The Metallicity Dimension of the Super Earth-cold Jupiter Correlation

The correlation between close-in super Earths and distant cold Jupiters in planetary systems has important implications for their formation and evolution.Contrary to some earlier findings,a recent study conducted by Bonomo et al.suggests that the occurrence of cold Jupiter companions is not excessive in super-Earth systems.Here we show that this discrepancy can be seen as a Simpson’s paradox and is resolved once the metallicity dependence of the super-Earth-cold Jupiter relation is taken into account.A common feature is noticed that almost all the cold Jupiter detections with inner super-Earth companions are found around metal-rich stars.Focusing on the Sun-like hosts with super-solar metallicities,we show that the frequency of cold Jupiters conditioned on the presence of inner super Earths is 39_(-11)^(+12)%,whereas the frequency of cold Jupiters in the same metallicity range is no more than 20%.Therefore,the occurrences of close-in super Earths and distant cold Jupiters appear correlated around metal-rich hosts.The relation between the two types of planets remains unclear for stars with metal-poor hosts due to the limited sample size and the much lower occurrence rate of cold Jupiters,but a correlation between the two cannot be ruled out.

Low Surface Brightness Galaxies Selected by Different Model Fitting

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.

Pulsar Glitch Activities:The Spin Parameters Approach

Glitch activity refers to the mean increase in pulsar spin frequency per year due to rotational glitches.It is an important tool for studying super-nuclear matter using neutron star interiors as templates.Glitch events are typically observed in the spin frequency(ν) and frequency derivative( ν) of pulsars.The rate of glitch recurrence decreases as the pulsar ages,and the activity parameter is usually measured by linear regression of cumulative glitches over a given period.This method is effective for pulsars with multiple regular glitch events.However,due to the scarcity of glitch events and the difficulty of monitoring all known pulsars,only a few have multiple records of glitch events.This limits the use of the activity parameter in studying neutron star interiors with multiple pulsars.In this study,we examined the relationship between the activity parameters and pulsar spin parameters(spin frequency,frequency derivative,and pulsar characteristic age).We found that a quadratic function provides a better fit for the relationship between activity parameters and spin parameters than the commonly used linear functions.Using this information,we were able to estimate the activity parameters of other pulsars that do not have records of glitches.Our analysis shows that the relationship between the estimated activity parameters and pulsar spin parameters is consistent with that of the observed activity parameters in the ensemble of pulsars.

Radio Frequency Interference Mitigation and Statistics in the Spectral Observations of FAST

In radio astronomy,radio frequency interference(RFI)becomes more and more serious for radio observational facilities.The RFI always influences the search and study of the interesting astronomical objects.Mitigating the RFI becomes an essential procedure in any survey data processing.The Five-hundred-meter Aperture Spherical radio Telescope(FAST)is an extremely sensitive radio telescope.It is necessary to find out an effective and precise RFI mitigation method for FAST data processing.In this work,we introduce a method to mitigate the RFI in FAST spectral observation and make a statistic for the RFI using~300 h FAST data.The details are as follows.First,according to the characteristics of FAST spectra,we propose to use the Asymmetrically Reweighted Penalized Least Squares algorithm for baseline fitting.Our test results show that it has a good performance.Second,we flag the RFI with four strategies,which are to flag extremely strong RFI,flag long-lasting RFI,flag polarized RFI,and flag beam-combined RFI,respectively.The test results show that all the RFI above a preset threshold could be flagged.Third,we make a statistic for the probabilities of polarized XX and YY RFI in FAST observations.The statistical results could tell us which frequencies are relatively quiescent.With such statistical data,we are able to avoid using such frequencies in our spectral observations.Finally,based on the~300 h FAST data,we obtained an RFI table,which is the most complete database currently for FAST.

The efficiency and accuracy of probability diagram, spatial statistic and fractal methods in the identification of shear zone gold mineralization: a case study of the Saqqez gold ore district,NW Iran

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.

Causal inference and related statistical methods

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.

Methods of de-noising the low frequency electromagnetic data

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.

Statistics of Galactic Supernova Remnants

We collected the basic parameters of 231 supernova remnants (SNRs) in ourGalaxy, namely, distances (d) from the Sun, linear diameters (D), Galactic heights (Z), estimatedages (t), luminosities (L), surface brightness (Σ) and flux densities (S_1) at 1-GHz frequency andspectral indices (α). We tried to find possible correlations between these parameters. As expected,the linear diameters were found to increase with ages for the shell-type remnants, and also to havea tendency to increase with the Galactic heights. Both the surface brightness and luminosity ofSNRs at 1-GHz tend to decrease with the linear diameter and with age. No other relations between theparameters were found.

