Groundwater quality assessment using multivariate analysis,geostatistical modeling, and water quality index(WQI): a case of study in the Boumerzoug-El Khroub valley of Northeast Algeria

In this study,the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-E1 khroub valley has been processed simultaneously with Multivariate analysis,geostatistical modeling,WQI,and geochemical modeling.Cluster analysis identified three main water types based on the major ion contents,where mineralization increased from group 1 to group 3.These groups were confirmed by FA/PCA,which demonstrated that groundwater quality is influenced by geochemical processes(water-rock interaction)and human practice(irrigation).The exponential semivariogram model WQI.Groundwater chemistry has a strong spatial structure for Mg,Na,Cl,and NO3,and a moderate spatial structure for EC,Ca,K,HCO3,and SO4.Water quality maps generated using ordinary Kriging are consistent with the HCA and PCA results.All water groups are supersaturated with respect to carbonate minerals,and dissolution of kaolinite and Ca-smectite is one of the processes responsible for hydrochemical evolution in the area.

ASSESSMENT OF LOCAL INFLUENCE IN MULTIVARIATE ANALYSIS

A recent method for assessing the local influence is introduced by Cook(1986), in which the normal curvature of the influence graph based on the likelihood displacement is used to monitor the influence of small perturbation. Since then this method has been applied to various kind of models. However, the local influence in multivariate analysis is still an unexplored area because the influence for many statistics in multivariate analysis is not convenient to handle based on the Cook's likelihood displacement. In this paper, we suggest a method with a slight modification in Cook's approach to assess the local influence of small perturbation on a certain statistic. The local influence of the perturbation on eigenvalue and eigenvector of variance-covariance matrix in theoretical and sample version is assessed, some results for the other statistics in multivariate analysis such as generalized variance, canonical correlations are studied. Finally, two examples are analysed for illustration.

Hydrogeochemical processes and multivariate analysis for groundwater quality in the arid Maadher region of Hodna,northern Algeria

This study focused on water quality and hydrogeochemical processes(evolution,origin)in the Maadher region,central Hodna in Algeria.In recent decades,the excessive exploitation of this resource due to urbanization,irrigation,and the effect of climate change reaching the countries of northern Africa have caused a decline in water levels and hydrochemical changes in the aquifer.The sampling campaign in 2019 based on 13 physicochemical parameters was carried out on the water from 32 boreholes in the study area,compared to data archives of both sampling campaigns in 1967 and 1996.The result revealed that the groundwater as a whole has moderate freshwater quality,due to its total dissolved solids(TDS)content and other dissolved ions of concern(nitrate NO),which exceed WHO standards.In addition,Piper diagram indicates that the hydrochemical facies of sulfate–chloride–nitrate–calcium(SO–Cl–NO–Catype),which globally characterizes the study area and these elements are the dominant dissolved ions.Principal component analysis and hierarchical cluster analysis(HCA)methodologies are applied in order to define the major control factors that affect the hydrochemistry of Maadher plain.Three distinct water groups were found,illustrating a different evolution of salinity(EC and TDS).The HCA indicated an interesting cluster with a distinct contamination signature and most likely with significantly higher sulfate,chloride,and nitrate concentrations.Anthropogenic processes also play an important role in the study area.The water resource comes from Bousaada Wadi,the exchange at the aquifer depth and the agricultural practices contribute to the deterioration of the quality.

