12 raw herbal materials were 95.07−209.73,9390−183064μv·s,5.928−21.33 min,22.62−106.69 min^(2),4.230−6.539,and 50530−974186μv·s,respectively.After integration,the ranges of these parameters were 10.00−88.00,9390−183064μv·s,5.951−22.02 min,22.27−104.73 min^(2),2.223−5.277,and 38159−807200μv·s,respectively.Correspondingly,the RSD of all the aforementioned pa-rameters before integration were 2.12%−9.15%,6.04%−49.78%,1.15%−23.10%,3.97%−25.79%,1.49%−19.86%,and 6.64%−51.20%,respectively.However,after integration,they changed to 0.00%,6.04%−49.87%,1.73%−23.02%,3.84%−26.85%,1.17%−16.54%,and 6.40%−48.59%,respectively.The results demonstrated that in the newly integrated fingerprint,the analytical units of constituent herbal materials,information entropy and information amount were significantly reduced(P<0.05),while the TQSM parameters remained unchanged(P>0.05).Additionally,the RSD of the TQSM parameters,information entropy,and information amount didn’t show significant difference before and after integration(P>0.05),but the RSD of the number and area of the integrated analytical units significantly decreased(P<0.05).Conclusion The MFSM method could reduce the analytical units of constituent herbal mate-rials while maintain the properties and variability from their original fingerprint.Thus,it could serve as a feasible and reliable tool to reduce difficulties in analyzing multi-compo-nents within CMMs and facilitating the evaluation of their quality.
文摘Aim To improve the efficiency of fatigue material tests and relevant statistical treatment of test data. Methods\ Least square approach and other special treatments were used. Results and Conclusion\ The concepts of each phase in fatigue tests and statistical treatment are clarified. The method proposed leads to three important properties. Reduced number of specimens brings to the advantage of lowering test expenditures. The whole test procedure has more flexibility for there is no need to conduct many tests at the same stress level as in traditional cases.
基金The authors would like to thank the Laboratory of Water Engineering,Fasa University for providing the facilities to perform this research.
文摘Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A large population also uses local groundwater for drinking purposes.Therefore,in this study,this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis(CA),Discriminant Analysis(DA),and Principal Component Analysis(PCA).Water quality data was monitored at 22 different wells,for five years(2009-2014)with 10 water quality parameters.By using cluster analysis,the sampling wells were grouped into two clusters with distinct water qualities at different locations.The Lasso Discriminant Analysis(LDA)technique was used to assess the spatial variability of water quality.Based on the results,all of the variables except sodium absorption ratio(SAR)are effective in the LDA model with all variables affording 92.80%correct assignation to discriminate between the clusters from the primary 10 variables.Principal component(PC)analysis and factor analysis reduced the complex data matrix into two main components,accounting for more than 95.93%of the total variance.The first PC contained the parameters of TH,Ca2+,and Mg2+.Therefore,the first dominant factor was hardness.In the second PC,Cl-,SAR,and Na+were the dominant parameters,which may indicate salinity.The originally acquired factors illustrate natural(existence of geological formations)and anthropogenic(improper disposal of domestic and agricultural wastes)factors which affect the groundwater quality.
文摘The flow of novel coronavirus(COVID-19)has affected almost every aspect of human life around the globe.Being the emerging ground and early sufferer of the virus,Wuhan city-data remains a case of multifold significance.Further,it is of notable importance to explore the impact of unique and unprecedented public health response of Chinese authorities—the extreme lockdown of the city.In this research,we investigate the statistical nature of the viral transmission concerning social distancing,extreme quarantine,and robust lockdown interventions.We observed highly convincing and statistically significant evidences in favor of quarantine and social distancing approaches.These findings might help countries,now facing,or likely to face the wave of the virus.We analyzed Wuhan-based data of“number of deaths”and“confirmed cases,”extracted from China CDC weekly database,dated from February 13,2020,to March 24,2020.To estimate the underlying group structure,the assembled data is further subdivided into three blocks,each consists of two weeks.Thus,the complete data set is studied in three phases,such as,phase 1(Ph 1)=February 13,2020,to February 26,2020;phase 2(Ph 2)=February 27,2020 to March 11,2020;and phase 3(Ph 3)=March 12,2020 to March 24,2020.We observed the overall median proportion of deaths in those six weeks remained 0.0127.This estimate is highly influenced by Ph1,when the early flaws of weak health response were still prevalent.Over the time,we witnessed a median decline of 92.12%in the death proportions.Moreover,a non-parametric version of the variability analysis of death data,estimated that the average rank of reported proportions in Ph 3 remained 7,which was 20.5 in Ph 2,and stayed 34.5 in the first phase.Similar patterns were observed,when studying the confirmed cases data.We estimated the overall median of the proportion of confirmed cases in Wuhan as 0.0041,which again,is highly inclined towards Ph 1 and Ph 2.We also witnessed minimum average rank proportions for Ph 3,such as 7,which was noticeably lower than Ph 2,21.71,and Ph 1, 32.29. Moreover, the varying degree of clustering indicates that the effectivenessof quarantine based policies is time-dependent. In general, the declinein coronavirus transmission in Wuhan significantly coincides with the lockdown.