Comparison of U-spatial Statistics Method with Classical Statistics Results in the Determination of Geochemical Anomalies of Epithermal Gold in Khoshnameh Area, Hashtjin, Iran

In this study,methods based on the distribution model(with and without personal opinion)were used for the separation of anomalous zones,which include two different methods of U-spatial statistics and mean plus values of standard deviation(X+nS).The primary purpose is to compare the results of these methods with each other.To increase the accuracy of comparison,regional geochemical data were used where occurrences and mineralization zones of epithermal gold have been introduced.The study area is part of the Hashtjin geological map,which is structurally part of the folded and thrust belt and part of the Alborz Tertiary magmatic complex.Samples were taken from secondary lithogeochemical environments.Au element data concerning epithermal gold reserves were used to investigate the efficacy of these two methods.In the U-spatial statistics method,and criteria were used to determine the threshold,and in the method,the element enrichment index of the region rock units was obtained with grouping these units.The anomalous areas were identified by,and criteria.Comparison of methods was made considering the position of discovered occurrences and the occurrences obtained from these methods,the flexibility of the methods in separating the anomalous zones,and the two-dimensional spatial correlation of the three elements As,Pb,and Ag with Au element.The ability of two methods to identify potential areas is acceptable.Among these methods,it seems the method with criteria has a high degree of flexibility in separating anomalous regions in the case of epithermal type gold deposits.

GRB jet beaming angle statistics

Existing theory and models suggest that a Type I （merger） GRB should have a larger jet beaming angle than a Type II （collapsar） GRB, but so far no statistical evidence is available to support this suggestion. In this paper, we obtain a sample of 37 beaming angles and calculate the probability that this is true. A correction is also devised to account for the scarcity of Type I GRBs in our sample. The probability is calculated to be 83% without the correction and 71% with it.

Practice and Thoughts on due Statistics for Four Classes of Enterprises Above Designated Size

“Four classes of enterprises above designated size”(hereinafter called four-classes enterprises)refer to objects of statistical survey that have reached a certain scale in China’s current statistical method system,including four classes in national economy,namely,industrial enterprises above designated size,construction and real estate development and management enterprises above qualifications,wholesale and retail,catering and accommodation enterprises,and service enterprises above designated size,which are the primary part of national economic and social development activities.This paper is focused on analyzing the practice and difficulties in the current statistics work of four-classes enterprises,and then this paper proposes some recommendations.

Searching for the Nano-Hertz Stochastic Gravitational Wave Background with the Chinese Pulsar Timing Array Data ReleaseⅠ

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.

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map

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.

Detection of 3D Human Posture Based on Improved Mediapipe

Based on the continuous development of motion capture technology for ordinary video images, unmarked optical motion capture has become the fastest human posture recognition technology. Compared with other technical products, Google’s 3D human body recognition framework—Mediapipe is the most mature representative in this field. However, Mediapipe also has many defects in the detection of 3D human posture. In this paper, firstly, to solve the problem of inaccurate detection of human posture by Mediapipe, the accuracy of 2D human posture detection is improved through the speed threshold correction method for each joint;According to the problem that the monocular camera can not detect the depth Z value in the human posture data accurately, the Z value of the joint point is corrected for the human tilt angle through statistics;Then, according to the inaccurate recognition of Z value caused by large body posture, the accurate correction of Z value of human posture under different body posture is realized by normalizing the simulation proportion of each body limb;Finally, in order to solve the problem of jitter, lag problem and periodic noise in multiple frames caused by the speed change of human joints, one euro filtering and mean filtering of joint data are carried out. This paper verifies that the accuracy of 3D human posture detection based on the improved Mediapipe is more than 90% through the multi-pose recognition test for people of different heights, weights, ages and gender.

Numerical Analysis of the Erosion Mechanism inside the Tube Sockets of Main Steam Thermometers in a Coal-Fired Power Plant

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.

Map Reconstruction of Radio Observations with Conditional Invertible Neural Networks

In radio astronomy,the challenge of reconstructing a sky map from time ordered data is known as an inverse problem.Standard map-making techniques and gridding algorithms are commonly employed to address this problem,each offering its own benefits such as producing minimum-variance maps.However,these approaches also carry limitations such as computational inefficiency and numerical instability in map-making and the inability to remove beam effects in grid-based methods.To overcome these challenges,this study proposes a novel solution through the use of the conditional invertible neural network(cINN)for efficient sky map reconstruction.With the aid of forward modeling,where the simulated time-ordered data(TODs)are generated from a given sky model with a specific observation,the trained neural network can produce accurate reconstructed sky maps.Using the Five-hundred-meter Aperture Spherical radio Telescope as an example,cINN demonstrates remarkable performance in map reconstruction from simulated TODs,achieving a mean squared error of2.29±2.14×10^(-4)K^(2),a structural similarity index of 0.968±0.002,and a peak signal-to-noise ratio of26.13±5.22 at the 1σlevel.Furthermore,by sampling in the latent space of cINN,the reconstruction errors for each pixel can be accurately quantified.

