Multivariate statistical techniques,cluster analysis,non-parametric tests,and factor analysis were applied to analyze a water quality dataset including 13 parameters at 37 sites of the Three Gorges area,China,from 200...Multivariate statistical techniques,cluster analysis,non-parametric tests,and factor analysis were applied to analyze a water quality dataset including 13 parameters at 37 sites of the Three Gorges area,China,from 2003–2008 to investigate spatio-temporal variations and identify potential pollution sources.Using cluster analysis,the twelve months of the year were classified into three periods of lowflow （LF）,normal-flow （NF）,and high-flow （HF）;and the 37 monitoring sites were divided into low pollution （LP）,moderate pollution （MP）,and high pollution （HP）.Dissolved oxygen （DO）,potassium permanganate index （COD Mn ）,and ammonia-nitrogen （NH 4 ＋-N） were identified as significant variables affecting temporal and spatial variations by non-parametric tests.Factor analysis identified that the major pollutants in the HP region were organic matters and nutrients during NF,heavy metals during LF,and petroleum during HF.In the MP region,the identified pollutants primarily included organic matter and heavy metals year-around,while in the LP region,organic pollution was significant during both NF and HF,and nutrient and heavy metal levels were high during both LF and HF.The main sources of pollution came from domestic wastewater and agricultural activities and runoff;however,they contributed differently to each region in regards to pollution levels.For the HP region,inputs from wastewater treatment plants were significant;but for MP and LP regions,water pollution was more likely from the combined effects of agriculture,domestic wastewater,and chemical industry.These results provide fundamental information for developing better water pollution control strategies for the Three Gorges area.展开更多
For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control mac...For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.展开更多
Surface water has become one of the most vulnerable resources on the earth due to deterioration of its quality from diverse sources of pollution. Understanding of the spatiotemporal distribution of pollutants and iden...Surface water has become one of the most vulnerable resources on the earth due to deterioration of its quality from diverse sources of pollution. Understanding of the spatiotemporal distribution of pollutants and identification of the sources in the river systems is a prerequisite for the protection and sustainable utilization of the water resources. Multivariate statistical techniques such as Principal Component Analysis (PCA) and Factor Analysis (FA) were applied in this study to investigate the temporal and spatial variations of water quality and appoint the major factors of pollution in the Shailmari River system. Water quality data for 14 physicochemical parameters from 11 monitoring sites over the year of 2014 in three sampling seasons were collected and analyzed for this study. Kruskal-Wallis test showed significant (p < 0.01) temporal and spatial variations in all of the water quality parameters of the river water. Principal component analysis (PCA) allowed extracting the contributing parameters affecting the seasonal water quality in the river system. Scatter plots of the PCs showed the tidal and spatial variation within river system and identified parameters controlling the behavior in each case. Factor analysis (FA) further reduced the data and extracted factors which are significantly responsible for water quality variation in the river. The results indicate that the parameters controlling the water quality in different seasons are related with salinity, anthropogenic pollution (sewage disposal, effluents) and agricultural runoff in pre-monsoon;precipitation induced surface runoff in monsoon;and erosion, oxidation or organic pollution (point and non-point sources) in post-monsoon. Therefore, the study reveals the applicability and usefulness of the multivariate statistical methods in assessing water quality of river by identifying the potential environmental factors controlling the water quality in different seasons which might help to better understand, monitor and manage the quality of the water resources.展开更多
This paper studied the expert system of genotype discrimination for the STR locus D5S818 based on near-infrared spectroscopy-principal discriminant variate (PDV).Six genotypes,i.e.genotypes 10-10,10-11,11-11,11-12,11-...This paper studied the expert system of genotype discrimination for the STR locus D5S818 based on near-infrared spectroscopy-principal discriminant variate (PDV).Six genotypes,i.e.genotypes 10-10,10-11,11-11,11-12,11-13 and 13-13,were selected as research subjects.Based on the optimum polymerase chain reaction (PCR) conditions,about 54 measuring samples for each genotype were obtained;these samples were tested by near-infrared spectroscopy directly.With differences between homozygote genotypes and heterozygote ones,and differences of the total number of core repeat units between the six genotypes,two types of genotyping-tree structure were constructed and their respective PDV models were studied using the near-infrared spectra of the samples as recognition variables.Finally,based on the classification ability of these two genotyping-tree structures,an optimum expert system of genotype discrimination was built using the PDV models.The result demonstrated that the built expert system had good discriminability and robustness;without any preprocessing for PCR products,the six genotypes studied could be discriminated rapidly and correctly.It provided a methodological support for establishing an expert system of genotype discrimination for all genotypes of locus D5S818 and other STR loci.展开更多
