A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in vari...A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.展开更多
The classification of the springtime water mass has an important influence on the hydrography,regional climate change and fishery in the Taiwan Strait.Based on 58 stations of CTD profiling data collected in the wester...The classification of the springtime water mass has an important influence on the hydrography,regional climate change and fishery in the Taiwan Strait.Based on 58 stations of CTD profiling data collected in the western and southwestern Taiwan Strait during the spring cruise of 2019,we analyze the spatial distributions of temperature(T)and salinity(S)in the investigation area.Then by using the fuzzy cluster method combined with the T-S similarity number,we classify the investigation area into 5 water masses:the Minzhe Coastal Water(MZCW),the Taiwan Strait Mixed Water(TSMW),the South China Sea Surface Water(SCSSW),the South China Sea Subsurface Water(SCSUW)and the Kuroshio Branch Water(KBW).The MZCW appears in the near surface layer along the western coast of Taiwan Strait,showing low-salinity(<32.0)tongues near the Minjiang River Estuary and the Xiamen Bay mouth.The TSMW covers most upper layer of the investigation area.The SCSSW is mainly distributed in the upper layer of the southwestern Taiwan Strait,beneath which is the SCSUW.The KBW is a high temperature(core value of 26.36℃)and high salinity(core value of 34.62)water mass located southeast of the Taiwan Bank and partially in the central Taiwan Strait.展开更多
Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel meth...Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel method based on CPSO.We first evaluate the clustering performance of this model using the variance ratio criterion(VRC)as the evaluation metric.The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization(PSO)algorithm.The CPSO aims to improve the VRC value while avoiding local optimal solutions.The simulated dataset is set at three levels of overlapping:non-overlapping,partial overlapping,and severe overlapping.Finally,we compare CPSO with two other methods.Results:By observing the comparative results,our proposed CPSO method performs outstandingly.In the conditions of non-overlapping,partial overlapping,and severe overlapping,our method has the best VRC values of 1683.2,620.5,and 275.6,respectively.The mean VRC values in these three cases are 1683.2,617.8,and 222.6.Conclusion:The CPSO performed better than other methods for cluster analysis problems.CPSO is effective for cluster analysis.展开更多
The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allo...The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.展开更多
The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine...The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.展开更多
The present study investigates the effect of nanoindentation on single-crystal magnesium specimens using the embedded-atom method potential in molecular dynamics simulation.Analyses are done under dynamic loading wher...The present study investigates the effect of nanoindentation on single-crystal magnesium specimens using the embedded-atom method potential in molecular dynamics simulation.Analyses are done under dynamic loading where the load-bearing capacity and change in the structural configuration are studied on the basal(Z-direction)and two prismatic planes(X-and Y-directions)with varying indenter velocities.The investigation of structural evolution is done using atomic displacement analyses to measure the net magnitude of displacement,atomic strain analyses to evaluate the shear strain developed in the process,and Wigner-Seitz defect analyses to calculate the total vacancies at varied timesteps.Furthermore,Voronoi analyses are done when indented on the basal plane to identify the cluster distribution at different planar depths of the specimen.From the analyses,it has been observed that the load-bearing capacity of the specimen varies with the indentation velocity and the direction of indentation on the specimen.Additionally,it is seen that the observed shear and total atomic displacement in the Z-direction is the least in comparison to the other two axes.The partial dislocation 1/3<-12-10>is seen to be majorly present and the population of dislocation loops is more abundant for lower indenter velocities.Furthermore,clusters<0,4,4,6>and<0,6,0,8>are the major indices developed during nanoindentation on the basal plane where they exhibit symmetrical distribution as observed from the Z-direction.展开更多
Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information.Precision agriculture uses datamining to advance agricultural developments.Many farmers are...Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information.Precision agriculture uses datamining to advance agricultural developments.Many farmers aren’t getting the most out of their land because they don’t use precision agriculture.They harvest crops without a well-planned recommendation system.Future crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical data.Most existing research still needs to address data preprocessing and crop categorization/classification.Furthermore,statistical analysis receives less attention,despite producing more accurate and valid results.The study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH values.The research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high productivity.The presented algorithm converts discrete numerals to factors first,then reduces levels.Second,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation.The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and geo-location.Finally,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.展开更多
Objective:To explore the general differentiation and treatment of insomnia by Professor Gao Ying through drug clustering and group correspondence analysis,and provide reference for clinical diagnosis and treatment.Met...Objective:To explore the general differentiation and treatment of insomnia by Professor Gao Ying through drug clustering and group correspondence analysis,and provide reference for clinical diagnosis and treatment.Methods:Collect retrospective case data from outpatient system,use SPSS20.0 software to perform frequency and cluster analysis on high-frequency symptoms and drug data,and perform corresponding analysis on the clustered drug syndrome groups.Results:A total of 349 consultations in 204 patients were included.Cluster analysis of 35 symptoms and 40 flavors with a frequency of more than 10%resulted in a corresponding relationship between 7 symptom groups,6 drug groups and 5 drug syndrome groups.The medicine symptom group has a high degree of matching;the doctors distinguish and tre at insomnia with calming,clearing heat,nourishing yin,liver,spleen,qi and phlegm as the core treatment,with consistent decoction,two to pill,lily ground Huang Tang,Lily Zhimu Decoction,Wendan Decoction,Sini San,Xiao Chai Hu Tang,Xiaoyao San,etc.are commonly used prescriptions;the physician's experience is to add or subtract Danshen and Zao Ren drink,which has a wide range of applicability to various insomnia syndrome.Conclusion:Based on the cluster analysis of drug symptoms and group correspondence analysis,it can reveal the pathogenesis,treatment and class information hidden in the data of drug symptoms,which can reflect the general law of physicians'syndrome differentiation and treatment of insomnia.This method has a reference for the exploration of TCM clinical experience significance;The results of this study can provide feedback to guide the clinical diagnosis and treatment of insomnia.展开更多
