On eigenvalues of a high-dimensional Kendall's rank correlation matrix with dependence

In this paper,we investigate the limiting spectral distribution of a high-dimensional Kendall’s rank correlation matrix.The underlying population is allowed to have a general dependence structure.The result no longer follows the generalized Marcenko-Pastur law,which is brand new.It is the first result on rank correlation matrices with dependence.As applications,we study Kendall’s rank correlation matrix for multivariate normal distributions with a general covariance matrix.From these results,we further gain insights into Kendall’s rank correlation matrix and its connections with the sample covariance/correlation matrix.

Variable Selection of Generalized Regression Models Based on Maximum Rank Correlation

In this paper, we investigate the variable selection problem of the generalized regression models. To estimate the regression parameter, a procedure combining the rank correlation method and the adaptive lasso technique is developed, which is proved to have oracle properties. A modified IMO （iterative marginal optimization） algorithm which directly aims to maximize the penalized rank correlation function is proposed. The effects of the estimating procedure are illustrated by simulation studies.

Spatiotemporal variability of rain-on-snow events in the arid region of Northwest China

Rain-on-snow(ROS)events involve rainfall on snow surfaces,and the occurrence of ROS events can exacerbate water scarcity and ecosystem vulnerability in the arid region of Northwest China(ARNC).In this study,using daily snow depth data and daily meteorological data from 68 meteorological stations provided by the China Meteorological Administration National Meteorological Information Centre,we investigated the spatiotemporal variability of ROS events in the ARNC from 1978 to 2015 and examined the factors affecting these events and possible changes of future ROS events in the ARNC.The results showed that ROS events in the ARNC mainly occurred from October to May of the following year and were largely distributed in the Qilian Mountains,Tianshan Mountains,Ili River Valley,Tacheng Prefecture,and Altay Prefecture,with the Ili River Valley,Tacheng City,and Altay Mountains exhibiting the most occurrences.Based on the intensity of ROS events,the areas with the highest risk of flooding resulting from ROS events in the ARNC were the Tianshan Mountains,Ili River Valley,Tacheng City,and Altay Mountains.The number and intensity of ROS events in the ARNC largely increased from 1978 to 2015,mainly influenced by air temperature and the number of rainfall days.However,due to the snowpack abundance in areas experiencing frequent ROS events in the ARNC,snowpack changes exerted slight impact on ROS events,which is a temporary phenomenon.Furthermore,elevation imposed lesser impact on ROS events in the ARNC than other factors.In the ARNC,the start time of rainfall and the end time of snowpack gradually advanced from the spring of the current year to the winter of the previous year,while the end time of rainfall and the start time of snowpack gradually delayed from autumn to winter.This may lead to more ROS events in winter in the future.These results could provide a sound basis for managing water resources and mitigating related disasters caused by ROS events in the ARNC.

Ray tracing/correlation approach to estimation of surface-based duct parameters from radar clutter

This paper describes a technique to estimate surface-based duct parameters by using a simple ray tracing/correlation method. The approach is novel in that it incorporates the Spearman rank-order correlation scheme between the observed surface clutter and the surface ray density for a given propagation path. The simulation results and the real data results both demonstrate the ability of this method to estimate surface-based duct parameters. Compared with the results obtained by a modified genetic algorithm combined with the parabolic wave equation, the results retrieved from the ray tracing/correlation scheme show a minor reduction in accuracy but a great improvement on computation time. Therefore the ray tracing/correlation method might be used as a precursor to more sophisticated and slower techniques, such as genetic algorithm and particle filters, by narrowing the parameter search space and providing a comprehensive and more efficient estimation algorithm.

基于Logistic回归的古代玻璃制品化学成分分析和研究

文章主要通过研究玻璃含有的不同化学成分,分析和鉴别古代的玻璃制品。首先分析了现有玻璃文物的数据,利用Spearman’s rank correlation coefficient和卡方检验表面分化、纹饰、颜色和玻璃类型之间的联系,采用Logistic回归鉴别未知文物,分析其所属的类型,并分析了分类结果的灵敏度。通过分析可知,文章采用的分类方法,能较好地通过化学成分对玻璃进行分类。

Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.