基金supported by the National Key R&D Program of China(2022YFF0503004,2020YFC2201200 and 2022YFF0503800)the National Natural Science Foundation of China(NSFC,grant No.12333009)supported by the National Space Administration of China under the Pre-research Program of Civil Space Technology"Design and Key Technologies of the Solar All-round Stereoscopic Detection System"。
文摘Solar magnetic field measurements mainly use the Zeeman effect,but this method has two problems,namely,low accuracy of the transverse magnetic field components and a 180°ambiguity.Multi-perspective observations can increase the measurement accuracy and resolve the ambiguity.This study investigates how combined observations from the Sun-Earth L5 point,Sun-Earth line,and solar polar-orbiting satellites improve the accuracy of the transverse solar magnetic field under different satellite positional configurations.A three-satellite model is developed using spherical trigonometry to establish coordinate relationships,and the error propagation formulas are applied to correc transverse field measurement errors.The magnetic field measurement error distribution of the Helioseismic and Magnetic Imager is analyzed,and the magnetograms from the three satellites are simulated.The improvement to the transverse field accuracy under various satellite configurations is then assessed based on simulation results.The results show that multi-perspective measurements can reduce transverse component errorsΔB_(x)to approximately 10%andΔB_(y)to about 15%compared to the error from a single satellite.An optimally designed polar orbit can decrease the transverse field error by nearly an order of magnitude for 80%of its operation time.
基金supported by the Ministry of Science and Technology of China(2020SKA0110401,2020SKA0110402 and 2020SKA0110100)the National Key Research and Development Program of China(2018YFA0404504,2018YFA0404601 and 2020YFC2201600)+2 种基金the National Natural Science Foundation of China(12373005,11890691,12205388,12220101003 and 12473097)the China Manned Space Project with numbers CMS-CSST-2021(A02,A03,B01)Guangdong Basic and Applied Basic Research Foundation(2024A1515012309)。
文摘In this study,we investigate the potential of mark-weighted angular correlation functions,which integrateβ-cosmic-web classification with angular correlation function analysis to improve cosmological constraints.Using SDSS DR12 CMASS-NGC galaxies and mock catalogs withΩ_(m)varying from 0.25 to 0.40,we assess the discriminative power of different statistics via the average improvement in chi-squared,ΔX^(2),across six redshift bins.This metric quantifies how effectively each statistic distinguishes between different cosmological models.Incorporating cosmic-web weights leads to substantial improvements.Using statistics weighted by the mean neighbor distance(Dnei)increasesΔX^(2)by approximately 40%–130%,while applying inverse mean neighbor distance weighting(1/Dnei)yields even larger gains,boostingΔX^(2)by a factor of 2–3 compared to traditional unweighted angular statistics.These enhancements are consistent with previous 3D clustering results,demonstrating the superior sensitivity of theβ-weighted approaches.Our method,based on thin redshift slices,is particularly suitable for slitless surveys(e.g.,Euclid,CSST)where redshift uncertainties limit 3D analyses.This study also offers a framework for applying marked statistics to 2D angular clustering.
文摘With the development of science and technology, the relationship between information and data and our life and production is getting closer and closer. Statistics play a very important role in promoting social and economic development. High-quality statistical data can intuitively reflect the current situation of social environment, market environment and political environment, and can also predict the future development trend. Based on this, this paper makes a comprehensive analysis of the value of statistical data and the current situation of statistical data work, and also summarizes the main factors affecting the quality of statistical data, so as to formulate more perfect countermeasures.