Objective Several clinical obstructive sleep apnea syndrome(OSAS)phenotypes associated with heterogeneous cardiovascular risk profiles have been recently identified.The purpose of this study was to identify clusters a...Objective Several clinical obstructive sleep apnea syndrome(OSAS)phenotypes associated with heterogeneous cardiovascular risk profiles have been recently identified.The purpose of this study was to identify clusters amongst these profiles that allow for the differentiation of patients.Methods This retrospective study included all moderate-to-severe OSAS patients referred to the sleep unit over a 5-year period.Demographic,symptom,comorbidity,polysomnographic,and continuous positive airway pressure(CPAP)adherence data were collected.Statistical analyses were performed to identify clusters of patients.Results A total of 567 patients were included(67%men,54±13 years,body mass index:32±7 kg/m2,65%Caucasian,32%European African).Five clusters were identified:less severe OSAS(n=172);healthier severe OSAS(n=160);poorly sleeping OSAS patients with cardiometabolic comorbidities(n=87);younger obese men with sleepiness at the wheel(n=94);sleepy obese men with very severe desaturating OSAS and cardiometabolic comorbidities(n=54).Patients in clusters 3 and 5 were older than those in clusters 2 and 4(P=0.034).Patients in clusters 4 and 5 were significantly more obese than those in the other clusters(P=0.04).No significant differences were detected in terms of symptoms and comorbidities.Polysomnographic profiles were very discriminating between clusters.CPAP adherence was similar in all clusters but,among adherent patients,daily usage was more important in cluster 1(less severe patients)than in cluster 5.Conclusion This study highlights that the typical sleepy obese middle-aged men with desaturating events represent only a minority of patients in our multi-ethnic moderate-to-severe OSAS cohort of 33%females.展开更多
Supervised machine learning techniques have become well established in the study of spectroscopy data.However,the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemomet...Supervised machine learning techniques have become well established in the study of spectroscopy data.However,the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis.This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data.In addition,we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain.This includes practices in data pre-processing,feature extraction,clustering distance metrics,clustering algorithms and validation techniques.Special consideration is given to the specific characteristics of IR and NIR spectroscopy data which typically includes high dimensionality and relatively low sample size.The findings highlighted a lack of quantitative analysis and evaluation in current practices for cluster analysis of IR and NIR spectroscopy data.With this in mind,we propose an analysis model or workflow with techniques specifically suited for cluster analysis of IR and NIR spectroscopy data along with a pragmatic application strategy.展开更多
Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy,namely,high dimensionality and small sample size.In order to improve cluster analysis outcomes,...Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy,namely,high dimensionality and small sample size.In order to improve cluster analysis outcomes,feature selection can be used to remove redundant or irrelevant features and reduce the dimensionality.However,for cluster analysis,this must be done in an unsupervised manner without the benefit of data labels.This paper presents a novel feature selection approach for cluster analysis,utilizing clusterability metrics to remove features that least contribute to a dataset’s tendency to cluster.Two versions are presented and evaluated:The Hopkins clusterability filter which utilizes the Hopkins test for spatial randomness and the Dip clusterability filter which utilizes the Dip test for unimodality.These new techniques,along with a range of existing filter and wrapper feature selection techniques were evaluated on eleven real-world spectroscopy datasets using internal and external clustering indices.Our newly proposed Hopkins clusterability filter performed the best of the six filter techniques evaluated.However,it was observed that results varied greatly for different techniques depending on the specifics of the dataset and the number of features selected,with significant instability observed for most techniques at low numbers of features.It was identified that the genetic algorithm wrapper technique avoided this instability,performed consistently across all datasets and resulted in better results on average than utilizing the all the features in the spectra.展开更多
In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of cl...In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of clustering,a robust cluster analysis method is proposed which is based on the diversity self-paced t-mixture model.This model firstly adopts the t-distribution as the submodel which tail is easily controllable.On this basis,it utilizes the entropy penalty expectation conditional maximal algorithm as a pre-clustering step to estimate the initial parameters.After that,this model introduces l2,1-norm as a self-paced regularization term and developes a new ECM optimization algorithm,in order to select high confidence samples from each component in training.Finally,experimental results on several real-world datasets in different noise environments show that the diversity self-paced t-mixture model outperforms the state-of-the-art clustering methods.It provides significant guidance for the construction of the robust mixture distribution model.展开更多
