The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide dist...The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide distribution and the lithotectonic regime of Darma Valley,Kumaun Himalaya.A landslide inventory comprising 295 landslides in the area has been prepared and several morphotectonic proxies such as valley floor width to height ratio(Vf),stream length gradient index(SL),and hypsometric integral(HI)have been used to infer tectonic regime.Morphometric analysis,including basic,linear,aerial,and relief aspects,of 59 fourth-order sub-basins,has been carried out to estimate erosion potential in the study area.The result demonstrates that 46.77%of the landslides lie in very high,20.32%in high,21.29%in medium,and 11.61%in low erosion potential zones respectively.In order to determine the key parameters controlling erosion potential,two multivariate statistical methods namely Principal Component Analysis(PCA)and Agglomerative Hierarchical Clustering(AHC)were utilized.PCA reveals that the Higher Himalayan Zone(HHZ)has the highest erosion potential due to the presence of elongated sub-basins characterized by steep slopes and high relief.The clusters created through AHC exhibit positive PCA values,indicating a robust correlation between PCA and AHC.Furthermore,the landslide density map shows two major landslide hotspots.One of these hotspots lies in the vicinity of highly active Munsiyari Thrust(MT),while the other is in the Pandukeshwar formation within the MT's hanging wall,characterized by a high exhumation rate.High SL and low Vf values along these hotspots further corroborate that the occurrence of landslides in the study area is influenced by tectonic activity.This study,by identifying erosionprone areas and elucidating the implications of tectonic activity on landslide distribution,empowers policymakers and government agencies to develop strategies for hazard assessment and effective landslide risk mitigation,consequently safeguarding lives and communities.展开更多
Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently d...Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.展开更多
Geochemical surveys are essential for understanding the spatial distribution of ore-forming elements.However,these surveys often involve compositional data,the weight concentrations,which do not meet the requirements ...Geochemical surveys are essential for understanding the spatial distribution of ore-forming elements.However,these surveys often involve compositional data,the weight concentrations,which do not meet the requirements of statistical methods due to the closure effect.In this study,we applied an integrated approach combining compositional data,multifractal,and multivariate statistical analyses to identify the nonlinear complexity of the spatial distributions of elemental concentrations in the Er’renshan ore field.Initially,the raw concentrations were transformed into log-ratios following the principles of composition data theory to alleviate the impact of the closure effect.Multifractal analysis was then conducted to characterise the nonlinear complexity of the concentration distributions.Furthermore,principal component analysis(PCA)and factor analysis(FA)were applied to identify spurious correlations and the potential factors controlling the distribution patterns.The results demonstrate that:a)the raw data are biased,while the log-ratio data are unbiased and more reliable;b)the spatial distributions of elemental concentrations exhibit nonlinear complexity;and c)the elemental distribution in the study area is largely controlled by structural factors.展开更多
Water quality is a pressing issue affecting the sustainable development of lakes.To elucidate the spatial and temporal characteristics of water quality in Bos ten Lake,China,this study constructed a comprehensive wate...Water quality is a pressing issue affecting the sustainable development of lakes.To elucidate the spatial and temporal characteristics of water quality in Bos ten Lake,China,this study constructed a comprehensive water quality index(CWQI) based on key water quality indicators,utilizing water quality data collected from 17 sampling sites spaning from 2011 to 2019.Key water quality indicators were determined using factor analysis,and the spatial and temporal characteristics of key water quality indicators and the CWQI were examined using multivariate statistical analysis.The key water quality indicators included pH,chemical oxygen demand(COD),water transparency(SD),NO3-,total dissolved solids(TDS),Cl-,SO42-,and electrical conductivity(EC).Furthermore,the contribution rates of all water quality indicators to the water quality were quantitatively elucidated using the SHapley Additive explanations(SHAP) values,thereby validating the factor analysis outcomes.Among the eight key water quality indicators,the COD had the most significant influence on the water quality of Bos ten Lake.The water quality condition of Bosten Lake has remained at Class Ⅲ from 2011 to 2019(CWQI ranging from3.19 to 3.90).The water quality of Bos ten Lake was characterized by distinct regional differences that arose from hydrodynamic processes within the lake and upstream water quality.The southwestern region exhibited the best water quality(mean CWQI of 3.47),whereas the northwestern region exhibited the worst(mean CWQI of 3.58).It is crucial to acknowledge that alongside the increase in industrial and agricultural effluent discharge monitoring,a series of ecological restoration projects for the lake basin have been initiated.Over time,the water quality of Bosten Lake showed gradual improvement(improvement rate of CWQI at 0.05/a).This study provides a critical scientific basis for enhancing the understanding and effective management of water quality in the Bosten Lake Basin through a comprehensive analysis of its spatial and temporal evolution and driving mechanisms.展开更多
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
Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares ...Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made.展开更多
Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the t...Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.展开更多
Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were ...Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.展开更多
To further understand the relationship between vegetation succession and soil fertility within farming-plantation ecotone in Ziwuling Mountains of the Loess Plateau, nine kinds of widely distributed communities at dif...To further understand the relationship between vegetation succession and soil fertility within farming-plantation ecotone in Ziwuling Mountains of the Loess Plateau, nine kinds of widely distributed communities at different succession stages were selected, and the effects of vegetation succession on soil fertility were studied through the methods of comparing two hierarchical clustering （similarity index： B） and other multivariate analysis. The results showed that： （i） the similarity in clustering pattern of nine communities which classified by plant species and soil nutrients respectively showed a trend of B ^-overall plant-soil0-10cn〉B^-overall plant-soil 10-20 cm 〉B^- overall plant-soil 20-40 cm, and for the top soil, it showed a trend of B^- grass-soil 0-10 cm 〉 B^-shrub-soil 0-10 cm 〉 Btree-soil0-10 cm; （ii） soil fertility increased during the succession process from abandoned land to forest community, and the soil fertility of forest community showed an increased order of coniferous forest →mixed forest →broadleaf forest; （iii） during the process of vegetation succession, the variation of topsoil fertility was higher than that of the subsurface soil （coefficient of variation： CV0-10 cm 〉CV 10-20 cm 〉 CV20-40 cm）, and when the succession developed into the stages of shrub and forest communities, the top soil fertility had been improved significantly; and （iv） for the subsurface soil of the communities at the advanced succession stages, the soil fertility also increased to some extent. Our results suggested that the method of comparing two hierarchical clustering reflected the similarity level of different cluster patterns, therefore, it was helpful to study the relationship between vegetation succession and soil fertility. There was a corresponding relationship between the change process of soil fertility from the top soil to subsurface soil and the process of vegetation succession from the early stages to the advanced stage. The differentiations of soil fertility in vertical space and horizontal space were both caused by vegetation succession, which was significant for both the shrub and forest communities. The improved level of forest soil fertility was related to forest vegetation types and the improved fertility level of broad-leaved forest-soil community was higher than that of the coniferous forest soil. In the practice on soil fertility ecological restoration of the loess plateau, it is important to carry out reasonably artificial forestation so as to enhance the restoration and improvement of soil fertility.展开更多
Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental...Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry(ICP-OES). Based on the contents of 14 inorganic elements(Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis(HCA) and principle component analysis(PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.展开更多
Dongguan （东莞） City, located in the Pearl River Delta, South China, is famous for its rapid industrialization in the past 30 years. A total of 90 topsoil samples have been collected from agricultural fields, includ...Dongguan （东莞） City, located in the Pearl River Delta, South China, is famous for its rapid industrialization in the past 30 years. A total of 90 topsoil samples have been collected from agricultural fields, including vegetable and orchard soils in the city, and eight heavy metals （As, Cu, Cd, Cr, Hg, Ni, Pb, and Zn） and other items （pH values and organic matter） have been analyzed, to evaluate the influence of anthropic activities on the environmental quality of agricultural soils and to identify the spatial distribution of trace elements and possible sources of trace elements. The elements Hg, Pb, and Cd have accumulated remarkably here, incomparison with the soil background content of elements in Guangdong （广东） Province. Pollution is more serious in the western plain and the central region, which are heavily distributed with industries and rivers. Multivariate and geostatistical methods have been applied to differentiate the influences of natural processes and human activities on the pollution of heavy metals in topsoils in the study area. The results of cluster analysis （CA） and factor analysis （FA） show that Ni, Cr, Cu, Zn, and As are grouped in factor F1, Pb in F2, and Cd and Hg in F3, respectively. The spatial pattern of the three factors may be well demonstrated by geostatistical analysis. It is shown that the first factor could be considered as a natural source controlled by parent rocks. The second factor could be referred to as ＂industrial and traffic pollution sources＂. The source of the third factor is mainly controlled by long-term anthropic activities, as a consequence of agricultural activities, fossil fuel consumption, and atmospheric deposition.展开更多
Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality dat...Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.展开更多
