Confidence Interval Estimation of the Correlation in the Presence of Non-Detects

This article deals with correlating two variables that have values that fall below the known limit of detection (LOD) of the measuring device;these values are known as non-detects (NDs). We use simulation to compare several methods for estimating the association between two such variables. The most commonly used method, simple substitution, consists of replacing each ND with some representative value such as LOD/2. Spearman’s correlation, in which all NDs are assumed to be tied at some value just smaller than the LOD, is also used. We evaluate each method under several scenarios, including small to moderate sample size, moderate to large censoring proportions, extreme imbalance in censoring proportions, and non-bivariate normal (BVN) data. In this article, we focus on the coverage probability of 95% confidence intervals obtained using each method. Confidence intervals using a maximum likelihood approach based on the assumption of BVN data have acceptable performance under most scenarios, even with non-BVN data. Intervals based on Spearman’s coefficient also perform well under many conditions. The methods are illustrated using real data taken from the biomarker literature.

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。

Change Trend of Chinese Urban Residents' Food-nitrogen Consumption

Change trend of Chinese urban residents＇ per capita food-nitrogen annual consumption from 1981 to 2007 was analyzed and predicted by using ARIMA time-series model in order to reveal the change of urban food-nitrogen consumption during the China＇s urbanization process.Results showed that after 1980s,the annual consumption of Chinese urban residents＇ food-nitrogen had a change trend of ＂ increase-decrease-increase＂ and generally presented as a slight increasing trend;With the acceleration of rapid economic development and urbanization process,Chinese urban residents＇ food-nitrogen consumption will still keep a rising trend in future,and also has a large rising space.

Covid-19’s Pandemic Relationship to Saudi Arabia’s Weather Using Statistical Analysis and GIS

The eruption of the novel Covid-19 has changed the socio-economic conditions of the world.The escalating number of infections and deaths seriously threatened human health when it became a pandemic from an epidemic.It developed into an alarming situation when the World Health Organization(WHO)declared a health emergency in MARCH 2020.The geographic settings and weather conditions are systematically linked to the spread of the epidemic.The concentration of population and weather attributes remains vital to study a pandemic such as Covid-19.The current work aims to explore the relationship of the population,weather conditions(humidity and temperature)with the reported novel Covid-19 cases in the Kingdom of Saudi Arabia(KSA).For the study,the data for the reported Covid-19 cases was secured from 11 March 2020,to 21 July 2020(132 days)from the 13 provinces of KSA.The Governorate level data was used to estimate the population data.A Geographic information system(GIS)analysis was utilised to visualise the relationship.The results suggested that a significant correlation existed between the population and Covid-19 cases.For the weather conditions,the data for the 13 provinces of KSA for the same period was utilised to estimate the relationship between the weather conditions and Covid-19 cases.Spearman’s rank correlation results confirmed that the humidity was significantly linked with the reported cases of Covid-19 in Makkah,Aseer,Najran,and Al Baha provinces.The temperature had a significant relation with the reported Covid-19 cases in Al-Riyad,Makkah,Al-Madinah,Aseer,Najran,and Al-Baha.The inconsistency of the results highlighted the variant behavior of Covid-19 in different regions of the KSA.More exploration is required beyond the weather-related variables.Suggestions for future research and policy direction are offered at the end of the study.

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.

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.

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.

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.

Temporary Crops in the Brazilian Agricultural Frontier

Food production remains one of the main challenges for humankind in this century, and Brazil is one of the largest food-producing countries that have yet some land for economically or technically profitable farming expansion. Moreover, knowing which areas constitute the Brazilian agricultural frontier is crucial for improving public policies and logistics infrastructure decisions. Data from the Brazilian Institute of Geography and Statistics from 1995 to 2019 were used in this study. We aimed to map and measure the expansion of agricultural areas in Brazil from 1995 to 2019 for temporary crops according to their mesoregions. We used a four-stage methodology, compared the results of two agglomerative clustering methods, and identified similar mesoregions based on their share trends in the Brazilian agricultural seeded area. Some mesoregions had higher positive trend values for their share of the Brazilian agricultural seeded area: Mato-grossense North (MT), Mato-grossense Northeast (MT), Mato Grosso do Sul Southwest (MS), Goiano South (GO), Extreme West Bahia (BA), Maranhense South (MA), Piauiense Southwest (PI), and Tocantins Eastern (TO). As a second leading group, the Paranaíba Upstream (MG), S&#227;o José do Rio Preto (SP), Mato-grossense Southeast (MT), and Goiano East (GO), must be emphasized. Further research is recommended, including extending the study to permanent crops and applying top-down analysis targeting microregions or municipalities in the identified mesoregions.

An index system for evaluation of agriculture green development at county level in China

Agriculture green development(AGD)has become an unavoidable choice to address the unique national circumstances of China.This study established a county-level AGD evaluation index system,comprised three dimensions,food production,ecological environment and socioeconomic development,using 20 indicators.The assessment delved into historical trend and current situation,utilizing Spearman rank correlation analysis to analyze trade-off and synergy relationships,using Quzhou County,Hebei Province as a case study.The main findings were in four areas.Firstly,the index for AGD in Quzhou County increased by 58.9%from 1978 to 2019.The major contribution were the social economy(65.8%)and food production(53.5%),whereas the ecological environment was found to have had a negative impact.Secondly,in 2019,the AGD index was only 56.4,indicating substantial potential for improvement relative to the target value.A notable difference in scores existed between the three dimensions,with the order being ecological environment(66.3)>food production(61.7)>socioeconomic(41.3).Also,90%of the indicators did not reach the target value.Thirdly,relationship analysis of the indicators revealed that the synergistic effect exceeded the trade-off effect.Specifically,46.3%of the indicators had no significant relationship,35.3%had a synergistic relationship,and 18.4%had a trade-off relationship.Finally,interdimensional indicator relationships exhibited a trade-off effect between the ecological environment and both food production and socioeconomic dimensions.However,a positive trend of synergy between production and ecology has emerged since 2015.In conclusion,the quantitative evaluation index system exposed the unbalanced development and significant potential relative to the target value of AGD in Quzhou County,despite notable progress.

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.

Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions

The incorporation of weather variables is crucial in developing an effective demand forecasting model because electricity demand is strongly influenced by weather conditions.The dependence of demand on weather conditions may change with time during a day.Therefore,the time stamped weather information is essential.In this paper,a multi-layer moving window approach is proposed to incorporate the significant weather variables,which are selected using Pearson and Spearman correlation techniques.The multi-layer moving window approach allows the layers to adjust their size to accommodate the weather variables based on their significance,which creates more flexibility and adaptability thereby improving the overall performance of the proposed approach.Furthermore,a recursive model is developed to forecast the demand in multi-step ahead.An electricity demand data for the state of New South Wales,Australia are acquired from the Australian Energy Market Operator and the associated results are reported in the paper.The results show that the proposed approach with dynamic incorporation of weather variables is promising for day-ahead and week-ahead load demand forecasting.