The rolling force learning function is an important part of the hot strip parameter calculation process, and the steel classification is the basis of the model parameter calculation,which has a direct impact on the mo...The rolling force learning function is an important part of the hot strip parameter calculation process, and the steel classification is the basis of the model parameter calculation,which has a direct impact on the model calculation precision,and the classification of steel quality will directly affect the accuracy of the model calculation.This paper is for the classification of a certain type of steel,in order to find the optimal classification of steel model to achieve greater precision of the rolling force set.Cluster analysis is a method for the study of affinities between samples or variables,in which Distance Coefficient method can be used to demonstrate the differences between the equivalent measured values of the steel deformation resistance in hot strip rolling.Based on the characteristics of the steel components the composition calculation formula CEQ is created by the trial and error method,so as to achieve the consistency between the CEQ and the equivalent deformation resistance by cluster analysis,that is the composition calculation formula CEQ can be used to characterize the equivalent deformation resistance of the steel in hot rolling.In the actual production process,through the composition formula CEQ,the classification of the steel is made,by which the steel rolling force learning calculation is classified and the rolling force setup deviation is reduced,the rolling stability is improved.展开更多
This paper establishes 13 evaluation indicators for the competitiveness of agri-food processing industry,uses factor analysis to evaluate the competitiveness of agri-food processing industry in 31 provinces(cities,aut...This paper establishes 13 evaluation indicators for the competitiveness of agri-food processing industry,uses factor analysis to evaluate the competitiveness of agri-food processing industry in 31 provinces(cities,autonomous regions)of China,and does cluster analysis to divide these regions into several categories according to the difference in competitiveness,in order to understand the level of competitiveness of agri-food processing industry in China.展开更多
[Objectives]To explore the compatibility rules of neonatal parenteral nutrition(PN)prescriptions based on association rules and hierarchical cluster analysis,thereby providing a reference for standardizing neonatal pa...[Objectives]To explore the compatibility rules of neonatal parenteral nutrition(PN)prescriptions based on association rules and hierarchical cluster analysis,thereby providing a reference for standardizing neonatal parenteral nutrition supportive therapy.[Methods]The data about neonatal PN formulations prepared by the Pharmacy Intravenous Admixture Services(PIVAS)of the Affiliated Hospital of Chengde Medical University from July 2015 to June 2021 were collected.The general information of the prescriptions and the frequency of drug use were analyzed with Excel 2019;the boxplot of drug dosing was drawn using GraphPad 8.0 software;and SPSS Modeler 18.0 and SPSS Statistics 26.0 were used to perform association rules and hierarchical cluster analysis.[Results]A total of 11488 PN prescriptions were collected from 1421 newborns,involving 18 kinds of drugs,which were divided into 11 types of nutrients.Association rules analysis yielded 84 nutrient substance combinations.The combination of fat emulsion-water-soluble vitamins-fat-soluble vitamins-glucose-amino acids had the highest confidence(99.95%).The hierarchical cluster analysis divided nutrients into 5 types.[Conclusions]The prescriptions of PN for newborns were composed of five types of nutrients:amino acids,fat emulsion,glucose,water-soluble vitamins,and fat-soluble vitamins.According to the lack of electrolytes and trace elements,appropriate drugs can be chosen to meet nutritional demands.This study provides reference basis for reasonable selection of drugs for neonatal PN prescriptions and further standardization of PN supportive therapy in newborns.展开更多
Hazard maps are usually prepared for each disaster, including seismic hazard maps, flood hazard maps, and landslide hazard maps. However, when the general public attempts to check their own disaster risk, most are lik...Hazard maps are usually prepared for each disaster, including seismic hazard maps, flood hazard maps, and landslide hazard maps. However, when the general public attempts to check their own disaster risk, most are likely not aware of the specific types of disaster. So, first of all, we need to know what kind<span style="font-family:;" "="">s</span><span style="font-family:;" "=""> of hazards are important. However, the information that integrates multiple hazards is not well maintained, and there are few such studies. On the other hand, in Japan, a lot of hazard information is being released on the Internet. So, we summarized and assessed hazard data that can be accessed online regarding shelters (where evacuees live during disasters) and their catchments (areas assigned to each shelter) in Yokohama City, Kanagawa Prefecture. Based on the results, we investigated whether a grouping by cluster analysis would allow for multi-hazard assessment. We used four natural disasters (seismic, flood, tsunami, sediment disaster) and six parameters of other population and senior population. However, since the characteristics of the population and the senior population were almost the same, only population data was used in the final examination. From the cluster analysis, it was found that it is appropriate to group the designated evacuation centers in Yokohama City into six groups. In addition, each of the six groups was found <span>to have explainable characteristics, confirming the effectiveness of multi-hazard</span> creation using cluster analysis. For example, we divided, all hazards are low, both flood and Seismic hazards are high, sediment hazards are high, etc. In many Japanese cities, disaster prevention measures have been constructed in consideration of ground hazards, mainly for earthquake disasters. In this paper, we confirmed the consistency between the evaluation results of the multi-hazard evaluated here and the existing ground hazard map and examined the usefulness of the designated evacuation center. Finally, the validity was confirmed by comparing this result with the ground hazard based on the actual measurement by the past research. In places where the seismic hazard is large, the two are consistent with the fact that the easiness of shaking by actual measurement is also large.</span>展开更多