A New Fracability Evaluation Approach for Shale Reservoirs Based on Multivariate Analysis: A Case Study in Zhaotong Shale Gas Demonstration Zone in Sichuan, China

Fracability characterizes the effectiveness of hydraulic fracturing.The existing assessment methods cannot reflect the actual value of the effectiveness due to a lack of comprehensive consideration and neglect of the influences of engineering factors.This study aims to solve this problem by implementing geological static data and production dynamic data in multivariate analysis in Zhaotong shale gas demonstration zone.First,the reservoir quality index(RQI)was introduced to evaluate the exploration potential by integrating the geological parameters with gray relational analysis.Moreover,the differences in fracturing fluid types and proppant sizes were considered,and the operating parameters were normalized on the basis of the equivalence principle.Finally,the general reservoir fracability index(GRFI)was proposed based on a dimensioned processing of the various parameters.A case study was conducted to verify the accuracy and feasibility of this new approach.The results demonstrate that(1)the organic carbon and gas content are adjusted to contribute the most to the calculation of the RQI,while the effective porosity contributes the least;(2)the fracturing scale is the main operating parameter determining the fracability,which has the strongest correlation with the effectiveness of fracking;and(3)the GRFI has a positive correlation with shale gas production,and the lower limit of the GRFI of 2,000 corresponds to a daily production of 50,000 m3/d;this value is defined as the threshold value of a stripper well.The GRFI is consistent with the productivity trend of shale gas wells in the research block,which suggests that the new model is accurate and practical for well candidate selection.

Multivariate analysis of serum surface-enhanced Raman spectroscopy of liver cancer patients

Early diagnosis of liver cancer plays a significant role in reducing its high mortality.In this preliminary study,the feasibility of using serum surface-enhanced Raman spectroscopy(SERS)to identify liver cancer was studied.Serum samples were obtained from liver cancer patients and healthy controls.The differences between the SERS spectra of pre-operation and postoperation of liver cancer patients were also analyzed.The general shape and trend of SERS spectra of health control and liver cancer patients were similar.Multivariate analysis,e.g.,PLSSVM,might be useful for the discrimination of serum SERS spectra of pre-operation and post-operation.

Multivariate analysis for ordination and comparison of size of Artemia cysts

It is known that the biometrics of brine shrimp cysts is of strain inherent. The analysis and comparison of the cyst charcters for the identification of unknown cysts from different geqraphical origin become mpible. In this paper, multivallate methods, mainly Principle Component Analysis (PCA) and Nonmetric Multi-dimensional Scaling (MDS), are used for the purpose. od results are gained with the analysis of the published data and some row data from cyst sizes. It shows that multivariate methods can cluster the similar characteristics of batch cysts and make out impure or mingled cysts of different origin. Amng others, MDS, with row data from the cyst measurement, can be easily used for the analysis of cyst ordination and comparison. The result demonstrates that the cysts of Chinese parthenopnetic brine shrimp are well discriminated by their geographic locations along the coastal China with larger size disttibuting in the south.

The Method for Optimum Estimation of COVID-19 Variant Type Virus Infection Status Analysis by the Multivariate Analysis Considering the Environmental Variability Impact in Japan

Currently, the estimated value of the effective reproduction number (ERN), which is an index for grasping the COVID-19 infection status, is used for important planning and evaluation of infection prevention measures. Since ERN in the Sequential SIR model fluctuates in multiple dimensions due to changes in the surrounding environment, it is difficult to set the appropriate accuracy of the uncertainty region of the estimated data. The challenge in this study is to build a mathematical model of infectious disease according to the characteristics and data characteristics of the infectious disease and select an appropriate estimation method. Highly accurate quantitative research that analyzes the validity of “how infectious diseases prevail” from an academic point of view is the key to prediction and estimation in appropriate infection situation analysis. In this study, we adopted a statistical multivariate analysis method (T method) that enables evaluation and prediction of important factors related to ERN estimation and analysis of phenomena that change in real time (time series analysis). It was clarified that it is possible to estimate with higher accuracy by applying the T method to the estimated value of ERN by the current SIR mathematical model.