This paper discusses an approximate algorithm method which can be used to generate arbitrary non-uniform continuous variates. Percentile calculations of arbitrary continuous distributions are given.In addition, the id...This paper discusses an approximate algorithm method which can be used to generate arbitrary non-uniform continuous variates. Percentile calculations of arbitrary continuous distributions are given.In addition, the idea Of the algorithm is applied to probability computing.展开更多
Following Nadarajah [1], we introduce a new bivariate correlated type Gamma distribution, whose joint density is expressed in two parts. Expressions for single and joint moments of the variates are derived. Bivariate ...Following Nadarajah [1], we introduce a new bivariate correlated type Gamma distribution, whose joint density is expressed in two parts. Expressions for single and joint moments of the variates are derived. Bivariate Correlated Wishart density follows on similar lines.展开更多
Dynamic variation of water quality in Meiliang Bay and part of West Taihu Lake has been analysed based on data from 1991 to 1992. Principal Component Analysis is used to reveal the mutual relationships of various fact...Dynamic variation of water quality in Meiliang Bay and part of West Taihu Lake has been analysed based on data from 1991 to 1992. Principal Component Analysis is used to reveal the mutual relationships of various factors. It is shown that there existis an obvious spatial and temporal variation in the main factors of water quality. Annual values of TP, CON, TN, Chl-a and conductivity decrease evidently from inner Meiliang Bay to the outer from north to south. TP and TN fluctuate seasonally with much higher value in winter. This is particularly true for the mouth of Liangxi River. In addition, the Chl-1 has a synchronous variation with water temperature, although being lagged a little, and closely relates to TP and TN. Finally, the results from Principal Component Analysis show that TP, TN, SS (or SD), water temperature and Chl-a are the most influential factors to water qualuty in this area, and both suspensions and algae can contribute to transparency to Taihu Lake.展开更多
The water quality of lakes can be degraded by excessive riverine nutrients.Riverine water quality generally varies depending on region and season because of the spatiotemporal variations in natural factors and anthrop...The water quality of lakes can be degraded by excessive riverine nutrients.Riverine water quality generally varies depending on region and season because of the spatiotemporal variations in natural factors and anthropogenic activities.Monthly water quality measurements of eight water quality variables were analyzed for two years at 16 sites of the Tianmuhu watershed.The variables were examined using hierarchical cluster analysis(HCA) and factor analysis/principal component analysis(FA/PCA) to reveal the spatiotemporal variations in riverine nutrients and to identify their potential sources.HCA revealed three geographical groups and three periods.Two drainages comprising towns and large villages were the most polluted, six drainages comprising widely distributed tea plantations and orchards were moderately polluted, and eight drainages without the factors were the least polluted.The river was most polluted in June when the first heavy rain(daily rainfall > 50 mm) occurs after fertilization and the number of rainy days is most(monthly number of rainy days > 20 days).Moderate pollution was observed from October to May, during which morethan 60% of the total nitrogen fertilizer and all of the phosphorus fertilizer are applied to the cropland, the total manure is applied to tea plantations and orchards, and a monthly rainfall ranging from 0 mm to 164 mm occurs.The remaining months were characterized by frequent raining(i.e., number of rainy days per month ranged from 5 to 24) and little use of fertilizers, and were thus least polluted.FA/PCA identified that the greatest pollution sources were the runoff from tea plantations and orchards,domestic pollution and the surface runoff from towns and villages, and rural sewage, which had extremely high contributions of riverine nitrogen, phosphorus,and chemical oxygen demand, respectively.The tea plantations and orchards promoted by the agricultural comprehensive development(ACD) were not environmentally friendly.Riverine nitrogen is a major water pollution parameter in hilly watersheds affected by ACD, and this parameter would not be reduced unless its loss load through the runoff from tea plantations and orchards is effectively controlled.展开更多
Barnyard millet(Echinochloa spp.) is one of the most underresearched crops with respect to characterization of genetic resources and genetic enhancement. A total of 95 germplasm lines representing global collection we...Barnyard millet(Echinochloa spp.) is one of the most underresearched crops with respect to characterization of genetic resources and genetic enhancement. A total of 95 germplasm lines representing global collection were evaluated in two rainy seasons at Almora,Uttarakhand, India for qualitative and quantitative traits and the data were subjected to multivariate analysis. High variation was observed for days to maturity, five-ear grain weight, and yield components. The first three principal component axes explained 73% of the total multivariate variation. Three major groups were detected by projection of the accessions on the first two principal components. The separation of accessions was based mainly on trait morphology. Almost all Indian and origin-unknown accessions grouped together to form an Echinochloa frumentacea group. Japanese accessions grouped together except for a few outliers to form an Echinochloa esculenta group. The third group contained accessions from Russia, Japan, Cameroon, and Egypt. They formed a separate group on the scatterplot and represented accessions with lower values for all traits except basal tiller number. The interrelationships between the traits indicated that accessions with tall plants, long and broad leaves, longer inflorescences, and greater numbers of racemes should be given priority as donors or parents in varietal development initiatives. Cluster analysis identified two main clusters based on agro-morphological characters.展开更多