The research investigation was carried out in the experimental area of Sher-e-Bangla Agricultural university, Sher-e-Bangla Nagar, Dhaka-1207, during the late Rabi season Mid December to May 2018 to study the genetic ...The research investigation was carried out in the experimental area of Sher-e-Bangla Agricultural university, Sher-e-Bangla Nagar, Dhaka-1207, during the late Rabi season Mid December to May 2018 to study the genetic diversity with cluster analysis for the 35 maize genotypes as experimental materials that were laid out in Randomized Complete Block Design (RCBD) with three replications. The research work was oriented to calculate and estimate the yield factor through analyzing genetic diversity involving the yield contributing characters. The maximum yield per plant (117.51 g) was estimated in the genotype G12 (Pacific) and the minimum yield per plant (51.89 g) was recorded in the genotype G17 (Dekalb Super). Due to the crossing among the 35 maize genotypes, a wide range of divergence was observed in this experiment. The highest genotypes were included in cluster number V with 12 genotypes: BHM-5, PAC-60, Pacific-98, HP-222, Khai Bhutta, AS-999, Pioneer, Duranta, Kaveri 218, Chamak-07 and Golden-984. Here, the intra cluster distance was observed in cluster I (1.23), II (0.00), III (0.76), IV (2.08) and V (1.89) respectively. The highest intra cluster was recorded in cluster IV (2.08) and the lowest in cluster III (0.76) that showed that the genotypes within the intra cluster distances were closely related and inter cluster distances were recorded higher and larger than intra cluster distances. Between inter and intra cluster mean values, inter cluster distances were recorded higher than the intra cluster distances which indicated wider genetic diversity among the genotypes of different groups involved. The maximum value for the cluster distance D2 was recorded in cluster III (18.740) followed by cluster II (15.470, 13.032). The farthest cluster distance was recorded in cluster III that means it represented the highest diversified genotypes than other clusters. The nearest cluster distance was recorded in cluster IV and cluster V with 3.441 values which denoted the less diversified genotypes. In this case, days to male flowering, number of rows per cob, number of seeds per cob and 100-seed weight contributed towards cluster mean performance, the maximum in cluster number V.展开更多
The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was need...The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measured are, the more accura te the MRA is, too more temperatures will hinder the analysis calculation. So it is necessary to identify the key temperatures of the machine tool. The selectio n of key temperatures decides the efficiency and precision of MRA. Because of th e complexities and multi-input and multi-output structure of the relationships , the exact quantitative portions as well as the unclear portions must be taken into consideration together to improve the identification of key temperatures. I n this paper, a fuzzy cluster analysis was used to select the key temperatures. The substance of identifying the key temperatures is to group all temperatures b y their relativity, and then to select a temperature from each group as the repr esentation. A fuzzy cluster analysis can uncover the relationships between t he thermal field and deformations more truly and thoroughly. A fuzzy cluster ana lysis is the cluster analysis based on fuzzy sets. Given U={u i|i=0,...,N}, in which u i is the temperature measured, a fuzzy matrix R can be obta ined. The transfer close package t(R) can be deduced from R. A fuzzy clu ster of U then conducts on the basis of t(R). Based on the fuzzy cluster analysis discussed above, this paper identified the k ey temperatures of a horizontal machining center. The number of the temperatures measured was reduced to 4 from 32, and then the multiple regression relationshi p models between the 4 temperatures and the thermal deformations of the spindle were drawn. The remnant errors between the regression models and measured deform ations reached a satisfying low level. At the same time, the decreasing of tempe rature variable number improved the efficiency of measure and analysis greatly.展开更多
Gold mining is now widely acknowledged as one of the significant sources of soil pollution in developed countries. In developing countries, the sources and levels of soil contamination have not been thoroughly address...Gold mining is now widely acknowledged as one of the significant sources of soil pollution in developed countries. In developing countries, the sources and levels of soil contamination have not been thoroughly addressed. Thus, this study was intended to determine the source of soil pollution and the level of contamination in the active and closed gold mining areas. The research paper presents the pollution load of heavy metals (lead-Pb, chromium-Cr, cadmium-Cd, copper-Cu, arsenic-As, manganese-Mn, and nickel-Ni) in 90 soil samples collected from the studied sites. Multivariate statistical analysis, including Principal Component Analysis (PCA) and Cluster Analysis (CA), coupled with correlation coefficient analysis, was performed to determine the possible sources of pollution in the study areas. The results indicated that Pb, Cr, Cu and Mn come from different sources than Cd, As and Ni. The results obtained from the metal pollution assessment using the Pollution Index (PI) and the Geoaccumulation Index (Igeo) confirmed that soils in the mining areas were contaminated in the range from moderately through strongly to highly contaminated soils. This study verified that soil contamination in the gold mining areas results from natural and anthropogenic processes. The current study findings would enhance our knowledge regarding the soil contamination level in the mining areas and the source of contamination. It is recommended to use PCA, CA, PI and Igeo to assess and monitor the heavy metal contaminated soil in gold mining areas.展开更多
Total recoverable concentration of five elements of concern: Aluminum, Iron, Manganese, Arsenic and Lead (Al, Fe, Mn, As, Pb) were measured by inductively coupled plasma atomic emission spectrometry, and mass spectrom...Total recoverable concentration of five elements of concern: Aluminum, Iron, Manganese, Arsenic and Lead (Al, Fe, Mn, As, Pb) were measured by inductively coupled plasma atomic emission spectrometry, and mass spectrometry. The results show that sediment texture plays a controlling role in the concentrations and their spatial distribution. Principal Component Analysis and Cluster Analysis were used to analyze the grain sizes of the sediments. Result of texture analysis classified the samples into three main components in percentages: sand, silt, and clay. Significant differences among the element concentrations in the three groups were observed, and the concentrations of the elements in each group are reported in this study. Most of the elements have their highest concentrations in the fine-grained samples with clay playing an important role, in comparison with the sand component of the soil/sediment samples. There appears to be a strong correlation between samples with high silt, and clay content with the areas of elevated concentrations for Al, Fe, and Mn. There was a strong correlation between aluminum and lead with clay;lead with silt;and sand with manganese, aluminum, and lead. However, there was no strong relationship between the soil textures and iron or arsenic. All elements measured were statistically significant (at P ≤ 0.05) by watershed. The upland areas, and depositional areas’ spatial variation of element concentrations in the sediments were also observed, which was in line with the spatial distribution of the grain size and was thought to be related to the watersheds hydrological dynamics.展开更多
文摘A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.