Viscum coloratum(Kom.)Nakai is a well-known medicinal hemiparasite widely distributed in Asia.The synthesis and accumulation of its metabolites are affected by both environmental factors and the host plants,while the ...Viscum coloratum(Kom.)Nakai is a well-known medicinal hemiparasite widely distributed in Asia.The synthesis and accumulation of its metabolites are affected by both environmental factors and the host plants,while the latter of which is usually overlooked.The purpose of this study was to comprehensively evaluate the effects of host and habitat on the metabolites in V.coloratum through multiple chemical and biological approaches.The metabolite profile of V.coloratum harvested from three different host plants in two habitats were determined by multiple chemical methods including high-performance liquid chromatography-ultraviolet(HPLC-UV),gas chromatography-flame ionization detector(GC-FID)and ultra-performance liquid chromatography quadrupole time of flight mass spectrometry(UPLC-QTOF/MS).The differences in antioxidant efficacy of V.coloratum were determined based on multiple in vitro models.The multivariate statistical analysis and data fusion strategy were applied to analyze the differences in metabolite profile and antioxidant activity of V.coloratum.Results indicated that the metabolite profile obtained by various chemical approaches was simultaneously affected by host and environment factors,and the environment plays a key role.Meanwhile,three main differential metabolites between two environment groups were identified.The results of antioxidant assay indicated that the environment has greater effects on the biological activity of V.coloratum than the host.Therefore,we conclude that the integration of various chemical and biological approaches combined with multivariate statistical and data fusion analysis,which can determine the influences of host plant and habitat on the metabolites,is a powerful strategy to control the quality of semi-parasitic herbal medicine.展开更多
Groundwater quality monitoring and geochemical characterization in the phreatic aquifer are critical for ensuring universal and equitable access to clean,reliable,and inexpensive drinking water for all.This research w...Groundwater quality monitoring and geochemical characterization in the phreatic aquifer are critical for ensuring universal and equitable access to clean,reliable,and inexpensive drinking water for all.This research was intended to investigate the hydrogeochemical attributes and mechanisms regulating the chemistry of groundwater as well as to assess spatial variation in groundwater quality in Satna district,India.To accomplish this,the groundwater data comprising 13 physio-chemical parameters from thirty-eight phreatic aquifer locations were analysed for May 2020 by combining entropy-weighted water quality index(EWQI),multivariate statistics,geochemical modelling,and geographical information system.The findings revealed that the groundwater is fresh and slightly alkaline.Hardness was a significant concern as 57.89% of samples were beyond the permissible limit of the World Health Organisation.The dominance of ions were in the order of Ca^(2+)> Na^(+)> Mg^(2+)> K^(+) and HCO_(3)^(-)> SO_(4)^(2-)> Cl^-> NO_(3)^(-)> F^(-).Higher concentration of these ions is mainly concentrated in the northeast and eastern regions.Pearson correlation analysis and principal component analysis(PCA) demonstrated that both natural and human factors regulate groundwater chemistry in the region.The analysis of Q-mode agglomerative hierarchical clustering highlighted three significant water clusters.Ca-HCO_3 was the most prevalent hydro-chemical facies in all three clusters.Geochemical modelling through various conventional plots indicated that groundwater chemistry in the research region is influenced by the dissolution of calcite/dolomite,reverse ion exchange,and by silicate and halite weathering.EWQI data of the study area disclosed that 73.69% of the samples were appropriate for drinking.Due to high salinity,Magnesium(Mg^(2+)),Nitrate(NO_(3)^(-)),and Bicarbonate(HCO_(3)^(-)) concentrations,the north-central and north-eastern regions are particularly susceptible.The findings of the study may be accomplished by policymakers and groundwater managers to achieve sustainable groundwater development at the regional scale.展开更多
Phyllostachys praecox is a bamboo species cultivated for edible shoots under intensive management.However,the potential pollution risk of heavy metals in bamboo soils is not clear under the intensive management for a ...Phyllostachys praecox is a bamboo species cultivated for edible shoots under intensive management.However,the potential pollution risk of heavy metals in bamboo soils is not clear under the intensive management for a long term.The objective of this study was to evaluate the effect of cultivation time on soil heavy metal accumulation and bioavailability in bamboo stands subjected to intensive management.Soil samples were collected from a chronosequence of bamboo stands which had been cultivated for 0,1,2,4,8,and 10 years in Lin’an,Zhejiang Province of China.Eight heavy metals(Cu,Zn,Pb,Ni,Cr,Cd,As,and Hg)present in the soil were selected,and their potential pollution risk was evaluated by chemical speciation analysis.Possible heavy metal sources were explored using multivariate and cluster analysis.Our results showed that Zn,Cu,Hg,and Cd contents in the soil increased with the cultivation time,while Ni,Cr,Pb,and As levels were similar among all stands.Furthermore,the bioavailabilities of all analyzed heavy metals increased with the cultivation time.Multivariate and cluster analysis showed that sources of Ni,Cr,Pb,and As were likely lithogenic in origin,whereas input of Zn,Cu,Hg,and Cd was mainly due to cultivation practices.Current bamboo management strategies raised the potential risks of heavy metal pollution in bamboo shoots in the long term.Soil acidification in P.praecox stands induced by intensive cultivation should be controlled since it stimulated and improved the bioavailability of heavy metals.Appropriate management strategies should thus be adopted to ensure safe and sustainable production of bamboo shoots.展开更多
