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
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
A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming probl...A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming problem can be converted into the single objective function by various methods as Chandra Sen’s method, weighted sum method, ranking function method, statistical averaging method. In this paper, Chandra Sen’s method and statistical averaging method both are used here for making single objective function from multi-objective function. Two multi-objective programming problems are solved to verify the result. One is numerical example and the other is real life example. Then the problems are solved by ordinary simplex method and fuzzy programming method. It can be seen that fuzzy programming method gives better optimal values than the ordinary simplex method.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
We present a study of low surface brightness galaxies(LSBGs) selected by fitting the images for all the galaxies inα.40 SDSS DR7 sample with two kinds of single-component models and two kinds of two-component models(...We present a study of low surface brightness galaxies(LSBGs) selected by fitting the images for all the galaxies inα.40 SDSS DR7 sample with two kinds of single-component models and two kinds of two-component models(disk+bulge):single exponential,single sersic,exponential+deVaucular(exp+deV),and exponential+sérsic(exp+ser).Under the criteria of the B band disk central surface brightness μ_(0,disk)(B)≥22.5 mag arcsec^(-2) and the axis ratio b/a> 0.3,we selected four none-edge-on LSBG samples from each of the models which contain 1105,1038,207,and 75 galaxies,respectively.There are 756 galaxies in common between LSBGs selected by exponential and sersic models,corresponding to 68.42% of LSBGs selected by the exponential model and 72.83% of LSBGs selected by the sersic model,the rest of the discrepancy is due to the difference in obtaining μ_(0) between the exponential and sersic models.Based on the fitting,in the range of 0.5≤n≤1.5,the relation of μ_(0) from two models can be written as μ_(0,sérsic)-μ_(0,exp)=-1.34(n-1).The LSBGs selected by disk+bulge models(LSBG_(2)comps) are more massive than LSBGs selected by single-component models(LSBG_1comp),and also show a larger disk component.Though the bulges in the majority of our LSBG_(2)comps are not prominent,more than 60% of our LSBG_(2)comps will not be selected if we adopt a single-component model only.We also identified 31 giant low surface brightness galaxies(gLSBGs) from LSBG_(2)comps.They are located at the same region in the color-magnitude diagram as other gLSBGs.After we compared different criteria of gLSBGs selection,we find that for gas-rich LSBGs,M_(*)> 10^(10)M_⊙ is the best to distinguish between gLSBGs and normal LSBGs with bulge.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
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
Ag-sheathed (Bi,Pb)2Sr2Ca2Cu3Ox 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 statist...Ag-sheathed (Bi,Pb)2Sr2Ca2Cu3Ox 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 significatly 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
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
Using observations from the Michelson Doppler Imager(MDI) onboard Solar and Heliospheric Observatory(SOHO), we develop a computational algorithm to automatically identify bipolar magnetic regions(BMRs) in active regio...Using observations from the Michelson Doppler Imager(MDI) onboard Solar and Heliospheric Observatory(SOHO), we develop a computational algorithm to automatically identify bipolar magnetic regions(BMRs) in active regions(ARs), and then study their statistical properties.The individual magnetic(positive or negative) pole of a BMR is determined from the region with an absolute strength above 55 G and with an area larger than 250 pixel^2(~495 Mm^2), while a BMR is identified as a pair of positive and negative poles with the shortest area-weight distance between them.Based on this method, 2234 BMRs are identified from MDI synoptic magnetograms between Carrington Rotations 1909(1996 May 06) and 2104(2010 December 10). 1005 of them are located in the northern hemisphere, while the other 1229 are in the southern hemisphere. We find that the BMR parameters(e.g., latitude, separation, fragment number and strength) are similar to those of ARs. Moreover, based on the maximum likelihood estimation(MLE) method, the frequency distributions representing the occurrence of these BMRs as functions of area and magnetic flux exhibit a power-law behavior, i.e.,dN/dx ∝ x^(-αx), with indices of α_A = 1.98 ± 0.06 and α_F = 1.93 ± 0.05 respectively. We also find that their orientation angles(θ) follow "Hale's Polarity Law" and deviate slightly toward the direction of the solar equator. Consistent with previous findings, we obtain the dependence of orientation angles on latitudes for normal BMRs during the 23 rd solar cycle. The north-south asymmetry of these BMRs is also detected here.展开更多