基金The National Natural Science Foundation of China under contract Nos 42106005,91958203,41676131,41876155.
文摘The classification of the springtime water mass has an important influence on the hydrography,regional climate change and fishery in the Taiwan Strait.Based on 58 stations of CTD profiling data collected in the western and southwestern Taiwan Strait during the spring cruise of 2019,we analyze the spatial distributions of temperature(T)and salinity(S)in the investigation area.Then by using the fuzzy cluster method combined with the T-S similarity number,we classify the investigation area into 5 water masses:the Minzhe Coastal Water(MZCW),the Taiwan Strait Mixed Water(TSMW),the South China Sea Surface Water(SCSSW),the South China Sea Subsurface Water(SCSUW)and the Kuroshio Branch Water(KBW).The MZCW appears in the near surface layer along the western coast of Taiwan Strait,showing low-salinity(<32.0)tongues near the Minjiang River Estuary and the Xiamen Bay mouth.The TSMW covers most upper layer of the investigation area.The SCSSW is mainly distributed in the upper layer of the southwestern Taiwan Strait,beneath which is the SCSUW.The KBW is a high temperature(core value of 26.36℃)and high salinity(core value of 34.62)water mass located southeast of the Taiwan Bank and partially in the central Taiwan Strait.
文摘Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel method based on CPSO.We first evaluate the clustering performance of this model using the variance ratio criterion(VRC)as the evaluation metric.The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization(PSO)algorithm.The CPSO aims to improve the VRC value while avoiding local optimal solutions.The simulated dataset is set at three levels of overlapping:non-overlapping,partial overlapping,and severe overlapping.Finally,we compare CPSO with two other methods.Results:By observing the comparative results,our proposed CPSO method performs outstandingly.In the conditions of non-overlapping,partial overlapping,and severe overlapping,our method has the best VRC values of 1683.2,620.5,and 275.6,respectively.The mean VRC values in these three cases are 1683.2,617.8,and 222.6.Conclusion:The CPSO performed better than other methods for cluster analysis problems.CPSO is effective for cluster analysis.
文摘The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.
基金funding from the National Natural Science Foundation of China(No.41572308)。
文摘The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.
文摘The present study investigates the effect of nanoindentation on single-crystal magnesium specimens using the embedded-atom method potential in molecular dynamics simulation.Analyses are done under dynamic loading where the load-bearing capacity and change in the structural configuration are studied on the basal(Z-direction)and two prismatic planes(X-and Y-directions)with varying indenter velocities.The investigation of structural evolution is done using atomic displacement analyses to measure the net magnitude of displacement,atomic strain analyses to evaluate the shear strain developed in the process,and Wigner-Seitz defect analyses to calculate the total vacancies at varied timesteps.Furthermore,Voronoi analyses are done when indented on the basal plane to identify the cluster distribution at different planar depths of the specimen.From the analyses,it has been observed that the load-bearing capacity of the specimen varies with the indentation velocity and the direction of indentation on the specimen.Additionally,it is seen that the observed shear and total atomic displacement in the Z-direction is the least in comparison to the other two axes.The partial dislocation 1/3<-12-10>is seen to be majorly present and the population of dislocation loops is more abundant for lower indenter velocities.Furthermore,clusters<0,4,4,6>and<0,6,0,8>are the major indices developed during nanoindentation on the basal plane where they exhibit symmetrical distribution as observed from the Z-direction.
基金This research work was funded by the Institutional Fund Projects under Grant No.(IFPIP:959-611-1443)The authors gratefully acknowledge the technical and financial support provided by the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information.Precision agriculture uses datamining to advance agricultural developments.Many farmers aren’t getting the most out of their land because they don’t use precision agriculture.They harvest crops without a well-planned recommendation system.Future crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical data.Most existing research still needs to address data preprocessing and crop categorization/classification.Furthermore,statistical analysis receives less attention,despite producing more accurate and valid results.The study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH values.The research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high productivity.The presented algorithm converts discrete numerals to factors first,then reduces levels.Second,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation.The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and geo-location.Finally,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.