Rock slope hazard assessment is an important part of risk analysis for open pit mines.The main parameters that can lead to rock slope failures are the parameters traditionally used in geomechanical classifications,the...Rock slope hazard assessment is an important part of risk analysis for open pit mines.The main parameters that can lead to rock slope failures are the parameters traditionally used in geomechanical classifications,the slope geometrical parameters and external factors like rainfall and blasting.This paper presents a methodology for a hazard assessment system for open pit mine slopes based on 88 cases collated around the world using principal components analysis,discriminant analysis and confidence ellipses.The historical cases used in this study included copper,gold,iron,diamond,lead and zinc,platinum and claystone mines.The variables used in the assessment methodology are uniaxial compressive strength of intact rock;spacing,persistence,opening,roughness,infilling and orientation of the main discontinuity set;weathering of the rock mass;groundwater;blasting method;and height and inclination of the pit.While principal component analysis was used to quantify the data,the discriminant analysis was used to establish a rule to classify new slopes about its stability condition.To provide a practical hazard assessment system,confidence ellipses were used to propose a hazard graph and generate the HAS-Q.The discriminant rule developed in this research has a high discrimination capacity with an error rate of 11.36%.展开更多
Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on s...Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on statistical process control (SPC) is investigated in detail by Monte Carlo experiments. It is revealed that in the sense of average performance, the false alarms rates (FAR) of principal component analysis (PCA), dynamic PCA are not affected by the time-series structures of process variables. Nevertheless, non-independent identical distribution will cause the actual FAR to deviate from its theoretic value apparently and result in unexpected consecutive false alarms for normal operating process. Dynamic PCA and ARMA-PCA are demonstrated to be inefficient to remove the influences of auto and cross correlations. Subspace identification-based PCA (SI-PCA) is proposed to improve the monitoring of dynamic processes. Through state space modeling, SI-PCA can remove the auto and cross corre-lations efficiently and avoid consecutive false alarms. Synthetic Monte Carlo experiments and the application in Tennessee Eastman challenge process illustrate the advantages of the proposed approach.展开更多
The Ziyoutun Cu-Au district is located in the Jizhong–Yanbian Metallogenic Belt and possesses excellent prospects.However,the thick regolith and complex tectonic settings present challenges in terms of detecting and ...The Ziyoutun Cu-Au district is located in the Jizhong–Yanbian Metallogenic Belt and possesses excellent prospects.However,the thick regolith and complex tectonic settings present challenges in terms of detecting and decomposition of weak geochemical anomalies.To address this challenge,we initially conducted a comprehensive analysis of 1:10,000-scale soil geochemical data.This analysis included multivariate statistical techniques,such as correlation analysis,R-mode cluster analysis,Q–Q plots and factor analysis.Subsequently,we decomposed the geochemical anomalies,identifying weak anomalies using spectrum-area modeling and local singularity analysis.The results indicate that the assemblage of Au-Cu-Bi-As-Sb represents the mineralization at Ziyoutun.In comparison to conventional methods,spectrumarea modeling and local singularity analysis outperform in terms of identification of anomalies.Ultimately,we considered four specific target areas(AP01,AP02,AP03 and AP04)for future exploration,based on geochemical anomalies and favorable geological factors.Within AP01 and AP02,the geochemical anomalies suggest potential mineralization at depth,whereas in AP03 and AP04 the surface anomalies require additional geological investigation.Consequently,we recommend conducting drilling,following more extensive surface fieldwork,at the first two targets and verifying surface anomalies in the last two targets.We anticipate these findings will significantly enhance future exploration in Ziyoutun.展开更多
In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effect...In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system.展开更多
基金CSIR for providing financial assistance(09/0420(11800)/2021EMR-I)。
文摘The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide distribution and the lithotectonic regime of Darma Valley,Kumaun Himalaya.A landslide inventory comprising 295 landslides in the area has been prepared and several morphotectonic proxies such as valley floor width to height ratio(Vf),stream length gradient index(SL),and hypsometric integral(HI)have been used to infer tectonic regime.Morphometric analysis,including basic,linear,aerial,and relief aspects,of 59 fourth-order sub-basins,has been carried out to estimate erosion potential in the study area.The result demonstrates that 46.77%of the landslides lie in very high,20.32%in high,21.29%in medium,and 11.61%in low erosion potential zones respectively.In order to determine the key parameters controlling erosion potential,two multivariate statistical methods namely Principal Component Analysis(PCA)and Agglomerative Hierarchical Clustering(AHC)were utilized.PCA reveals that the Higher Himalayan Zone(HHZ)has the highest erosion potential due to the presence of elongated sub-basins characterized by steep slopes and high relief.The clusters created through AHC exhibit positive PCA values,indicating a robust correlation between PCA and AHC.Furthermore,the landslide density map shows two major landslide hotspots.One of these hotspots lies in the vicinity of highly active Munsiyari Thrust(MT),while the other is in the Pandukeshwar formation within the MT's hanging wall,characterized by a high exhumation rate.High SL and low Vf values along these hotspots further corroborate that the occurrence of landslides in the study area is influenced by tectonic activity.This study,by identifying erosionprone areas and elucidating the implications of tectonic activity on landslide distribution,empowers policymakers and government agencies to develop strategies for hazard assessment and effective landslide risk mitigation,consequently safeguarding lives and communities.