Fast radio bursts(FRBs) are extremely strong radio flares lasting several milliseconds,most of which come from unidentified objects at a cosmological distance.They can be apparently repeating or not.In this paper,we a...Fast radio bursts(FRBs) are extremely strong radio flares lasting several milliseconds,most of which come from unidentified objects at a cosmological distance.They can be apparently repeating or not.In this paper,we analyzed 18 repeaters and 12 non-repeating FRBs observed in the frequency bands of 400–800 MHz from Canadian Hydrogen Intensity Mapping Experiment(CHIME).We investigated the distributions of FRB isotropic-equivalent radio luminosity,considering the K correction.Statistically,the luminosity distribution can be better fitted by Gaussian form than by power-law.Based on the above results,together with the observed FRB event rate,pulse duration,and radio luminosity,FRB origin models are evaluated and constrained such that the gamma-ray bursts(GRBs) may be excluded for the non-repeaters while magnetars or neutron stars(NSs) emitting the supergiant pulses are preferred for the repeaters.We also found the necessity of a small FRB emission beaming solid angle(about 0.1 sr) from magnetars that should be considered,and/or the FRB association with soft gamma-ray repeaters(SGRs) may lie at a low probability of about 10%.Finally,we discussed the uncertainty of FRB luminosity caused by the estimation of the distance that is inferred by the simple relation between the redshift and dispersion measure(DM).展开更多
The quality of the low frequency electromagnetic data is affected by the spike and the trend noises.Failure in removal of the spikes and the trends reduces the credibility of data explanation.Based on the analyses of ...The quality of the low frequency electromagnetic data is affected by the spike and the trend noises.Failure in removal of the spikes and the trends reduces the credibility of data explanation.Based on the analyses of the causes and characteristics of these noises,this paper presents the results of a preset statistics stacking method(PSSM)and a piecewise linear fitting method(PLFM)in de-noising the spikes and trends,respectively.The magnitudes of the spikes are either higher or lower than the normal values,which leads to distortion of the useful signal.Comparisons have been performed in removing of the spikes among the average,the statistics and the PSSM methods,and the results indicate that only the PSSM can remove the spikes successfully.On the other hand,the spectrums of the linear and nonlinear trends mainly lie in the low frequency band and can change the calculated resistivity significantly.No influence of the trends is observed when the frequency is higher than a certain threshold value.The PLSM can remove effectively both the linear and nonlinear trends with errors around 1% in the power spectrum.The proposed methods present an effective way for de-noising the spike and the trend noises in the low frequency electromagnetic data,and establish a research basis for de-noising the low frequency noises.展开更多
To improve the location accuracy, a hybrid location algorithm based on cuckoo and statistical manifold method is proposed. It combines the cuckoo algorithm's strong global optimization ability and the statistical ...To improve the location accuracy, a hybrid location algorithm based on cuckoo and statistical manifold method is proposed. It combines the cuckoo algorithm's strong global optimization ability and the statistical manifold<span>’</span><span>s accurate positioning ability fully. The simulation results show that the hybrid location algorithm has higher accuracy and reduces the influence of initial value selection on location accuracy.</span>展开更多
Increasing contamination of water resources in the world and our country and decreasing water quality over time, not having met the objectives of utilization of water resources;it has increased the importance of water...Increasing contamination of water resources in the world and our country and decreasing water quality over time, not having met the objectives of utilization of water resources;it has increased the importance of water management. The monitoring of the water resources and evaluation of these monitoring results have given direction to the studies’ outcome in order to control factors that pollute water resources and reduce water quality. Nilüfer Creek is very important for both being a source of drinking and potable water and a discharge area for wastewaters for the city of Bursa. In this study, the results of the analysis belonging to the period between 2002-2010 which are taken from 15 points by General Directorate of Bursa Water and Sewerage Administration (BUWSA) were evaluated in relation to water quality of the Nilüfer Creek. Non-parametric methods were used in the evaluation of the water quality data due to the lack of normally distributed data. The identification of the best represented parameters of the water quality was provided by applying Principal Component Analysis. According to results of the analysis, the best representative 9 parameters from the 19 water quality parameters were defined as parameters of BOD5, COD, TSS, T.Fe, Zn, conductivity, NO2-N, Ni and NO3-N that taking part of the first two components.展开更多