文摘Identification of modal parameters of a linear structure with output-only measurements has received much attention over the past decades. In the paper, the Natural Excitation Technique (NExT) is used for acquisition of the impulse signals from the structural responses. Then Eigensystem Realization Algorithm (ERA) is utilized for modal identification. For disregarding the fictitious ‘computational modes', a procedure, Statistically Averaging Modal Frequency Method (SAMFM), is developed to distinguish the true modes from noise modes, and to improve the precision of the identified modal frequencies of the structure. An offshore platform is modeled with the finite element method. The theoretical modal parameters are obtained for a comparison with the identified values. The dynamic responses of the platform under random wave loading are computed for providing the output signals used for identification with ERA. Results of simulation demonstrate that the proposed method can determine the system modal frequency with high precision.
基金Natural Natural Science Foundation of China Under Grant No 50778077 & 50608036the Graduate Innovation Fund of Huazhong University of Science and Technology Under Grant No HF-06-028
文摘A novel damage detection method is applied to a 3-story frame structure, to obtain statistical quantification control criterion of the existence, location and identification of damage. The mean, standard deviation, and exponentially weighted moving average （EWMA） are applied to detect damage information according to statistical process control （SPC） theory. It is concluded that the detection is insignificant with the mean and EWMA because the structural response is not independent and is not a normal distribution. On the other hand, the damage information is detected well with the standard deviation because the influence of the data distribution is not pronounced with this parameter. A suitable moderate confidence level is explored for more significant damage location and quantification detection, and the impact of noise is investigated to illustrate the robustness of the method.
文摘In this study,geochemical anomaly separation was carried out with methods based on the distribution model,which includes probability diagram(MPD),fractal(concentration-area technique),and U-statistic methods.The main objective is to evaluate the efficiency and accuracy of the methods in separation of anomalies on the shear zone gold mineralization.For this purpose,samples were taken from the secondary lithogeochemical environment(stream sediment samples)on the gold mineralization in Saqqez,NW of Iran.Interpretation of the histograms and diagrams showed that the MPD is capable of identifying two phases of mineralization.The fractal method could separate only one phase of change based on the fractal dimension with high concentration areas of the Au element.The spatial analysis showed two mixed subpopulations after U=0 and another subpopulation with very high U values.The MPD analysis followed spatial analysis,which shows the detail of the variations.Six mineralized zones detected from local geochemical exploration results were used for validating the methods mentioned above.The MPD method was able to identify the anomalous areas higher than 90%,whereas the two other methods identified 60%(maximum)of the anomalous areas.The raw data without any estimation for the concentration was used by the MPD method using aminimum of calculations to determine the threshold values.Therefore,the MPD method is more robust than the other methods.The spatial analysis identified the detail soft hegeological and mineralization events that were affected in the study area.MPD is recommended as the best,and the spatial U-analysis is the next reliable method to be used.The fractal method could show more detail of the events and variations in the area with asymmetrical grid net and a higher density of sampling or at the detailed exploration stage.
文摘Making use of the 2MASS Data Release, we have searched for nearinfrared （JHK） counterparts to 268 blazars from Donato et al. and obtained 238 counterparts within 5＇＇ in the area covered by 2MASS. It provides us a sample with infrared data several times larger than the previous one of the same kind. Based on our sample and the sample by Donato et al., we have compared in detail the properties of HBLs, LBLs and FSRQs from five aspects and found that HBLs are significantly different from LBLs and FSRQs while LBLs are not obviously different from FSRQs. Our results strongly support the division of BL Lac objects into the high-frequency peaked （HBL） and low-frequency peaked （LBL） objects introduced by Padovani ＆ Giommi and show that HBLs and LBLs are two kinds of blazar having different physical properties.