Guided Wave Based Damage Detection Method for Aircraft Composite Structures under Varying Temperatures

Guided waves based damage detection methods using base signals offer the advantages of simplicity of signal generation and reception,sensitivity to damage,and large area coverage;however,applications of the technology are limited by the sensitivity to environmental temperature variations.In this paper,a Spearman Damage Index-based damage diagnosis method for structural health condition monitoring under varying temperatures is presented.First,a PZT sensor-based Guided wave propagation model is proposed and employed to analyze the temperature effect.The result of the analysis shows the wave speed of the Guided wave signal has higher temperature sensitivity than the signal fluctuation features.Then,a Spearman rank correlation coefficient-based damage index is presented to identify damage of the structure under varying temperatures.Finally,a damage detection test on a composite plate is conducted to verify the effectiveness of the Spearman Damage Index-based damage diagnosis method.Experimental results show that the proposed damage diagnosis method is capable of detecting the existence of the damage and identify its location under varying temperatures.

Characteristics of Ambient Air Quality in the Central Urban Area of Western Guizhou in the Past Five Years

Air is an important condition for human activities and survival,and its quality is closely related to the quality of life and level of health for the people.In recent years,the problem caused by air quality has become one of the main problems that endanger human health and restrict economic development,which has been widely concerned.In this paper,the air quality status and its changing trend were analyzed by using the methods of the comprehensive index of ambient air quality and Spearman s rank correlation coefficient,based on the hourly pollutant concentration data of five national ambient air quality monitoring stations in the central urban area of Liupanshui City,Guizhou Province from January 1,2015 to December 31,2019.The results showed that the concentration of air pollutants in the atmosphere in the past five years showed a downward trend in the central urban area of Liupanshui City.During 2018-2019,the air quality has been up to the standard for two consecutive years,and it was developing to a higher quality direction.The air quality was better in summer half year than in winter half year.In one year,the air quality was the best in June and the worst in February.The air quality was the best at 07:00 and the worst at 21:00 every day.The air quality in the east and the west of the city was better than that in the middle.In most years,the activities,making and burning paper to resemble money as an offering sacrifices to gods or ancestors in Zhongyuan Festival,caused serious pollution.

Spatio-temporal Distribution Characteristics and Environmental Impact Factors of Lung Cancer Mortality: A Case Study of Yuhui District in Bengbu City, China

Among cancers, lung cancer is the most common cause of death in China. For the prevention and control of lung cancer, it is necessary to investigate the spatial and temporal distribution of lung cancer mortality, as well as the changes in the trend and the affecting mechanism. Based on statistics and auto-correlation analysis, this paper studied the spatial and temporal distribution of lung cancer mortality in Yuhui District, Bengbu, Huaihe River Basin, from 2017 to 2020. In addition, Spearman’s Rank Correlation Assessment Model and Geographic Detector Model were used to examine the relationship between environmental factors and lung cancer mortality to identify impact factors and their mechanisms. The findings indicated that: 1) from the characteristics of temporal distribution, the number of lung cancer deaths exhibited a linear growth tendency, with the highest mortality in winter;2) from the characteristics of spatial distribution, lung cancer mortality showed a strong spatial agglomeration form, concentrating on two clustering areas, located in the old city and the central city of Bengbu, near the Huaihe River;3) from the point of view of the whole research area, there were 15 impact factors with significant correlation in the built and natural environment factors. The significant impacting factors in the built environment included land use, road traffic, spatial form and blue-green space, which could indirectly affect lung cancer mortality, while air pollution and temperature constituted the significant impacting factors in the natural environment;4) the influence of screened environmental factors on lung cancer mortality was different. Spatial stratified heterogeneity assessment, the interaction among environmental factors demonstrated statistical significance, it was found that the interaction between environmental factors in pairs had a significant enhancement effect on lung cancer mortality. To some extent, urban planning and policies could reduce lung cancer mortality.