In this paper, the statistical averaging method and the new statistical averaging methods have been used to solve the fuzzy multi-objective linear programming problems. These methods have been applied to form a single...In this paper, the statistical averaging method and the new statistical averaging methods have been used to solve the fuzzy multi-objective linear programming problems. These methods have been applied to form a single objective function from the fuzzy multi-objective linear programming problems. At first, a numerical example of solving fuzzy multi-objective linear programming problem has been provided to validate the maximum risk reduction by the proposed method. The proposed method has been applied to assess the risk of damage due to natural calamities like flood, cyclone, sidor, and storms at the coastal areas in Bangladesh. The proposed method of solving the fuzzy multi-objective linear programming problems by the statistical method has been compared with the Chandra Sen’s method. The numerical results show that the proposed method maximizes the risk reduction capacity better than Chandra Sen’s method.展开更多
Observing and timing a group of millisecond pulsars with high rotational stability enables the direct detection of gravitational waves(GWs).The GW signals can be identified from the spatial correlations encoded in the...Observing and timing a group of millisecond pulsars with high rotational stability enables the direct detection of gravitational waves(GWs).The GW signals can be identified from the spatial correlations encoded in the times-of-arrival of widely spaced pulsar-pairs.The Chinese Pulsar Timing Array(CPTA)is a collaboration aiming at the direct GW detection with observations carried out using Chinese radio telescopes.This short article serves as a“table of contents”for a forthcoming series of papers related to the CPTA Data Release 1(CPTA DR1)which uses observations from the Five-hundred-meter Aperture Spherical radio Telescope.Here,after summarizing the time span and accuracy of CPTA DR1,we report the key results of our statistical inference finding a correlated signal with amplitude logA_(c)=-14.4_(-2.8)^(+1.0)for spectral index in the range ofα∈[-1.8,1.5]assuming a GW background(GWB)induced quadrupolar correlation.The search for the Hellings–Downs(HD)correlation curve is also presented,where some evidence for the HD correlation has been found that a 4.6σstatistical significance is achieved using the discrete frequency method around the frequency of 14 n Hz.We expect that the future International Pulsar Timing Array data analysis and the next CPTA data release will be more sensitive to the n Hz GWB,which could verify the current results.展开更多
A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-effi...A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.展开更多
Leakage occurring in the tube sockets of the main steam thermometers can seriously threaten the safe operation of coal-fired power plants.Here,assuming a 300 MW unit as a relevant testbed,this problem is investigated ...Leakage occurring in the tube sockets of the main steam thermometers can seriously threaten the safe operation of coal-fired power plants.Here,assuming a 300 MW unit as a relevant testbed,this problem is investigated numerically through solution of the equations of fluid-dynamics in synergy with the mathematical treatment of relevant statistics.The results indicate that the steam can form a large-scale spiral flow inside the tube socket and continuously scour the inner wall.In the model with the protective casing setting angle of 60°,the average tangential fluid velocity can reach up to 4.8 m/s,which is about twice higher than that in the model with the protective casing setting angle of 0°.The wake disturbance generated by the flow around the thermo-sensitive body leads to differences in the fluid motion inside the tube sockets between the upstream and downstream thermometers.These differences are affected by the distance between the thermometers,the setting angle of protective casing,and other factors.The pressure of the main steam inside the tube socket for a R3 thermometer,located outside the curved pipeline,is about 1756 Pa higher than that of the L3 thermometer located outside the straight pipeline,indicating that the secondary flow generated in the curved pipeline is able to provide stronger energy for the large-scale spiral flow inside the tube socket.On the basis of these findings,an improvement scheme for the installation of long-itudinal ribs in the tube sockets is proposed.The simulation results show that the average tangential velocity of the fluid within the near-wall area of tube sockets decreases by more than 90%,which should be enough to effectively alleviate the damage to the inner wall caused by high-pressure fluid or particles.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.