Multivariate Analysis of Femoral Adductor Muscle Contracture after Total Hip Arthroplasty in Patients with Avascular Necrosis of Femoral Head

Objective:This study is to investigate the nisk factors of femoral head contracture after total hip arthroplasty(THA)in patients with avascular necrosis of femoral head.Methods:Retrospective analysis was perfomed in 361 cases of femoral head necrosis patients taking THA fom September 2016 to December 2017.A total of 179 patents with no significant preoperative adductor muscle contraction were finally enrolled in this study.These 179 patients were further divided into two groups:contracture group(64 cases)and noncompaction group(115 cases).The chi-square test was used to compare the differences between the two groups.Risk factors were identifed by logistic regession analysis.Results:of the patients included,64 patients (35.75%)developed into end adductor muscle contracture.There were signuificant differences in limb shortening.surgical history,whether taction,surgical approach,surgical methods,and fumctional training between the two goups(P<0.05).Logistic regression analysis showed that shortness of extremity,sugical approach,effective taction,surgical history,and etiology were the factors affecting femoral head contracture after THA in patients with avascular necrosis of femoral head.Conclusions:Preoperative traction therapy,surgical methods,and postoperative functional training are the factors that affect the adductor muscle contraction after THA.

Factors contributing to myasthenic crisis after thymectomy-a multivariate analysis study

Objective To evaluate the risk factors of myasthenic crisis (MC) after thymectomy for myasthenia gravis (MG) . Methods A retrospective study was conducted in 84 patients with MG from July 1995 to December 2009. The following factors were evaluated: sex,age,history of preoperative myasthenic crisis,Ossermen classification, steroid hormones use and anticholinesterase drugs before operation,operation methods,operation time,thymoma,

Subnanometer-resolved chemical imaging via multivariate analysis of tip-enhanced Raman maps

Tip-enhanced Raman spectroscopy(TERS)is a powerful surface analysis technique that can provide subnanometer-resolved images of nanostructures with site-specific chemical fingerprints.However,due to the limitation of weak Raman signals and the resultant difficulty in achieving TERS imaging with good signal-to-noise ratios(SNRs),the conventional single-peak analysis is unsuitable for distinguishing complex molecular architectures at the subnanometer scale.Here we demonstrate that the combination of subnanometer-resolved TERS imaging and advanced multivariate analysis can provide an unbiased panoramic view of the chemical identity and spatial distribution of different molecules on surfaces,yielding high-quality chemical images despite limited SNRs in individual pixel-level spectra.This methodology allows us to exploit the full power of TERS imaging and unambiguously distinguish between adjacent molecules with a resolution of~0.4 nm,as well as to resolve submolecular features and the differences in molecular adsorption configurations.Our results provide a promising methodology that promotes TERS imaging as a routine analytical technique for the analysis of complex nanostructures on surfaces.

Multivariate analysis of subjective evaluation of indoor lighting environment

Though subjective feelings affected by lighting have been studied a lot,multivariate experimental studies are lacking in this research field.Central composite rotatable design(CCRD)was applied to investigate the influence of illuminance,correlated color temperature(CCT)and illuminance uniformity on satisfaction in this study.A series of subject experiments were carried out and polynomial regression models of luminous satisfaction for different activity scenarios were generated.Illuminance and CCT affected the satisfaction significantly in visual task scenarios,and the effect of them was independent with each other,which was demonstrated by the regression model(R2=0.89).The variation of luminous satisfaction in general activity scenarios cannot be fully explained by the three investigated factors.The results contributed to the knowledge of multivariate analysis of luminous environment and indicated that much could be expected referring to the application of CCRD in this field.

Comparative Multivariate Analysis of Codon and Amino Acid Usage in Three Leishmania Genomes

Multivariate analysis of codon and amino acid usage was performed for three Leishmania species, including L. donovani, L. infantum and L. major. It was revealed that all three species are under mutational bias and translational selection. Lower GC12 and higher GC3s in all three parasites suggests that the ancestral highly expressed genes （HEGs）, compared to lowly expressed genes （LEGs）, might have been rich in AT-content. This also suggests that there must have been a faster rate of evolution under GC-bias in LEGs. It was observed from the esti- mation of synonymous/non-synonymous substitutions in HEGs that the HEG dataset of L. donovani is much closer to L. major evolutionarily. This is also supported by the higher ds value as compared to ds between L. donovani and L. major, suggesting the conservation of synonymous codon positions between these two species and the role of translational selection in shaping the composition of protein-coding genes.