In order to analyze the characteristics of surface water resource quality for the reconstruction of old water treatment plant,multivariate statistical techniques such as cluster analysis and factor analysis were appli...In order to analyze the characteristics of surface water resource quality for the reconstruction of old water treatment plant,multivariate statistical techniques such as cluster analysis and factor analysis were applied to the data of Yuqiao Reservoir-surface water resource of the Luan River,China.The results of cluster analysis demonstrate that the months of one year were divided into 3 groups and the characteristic of clusters was agreed with the seasonal characteristics in North China.Three factors were derived from the complicated set using factor analysis.Factor 1 included turbidity and chlorophyll,which seemed to be related to the anthropogenic activities;factor 2 included alkaline and hardness,which were related to the natural characteristic of surface water;and factor 3 included Cl and NO2-N affected by mineral and agricultural activities.The sinusoidal shape of the score plots of the three factors shows that the temporal variations caused by natural and human factors are linked to seasonality.展开更多
Due to the pulse interference, measurement outliers and artificial modeling errors, the multivariate skew t noise widely exists in the real environment. However, to date, little attention has been paid to the state es...Due to the pulse interference, measurement outliers and artificial modeling errors, the multivariate skew t noise widely exists in the real environment. However, to date, little attention has been paid to the state estimation for systems in which the process noise and the measurement noise are both modeled as the heavy-tailed and skew non-Gaussian noise. In this paper, the multivariate skew t distribution is utilized to model the heavy-tailed and skew non-Gaussian noise. Then a probabilistic graphical form of the multivariate skew t distribution is given and proved. Based on the probabilistic graphical form, a hierarchical Gaussian state space model for stochastic uncertain systems is proposed, which transforms the estimation problem for systems with the heavy-tailed and skew non-Gaussian noises into the one with a hierarchical Gaussian state space model. Next, given the designed Gaussian state space model, the robust Bayesian filter and smoother based on the variational Bayesian inference are proposed to approximately estimate the system state and the unknown noise parameters. Furthermore, the complexity analysis together with the controllability and observability for stochastic uncertain systems with multivariate skew t noises is given. Finally,the simulation results of the target tracking scenario verify the validity of the proposed algorithms.展开更多
Water quality of Litani River was deteriorated due to rapid population growth and industrial and agricultural activity. Multivariate analysis of spatio-temporal variation of water quality is useful to improve the proj...Water quality of Litani River was deteriorated due to rapid population growth and industrial and agricultural activity. Multivariate analysis of spatio-temporal variation of water quality is useful to improve the projects of water quality management and treatment of the river. In this work, analysis of samples from different locations at different seasons was investigated. The spatio-temporal variation of physico-chemical parameters of the water was determined. A total of 11 water quality parameters were monitored over 12 months during 2018 at 3 sites located in different areas of the river. Multivariate statistical techniques were used to study the spatio-temporal evolution of the studied parameters and the correlation between the different factors. Principal Component Analysis (PCA) was applied to the responsible factors for water quality variations during wet and dry periods. The multivariate analysis of variance (MANOVA) was also applied to the same factors and gives the best results for both spatial and temporal analysis. A black point of agricultural, industrial and sewage water pollution was identified in Jeb-Jennine station from the high concentrations of ammonia, sulfate and phosphate. This difference was proved by the major changes in the values of the parameters from one station to the other. Jeb-Jennine represents a main pollution area in the river. The high ammonia, sulfate and phosphate concentrations result from the important agricultural, industrial and sewage water pollution in the area. A high bacterial activity was highlighted in Jeb-Jennine and Quaroun stations because of the presence of the high nitrite concentrations in the two locations. All parameters are highly affected by climate factors, especially temperature and precipitation. TDS, salinity, electrical conductivity and the concentrations of all pollutants increase during wet season affected by the runoff. Other factors can affect the water quality of the river for example geographical features of the region and seasonal human activity like tourism. The correlation between different parameters was evaluated using PCA statistical method. This correlation is not stable, and evolves between wet and dry season.展开更多