基金Traditional Chinese Medicine Inheritance and Innovation"Hundreds of Millions"Talent Project(QiHuang Project)-Qihuang Scholars(National Education and Development of Traditional Chinese Medicine[2018]No.12)。
文摘Objective:To explore the general differentiation and treatment of insomnia by Professor Gao Ying through drug clustering and group correspondence analysis,and provide reference for clinical diagnosis and treatment.Methods:Collect retrospective case data from outpatient system,use SPSS20.0 software to perform frequency and cluster analysis on high-frequency symptoms and drug data,and perform corresponding analysis on the clustered drug syndrome groups.Results:A total of 349 consultations in 204 patients were included.Cluster analysis of 35 symptoms and 40 flavors with a frequency of more than 10%resulted in a corresponding relationship between 7 symptom groups,6 drug groups and 5 drug syndrome groups.The medicine symptom group has a high degree of matching;the doctors distinguish and tre at insomnia with calming,clearing heat,nourishing yin,liver,spleen,qi and phlegm as the core treatment,with consistent decoction,two to pill,lily ground Huang Tang,Lily Zhimu Decoction,Wendan Decoction,Sini San,Xiao Chai Hu Tang,Xiaoyao San,etc.are commonly used prescriptions;the physician's experience is to add or subtract Danshen and Zao Ren drink,which has a wide range of applicability to various insomnia syndrome.Conclusion:Based on the cluster analysis of drug symptoms and group correspondence analysis,it can reveal the pathogenesis,treatment and class information hidden in the data of drug symptoms,which can reflect the general law of physicians'syndrome differentiation and treatment of insomnia.This method has a reference for the exploration of TCM clinical experience significance;The results of this study can provide feedback to guide the clinical diagnosis and treatment of insomnia.
文摘Objective Several clinical obstructive sleep apnea syndrome(OSAS)phenotypes associated with heterogeneous cardiovascular risk profiles have been recently identified.The purpose of this study was to identify clusters amongst these profiles that allow for the differentiation of patients.Methods This retrospective study included all moderate-to-severe OSAS patients referred to the sleep unit over a 5-year period.Demographic,symptom,comorbidity,polysomnographic,and continuous positive airway pressure(CPAP)adherence data were collected.Statistical analyses were performed to identify clusters of patients.Results A total of 567 patients were included(67%men,54±13 years,body mass index:32±7 kg/m2,65%Caucasian,32%European African).Five clusters were identified:less severe OSAS(n=172);healthier severe OSAS(n=160);poorly sleeping OSAS patients with cardiometabolic comorbidities(n=87);younger obese men with sleepiness at the wheel(n=94);sleepy obese men with very severe desaturating OSAS and cardiometabolic comorbidities(n=54).Patients in clusters 3 and 5 were older than those in clusters 2 and 4(P=0.034).Patients in clusters 4 and 5 were significantly more obese than those in the other clusters(P=0.04).No significant differences were detected in terms of symptoms and comorbidities.Polysomnographic profiles were very discriminating between clusters.CPAP adherence was similar in all clusters but,among adherent patients,daily usage was more important in cluster 1(less severe patients)than in cluster 5.Conclusion This study highlights that the typical sleepy obese middle-aged men with desaturating events represent only a minority of patients in our multi-ethnic moderate-to-severe OSAS cohort of 33%females.
基金This research is supported by the Commonwealth of Australia as represented by the Defence Science and Technology Group of the Department of Defence,and by an Australian Government Research Training Program(RTP)Scholarship。
文摘Supervised machine learning techniques have become well established in the study of spectroscopy data.However,the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis.This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data.In addition,we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain.This includes practices in data pre-processing,feature extraction,clustering distance metrics,clustering algorithms and validation techniques.Special consideration is given to the specific characteristics of IR and NIR spectroscopy data which typically includes high dimensionality and relatively low sample size.The findings highlighted a lack of quantitative analysis and evaluation in current practices for cluster analysis of IR and NIR spectroscopy data.With this in mind,we propose an analysis model or workflow with techniques specifically suited for cluster analysis of IR and NIR spectroscopy data along with a pragmatic application strategy.
文摘Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy,namely,high dimensionality and small sample size.In order to improve cluster analysis outcomes,feature selection can be used to remove redundant or irrelevant features and reduce the dimensionality.However,for cluster analysis,this must be done in an unsupervised manner without the benefit of data labels.This paper presents a novel feature selection approach for cluster analysis,utilizing clusterability metrics to remove features that least contribute to a dataset’s tendency to cluster.Two versions are presented and evaluated:The Hopkins clusterability filter which utilizes the Hopkins test for spatial randomness and the Dip clusterability filter which utilizes the Dip test for unimodality.These new techniques,along with a range of existing filter and wrapper feature selection techniques were evaluated on eleven real-world spectroscopy datasets using internal and external clustering indices.Our newly proposed Hopkins clusterability filter performed the best of the six filter techniques evaluated.However,it was observed that results varied greatly for different techniques depending on the specifics of the dataset and the number of features selected,with significant instability observed for most techniques at low numbers of features.It was identified that the genetic algorithm wrapper technique avoided this instability,performed consistently across all datasets and resulted in better results on average than utilizing the all the features in the spectra.
基金Supported by the 13th 5-Year National Science and Technology Supporting Project(2018YFC2000302)。
文摘In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of clustering,a robust cluster analysis method is proposed which is based on the diversity self-paced t-mixture model.This model firstly adopts the t-distribution as the submodel which tail is easily controllable.On this basis,it utilizes the entropy penalty expectation conditional maximal algorithm as a pre-clustering step to estimate the initial parameters.After that,this model introduces l2,1-norm as a self-paced regularization term and developes a new ECM optimization algorithm,in order to select high confidence samples from each component in training.Finally,experimental results on several real-world datasets in different noise environments show that the diversity self-paced t-mixture model outperforms the state-of-the-art clustering methods.It provides significant guidance for the construction of the robust mixture distribution model.