Extraction and Analysis of Coronal High-temperature Components Based on Outlier Detection

The extraction of high-temperature regions in active regions(ARs)is an important means to help understand the mechanism of coronal heating.The important observational means of high-temperature radiation in ARs is the main emission line of Fe XVⅢin the 94?of the Atmospheric Imaging Assembly.However,the diagnostic algorithms for Fe XVⅢ,including the differential emission measure(DEM)and linear diagnostics proposed by Del based on the DEM,have been greatly limited for a long time,and the results obtained are different from the predictions.In this paper,we use the outlier detection method to establish the nonlinear correlation between 94?and 171,193,211?based on the former researches by others.A neural network based on 171,193,211?is constructed to replace the low-temperature emission lines in the ARs of 94?.The predicted results are regarded as the low-temperature components of 94?,and then the predicted results are subtracted from 94?to obtain the outlier component of 94?,or Fe XVⅢ.Then,the outlier components obtained by neural network are compared with the Fe XVⅢobtained by DEM and Del's method,and a high similarity is found,which proves the reliability of neural network to obtain the high-temperature components of ARs,but there are still many differences.In order to analyze the differences between the Fe XVⅢobtained by the three methods,we subtract the Fe XVⅢobtained by the DEM and Del's method from the Fe XVⅢobtained by the neural network to obtain the residual value,and compare it with the results of Fe XIV in the temperature range of 6.1-6.45 MK.It is found that there is a great similarity,which also shows that the Fe XVⅢobtained by DEM and Del's method still has a large low-temperature component dominated by Fe XIV,and the Fe XVⅢobtained by neural network is relatively pure.

Galaxy Interactions in Filaments and Sheets:Insights from EAGLE Simulations

We study the color and star formation rates of paired galaxies in filaments and sheets using the EAGLE simulations.We find that the major pairs with pair separation<50 kpc are bluer and more star-forming in filamentary environments compared to those hosted in sheet-like environments.This trend reverses beyond a pair separation of~50 kpc.The interacting pairs with larger separations(>50 kpc)in filaments are on average redder and low-star-forming compared to those embedded in sheets.The galaxies in filaments and sheets may have different stellar mass and cold gas mass distributions.Using a KS test,we find that for paired galaxies with pair separation<50 kpc,there are no significant differences in these properties in sheets and filaments.The filaments transport gas toward the cluster of galaxies.Some earlier studies find preferential alignment of galaxy pairs with the filament axis.Such alignment of galaxy pairs may lead to different gas accretion efficiency in galaxies residing in filaments and sheets.We propose that the enhancement of star formation rate at smaller pair separation in filaments is caused by the alignment of galaxy pairs.A recent study with SDSS data reports the same findings.The confirmation of these results by the EAGLE simulations suggests that the hydrodynamical simulations are powerful theoretical tools for studying galaxy formation and evolution in the cosmic web.

Solving Multi-Objective Linear Programming Problem by Statistical Averaging Method with the Help of Fuzzy Programming Method

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.

Fuzzy Synthetic Evaluation Model for Smart Washing Machine

Generally fuzzy control system (FCS) is worked in washing machine. For the fuzzy set theory, membership functions are the building blocks. In a fuzzy set, fuzziness is determined by its membership functions. The shapes of membership functions are important, because it has an effect on fuzzy inference system. The shapes of membership functions can be triangular, trapezoidal and gaussian. The most widely used triangular membership function is used in this paper, because it can capture the short time period. In washing machine, open loop control system is found. This paper applies a fuzzy synthetic evaluation method (FSEM) for washing cloth in washing machine as FSEM can handle the multiple criteria with the help of evaluation matrix generated from membership function and weight matrix generated by Analytical Hierarchy Process (AHP). The purpose of this research is to minimize the wash time. By applying FSEM, we get a wash time which is less than that wash time got from applying the Mamdani approach in FCS. An example is given for illustration. For more reduction of wash time, statistical averaging method is also used. To reduce the wash time, statistical averaging method can be used in Mamdani approach also.