This paper presents an efficient class of estimators for estimating the population mean of the variate under study in two-phase sampling using information on several auxiliary variates.The expressions for bias and mea...This paper presents an efficient class of estimators for estimating the population mean of the variate under study in two-phase sampling using information on several auxiliary variates.The expressions for bias and mean square error(MSE)of the proposed class have been obtained using Taylor series method.In addition,the minimum attainableMSE of the proposed class is obtained to the first order of approximation.The proposed class encompasses a wide range of estimators of the sampling literature.Efficiency comparison has been made for demonstrating the performance of the proposed class.An attempt has been made to find optimum sample sizes under a known fixed cost function.Numerical illustrations are given in support of theoretical findings.展开更多
This paper presents exponential-type ratio and product estimators for a finite population mean in double sampling using information on several auxiliary variates.The proposed estimators can be viewed as a generalizati...This paper presents exponential-type ratio and product estimators for a finite population mean in double sampling using information on several auxiliary variates.The proposed estimators can be viewed as a generalization over the estimators suggested by Singh and Vishwakarma(Austrian J Stat 36(3):217–225,2007).The expressions for biases and mean square errors(MSEs)of the proposed estimators have been derived to the first degree of approximation.In addition,the expressions for minimum attainable MSEs are also investigated using the criterion for optimality of the weights.An empirical study is carried out in the support of the present study.Both theoretical andempirical findings are encouraging and support thesoundness that the proposed procedures for mean estimation perform better than the usual unbiased estimators and other well-known estimators under some realistic conditions.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
In the context of multivariate regular variation,the authors establish the first-order asymptotics of the spectral risk measure of portfolio loss.Furthermore,by the notion of second-order regular variation,the second-...In the context of multivariate regular variation,the authors establish the first-order asymptotics of the spectral risk measure of portfolio loss.Furthermore,by the notion of second-order regular variation,the second-order asymptotics of the spectral risk measure of portfolio loss is also presented.In order to illustrate the derived results,a numerical example with Monte Carlo simulation is carried out.展开更多
Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LM...Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.展开更多
Phthalate esters(PAEs),recognized as endocrine disruptors,are released into the environment during usage,thereby exerting adverse ecological effects.This study investigates the occurrence,sources,and risk assessment o...Phthalate esters(PAEs),recognized as endocrine disruptors,are released into the environment during usage,thereby exerting adverse ecological effects.This study investigates the occurrence,sources,and risk assessment of PAEs in surface water obtained from 36 sampling points within the Yellow River and Yangtze River basins.The total concentration of PAEs in the Yellow River spans from124.5 to 836.5 ng/L,with Dimethyl phthalate(DMP)(75.4±102.7 ng/L)and Diisobutyl phthalate(DiBP)(263.4±103.1 ng/L)emerging as the predominant types.Concentrations exhibit a pattern of upstream(512.9±202.1 ng/L)>midstream(344.5±135.3 ng/L)>downstream(177.8±46.7 ng/L).In the Yangtze River,the total concentration ranges from 81.9 to 441.6 ng/L,with DMP(46.1±23.4 ng/L),Diethyl phthalate(DEP)(93.3±45.2 ng/L),and DiBP(174.2±67.6 ng/L)as the primary components.Concentration levels follow a midstream(324.8±107.3 ng/L)>upstream(200.8±51.8 ng/L)>downstream(165.8±71.6 ng/L)pattern.Attention should be directed towards the moderate ecological risks of DiBP in the upstream of HH,and both the upstream and midstream of CJ need consideration for the moderate ecological risks associated with Di-n-octyl phthalate(DNOP).Conversely,in other regions,the associated risk with PAEs is either low or negligible.The main source of PAEs in Yellow River is attributed to the release of construction land,while in the Yangtze River Basin,it stems from the accumulation of pollutants in lakes and forests discharged into the river.These findings are instrumental for pinpointing sources of PAEs pollution and formulating control strategies in the Yellow and Yangtze Rivers,providing valuable insights for global PAEs research in other major rivers.展开更多
Epigenetics-mediated breeding(epibreeding)involves engineering crop traits and stress responses through the targeted manipulation of key epigenetic features to enhance agricultural productivity.While conventional bree...Epigenetics-mediated breeding(epibreeding)involves engineering crop traits and stress responses through the targeted manipulation of key epigenetic features to enhance agricultural productivity.While conventional breeding methods raise concerns about reduced genetic diversity,epibreeding propels crop improvement through epigenetic variations that regulate gene expression,ultimately impacting crop yield.Epigenetic regulation in crops encompasses various modes,including histone modification,DNA modification,RNA modification,non-coding RNA,and chromatin remodeling.This review summarizes the epigenetic mechanisms underlying major agronomic traits in maize and identifies candidate epigenetic landmarks in the maize breeding process.We propose a valuable strategy for improving maize yield through epibreeding,combining CRISPR/Cas-based epigenome editing technology and Synthetic Epigenetics(SynEpi).Finally,we discuss the challenges and opportunities associated with maize trait improvement through epibreeding.展开更多