基金supported in part by the National Science Fund for Distinguished Young Scholars of China(62225303)the National Natural Science Fundation of China(62303039,62433004)+2 种基金the China Postdoctoral Science Foundation(BX20230034,2023M730190)the Fundamental Research Funds for the Central Universities(buctrc202201,QNTD2023-01)the High Performance Computing Platform,College of Information Science and Technology,Beijing University of Chemical Technology
文摘Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.
基金supported by the Doctoral Research Start-up Fund,East China University of Technology(DHBK2019313)the Open Research Fund Program of Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University),the Ministry of Education(2020YSJS10)+1 种基金the Open Research Fund Program of Shandong Provincial Engineering Laboratory of Application and Development of Big Data for Deep Gold Exploration(SDK202224)the Basic Scientific Research Fund of the Institute of Geophysical and Geochemical Exploration,Chinese Academy of Geological Sciences(AS2022P03).
文摘Geochemical surveys are essential for understanding the spatial distribution of ore-forming elements.However,these surveys often involve compositional data,the weight concentrations,which do not meet the requirements of statistical methods due to the closure effect.In this study,we applied an integrated approach combining compositional data,multifractal,and multivariate statistical analyses to identify the nonlinear complexity of the spatial distributions of elemental concentrations in the Er’renshan ore field.Initially,the raw concentrations were transformed into log-ratios following the principles of composition data theory to alleviate the impact of the closure effect.Multifractal analysis was then conducted to characterise the nonlinear complexity of the concentration distributions.Furthermore,principal component analysis(PCA)and factor analysis(FA)were applied to identify spurious correlations and the potential factors controlling the distribution patterns.The results demonstrate that:a)the raw data are biased,while the log-ratio data are unbiased and more reliable;b)the spatial distributions of elemental concentrations exhibit nonlinear complexity;and c)the elemental distribution in the study area is largely controlled by structural factors.
基金supported by the National Natural Science Foundation of China(42377072,52409105).
文摘Water quality is a pressing issue affecting the sustainable development of lakes.To elucidate the spatial and temporal characteristics of water quality in Bos ten Lake,China,this study constructed a comprehensive water quality index(CWQI) based on key water quality indicators,utilizing water quality data collected from 17 sampling sites spaning from 2011 to 2019.Key water quality indicators were determined using factor analysis,and the spatial and temporal characteristics of key water quality indicators and the CWQI were examined using multivariate statistical analysis.The key water quality indicators included pH,chemical oxygen demand(COD),water transparency(SD),NO3-,total dissolved solids(TDS),Cl-,SO42-,and electrical conductivity(EC).Furthermore,the contribution rates of all water quality indicators to the water quality were quantitatively elucidated using the SHapley Additive explanations(SHAP) values,thereby validating the factor analysis outcomes.Among the eight key water quality indicators,the COD had the most significant influence on the water quality of Bos ten Lake.The water quality condition of Bosten Lake has remained at Class Ⅲ from 2011 to 2019(CWQI ranging from3.19 to 3.90).The water quality of Bos ten Lake was characterized by distinct regional differences that arose from hydrodynamic processes within the lake and upstream water quality.The southwestern region exhibited the best water quality(mean CWQI of 3.47),whereas the northwestern region exhibited the worst(mean CWQI of 3.58).It is crucial to acknowledge that alongside the increase in industrial and agricultural effluent discharge monitoring,a series of ecological restoration projects for the lake basin have been initiated.Over time,the water quality of Bosten Lake showed gradual improvement(improvement rate of CWQI at 0.05/a).This study provides a critical scientific basis for enhancing the understanding and effective management of water quality in the Bosten Lake Basin through a comprehensive analysis of its spatial and temporal evolution and driving mechanisms.
