Actual Correlation Coefficients Research Articles

Image Genetic Analysis and Application Research Based on QRFPR and Other Neural Network-Related SNP Loci.

The development of neuroimaging technology and molecular genetics has produced a large amount of imaging genetics data, which has greatly promoted the study of complex mental diseases. However, because the feature dimension of the data is too high, the correlation measure assumes that the data obey Gaussian distribution, and traditional algorithms often cannot explain these two types of data well. This article mainly studies image genetics analysis and its application based on neural network. In this paper, based on the theory and application technology of neural network, the tree structure is established by prior knowledge, that is, each SNP site is used as a leaf node of the tree, and the LD block and genome formed by the linkage imbalance of multiple SNP sites are used as intermediate nodes. Then, the hierarchical relationship of features was introduced. On this basis, a sparse learning method based on tree structure guidance is used to select features from multiple features of multiple SNPs locus regression candidate brain regions. Finally, the identification of SNPs in feature selection is used to predict quantitative traits of brain regions. The distribution of the typical vector values obtained by the algorithm in the experimental data is basically consistent with the distribution of the median of the actual data, and the correlation coefficient obtained is closest to the actual correlation coefficient in the data set. The average correlation coefficient of the algorithm reaches 82.3%, which is about 4.2% higher than the control algorithm. Experimental results show that this method can not only significantly improve the regression performance but also detect the risk gene SNPs loci with spatial clustering features and functional interpretation significance. It is practical and effective to use it in clinical trials.

Efficient Loads Surrogates for Waked Turbines in an Array

Accurately and efficiently predicting wind turbine structural loading is a crucial step in wind farm design. Without considering structural loading, wind farm optimization could negatively impact turbine fatigue and ultimate loads, especially for waked and partially waked turbines, which could result in higher maintenance costs and reduced turbine lifetime. However, predicting turbine loads throughout an array is a costly step, as these quantities require time-accurate results across long time histories, which is often intractable for large array optimization. Therefore, surrogate models that link array spacing to load outputs are often used, but the surrogates are then unique to the inflow conditions and array configurations in the training library. This work develops surrogate models for many wind turbine load outputs based solely on rotor plane velocity measurements, with no required input about array configuration or freestream inflow parameters. Surrogate models were constructed for many turbine quantities of interest (QoI), considering mean, standard deviation, ultimate, and fatigue loads. In general, most QoI statistics were accurately captured, as measured by predicted vs. actual correlation coefficient, confirming the suitability of the approach. Temporal mean values of the QoI required only temporal mean measurements of the rotor plane inflow velocity. However, accurate prediction of temporal standard deviation, ultimate, and fatigue values of QoI also required temporal standard deviations of the rotor plane velocity field. Poor surrogate performance was observed when too many correlated inputs were used, such as multiple velocity components. If the fewest inflow parameters are used to construct the surrogates, the average correlation coefficient value for all output QoI statistics is 0.89. Surrogates for standard deviations and damage equivalent loads (DELs) of turbine QoIs generally had lower accuracy and tower-base and shaft load channels posed the most difficult to capture accurately. The results suggest that these surrogates could be easily paired with analytic wake models, which are frequently used for pre-construction wind farm array optimization, to account for turbine loading in addition to power production. By including the optimal inflow conditions, the surrogate accuracy can improve to an average correlation coefficient value for all output QoI statistics of 0.92. This work has established the ability to build accurate surrogates for mean, standard deviation, ultimate load, and DEL turbine QoI values based on the rotor plane inflow velocity, and identified which inflow conditions lead to greater surrogate accuracy.

Correlation range query for effective recommendations

Efficient correlation computation has been an active research area of data mining. Given a large dataset and a specified query item, we are interested in finding items in the dataset that are within certain range of correlation with the query item. Such a problem, known as the correlation range query, has been a common task in many application domains. In this paper, we identify piecewise monotone properties of the upper and lower bounds of the ? coefficient, and propose an efficient correlation range query algorithm, called CORAQ. The CORAQ algorithm effectively prunes many items without computing their actual correlation coefficients with the query item. CORAQ also attains completeness and correctness of the query results. Experiments with large benchmark datasets show that this algorithm is much faster than its brute-force alternative and scales well with large datasets. As case studies, real-world datasets from recommendation applications are analyzed to demonstrate that CORAQ can help effectively identify interesting items to recommend to users.