基金supported by the National Water Special Project (No.2009ZX07526-005)the Strategic Environmental Assessment Project (No.HP1080901)
文摘Multivariate statistical techniques,cluster analysis,non-parametric tests,and factor analysis were applied to analyze a water quality dataset including 13 parameters at 37 sites of the Three Gorges area,China,from 2003–2008 to investigate spatio-temporal variations and identify potential pollution sources.Using cluster analysis,the twelve months of the year were classified into three periods of lowflow （LF）,normal-flow （NF）,and high-flow （HF）;and the 37 monitoring sites were divided into low pollution （LP）,moderate pollution （MP）,and high pollution （HP）.Dissolved oxygen （DO）,potassium permanganate index （COD Mn ）,and ammonia-nitrogen （NH 4 ＋-N） were identified as significant variables affecting temporal and spatial variations by non-parametric tests.Factor analysis identified that the major pollutants in the HP region were organic matters and nutrients during NF,heavy metals during LF,and petroleum during HF.In the MP region,the identified pollutants primarily included organic matter and heavy metals year-around,while in the LP region,organic pollution was significant during both NF and HF,and nutrient and heavy metal levels were high during both LF and HF.The main sources of pollution came from domestic wastewater and agricultural activities and runoff;however,they contributed differently to each region in regards to pollution levels.For the HP region,inputs from wastewater treatment plants were significant;but for MP and LP regions,water pollution was more likely from the combined effects of agriculture,domestic wastewater,and chemical industry.These results provide fundamental information for developing better water pollution control strategies for the Three Gorges area.
基金National Natural Science Foundation of China (70931004)
文摘For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.
文摘Surface water has become one of the most vulnerable resources on the earth due to deterioration of its quality from diverse sources of pollution. Understanding of the spatiotemporal distribution of pollutants and identification of the sources in the river systems is a prerequisite for the protection and sustainable utilization of the water resources. Multivariate statistical techniques such as Principal Component Analysis (PCA) and Factor Analysis (FA) were applied in this study to investigate the temporal and spatial variations of water quality and appoint the major factors of pollution in the Shailmari River system. Water quality data for 14 physicochemical parameters from 11 monitoring sites over the year of 2014 in three sampling seasons were collected and analyzed for this study. Kruskal-Wallis test showed significant (p < 0.01) temporal and spatial variations in all of the water quality parameters of the river water. Principal component analysis (PCA) allowed extracting the contributing parameters affecting the seasonal water quality in the river system. Scatter plots of the PCs showed the tidal and spatial variation within river system and identified parameters controlling the behavior in each case. Factor analysis (FA) further reduced the data and extracted factors which are significantly responsible for water quality variation in the river. The results indicate that the parameters controlling the water quality in different seasons are related with salinity, anthropogenic pollution (sewage disposal, effluents) and agricultural runoff in pre-monsoon;precipitation induced surface runoff in monsoon;and erosion, oxidation or organic pollution (point and non-point sources) in post-monsoon. Therefore, the study reveals the applicability and usefulness of the multivariate statistical methods in assessing water quality of river by identifying the potential environmental factors controlling the water quality in different seasons which might help to better understand, monitor and manage the quality of the water resources.
基金supported by grants from the National Natural Science Foundation of China (Grant no. 81001686)
文摘This paper studied the expert system of genotype discrimination for the STR locus D5S818 based on near-infrared spectroscopy-principal discriminant variate (PDV).Six genotypes,i.e.genotypes 10-10,10-11,11-11,11-12,11-13 and 13-13,were selected as research subjects.Based on the optimum polymerase chain reaction (PCR) conditions,about 54 measuring samples for each genotype were obtained;these samples were tested by near-infrared spectroscopy directly.With differences between homozygote genotypes and heterozygote ones,and differences of the total number of core repeat units between the six genotypes,two types of genotyping-tree structure were constructed and their respective PDV models were studied using the near-infrared spectra of the samples as recognition variables.Finally,based on the classification ability of these two genotyping-tree structures,an optimum expert system of genotype discrimination was built using the PDV models.The result demonstrated that the built expert system had good discriminability and robustness;without any preprocessing for PCR products,the six genotypes studied could be discriminated rapidly and correctly.It provided a methodological support for establishing an expert system of genotype discrimination for all genotypes of locus D5S818 and other STR loci.