文摘The rolling force learning function is an important part of the hot strip parameter calculation process, and the steel classification is the basis of the model parameter calculation,which has a direct impact on the model calculation precision,and the classification of steel quality will directly affect the accuracy of the model calculation.This paper is for the classification of a certain type of steel,in order to find the optimal classification of steel model to achieve greater precision of the rolling force set.Cluster analysis is a method for the study of affinities between samples or variables,in which Distance Coefficient method can be used to demonstrate the differences between the equivalent measured values of the steel deformation resistance in hot strip rolling.Based on the characteristics of the steel components the composition calculation formula CEQ is created by the trial and error method,so as to achieve the consistency between the CEQ and the equivalent deformation resistance by cluster analysis,that is the composition calculation formula CEQ can be used to characterize the equivalent deformation resistance of the steel in hot rolling.In the actual production process,through the composition formula CEQ,the classification of the steel is made,by which the steel rolling force learning calculation is classified and the rolling force setup deviation is reduced,the rolling stability is improved.
基金Supported by Humanities and Social Sciences Project of Hubei Provincial Department of Education(14Q033)
文摘This paper establishes 13 evaluation indicators for the competitiveness of agri-food processing industry,uses factor analysis to evaluate the competitiveness of agri-food processing industry in 31 provinces(cities,autonomous regions)of China,and does cluster analysis to divide these regions into several categories according to the difference in competitiveness,in order to understand the level of competitiveness of agri-food processing industry in China.
基金Supported by Science and Technology Research and Development Project of Chengde City,Hebei Province(201706A043)Young Scholar Program of Hebei Pharmaceutical Association Hospital Pharmaceutical Research Project(2020—Hbsyxhqn0029).
文摘[Objectives]To explore the compatibility rules of neonatal parenteral nutrition(PN)prescriptions based on association rules and hierarchical cluster analysis,thereby providing a reference for standardizing neonatal parenteral nutrition supportive therapy.[Methods]The data about neonatal PN formulations prepared by the Pharmacy Intravenous Admixture Services(PIVAS)of the Affiliated Hospital of Chengde Medical University from July 2015 to June 2021 were collected.The general information of the prescriptions and the frequency of drug use were analyzed with Excel 2019;the boxplot of drug dosing was drawn using GraphPad 8.0 software;and SPSS Modeler 18.0 and SPSS Statistics 26.0 were used to perform association rules and hierarchical cluster analysis.[Results]A total of 11488 PN prescriptions were collected from 1421 newborns,involving 18 kinds of drugs,which were divided into 11 types of nutrients.Association rules analysis yielded 84 nutrient substance combinations.The combination of fat emulsion-water-soluble vitamins-fat-soluble vitamins-glucose-amino acids had the highest confidence(99.95%).The hierarchical cluster analysis divided nutrients into 5 types.[Conclusions]The prescriptions of PN for newborns were composed of five types of nutrients:amino acids,fat emulsion,glucose,water-soluble vitamins,and fat-soluble vitamins.According to the lack of electrolytes and trace elements,appropriate drugs can be chosen to meet nutritional demands.This study provides reference basis for reasonable selection of drugs for neonatal PN prescriptions and further standardization of PN supportive therapy in newborns.
文摘Hazard maps are usually prepared for each disaster, including seismic hazard maps, flood hazard maps, and landslide hazard maps. However, when the general public attempts to check their own disaster risk, most are likely not aware of the specific types of disaster. So, first of all, we need to know what kind<span style="font-family:;" "="">s</span><span style="font-family:;" "=""> of hazards are important. However, the information that integrates multiple hazards is not well maintained, and there are few such studies. On the other hand, in Japan, a lot of hazard information is being released on the Internet. So, we summarized and assessed hazard data that can be accessed online regarding shelters (where evacuees live during disasters) and their catchments (areas assigned to each shelter) in Yokohama City, Kanagawa Prefecture. Based on the results, we investigated whether a grouping by cluster analysis would allow for multi-hazard assessment. We used four natural disasters (seismic, flood, tsunami, sediment disaster) and six parameters of other population and senior population. However, since the characteristics of the population and the senior population were almost the same, only population data was used in the final examination. From the cluster analysis, it was found that it is appropriate to group the designated evacuation centers in Yokohama City into six groups. In addition, each of the six groups was found <span>to have explainable characteristics, confirming the effectiveness of multi-hazard</span> creation using cluster analysis. For example, we divided, all hazards are low, both flood and Seismic hazards are high, sediment hazards are high, etc. In many Japanese cities, disaster prevention measures have been constructed in consideration of ground hazards, mainly for earthquake disasters. In this paper, we confirmed the consistency between the evaluation results of the multi-hazard evaluated here and the existing ground hazard map and examined the usefulness of the designated evacuation center. Finally, the validity was confirmed by comparing this result with the ground hazard based on the actual measurement by the past research. In places where the seismic hazard is large, the two are consistent with the fact that the easiness of shaking by actual measurement is also large.</span>
文摘The research investigation was carried out in the experimental area of Sher-e-Bangla Agricultural university, Sher-e-Bangla Nagar, Dhaka-1207, during the late Rabi season Mid December to May 2018 to study the genetic diversity with cluster analysis for the 35 maize genotypes as experimental materials that were laid out in Randomized Complete Block Design (RCBD) with three replications. The research work was oriented to calculate and estimate the yield factor through analyzing genetic diversity involving the yield contributing characters. The maximum yield per plant (117.51 g) was estimated in the genotype G12 (Pacific) and the minimum yield per plant (51.89 g) was recorded in the genotype G17 (Dekalb Super). Due to the crossing among the 35 maize genotypes, a wide range of divergence was observed in this experiment. The highest genotypes were included in cluster number V with 12 genotypes: BHM-5, PAC-60, Pacific-98, HP-222, Khai Bhutta, AS-999, Pioneer, Duranta, Kaveri 218, Chamak-07 and Golden-984. Here, the intra cluster distance was observed in cluster I (1.23), II (0.00), III (0.76), IV (2.08) and V (1.89) respectively. The highest intra cluster was recorded in cluster IV (2.08) and the lowest in cluster III (0.76) that showed that the genotypes within the intra cluster distances were closely related and inter cluster distances were recorded higher and larger than intra cluster distances. Between inter and intra cluster mean values, inter cluster distances were recorded higher than the intra cluster distances which indicated wider genetic diversity among the genotypes of different groups involved. The maximum value for the cluster distance D2 was recorded in cluster III (18.740) followed by cluster II (15.470, 13.032). The farthest cluster distance was recorded in cluster III that means it represented the highest diversified genotypes than other clusters. The nearest cluster distance was recorded in cluster IV and cluster V with 3.441 values which denoted the less diversified genotypes. In this case, days to male flowering, number of rows per cob, number of seeds per cob and 100-seed weight contributed towards cluster mean performance, the maximum in cluster number V.