基金supported by the National Natural Science Foundation of China(U1931207,61602278 and 61702306)Sci.&Tech.Development Fund of Shandong Province of China(2016ZDJS02A11,ZR2017BF015 and ZR2017MF027)+1 种基金the Humanities and Social Science Research Project of the Ministry of Education(18YJAZH017)the Taishan Scholar Program of Shandong Province,and the Science and Technology Support Plan of Youth Innovation Team of Shandong Higher School(2019KJN024)。
文摘Predicting seeing of astronomical observations can provide hints of the quality of optical imaging in the near future,and facilitate flexible scheduling of observation tasks to maximize the use of astronomical observatories.Traditional approaches to seeing prediction mostly rely on regional weather models to capture the in-dome optical turbulence patterns.Thanks to the developing of data gathering and aggregation facilities of astronomical observatories in recent years,data-driven approaches are becoming increasingly feasible and attractive to predict astronomical seeing.This paper systematically investigates data-driven approaches to seeing prediction by leveraging various big data techniques,from traditional statistical modeling,machine learning to new emerging deep learning methods,on the monitoring data of the Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST).The raw monitoring data are preprocessed to allow for big data modeling.Then we formulate the seeing prediction task under each type of modeling framework and develop seeing prediction models through using representative big data techniques,including ARIMA and Prophet for statistical modeling,MLP and XGBoost for machine learning,and LSTM,GRU and Transformer for deep learning.We perform empirical studies on the developed models with a variety of feature configurations,yielding notable insights into the applicability of big data techniques to the seeing prediction task.
文摘We present an analysis of the timing observations on 27 radio pulsars, collected at Hartebeesthoek Radio Astronomy Observatory （HartRAO）, with time spans ranging between - 9 and 14yr. Our results show that the measured pulsar frequency second derivatives are non-stationary. Both the magnitude and the sign of the v values depend upon the choice of epoch and data span. A simple statistical analysis of the observed second time derivative of the pulse frequency （v obs） of a large sample of 391 （25 HartRAO and 366 Jodrell Bank Observatory）. pulsars reveals that v is only marginally correlated with both the pulsar spindown rate （P） and the characteristic age （τc）. We find correlation coefficients of ,- 0.20 and -0.30 between the measured braking indices and, respectively,P and τc. This result reaffirms earlier conclusions that the braking indices of most radio pulsars, obtained through the standard timing technique, are strongly dominated by sustained random fluctuations in the observed pulse phase.
基金supported by the National Natural Science Foundation of China (Grant No. 11103004)the Foundation for the Authors of National Excellent Doctoral Dissertations of China (Grant No. 201225)
文摘With the assistance of the identified/associated sources in the second Fermi Large Area Telescope （LAT） catalog, we analyze and resolve the spatial distribution and the distributions of the gamma-ray spectral and variability indices of the remaining 575 unassociated Fermi LAT sources. Consequently, it is suggested that the unassociated sources could statistically consist of Galactic supernova remnants/pulsar wind nebulae, BL Lacertae objects, fiat spectrum radio quasars and other types of active galaxies with fractions of 25%, 29%, 41% and 5%, respectively.
文摘Ag-sheathed (Bi,Pb)(2)SoCa(2)Cu(3)O(x) tapes were prepared by the powder-in-tube method. The influences of rolling parameters on superconducting characteristics of Bi(2223)/Ag tapes were analyzed qualitatively with a statistical method. The results demonstrate that roll diameter and reduction per pass significantly influence the properties of Bi(2223)/Ag superconducting tapes while roll speed does less and working friction the least. An optimized rolling process was therefore achieved according to the above results.
基金the APC was funded by“Stefan cel Mare”University of Suceava,Romania。
文摘Identifying patterns,recognition systems,prediction methods,and detection methods is a major challenge in solving different medical issues.Few categories of devices for personal and professional assessment of body composition are available.Bioelectrical impedance analyzer is a simple,safe,affordable,mobile,non-invasive,and less expensive alternative device for body composition assessment.Identifying the body composition pattern of different groups with varying age and gender is a major challenge in defining an optimal level because of the body shape,body mass,energy requirements,physical fitness,health status,and metabolic profile.Thus,this research aims to identify the statistical medical pattern recognition of body composition data by using a bioelectrical impedance analyzer.In previous studies,a pattern was identified for four indicators that concern body composition(e.g.,body mass index(BMI),body fat,muscle mass,and total body water).The novelty of our study is the fact that we identified a recognition pattern by using medical statistical methods for a body composition that contains seven indicators(e.g.,body fat,visceral fat,BMI,muscle mass,skeletal muscle mass,sarcopenic index,and total body water).The youth that exhibited the body composition pattern identified in our study could be considered healthy.Every deviation of one or more parameters outside the margins of the pattern for body composition could be associated with health issues,and more medical investigations would be needed for a diagnosis.BIA is considered a valid and reliable device to assess body composition along with medical statistical methods to identify a pattern for body composition according to the age,gender,and other relevant parameters.