文摘This paper discusses an approximate algorithm method which can be used to generate arbitrary non-uniform continuous variates. Percentile calculations of arbitrary continuous distributions are given.In addition, the idea Of the algorithm is applied to probability computing.
文摘Following Nadarajah [1], we introduce a new bivariate correlated type Gamma distribution, whose joint density is expressed in two parts. Expressions for single and joint moments of the variates are derived. Bivariate Correlated Wishart density follows on similar lines.
文摘Dynamic variation of water quality in Meiliang Bay and part of West Taihu Lake has been analysed based on data from 1991 to 1992. Principal Component Analysis is used to reveal the mutual relationships of various factors. It is shown that there existis an obvious spatial and temporal variation in the main factors of water quality. Annual values of TP, CON, TN, Chl-a and conductivity decrease evidently from inner Meiliang Bay to the outer from north to south. TP and TN fluctuate seasonally with much higher value in winter. This is particularly true for the mouth of Liangxi River. In addition, the Chl-1 has a synchronous variation with water temperature, although being lagged a little, and closely relates to TP and TN. Finally, the results from Principal Component Analysis show that TP, TN, SS (or SD), water temperature and Chl-a are the most influential factors to water qualuty in this area, and both suspensions and algae can contribute to transparency to Taihu Lake.
基金jointly sponsored by the National Natural Science Foundation of China(41030745,41271500)Key Project of Chinese Academy of Sciences(KZZDEW-10-4)+1 种基金Key"135"Project of Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences(NIGLAS2012135005)the Scientific Research Foundation of Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences(Y4SL011036)
文摘The water quality of lakes can be degraded by excessive riverine nutrients.Riverine water quality generally varies depending on region and season because of the spatiotemporal variations in natural factors and anthropogenic activities.Monthly water quality measurements of eight water quality variables were analyzed for two years at 16 sites of the Tianmuhu watershed.The variables were examined using hierarchical cluster analysis(HCA) and factor analysis/principal component analysis(FA/PCA) to reveal the spatiotemporal variations in riverine nutrients and to identify their potential sources.HCA revealed three geographical groups and three periods.Two drainages comprising towns and large villages were the most polluted, six drainages comprising widely distributed tea plantations and orchards were moderately polluted, and eight drainages without the factors were the least polluted.The river was most polluted in June when the first heavy rain(daily rainfall > 50 mm) occurs after fertilization and the number of rainy days is most(monthly number of rainy days > 20 days).Moderate pollution was observed from October to May, during which morethan 60% of the total nitrogen fertilizer and all of the phosphorus fertilizer are applied to the cropland, the total manure is applied to tea plantations and orchards, and a monthly rainfall ranging from 0 mm to 164 mm occurs.The remaining months were characterized by frequent raining(i.e., number of rainy days per month ranged from 5 to 24) and little use of fertilizers, and were thus least polluted.FA/PCA identified that the greatest pollution sources were the runoff from tea plantations and orchards,domestic pollution and the surface runoff from towns and villages, and rural sewage, which had extremely high contributions of riverine nitrogen, phosphorus,and chemical oxygen demand, respectively.The tea plantations and orchards promoted by the agricultural comprehensive development(ACD) were not environmentally friendly.Riverine nitrogen is a major water pollution parameter in hilly watersheds affected by ACD, and this parameter would not be reduced unless its loss load through the runoff from tea plantations and orchards is effectively controlled.
文摘Barnyard millet(Echinochloa spp.) is one of the most underresearched crops with respect to characterization of genetic resources and genetic enhancement. A total of 95 germplasm lines representing global collection were evaluated in two rainy seasons at Almora,Uttarakhand, India for qualitative and quantitative traits and the data were subjected to multivariate analysis. High variation was observed for days to maturity, five-ear grain weight, and yield components. The first three principal component axes explained 73% of the total multivariate variation. Three major groups were detected by projection of the accessions on the first two principal components. The separation of accessions was based mainly on trait morphology. Almost all Indian and origin-unknown accessions grouped together to form an Echinochloa frumentacea group. Japanese accessions grouped together except for a few outliers to form an Echinochloa esculenta group. The third group contained accessions from Russia, Japan, Cameroon, and Egypt. They formed a separate group on the scatterplot and represented accessions with lower values for all traits except basal tiller number. The interrelationships between the traits indicated that accessions with tall plants, long and broad leaves, longer inflorescences, and greater numbers of racemes should be given priority as donors or parents in varietal development initiatives. Cluster analysis identified two main clusters based on agro-morphological characters.