Seasonal Variability of Biofouling Community Structure in the Gulf of Mannar,Southeast Coast of India:A Multivariate Approach

In this research,an Underwater Biofouling Panel(UWBFP)system was erected for the qualitative and quantitative estimation of macro fouling organisms in the Gulf of Mannar.Forty-four biofoulers were identified from four types of selected test panels.Among these biofoulers,Amphibalanus amphitrite(Darwin,1854)was the dominant one.The concrete panel encouraged the highest barnacle density compared to the other panels.Three series of test panels were used to assess the seasonal density of biofouling communities.The overall variation in barnacle count in the seaward and shoreward sides of all these three series were tested.They were found to be significantly different from each other.The greater variations in the barnacle density observed in this study in A-series of test panels could be due to the lack of or absence of other foulers to compete within the fortnight.The Shannon-Wiener species diversity index showed the highest diversity in wood substratum among the three series with greater accumulation of different types of fouling organisms.Multivariate analyses were also performed to understand the seasonal variation as well as the settlement pattern on the different directions of test panels based on validated data.PCA showed a strong variability(PC1 between 70.8%and 98.6%variance)between the directions of the panels in connection with barnacle density.The shade plot and CAP analysis segregated the short-term A-series test panels from the long-term(B-and C-series)test panels.Hence,the output was helpful in understanding the recruitment status of various faunal resources involved in the biofouling processes.

Dynamic Ensemble Multivariate Time Series Forecasting Model for PM2.5

In forecasting real time environmental factors,large data is needed to analyse the pattern behind the data values.Air pollution is a major threat towards developing countries and it is proliferating every year.Many methods in time ser-ies prediction and deep learning models to estimate the severity of air pollution.Each independent variable contributing towards pollution is necessary to analyse the trend behind the air pollution in that particular locality.This approach selects multivariate time series and coalesce a real time updatable autoregressive model to forecast Particulate matter(PM)PM2.5.To perform experimental analysis the data from the Central Pollution Control Board(CPCB)is used.Prediction is car-ried out for Chennai with seven locations and estimated PM’s using the weighted ensemble method.Proposed method for air pollution prediction unveiled effective and moored performance in long term prediction.Dynamic budge with high weighted k-models are used simultaneously and devising an ensemble helps to achieve stable forecasting.Computational time of ensemble decreases with paral-lel processing in each sub model.Weighted ensemble model shows high perfor-mance in long term prediction when compared to the traditional time series models like Vector Auto-Regression(VAR),Autoregressive Integrated with Mov-ing Average(ARIMA),Autoregressive Moving Average with Extended terms(ARMEX).Evaluation metrics like Root Mean Square Error(RMSE),Mean Absolute Error(MAE)and the time to achieve the time series are compared.

Comprehensive multivariate grey incidence degree based on principal component analysis

To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.

Surgical mortality in patients with malignant obstructive jaundice: a multivariate discriminant analysis

OBJECTIVE: To estimate the operative mortality in patients with malignant obstructive jaundice. METHODS: Twelve risk factors were analyzed using multivariate discriminant analysis in 90 patients who had been operated on. RESULTS: Operative mortality was significantly related to the following factors: age, duration of jaundice, packed RBC volume, white blood cell count and concentration of blood urine nitrogen; it was not significantly related to diseases and types of operation. The following formula was obtained: packed RBC volume×0.09954-age×0. 04018-blood urine nitrogen×0. 23693-duration of jaundice× 2. 07388-WBC count×0. 21118+5. 26593. With this formula, an operative mortality of 77. 8% was predicted. CONCLUSION: With a positive value from the formula, the patient should be operated on; otherwise non-operative treatment is advocated.