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.
文摘A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming problem can be converted into the single objective function by various methods as Chandra Sen’s method, weighted sum method, ranking function method, statistical averaging method. In this paper, Chandra Sen’s method and statistical averaging method both are used here for making single objective function from multi-objective function. Two multi-objective programming problems are solved to verify the result. One is numerical example and the other is real life example. Then the problems are solved by ordinary simplex method and fuzzy programming method. It can be seen that fuzzy programming method gives better optimal values than the ordinary simplex method.
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.
基金supported by the National Key R&D Program of China (grant No.2022YFA1602901)support of the National Natural Science Foundation of China(NSFC) grant Nos. 12090040, 12090041, and 12003043+5 种基金supported by the Youth Innovation Promotion AssociationCAS (No. 2020057)the science research grants of CSST from the China Manned Space Projectsupport of the NSFC grant Nos.11733006 and U1931109supported by the Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No. XDB0550100partially supported by the Open Project Program of the Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciences。
文摘We present a study of low surface brightness galaxies(LSBGs) selected by fitting the images for all the galaxies inα.40 SDSS DR7 sample with two kinds of single-component models and two kinds of two-component models(disk+bulge):single exponential,single sersic,exponential+deVaucular(exp+deV),and exponential+sérsic(exp+ser).Under the criteria of the B band disk central surface brightness μ_(0,disk)(B)≥22.5 mag arcsec^(-2) and the axis ratio b/a> 0.3,we selected four none-edge-on LSBG samples from each of the models which contain 1105,1038,207,and 75 galaxies,respectively.There are 756 galaxies in common between LSBGs selected by exponential and sersic models,corresponding to 68.42% of LSBGs selected by the exponential model and 72.83% of LSBGs selected by the sersic model,the rest of the discrepancy is due to the difference in obtaining μ_(0) between the exponential and sersic models.Based on the fitting,in the range of 0.5≤n≤1.5,the relation of μ_(0) from two models can be written as μ_(0,sérsic)-μ_(0,exp)=-1.34(n-1).The LSBGs selected by disk+bulge models(LSBG_(2)comps) are more massive than LSBGs selected by single-component models(LSBG_1comp),and also show a larger disk component.Though the bulges in the majority of our LSBG_(2)comps are not prominent,more than 60% of our LSBG_(2)comps will not be selected if we adopt a single-component model only.We also identified 31 giant low surface brightness galaxies(gLSBGs) from LSBG_(2)comps.They are located at the same region in the color-magnitude diagram as other gLSBGs.After we compared different criteria of gLSBGs selection,we find that for gas-rich LSBGs,M_(*)> 10^(10)M_⊙ is the best to distinguish between gLSBGs and normal LSBGs with bulge.
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.
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Ag-sheathed (Bi,Pb)2Sr2Ca2Cu3Ox 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 significatly 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.
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.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11603077, 11573072 and 11333009)the Youth Fund of Jiangsu (No. BK20161095)the Laboratory (No. 2010DP173032)
文摘Using observations from the Michelson Doppler Imager(MDI) onboard Solar and Heliospheric Observatory(SOHO), we develop a computational algorithm to automatically identify bipolar magnetic regions(BMRs) in active regions(ARs), and then study their statistical properties.The individual magnetic(positive or negative) pole of a BMR is determined from the region with an absolute strength above 55 G and with an area larger than 250 pixel^2(~495 Mm^2), while a BMR is identified as a pair of positive and negative poles with the shortest area-weight distance between them.Based on this method, 2234 BMRs are identified from MDI synoptic magnetograms between Carrington Rotations 1909(1996 May 06) and 2104(2010 December 10). 1005 of them are located in the northern hemisphere, while the other 1229 are in the southern hemisphere. We find that the BMR parameters(e.g., latitude, separation, fragment number and strength) are similar to those of ARs. Moreover, based on the maximum likelihood estimation(MLE) method, the frequency distributions representing the occurrence of these BMRs as functions of area and magnetic flux exhibit a power-law behavior, i.e.,dN/dx ∝ x^(-αx), with indices of α_A = 1.98 ± 0.06 and α_F = 1.93 ± 0.05 respectively. We also find that their orientation angles(θ) follow "Hale's Polarity Law" and deviate slightly toward the direction of the solar equator. Consistent with previous findings, we obtain the dependence of orientation angles on latitudes for normal BMRs during the 23 rd solar cycle. The north-south asymmetry of these BMRs is also detected here.