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Supported by the National High-Tech Development Program of China(No.863-511-920-011,2001AA411230).
文摘Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made.
基金Supported by the National Natural Science Foundation of China （No.60574047） and the Doctorate Foundation of the State Education Ministry of China （No.20050335018）.
文摘Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.
基金supported by the Major State Basic Research Development Program (No. 2010CB428800)the Geological Survey Projects Foundation of Institute of Hydrogeology and Environmental Geology (No. SK201308)
文摘Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.
基金supported by the National Key Basic Research Program of China(973Program,2002CB111505)
文摘To further understand the relationship between vegetation succession and soil fertility within farming-plantation ecotone in Ziwuling Mountains of the Loess Plateau, nine kinds of widely distributed communities at different succession stages were selected, and the effects of vegetation succession on soil fertility were studied through the methods of comparing two hierarchical clustering （similarity index： B） and other multivariate analysis. The results showed that： （i） the similarity in clustering pattern of nine communities which classified by plant species and soil nutrients respectively showed a trend of B ^-overall plant-soil0-10cn〉B^-overall plant-soil 10-20 cm 〉B^- overall plant-soil 20-40 cm, and for the top soil, it showed a trend of B^- grass-soil 0-10 cm 〉 B^-shrub-soil 0-10 cm 〉 Btree-soil0-10 cm; （ii） soil fertility increased during the succession process from abandoned land to forest community, and the soil fertility of forest community showed an increased order of coniferous forest →mixed forest →broadleaf forest; （iii） during the process of vegetation succession, the variation of topsoil fertility was higher than that of the subsurface soil （coefficient of variation： CV0-10 cm 〉CV 10-20 cm 〉 CV20-40 cm）, and when the succession developed into the stages of shrub and forest communities, the top soil fertility had been improved significantly; and （iv） for the subsurface soil of the communities at the advanced succession stages, the soil fertility also increased to some extent. Our results suggested that the method of comparing two hierarchical clustering reflected the similarity level of different cluster patterns, therefore, it was helpful to study the relationship between vegetation succession and soil fertility. There was a corresponding relationship between the change process of soil fertility from the top soil to subsurface soil and the process of vegetation succession from the early stages to the advanced stage. The differentiations of soil fertility in vertical space and horizontal space were both caused by vegetation succession, which was significant for both the shrub and forest communities. The improved level of forest soil fertility was related to forest vegetation types and the improved fertility level of broad-leaved forest-soil community was higher than that of the coniferous forest soil. In the practice on soil fertility ecological restoration of the loess plateau, it is important to carry out reasonably artificial forestation so as to enhance the restoration and improvement of soil fertility.
基金supposed by the Program for Science and Technology of Shandong Province (2011GHY11521)the Department of Education of Shandong Province (No. J11LB07)the Natural Science Foundation of Qingdao City (Nos. 12-1-3-52-(1)-nsh and 12-1-4-16-(7)-jch)
文摘Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry(ICP-OES). Based on the contents of 14 inorganic elements(Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis(HCA) and principle component analysis(PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.
基金supported by the Ministry of Land and Resources of China (No. [2005]011-16)State Environment Protection Administration of China (No. 2001-1-2)+2 种基金State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciencesthe Guangdong Provincial Office of SciencesTechnology via NSF Team Project and Key Project (Nos. 06202438, 2004A3030800)
文摘Dongguan （东莞） City, located in the Pearl River Delta, South China, is famous for its rapid industrialization in the past 30 years. A total of 90 topsoil samples have been collected from agricultural fields, including vegetable and orchard soils in the city, and eight heavy metals （As, Cu, Cd, Cr, Hg, Ni, Pb, and Zn） and other items （pH values and organic matter） have been analyzed, to evaluate the influence of anthropic activities on the environmental quality of agricultural soils and to identify the spatial distribution of trace elements and possible sources of trace elements. The elements Hg, Pb, and Cd have accumulated remarkably here, incomparison with the soil background content of elements in Guangdong （广东） Province. Pollution is more serious in the western plain and the central region, which are heavily distributed with industries and rivers. Multivariate and geostatistical methods have been applied to differentiate the influences of natural processes and human activities on the pollution of heavy metals in topsoils in the study area. The results of cluster analysis （CA） and factor analysis （FA） show that Ni, Cr, Cu, Zn, and As are grouped in factor F1, Pb in F2, and Cd and Hg in F3, respectively. The spatial pattern of the three factors may be well demonstrated by geostatistical analysis. It is shown that the first factor could be considered as a natural source controlled by parent rocks. The second factor could be referred to as ＂industrial and traffic pollution sources＂. The source of the third factor is mainly controlled by long-term anthropic activities, as a consequence of agricultural activities, fossil fuel consumption, and atmospheric deposition.