基金Project supported by the Hi-Tech Research and Development(863)Program of China(No.2006AA06Z311)Development Program for Outstanding Young Teachers in Harbin Institute of Technology(No.HITQNJS.2008.044),China
文摘In order to analyze the characteristics of surface water resource quality for the reconstruction of old water treatment plant,multivariate statistical techniques such as cluster analysis and factor analysis were applied to the data of Yuqiao Reservoir-surface water resource of the Luan River,China.The results of cluster analysis demonstrate that the months of one year were divided into 3 groups and the characteristic of clusters was agreed with the seasonal characteristics in North China.Three factors were derived from the complicated set using factor analysis.Factor 1 included turbidity and chlorophyll,which seemed to be related to the anthropogenic activities;factor 2 included alkaline and hardness,which were related to the natural characteristic of surface water;and factor 3 included Cl and NO2-N affected by mineral and agricultural activities.The sinusoidal shape of the score plots of the three factors shows that the temporal variations caused by natural and human factors are linked to seasonality.
基金supported by the National Natural Science Foundation of China (Nos.61603040 and 61433003)Yunnan Applied Basic Research Project of China (No.201701CF00037)Yunnan Provincial Science and Technology Department Key Research Program (Engineering), China (No.2018BA070)。
文摘Due to the pulse interference, measurement outliers and artificial modeling errors, the multivariate skew t noise widely exists in the real environment. However, to date, little attention has been paid to the state estimation for systems in which the process noise and the measurement noise are both modeled as the heavy-tailed and skew non-Gaussian noise. In this paper, the multivariate skew t distribution is utilized to model the heavy-tailed and skew non-Gaussian noise. Then a probabilistic graphical form of the multivariate skew t distribution is given and proved. Based on the probabilistic graphical form, a hierarchical Gaussian state space model for stochastic uncertain systems is proposed, which transforms the estimation problem for systems with the heavy-tailed and skew non-Gaussian noises into the one with a hierarchical Gaussian state space model. Next, given the designed Gaussian state space model, the robust Bayesian filter and smoother based on the variational Bayesian inference are proposed to approximately estimate the system state and the unknown noise parameters. Furthermore, the complexity analysis together with the controllability and observability for stochastic uncertain systems with multivariate skew t noises is given. Finally,the simulation results of the target tracking scenario verify the validity of the proposed algorithms.
文摘Water quality of Litani River was deteriorated due to rapid population growth and industrial and agricultural activity. Multivariate analysis of spatio-temporal variation of water quality is useful to improve the projects of water quality management and treatment of the river. In this work, analysis of samples from different locations at different seasons was investigated. The spatio-temporal variation of physico-chemical parameters of the water was determined. A total of 11 water quality parameters were monitored over 12 months during 2018 at 3 sites located in different areas of the river. Multivariate statistical techniques were used to study the spatio-temporal evolution of the studied parameters and the correlation between the different factors. Principal Component Analysis (PCA) was applied to the responsible factors for water quality variations during wet and dry periods. The multivariate analysis of variance (MANOVA) was also applied to the same factors and gives the best results for both spatial and temporal analysis. A black point of agricultural, industrial and sewage water pollution was identified in Jeb-Jennine station from the high concentrations of ammonia, sulfate and phosphate. This difference was proved by the major changes in the values of the parameters from one station to the other. Jeb-Jennine represents a main pollution area in the river. The high ammonia, sulfate and phosphate concentrations result from the important agricultural, industrial and sewage water pollution in the area. A high bacterial activity was highlighted in Jeb-Jennine and Quaroun stations because of the presence of the high nitrite concentrations in the two locations. All parameters are highly affected by climate factors, especially temperature and precipitation. TDS, salinity, electrical conductivity and the concentrations of all pollutants increase during wet season affected by the runoff. Other factors can affect the water quality of the river for example geographical features of the region and seasonal human activity like tourism. The correlation between different parameters was evaluated using PCA statistical method. This correlation is not stable, and evolves between wet and dry season.
文摘This paper presents an efficient class of estimators for estimating the population mean of the variate under study in two-phase sampling using information on several auxiliary variates.The expressions for bias and mean square error(MSE)of the proposed class have been obtained using Taylor series method.In addition,the minimum attainableMSE of the proposed class is obtained to the first order of approximation.The proposed class encompasses a wide range of estimators of the sampling literature.Efficiency comparison has been made for demonstrating the performance of the proposed class.An attempt has been made to find optimum sample sizes under a known fixed cost function.Numerical illustrations are given in support of theoretical findings.