Discrimination of aqueous and vinegary extracts of Shixiao San using metabolomics coupled with multivariate data analysis and evaluation of antihyperlipidemic effect

A novel study using LCeMS(Liquid chromatography tandem mass spectrometry)coupled with multivariate data analysis and bioactivity evaluation was established for discrimination of aqueous extract and vinegar extract of Shixiao San.Batches of these two kinds of samples were subjected to analysis,and the datasets of sample codes,tR-m/z pairs and ion intensities were processed with principal component analysis(PCA).The result of score plot showed a clear classification of the aqueous and vinegar groups.And the chemical markers having great contributions to the differentiation were screened out on the loading plot.The identities of the chemical markers were performed by comparing the mass fragments and retention times with those of reference compounds and/or the known compounds published in the literatures.Based on the proposed strategy,quercetin-3-Oneohesperidoside,isorhamnetin-3-O-neohespeeridoside,kaempferol-3-O-neohesperidoside,isorhamnetin-3-O-rutinoside and isorhamnetin-3-O-(2G-a-l-rhamnosyl)-rutinoside were explored as representative markers in distinguishing the vinegar extract from the aqueous extract.The anti-hyperlipidemic activities of two processed extracts of Shixiao San were examined on serum levels of lipids,lipoprotein and blood antioxidant enzymes in a rat hyperlipidemia model,and the vinegary extract,exerting strong lipid-lowering and antioxidative effects,was superior to the aqueous extract.Therefore,boiling with vinegary was predicted as the greatest processing procedure for anti-hyperlipidemic effect of Shixiao San.Furthermore,combining the changes in the metabolic profiling and bioactivity evaluation,the five representative markers may be related to the observed antihyperlipidemic effect.

The Bio-Geographical Regions Division of Global Terrestrial Animal by Multivariate Similarity Clustering Analysis Method

A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorithms. The faunal dataset included almost all terrestrial and freshwater fauna, a total of 4631 families, 141,814 genera, and 1,334,834 species. Our findings demonstrated that suitable results were only obtained with the MSCA method, which was associated with distinct hierarchies, reasonable structuring, and furthermore, conformed to biogeographical criteria. A total of seven kingdoms and 20 sub-kingdoms were identified. We discovered that the clustering results for the higher and lower animals did not differ significantly, leading us to consider that the analysis result is convincing as the first zoogeographical division scheme for global all terrestrial animals.

Volatile profile and multivariant analysis of Sanhuang chicken breast in combination with Chinese 5-spice blend and garam masala

Sanhuang chicken is a popular native breed in China and well-known for delicious flavour.Spices could enhance the chicken meat flavour and work well in preservation.Chinese 5-spice blend(CS)and garam masala(GM)are routinely using spices in China and Pakistan,respectively.The flavour profiles of Sanhuang chicken breast(CB)and its blends with CS and GM were obtained by electronic nose(E-nose),solid-phase microextraction gas chromatography-mass spectrometry(SPME GC-MS)and GC-ion mobility spectrometry(GC-IMS).Principal component analysis(PCA)efficiently discriminated the aroma profiles of three chicken formulations.The GC-chromatographs revealed the significant aroma alterations of chicken breast meat after marination with spices.Aldehydes were the major contributors of chicken aroma,while most of the aromatic hydrocarbons were generated by spices.Almost all chicken key-compounds produced by oxidation reaction were either reduced or eliminated by marination,showing the antioxidation capacity of spices leading to meat preservation.GC-IMS is not only a rapid and comprehensive detection method,but also proved to be more sensitive than GC-MS.The substantial role of both traditional spices in enhancing flavour quality of chicken meat,and their exposure as functional ingredients in Chinese and Pakistan cuisines could lead to the cross-cultural meat trade opportunities.