Projecting impact of CPEC on Pakistan’s electric power crisis

The commencement of China–Pakistan Economic Corridor has led to the appreciation of Pakistan’s economic outlook from 5.4%to 5.8%by the World Bank.The upgraded outlook is a welcome sign but it is still trivial,essentially attributable to the electric power crisis,which approximately trims 2%of Pakistan’s economic growth annually.Almost 60%of the CPEC(China–Pakistan Economic Corridor)funds are directed at Pakistan’s energy sector,hence,demanding careful attention of both researchers and policy analysts alike.The study is based upon a meta-analytic review of literature concerning CPEC and Pakistan’s energy sector.The results of the study demonstrate that CPEC is an easing agent for Pakistan’s energy crisis(82.30%).The results also highlight points of concern,including inadequate planning(47%),dilapidated electricity distribution system causing losses(64.7%),and an unsustainable energy mix(64.7%).The study further validates the findings via Spearman’s Rho-Correlation.The rρvalue for the possible“resolution of Pakistan’s energy crisis”is 0.5426 achieving a significance level of 98%and a corresponding p-value of 0.0252.The significant negative rρvalue attained is−0.4894 which establishes the fact that lack of planning can hinder the energy crisis resolution.

Additive Main Effects and Multiplicative Interaction and Other Stability Analyses of Tef [<i>Eragrostis tef</i>(Zucc.)Trotter] Grain Yield

Tef [Eragrostis tef (Zucc.)Trotter]) is one of the most important cereal crops grown in Ethiopia. Tef production has been partly constrained by low yield and less stability of the genotypes under cultivation. Field experiments were carried out in Halaba, Loka Abaya, Bensa and Areka, South Ethiopia, from August to November, during 2016 and 2017 main cropping seasons, in order to estimate yield stability and the association between AMMI analysis and other stability parameters. Experiments were laid out in randomized complete block design with three replications using fourteen improved tef genotypes. Mean yield for Halaba, Loka Abaya2016, Loka Abaya2017, Bensa, Areka2016 and Areka2017 was 0.99, 0.45, 0.48, 1.50, 1.62 and 0.77 tons/ha, respectively. Genotypes Amarach, Boset, Simada, and Tseday exhibited high mean yield of 1.09, 1.10, 1.07 and 1.07 tons/ha, respectively. AMMI stability value (ASV) ranged from 0.17 (genotype Lakech) to 1.40 (Amarach);yield stability index (YSI) from 7 (Lakech) to 25 (Quncho);and superiority measure (Pi) from 0.015 (Boset) to 0.145 (Dega Tef). Rank correlation of yield with Pi (r = 0.97, p < 0.01), ASV with Wi and δi2 (r = 0.85, p < 0.01) and that between Wi and δi2 (r = 1.00, p < 0.01) was high. Rank correlation of YSI with yield (r = 0.57, p < 0.05), ASV (r = 0.75, p < 0.01), and Pi (r = 0.68, p < 0.01) and Wi and δi2 (r = 0.67, p < 0.01) was also positive. The present study showed that genotypes Etsub, Simada and Tseday would be recommended for high yield and wide adaptation, and ASV would be used alone or jointly with YSI, Wi and δi2 for ranking of genotypes.

Parameter Sensitivity and Qualitative Analysis of Dynamics of Ovarian Tumor Growth Model with Treatment Strategy

In this paper, we are interested to find the most sensitive parameter, local and global stability of ovarian tumor growth model. For sensitivity analysis, we use Latin Hypercube Sampling (LHS) method to generate sample points and Partial Rank Correlation Coefficient (PRCC) method, uses those sample points to find out which parameters are important for the model. Based on our findings, we suggest some treatment strategies. We investigate the sensitivity of the parameters for tumor volume, y, cell nutrient density, Q and maximum tumor size, ymax. We also use Scatter Plot method using LHS samples to show the consistency of the results obtained by using PRCC. Moreover, we discuss the qualitative analysis of ovarian tumor growth model investigating the local and global stability.