基金Project (2012ZX07501002-001) supported by the Ministry of Science and Technology of China
文摘Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.
基金funded by the National Natural Science Foundation of China(Grant No.:30901967)the Natural Science Foundation of Liaoning Province(Grant No.:2013020223)Shenyang Pharmaceutical University Student Science and Technology Innovation Project(Grant No.:12)。
文摘Viscum coloratum(Kom.)Nakai is a well-known medicinal hemiparasite widely distributed in Asia.The synthesis and accumulation of its metabolites are affected by both environmental factors and the host plants,while the latter of which is usually overlooked.The purpose of this study was to comprehensively evaluate the effects of host and habitat on the metabolites in V.coloratum through multiple chemical and biological approaches.The metabolite profile of V.coloratum harvested from three different host plants in two habitats were determined by multiple chemical methods including high-performance liquid chromatography-ultraviolet(HPLC-UV),gas chromatography-flame ionization detector(GC-FID)and ultra-performance liquid chromatography quadrupole time of flight mass spectrometry(UPLC-QTOF/MS).The differences in antioxidant efficacy of V.coloratum were determined based on multiple in vitro models.The multivariate statistical analysis and data fusion strategy were applied to analyze the differences in metabolite profile and antioxidant activity of V.coloratum.Results indicated that the metabolite profile obtained by various chemical approaches was simultaneously affected by host and environment factors,and the environment plays a key role.Meanwhile,three main differential metabolites between two environment groups were identified.The results of antioxidant assay indicated that the environment has greater effects on the biological activity of V.coloratum than the host.Therefore,we conclude that the integration of various chemical and biological approaches combined with multivariate statistical and data fusion analysis,which can determine the influences of host plant and habitat on the metabolites,is a powerful strategy to control the quality of semi-parasitic herbal medicine.
文摘Groundwater quality monitoring and geochemical characterization in the phreatic aquifer are critical for ensuring universal and equitable access to clean,reliable,and inexpensive drinking water for all.This research was intended to investigate the hydrogeochemical attributes and mechanisms regulating the chemistry of groundwater as well as to assess spatial variation in groundwater quality in Satna district,India.To accomplish this,the groundwater data comprising 13 physio-chemical parameters from thirty-eight phreatic aquifer locations were analysed for May 2020 by combining entropy-weighted water quality index(EWQI),multivariate statistics,geochemical modelling,and geographical information system.The findings revealed that the groundwater is fresh and slightly alkaline.Hardness was a significant concern as 57.89% of samples were beyond the permissible limit of the World Health Organisation.The dominance of ions were in the order of Ca^(2+)> Na^(+)> Mg^(2+)> K^(+) and HCO_(3)^(-)> SO_(4)^(2-)> Cl^-> NO_(3)^(-)> F^(-).Higher concentration of these ions is mainly concentrated in the northeast and eastern regions.Pearson correlation analysis and principal component analysis(PCA) demonstrated that both natural and human factors regulate groundwater chemistry in the region.The analysis of Q-mode agglomerative hierarchical clustering highlighted three significant water clusters.Ca-HCO_3 was the most prevalent hydro-chemical facies in all three clusters.Geochemical modelling through various conventional plots indicated that groundwater chemistry in the research region is influenced by the dissolution of calcite/dolomite,reverse ion exchange,and by silicate and halite weathering.EWQI data of the study area disclosed that 73.69% of the samples were appropriate for drinking.Due to high salinity,Magnesium(Mg^(2+)),Nitrate(NO_(3)^(-)),and Bicarbonate(HCO_(3)^(-)) concentrations,the north-central and north-eastern regions are particularly susceptible.The findings of the study may be accomplished by policymakers and groundwater managers to achieve sustainable groundwater development at the regional scale.