基金supported by the National Natural Science Foundation of China (Grant Nos.11988101,U1938117,U1731238,11703003 and 11725313)the International Partnership Program of Chinese Academy of Sciences (Grant No.114A11KYSB20160008)+1 种基金the National Key R&D Program of China (No.2016YFA0400702)the Guizhou Provincial Science and Technology Foundation (Grant No.[2020]1Y019)。
文摘Fast radio bursts(FRBs) are extremely strong radio flares lasting several milliseconds,most of which come from unidentified objects at a cosmological distance.They can be apparently repeating or not.In this paper,we analyzed 18 repeaters and 12 non-repeating FRBs observed in the frequency bands of 400–800 MHz from Canadian Hydrogen Intensity Mapping Experiment(CHIME).We investigated the distributions of FRB isotropic-equivalent radio luminosity,considering the K correction.Statistically,the luminosity distribution can be better fitted by Gaussian form than by power-law.Based on the above results,together with the observed FRB event rate,pulse duration,and radio luminosity,FRB origin models are evaluated and constrained such that the gamma-ray bursts(GRBs) may be excluded for the non-repeaters while magnetars or neutron stars(NSs) emitting the supergiant pulses are preferred for the repeaters.We also found the necessity of a small FRB emission beaming solid angle(about 0.1 sr) from magnetars that should be considered,and/or the FRB association with soft gamma-ray repeaters(SGRs) may lie at a low probability of about 10%.Finally,we discussed the uncertainty of FRB luminosity caused by the estimation of the distance that is inferred by the simple relation between the redshift and dispersion measure(DM).
文摘The quality of the low frequency electromagnetic data is affected by the spike and the trend noises.Failure in removal of the spikes and the trends reduces the credibility of data explanation.Based on the analyses of the causes and characteristics of these noises,this paper presents the results of a preset statistics stacking method(PSSM)and a piecewise linear fitting method(PLFM)in de-noising the spikes and trends,respectively.The magnitudes of the spikes are either higher or lower than the normal values,which leads to distortion of the useful signal.Comparisons have been performed in removing of the spikes among the average,the statistics and the PSSM methods,and the results indicate that only the PSSM can remove the spikes successfully.On the other hand,the spectrums of the linear and nonlinear trends mainly lie in the low frequency band and can change the calculated resistivity significantly.No influence of the trends is observed when the frequency is higher than a certain threshold value.The PLSM can remove effectively both the linear and nonlinear trends with errors around 1% in the power spectrum.The proposed methods present an effective way for de-noising the spike and the trend noises in the low frequency electromagnetic data,and establish a research basis for de-noising the low frequency noises.
文摘To improve the location accuracy, a hybrid location algorithm based on cuckoo and statistical manifold method is proposed. It combines the cuckoo algorithm's strong global optimization ability and the statistical manifold<span>’</span><span>s accurate positioning ability fully. The simulation results show that the hybrid location algorithm has higher accuracy and reduces the influence of initial value selection on location accuracy.</span>
文摘Increasing contamination of water resources in the world and our country and decreasing water quality over time, not having met the objectives of utilization of water resources;it has increased the importance of water management. The monitoring of the water resources and evaluation of these monitoring results have given direction to the studies’ outcome in order to control factors that pollute water resources and reduce water quality. Nilüfer Creek is very important for both being a source of drinking and potable water and a discharge area for wastewaters for the city of Bursa. In this study, the results of the analysis belonging to the period between 2002-2010 which are taken from 15 points by General Directorate of Bursa Water and Sewerage Administration (BUWSA) were evaluated in relation to water quality of the Nilüfer Creek. Non-parametric methods were used in the evaluation of the water quality data due to the lack of normally distributed data. The identification of the best represented parameters of the water quality was provided by applying Principal Component Analysis. According to results of the analysis, the best representative 9 parameters from the 19 water quality parameters were defined as parameters of BOD5, COD, TSS, T.Fe, Zn, conductivity, NO2-N, Ni and NO3-N that taking part of the first two components.