An Automatic Monitoring and Alarming Method for the Power Supply of Weather Radars

As weather radar stations require headroom environment to operate,they were mostly built on highlands which are usually unattended.The mains supply is relatively poor,and the risk of radar stoppages due to power outage is therefore ever-present.As such,the radar construction program is used to build a complementary security video monitoring system.By collecting monitoring images of the regulated power supply in real-time from power supply auto transfer systems in distribution rooms and radar transceiver rooms,using Spearman’s rank correlation coefficient to analyse pixel variation trends,and supplementing statistical analysis of pixel characteristics difference to eliminate misjudgments resulting from low image contrast in special scenarios,a software can be developed through C#.It has the function of automatically monitoring mains supply and alerting staff on duty to handle the power outage in a timely manner via text message so that any potential risk is neutralised before it can cause damage.This monitoring and auto-alerting approach is generally applicable to unattended rooms with large amounts of electronical equipment.

Decomposition dynamic of higher plant pigments by HPLC analysis

The fate of the litter of dominant vegetation(willows and reeds) is one of the aspects studied in the frame of the project “Onderzoek Milieu Effecten Sigmaplan”. One of the questions to be considered is how long the litter stays within the estuary. In this paper, the time the leaf litter(Salix triandra and Phragmites australis) stayed in the Schelde estuary was studied by using plant pigment as biomarkers with HPLC application. After analyzing the original data from the incubation experiment described by Dubuison and Geers(1999), the decomposition dynamics patterns of pigments were analyzed and described, and these decomposition dynamics patterns were used as calibration patterns. By using Spearman Rank Order Correlation, the calibration patterns of the pigments which were significant(p<0.05) were grouped. In this way, several groups of the calibration patterns of pigment decomposition were achieved. The presence or absence of these groups of pigments (whether they can be detected or not from HPLC) was shown to be useful in determining the time the litter has stayed in the water. Combining data of DW and POC, more precise timing can be obtained.

Local Dependence Test Between Random Vectors Based on the Robust Conditional Spearman's ρ and Kendall's τ

This paper introduces two local conditional dependence matrices based on Spearman'sρand Kendall'sτgiven the condition that the underlying random variables belong to the intervals determined by their quantiles.The robustness is studied by means of the influence functions of conditional Spearman'sρand Kendall'sτ.Using the two matrices,we construct the corresponding test statistics of local conditional dependence and derive their limit behavior including consistency,null and alternative asymptotic distributions.Simulation studies illustrate a superior power performance of the proposed Kendall-based test.Real data analysis with proposed methods provides a precise description and explanation of some financial phenomena in terms of mathematical statistics.

Data quality evaluation and calibration of on-road remote sensing systems based on exhaust plumes

In recent years,with rapid increases in the number of vehicles in China,the contribution of vehicle exhaust emissions to air pollution has become increasingly prominent.To achieve the precise control of emissions,on-road remote sensing(RS)technology has been developed and applied for law enforcement and supervision.However,data quality is still an existing issue affecting the development and application of RS.In this study,the RS data from a cross-road RS system used at a single site(from 2012 to 2015)were collected,the data screening process was reviewed,the issues with data quality were summarized,a new method of data screening and calibration was proposed,and the effectiveness of the improved data quality control methods was finally evaluated.The results showed that this method reduces the skewness and kurtosis of the data distribution by up to nearly 67%,which restores the actual characteristics of exhaust diffusion and is conducive to the identification of actual clean and high-emission vehicles.The annual variability of emission factors of nitric oxide decreases by 60%-on average-eliminating the annual drift of fleet emissions and improving data reliability.