基金supported by the National Key R&D Program of China(No.2016FYE0112700)the National Natural Science Foundation of China(No.41671296)the Science and Technology Department of Zhejiang Province of China(No.2017C02016)
文摘Phyllostachys praecox is a bamboo species cultivated for edible shoots under intensive management.However,the potential pollution risk of heavy metals in bamboo soils is not clear under the intensive management for a long term.The objective of this study was to evaluate the effect of cultivation time on soil heavy metal accumulation and bioavailability in bamboo stands subjected to intensive management.Soil samples were collected from a chronosequence of bamboo stands which had been cultivated for 0,1,2,4,8,and 10 years in Lin’an,Zhejiang Province of China.Eight heavy metals(Cu,Zn,Pb,Ni,Cr,Cd,As,and Hg)present in the soil were selected,and their potential pollution risk was evaluated by chemical speciation analysis.Possible heavy metal sources were explored using multivariate and cluster analysis.Our results showed that Zn,Cu,Hg,and Cd contents in the soil increased with the cultivation time,while Ni,Cr,Pb,and As levels were similar among all stands.Furthermore,the bioavailabilities of all analyzed heavy metals increased with the cultivation time.Multivariate and cluster analysis showed that sources of Ni,Cr,Pb,and As were likely lithogenic in origin,whereas input of Zn,Cu,Hg,and Cd was mainly due to cultivation practices.Current bamboo management strategies raised the potential risks of heavy metal pollution in bamboo shoots in the long term.Soil acidification in P.praecox stands induced by intensive cultivation should be controlled since it stimulated and improved the bioavailability of heavy metals.Appropriate management strategies should thus be adopted to ensure safe and sustainable production of bamboo shoots.
基金Federal Agency for Support and Evaluation of Graduate Education (CAPES – Brazil, Grant ID 19/2016)Fondation for Research Support of Minas Gerais (FAPEMIG – Brazil)University of New South Wales (UNSW – Australia)
文摘Rock slope hazard assessment is an important part of risk analysis for open pit mines.The main parameters that can lead to rock slope failures are the parameters traditionally used in geomechanical classifications,the slope geometrical parameters and external factors like rainfall and blasting.This paper presents a methodology for a hazard assessment system for open pit mine slopes based on 88 cases collated around the world using principal components analysis,discriminant analysis and confidence ellipses.The historical cases used in this study included copper,gold,iron,diamond,lead and zinc,platinum and claystone mines.The variables used in the assessment methodology are uniaxial compressive strength of intact rock;spacing,persistence,opening,roughness,infilling and orientation of the main discontinuity set;weathering of the rock mass;groundwater;blasting method;and height and inclination of the pit.While principal component analysis was used to quantify the data,the discriminant analysis was used to establish a rule to classify new slopes about its stability condition.To provide a practical hazard assessment system,confidence ellipses were used to propose a hazard graph and generate the HAS-Q.The discriminant rule developed in this research has a high discrimination capacity with an error rate of 11.36%.
基金National Natural Foundation of China (No.60421002, No.70471052)
文摘Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on statistical process control (SPC) is investigated in detail by Monte Carlo experiments. It is revealed that in the sense of average performance, the false alarms rates (FAR) of principal component analysis (PCA), dynamic PCA are not affected by the time-series structures of process variables. Nevertheless, non-independent identical distribution will cause the actual FAR to deviate from its theoretic value apparently and result in unexpected consecutive false alarms for normal operating process. Dynamic PCA and ARMA-PCA are demonstrated to be inefficient to remove the influences of auto and cross correlations. Subspace identification-based PCA (SI-PCA) is proposed to improve the monitoring of dynamic processes. Through state space modeling, SI-PCA can remove the auto and cross corre-lations efficiently and avoid consecutive false alarms. Synthetic Monte Carlo experiments and the application in Tennessee Eastman challenge process illustrate the advantages of the proposed approach.
基金project was supported by the Enterprise Authorized Item from the Jilin Sanhe Mining Development Co.,Ltd.(3-4-2021-120)the Fundamental Research Funds for the Central Universities(2-9-2020-010)。
文摘The Ziyoutun Cu-Au district is located in the Jizhong–Yanbian Metallogenic Belt and possesses excellent prospects.However,the thick regolith and complex tectonic settings present challenges in terms of detecting and decomposition of weak geochemical anomalies.To address this challenge,we initially conducted a comprehensive analysis of 1:10,000-scale soil geochemical data.This analysis included multivariate statistical techniques,such as correlation analysis,R-mode cluster analysis,Q–Q plots and factor analysis.Subsequently,we decomposed the geochemical anomalies,identifying weak anomalies using spectrum-area modeling and local singularity analysis.The results indicate that the assemblage of Au-Cu-Bi-As-Sb represents the mineralization at Ziyoutun.In comparison to conventional methods,spectrumarea modeling and local singularity analysis outperform in terms of identification of anomalies.Ultimately,we considered four specific target areas(AP01,AP02,AP03 and AP04)for future exploration,based on geochemical anomalies and favorable geological factors.Within AP01 and AP02,the geochemical anomalies suggest potential mineralization at depth,whereas in AP03 and AP04 the surface anomalies require additional geological investigation.Consequently,we recommend conducting drilling,following more extensive surface fieldwork,at the first two targets and verifying surface anomalies in the last two targets.We anticipate these findings will significantly enhance future exploration in Ziyoutun.
基金Project(2003AA430200) supported by the National High-Tech Research and Development Program of China
文摘In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system.