Effect of correlated input parameters on the failure probability of pipelines with corrosion defects by using FITNET FFS procedure

The paper presents a probabilistic methodology considering the correlations between the input variables for the failure probability evaluation of corroding pipelines based on the corrosion module of the FITNET FFS procedure. A computer program based on this method is developed to calculate the failure probability of pipelines by considering different numbers of defects and different elapsed times. In case of one defect, the correlation between the initial defect depth and the initial defect length has the most significant impact on the failure probability of the pipeline. If the correlations between these two parameters for an individual defect are not considered, the prediction results are nonconservative when the failure probability is below 40% and conservative when it is above 40%. In case of multiple defects, the independent assumption of variables generally leads to a conservative estimate of the failure probability. The conservatism increases if the elapsed time and/or the actual correlation coefficients of the variables increase. The correlation of the operating pressure, the initial defect depth and material ultimate tensile strength at the location of different defects has a larger impact on the failure probability than the correlation of other parameters at different defects. The upper bound failure probability calculated by engineering method corresponds to that calculated using the presented method without considering the correlation between input parameters. This confirms the validity of the presented model.

A Globular Cluster Metallicity Scale Based on the Abundance of Feii

ABSTRACTAssuming that in the atmospheres of low‐mass, metal‐poor red giant stars, one‐dimensional models based on local thermodynamic equilibrium accurately predict the abundance of iron from Feii, we derive a globular cluster metallicity scale based on the equivalent widths of Feii lines measured from high‐resolution spectra of giants in 16 key clusters lying in the abundance range −2.4 < [Fe/H]II < −0.7. We base the scale largely on the analysis of spectra of 149 giant stars in 11 clusters by the Lick‐Texas group supplemented by high‐resolution studies of giants in five other clusters. We also derive ab initio the true distance moduli for certain key clusters (M5, M3, M13, M92, and M15) as a means of setting stellar surface gravities. Allowances are made for changes in the abundance scale if one employs (1) Kurucz models with and without convective overshooting to represent giant star atmospheres in place of MARCS models and (2) the Houdashelt et al. color‐temperature scale in place of the Alonso et al. scale.We find that [Fe/H]II is correlated linearly with W′, the reduced strength of the near‐infrared Caii triplet defined by Rutledge et al., although the actual correlation coefficients depend on the atmospheric model employed. The correlations, limited to the range −2.4 < [Fe/H]II < −0.7, are as follows:1. [Fe/H]II = 0.531W′ - 3.279 (MARCS),2. [Fe/H]II = 0.537W′ - 3.225 (Kurucz with convective overshooting),3. [Fe/H]II = 0.562W′ - 3.329 (Kurucz without convective overshooting).We also discuss how to estimate [X/Fe] ratios. We suggest that C, N, and O, as well as elements appearing in the spectrum in the singly ionized state, e.g., Ti, Sc, Ba, La, and Eu, should be normalized to the abundance of Feii. Other elements, which appear mostly in the neutral state, but for which the dominant species is nevertheless the ionized state, are probably best normalized to Fei, but uncertainties remain.

Eye on the Ionosphere: The Spatial Variability of Ionospheric Range Delay

The ionospheric range delay is highly variable both in space and in time. The ionospheric correlation distance is defined as the distance over which a measurement of the difference from monthly mean ionospheric range delay at one location can be used to determine the difference from mean conditions at a second location. The percentage improvement required is dependent upon the actual correlation coefficient, which must be relatively large to produce a significant improvement over differences from average conditions at two stations. Results of studies of the correlation distance show that, in order to obtain even a 28% improvement over monthly median conditions, the measurement location must be within approximately 3,000 km in longitude or 1,800 km in latitude from the location where the update from monthly mean conditions is required, at least in the mid-latitude region. Another study showed that, in order to be able to determine an improvement of 1 m in ionospheric range delay from average ionospheric conditions at a location remote from where the actual measurement of range delay is made, the measurement must be within approximately 500 km of that location. © 2000 John Wiley & Sons, Inc.