Wind farm polymerization influences on security and economic operation in power system based on Copula function

Wind speed dependences on different areas in a wind farm have influences on security and economic operation in power system.In order to simulate the correlation of wind speed series between different positions,this paper applies Copula function and rank correlation matrix methods to measure the coherence of wind speed in a wind farm.The correlated wind sample space is established.According to active power output characteristics of wind turbines,the polymerization model in a wind farm can be achieved.Monte Carlo optimal power flow is applied to IEEE-30 and IEEE-300 bus systems based on the principle of energy saving dispatching.The study shows that the accuracy of outputs is improved,thus reducing the fluctuation ranges in unit generating costs and power flow in branches while considering wind speed polymerization.This approach provides a new method to improve the effectiveness of energy saving dispatching and system operation arrangement.Results have been tested to be effective.

Comprehensive evaluation method for tidal current power generation device

There are many types of tidal current power generation devices,and it is necessary to make comprehensive evaluation of tidal current power generation devices in order to provide valuable reference for the improvement of their performance indexes.On the basis of the analysis of the tidal current power generation device performance indexes,the hierarchical model for comprehensive evaluation of device performance is given in this paper.By normalizing the membership matrix elements based on fuzzy comprehensive evaluation model,all the values of the matrix elements are restrained in the range of0.0 to 1.0,hence the complexity of the calculations is reduced.Vector similarity is used to determine the expert weights which reflect the knowledge and experience of the experts.This paper presents an improved method for rank correlation analysis,and calculates the comprehensive weight value and the final evaluation results of tidal current power generation devices.The presented method improves the credibility of the evaluation.In the end,measured data of two units of tidal current power generation devices are evaluated in the paper,and the effectiveness of the presented method is verified.

Variability in Regional Ecological Vulnerability: A Case Study of Sichuan Province, China

Rapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide.Using land use,socioeconomic,and natural hazards data,we conducted an assessment of the ecological vulnerability of prefectures in Sichuan Province for the years 2005,2010,and 2015 to capture variations in its capacity to modulate in response to disturbances and to explore potential factors driving these variations.We selected five landscape metrics and two topological indicators for the proposed ecological vulnerability index(EVI),and constructed the EVI using a principal component analysis-based entropy method.A series of correlation analyses were subsequently performed to identify the factors driving variations in ecological vulnerability.The results show that:(1)for each of the study years,prefectures with high ecological vulnerability were located mainly in southern and eastern Sichuan,whereas prefectures in central and western Sichuan were of relatively low ecological vulnerability;(2)Sichuan’s ecological vulnerability increased significantly(p=0.011)during2005–2010;(3)anthropogenic activities were the main factors driving variations in ecological vulnerability.These findings provide a scientific basis for implementing ecological protection and restoration in Sichuan as well as guidelines for achieving integrated disaster risk reduction.

Global sensitivity analysis of a single-cell HBV model for viral dynamics in the liver

The predictive accuracy of mathematical models representing anything ranging from the meteorological to the biological system profoundly depends on the quality of model parameters derived from experimental data.Hence,robust sensitivity analysis(SA)of these critical model parameters aids in sifting the influential from the negligible out of typically vast parameter regimes,thus illuminating key components of the system under study.We here move beyond traditional local sensitivity analysis to the adoption of global SA techniques.Partial rank correlation coefficient(PRCC)based on Latin hypercube sampling is compared with the variance-based Sobol method.We selected for this SA investigation an infection model for the hepatitis-B virus(HBV)that describes infection dynamics and clearance of HBV in the liver[Murray&Goyal,2015].The model tracks viral particles such as the tenacious and nearly ineradicable covalently closed circular DNA(cccDNA)embedded in infected nuclei and an HBV protein known as p36.Our application of these SA methods to the HBV model illuminates,especially over time,the quantitative relationships between cccDNA synthesis rate and p36 synthesis and export.Our results reinforce previous observations that the viral protein,p36,is by far the most influential factor for cccDNA replication.Moreover,both methods are capable of finding crucial parameters of the model.Though the Sobol method is independent of model structure(e.g.,linearity and monotonicity)and well suited for SA,our results ensure that LHS-PRCC suffices for SA of a non-linear model if it is monotonic.