文摘This paper presents exponential-type ratio and product estimators for a finite population mean in double sampling using information on several auxiliary variates.The proposed estimators can be viewed as a generalization over the estimators suggested by Singh and Vishwakarma(Austrian J Stat 36(3):217–225,2007).The expressions for biases and mean square errors(MSEs)of the proposed estimators have been derived to the first degree of approximation.In addition,the expressions for minimum attainable MSEs are also investigated using the criterion for optimality of the weights.An empirical study is carried out in the support of the present study.Both theoretical andempirical findings are encouraging and support thesoundness that the proposed procedures for mean estimation perform better than the usual unbiased estimators and other well-known estimators under some realistic conditions.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金supported by the Important Natural Science Foundation of Colleges and Universities of Anhui Province under Grant No.KJ2020A0122the Scientific Research Start-up Foundation of Hefei Normal University。
文摘In the context of multivariate regular variation,the authors establish the first-order asymptotics of the spectral risk measure of portfolio loss.Furthermore,by the notion of second-order regular variation,the second-order asymptotics of the spectral risk measure of portfolio loss is also presented.In order to illustrate the derived results,a numerical example with Monte Carlo simulation is carried out.
基金National Natural Science Foundation of China,No.42161006Yunnan Fundamental Research Projects No.202201AT070094,No.202301BF070001-004+1 种基金Special Project for High-level Talents of Yunnan Province for Young Top Talents,No.C6213001159European Research Council(ERC)Starting-Grant STORIES,No.101040939。
文摘Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.
基金supported by the Ministry of Science and Technology of China(Nos.2021YFC3200904 and 2022YFC3203705)the National Natural Science Foundation of China(Nos.52270012 and 52070184).
文摘Phthalate esters(PAEs),recognized as endocrine disruptors,are released into the environment during usage,thereby exerting adverse ecological effects.This study investigates the occurrence,sources,and risk assessment of PAEs in surface water obtained from 36 sampling points within the Yellow River and Yangtze River basins.The total concentration of PAEs in the Yellow River spans from124.5 to 836.5 ng/L,with Dimethyl phthalate(DMP)(75.4±102.7 ng/L)and Diisobutyl phthalate(DiBP)(263.4±103.1 ng/L)emerging as the predominant types.Concentrations exhibit a pattern of upstream(512.9±202.1 ng/L)>midstream(344.5±135.3 ng/L)>downstream(177.8±46.7 ng/L).In the Yangtze River,the total concentration ranges from 81.9 to 441.6 ng/L,with DMP(46.1±23.4 ng/L),Diethyl phthalate(DEP)(93.3±45.2 ng/L),and DiBP(174.2±67.6 ng/L)as the primary components.Concentration levels follow a midstream(324.8±107.3 ng/L)>upstream(200.8±51.8 ng/L)>downstream(165.8±71.6 ng/L)pattern.Attention should be directed towards the moderate ecological risks of DiBP in the upstream of HH,and both the upstream and midstream of CJ need consideration for the moderate ecological risks associated with Di-n-octyl phthalate(DNOP).Conversely,in other regions,the associated risk with PAEs is either low or negligible.The main source of PAEs in Yellow River is attributed to the release of construction land,while in the Yangtze River Basin,it stems from the accumulation of pollutants in lakes and forests discharged into the river.These findings are instrumental for pinpointing sources of PAEs pollution and formulating control strategies in the Yellow and Yangtze Rivers,providing valuable insights for global PAEs research in other major rivers.
基金supported by funding from the National Key R&D Program of China(2023ZD0407304)the Sci-Tech Innovation 2030 Agenda(2022ZD0115703)Fundamental Research Funds for Central Non-Profit of Chinese Academy of Agricultural Sciences(Y2023PT20).
文摘Epigenetics-mediated breeding(epibreeding)involves engineering crop traits and stress responses through the targeted manipulation of key epigenetic features to enhance agricultural productivity.While conventional breeding methods raise concerns about reduced genetic diversity,epibreeding propels crop improvement through epigenetic variations that regulate gene expression,ultimately impacting crop yield.Epigenetic regulation in crops encompasses various modes,including histone modification,DNA modification,RNA modification,non-coding RNA,and chromatin remodeling.This review summarizes the epigenetic mechanisms underlying major agronomic traits in maize and identifies candidate epigenetic landmarks in the maize breeding process.We propose a valuable strategy for improving maize yield through epibreeding,combining CRISPR/Cas-based epigenome editing technology and Synthetic Epigenetics(SynEpi).Finally,we discuss the challenges and opportunities associated with maize trait improvement through epibreeding.