A generalised algorithm for polarity correlation estimation using binary patterns

A recently developed binary-pattern correlator uses the binary patterns of a group of successive samples of the polarity-sampled signal for correlation estimation. At high sampling rates (compared to the bandwidth of the signal), only a limited number of polarity changes may take place within a finite signal duration. Thus, a finite interval of a polarity-sampled signal may only have a limited set of binary patterns. The correlator estimates the frequency of occurrence of these patterns on-line and stores them in tables. The actual correlation coefficients are then deduced from these tables. Because the number of patterns is limited, efficient operation results. A general binary-pattern correlation algorithm is presented, with examples implemented on a Z-80 microprocessor. These examples represent different implementations of the algorithm. They differ in the number of signal samples effectively input each time, and in the size of the frequency of pattern-occurrence tables. As a result they have different sampling rates, and different off-line processing intervals. However, there is a trade-off between the achieved sampling rate and the amount of off-line processing.

Serum alpha 1 antichymotrypsin concentration as a marker of disease activity in rheumatoid arthritis.

Serum alpha 1 antichymotrypsin (alpha 1ACT), C reactive protein (CRP), orosomucoid, and erythrocyte sedimentation rate (ESR) were measured sequentially in 20 patients with rheumatoid arthritis (RA) treated with gold or penicillamine. Pain score, morning stiffness, grip strength, and articular index were measured and a Mallya score calculated. Based on a total of 148 sets of observations, significant correlations were found between alpha 1ACT and other variables (p less than 0.001 except morning stiffness at p less than 0.05). The actual correlation coefficients indicated a closer association with the other laboratory tests, CRP (0.62), orosomucoid (0.69), and ESR (0.61), than with clinical measurements: pain score (0.38), articular index (0.41), grip strength (-0.3), morning stiffness (0.19), and Mallya score (0.5). Sequential data on individual patients showed differing patterns of change in the variables indicating the importance of measuring more than one acute phase protein (APP), especially when CRP is inappropriately low. Serum alpha 1ACT concentration does reflect disease activity in RA. Its potential advantages are discussed.

ON THE SPATIAL CORRELATION OF MONTHLY PRECIPITATION AND THE ACCURACY OF HYDROLOGICAL MAP

When making a hydrological map, it is necessary to evaluate the accuracy of the obtained map and specify it on the map. The author has studied the spatial correlation of monthly precipitation and proposed a method of expressing the accuracy of monthly precipitation map by using spatial correlations. The calculation of spatial correlation is done among all the stations over the area. The accuracy is measured by the maximum value which is smaller than any actual correlation coefficients between all the neighboring observation stations. The data used in the analysis are the monthly precipitations observed in the northern and the middle parts of the Kanto District from January 1961 to December 1970. Iso-lines of spatial correlations of monthly precipitation are shown in Figs. 1_??_5. There are some distinctive features in the patterns of iso-lines. The iso-lines of 0.90 and 0.95 are not always circular but slender except the case of iso-line map for Tokyo. The area of the closed iso-lines of spatial correlations is large in the middle part of the Kanto Plain, and small in the area adjacent to the mountainous area. It is important to arrange the observation stations by the synthetic consideration of spatial correlations of the precipitation.

A study of the effects of truncation in bivariate normal distribution

AbstractThe moments of bivariate normal distribution, which is truncated with respect to both the random variables, are obtained by using the orthogonal expansion of the distribution and the properties of HERMITE polynomials. In particular the correlation coefficient of the truncated distribution is derived in terms of the actual correlation coefficient. In order to study the effect of truncation tables have been prepared of this correlation coefficient for certain given values of the actual correlation coefficient and for a few selected values of the points of truncation. A listing of the computer program for this purpose is also given.

Correlation between Solar Activity and Brightness of Jupiter's Great Red Spot

THE “pronounced correlation” claimed by Graf, Smith and McDevitt1 between Zurich sunspot numbers and the relative brightness of the Jovian red spot is not readily apparent in their Fig. 1. It is therefore worthwhile to calculate r, the actual correlation coefficient between the two functions plotted. Using values scaled from Fig. 1, the result is r=0.27. This value of r implies2 that only 7.5 per cent of the variation of the red spot can possibly be attributed to the solar activity responsible for the Zurich sunspot number. For the fifty values of the two functions compared, however, a correlation coefficient of 0.27 is not significant at the 5 per cent confidence level usually adopted in studies of this kind. In view of these considerations the relationship between the two phenomena will remain unestablished until better correlations can be obtained.