文摘The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measured are, the more accura te the MRA is, too more temperatures will hinder the analysis calculation. So it is necessary to identify the key temperatures of the machine tool. The selectio n of key temperatures decides the efficiency and precision of MRA. Because of th e complexities and multi-input and multi-output structure of the relationships , the exact quantitative portions as well as the unclear portions must be taken into consideration together to improve the identification of key temperatures. I n this paper, a fuzzy cluster analysis was used to select the key temperatures. The substance of identifying the key temperatures is to group all temperatures b y their relativity, and then to select a temperature from each group as the repr esentation. A fuzzy cluster analysis can uncover the relationships between t he thermal field and deformations more truly and thoroughly. A fuzzy cluster ana lysis is the cluster analysis based on fuzzy sets. Given U={u i|i=0,...,N}, in which u i is the temperature measured, a fuzzy matrix R can be obta ined. The transfer close package t(R) can be deduced from R. A fuzzy clu ster of U then conducts on the basis of t(R). Based on the fuzzy cluster analysis discussed above, this paper identified the k ey temperatures of a horizontal machining center. The number of the temperatures measured was reduced to 4 from 32, and then the multiple regression relationshi p models between the 4 temperatures and the thermal deformations of the spindle were drawn. The remnant errors between the regression models and measured deform ations reached a satisfying low level. At the same time, the decreasing of tempe rature variable number improved the efficiency of measure and analysis greatly.
文摘Gold mining is now widely acknowledged as one of the significant sources of soil pollution in developed countries. In developing countries, the sources and levels of soil contamination have not been thoroughly addressed. Thus, this study was intended to determine the source of soil pollution and the level of contamination in the active and closed gold mining areas. The research paper presents the pollution load of heavy metals (lead-Pb, chromium-Cr, cadmium-Cd, copper-Cu, arsenic-As, manganese-Mn, and nickel-Ni) in 90 soil samples collected from the studied sites. Multivariate statistical analysis, including Principal Component Analysis (PCA) and Cluster Analysis (CA), coupled with correlation coefficient analysis, was performed to determine the possible sources of pollution in the study areas. The results indicated that Pb, Cr, Cu and Mn come from different sources than Cd, As and Ni. The results obtained from the metal pollution assessment using the Pollution Index (PI) and the Geoaccumulation Index (Igeo) confirmed that soils in the mining areas were contaminated in the range from moderately through strongly to highly contaminated soils. This study verified that soil contamination in the gold mining areas results from natural and anthropogenic processes. The current study findings would enhance our knowledge regarding the soil contamination level in the mining areas and the source of contamination. It is recommended to use PCA, CA, PI and Igeo to assess and monitor the heavy metal contaminated soil in gold mining areas.
文摘Total recoverable concentration of five elements of concern: Aluminum, Iron, Manganese, Arsenic and Lead (Al, Fe, Mn, As, Pb) were measured by inductively coupled plasma atomic emission spectrometry, and mass spectrometry. The results show that sediment texture plays a controlling role in the concentrations and their spatial distribution. Principal Component Analysis and Cluster Analysis were used to analyze the grain sizes of the sediments. Result of texture analysis classified the samples into three main components in percentages: sand, silt, and clay. Significant differences among the element concentrations in the three groups were observed, and the concentrations of the elements in each group are reported in this study. Most of the elements have their highest concentrations in the fine-grained samples with clay playing an important role, in comparison with the sand component of the soil/sediment samples. There appears to be a strong correlation between samples with high silt, and clay content with the areas of elevated concentrations for Al, Fe, and Mn. There was a strong correlation between aluminum and lead with clay;lead with silt;and sand with manganese, aluminum, and lead. However, there was no strong relationship between the soil textures and iron or arsenic. All elements measured were statistically significant (at P ≤ 0.05) by watershed. The upland areas, and depositional areas’ spatial variation of element concentrations in the sediments were also observed, which was in line with the spatial distribution of the grain size and was thought to be related to the watersheds hydrological dynamics.