文摘In this paper, the statistical averaging method and the new statistical averaging methods have been used to solve the fuzzy multi-objective linear programming problems. These methods have been applied to form a single objective function from the fuzzy multi-objective linear programming problems. At first, a numerical example of solving fuzzy multi-objective linear programming problem has been provided to validate the maximum risk reduction by the proposed method. The proposed method has been applied to assess the risk of damage due to natural calamities like flood, cyclone, sidor, and storms at the coastal areas in Bangladesh. The proposed method of solving the fuzzy multi-objective linear programming problems by the statistical method has been compared with the Chandra Sen’s method. The numerical results show that the proposed method maximizes the risk reduction capacity better than Chandra Sen’s method.
基金supported by the National SKA Program of China(2020SKA0120100)the National Natural Science Foundation of China(Nos.12041303 and 12250410246)+1 种基金the CAS-MPG LEGACY projectfunding from the Max-Planck Partner Group。
文摘Observing and timing a group of millisecond pulsars with high rotational stability enables the direct detection of gravitational waves(GWs).The GW signals can be identified from the spatial correlations encoded in the times-of-arrival of widely spaced pulsar-pairs.The Chinese Pulsar Timing Array(CPTA)is a collaboration aiming at the direct GW detection with observations carried out using Chinese radio telescopes.This short article serves as a“table of contents”for a forthcoming series of papers related to the CPTA Data Release 1(CPTA DR1)which uses observations from the Five-hundred-meter Aperture Spherical radio Telescope.Here,after summarizing the time span and accuracy of CPTA DR1,we report the key results of our statistical inference finding a correlated signal with amplitude logA_(c)=-14.4_(-2.8)^(+1.0)for spectral index in the range ofα∈[-1.8,1.5]assuming a GW background(GWB)induced quadrupolar correlation.The search for the Hellings–Downs(HD)correlation curve is also presented,where some evidence for the HD correlation has been found that a 4.6σstatistical significance is achieved using the discrete frequency method around the frequency of 14 n Hz.We expect that the future International Pulsar Timing Array data analysis and the next CPTA data release will be more sensitive to the n Hz GWB,which could verify the current results.
基金funded by the National Natural Science Foundation of China(41971226,41871357)the Major Research and Development and Achievement Transformation Projects of Qinghai,China(2022-QY-224)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28110502,XDA19030303).
文摘A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.
文摘Leakage occurring in the tube sockets of the main steam thermometers can seriously threaten the safe operation of coal-fired power plants.Here,assuming a 300 MW unit as a relevant testbed,this problem is investigated numerically through solution of the equations of fluid-dynamics in synergy with the mathematical treatment of relevant statistics.The results indicate that the steam can form a large-scale spiral flow inside the tube socket and continuously scour the inner wall.In the model with the protective casing setting angle of 60°,the average tangential fluid velocity can reach up to 4.8 m/s,which is about twice higher than that in the model with the protective casing setting angle of 0°.The wake disturbance generated by the flow around the thermo-sensitive body leads to differences in the fluid motion inside the tube sockets between the upstream and downstream thermometers.These differences are affected by the distance between the thermometers,the setting angle of protective casing,and other factors.The pressure of the main steam inside the tube socket for a R3 thermometer,located outside the curved pipeline,is about 1756 Pa higher than that of the L3 thermometer located outside the straight pipeline,indicating that the secondary flow generated in the curved pipeline is able to provide stronger energy for the large-scale spiral flow inside the tube socket.On the basis of these findings,an improvement scheme for the installation of long-itudinal ribs in the tube sockets is proposed.The simulation results show that the average tangential velocity of the fluid within the near-wall area of tube sockets decreases by more than 90%,which should be enough to effectively alleviate the damage to the inner wall caused by high-pressure fluid or particles.
