Application of multivariate statistical techniques in assessment of surface water quality in Second Songhua River basin,China

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.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

Chemical comparison of Semen Euphorbiae and Semen Euphorbiae Pulveratum by UPLC-Q-TOF/MS coupled with multivariate statistical techniques

In the present study,we aimed to assess the chemical composition changes of Semen Euphorbiae(SE)and Semen Euphorbiae Pulveratum(SEP)by UPLC-Q-TOF/MS and multivariate statistical methods.The UPLC-Q-TOF/MS method and SIMCA-P software were used to analyze the changes of chemical components of SE and SEP based on PCA and PLS-DA multivariate statistical methods.A"component-target-disease"network model was constructed by Intelligent Platform for Life Sciences of traditional Chinese medicine(TCM)to predict potential related diseases.The differences of chemical composition were significant between SE and SEP.Under positive ion mode,the amounts of Euphorbia factor L2,L3,L7a,L8,L9 and lathyrol were obviously decreased after processing.Under negative ion mode,the amounts of aesculetin,bisaesculetin,ingenol and cetylic acid were increased obviously,while Euphorbia factor L1,L4 and L5 were decreased obviously after processing,and the components of euphobiasteroid,aesculetin,lathyrol and linoleic acid among the 14 differentiated compounds were closely related to the SE-related diseases through the"component-target-disease"network model.UPLC-Q-TOF/MS technology in combination with multivariate statistical methods had certain advantages in studying the complex changes of chemical composition before and after manufacturing pulveratum of SE.It provided a basis for clarifying the toxicity-attenuated mechanisms of SE manufacturing pulveratum,and laid the foundation for its further development and utilization.

40 Years of Applied Linguistics: Investigating Content Areas, Research Methods, and Statistical Techniques

This review study was designed to map out the research trends through an intensive text analysis of 1,366 research articles （RAs） of applied linguistics during the past 40 years （from 1976 to 2015）. RAs were coded and analyzed by four analysts to identify their content of research, research methods, and statistical procedures. It was found that there has been an increase in the number and the average length of articles. The average length has been on the rise from 8.09 pages in 1976-1985 to 14.38 during 2006-2015. The extensive review of the RAs also revealed a broad range of themes that belonged to 34 research domains. SLA, Technology 8： Language Learning, Language Teaching Methodology, Language Testing, and Psycholinguistics were the most widely researched areas. The qualitative method with 33.97% was the dominant research method in the journals. Regarding the statistical techniques, it was illustrated that descriptive statistics, Pearson correlation, ANOVA, and t-test were the most commonly used procedures in the applied linguistic RAs.

Temporal and spatial variations hydrochemical components and driving factors in Baiyangdian Lake in the Northern Plain of China

Understanding the temporal and spatial variation of hydrochemical components in large freshwater lakes is crucial for effective management and conversation.In this study,we identify the temporalspatial characteristics and driving factors of the hydrochemical components in Baiyangdian Lake using geochemical methods(Gibbs diagram,Piper diagram and End-element diagram of ion ratio)and multivariate statistical techniques(Principal component analysis and Correlation analysis).16 sets of samples were collected from Baiyangdian Lake in May(normal season),July(flood season),and December(dry season)of 2022.Results indicate significant spatial variation in Nat,ci,SO and NO,,suggesting a strong influence of human activities.Cation concentrations exhibit greater seasonal variation in the dry season compared to the flood season,while the concentrations of the four anions show inconsistent seasonal changes due to the combined effects of river water chemical composition and human activities.The hydrochemical type of Baiyangdian Lake is primarily HCO,Cl-Na.Ca,Mg*and HCO,originate mainly from silicate and carbonate rock dissolution,while Kt,Nat and CI originate mainly from sewage and salt dissolution in sediments.SO42 may mainly stem from industrial wastewater,while NO,primarily originates from animal feces and domestic sewage.Through the use of Principal Component Analysis,it is identified that water-rock interaction(silicate and carbonate rocks dissolution,and dissolution of salt in sediments),carbonate sedimentation,sewage,agricultural fertilizer and manure,and nitrification are the main driving factors of the variation of hydrochemical components of Baiyangdian Lake across three hydrological seasons.These findings suggest the need for effective control of substandard domestic sewage discharge,optimization of agricultural fertilization strategies,and proper management of animal manure to comprehensively improve the water environment in Baiyangdian Lake.

Method“Monte Carlo”in healthcare

In public health,simulation modeling stands as an invaluable asset,enabling the evaluation of new systems without their physical implementation,experimentation with existing systems without operational adjustments,and testing system limits without real-world repercussions.In simulation modeling,the Monte Carlo method emerges as a powerful yet underutilized tool.Although the Monte Carlo method has not yet gained widespread prominence in healthcare,its technological capabilities hold promise for substantial cost reduction and risk mitigation.In this review article,we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts.We underscore the significance of experiential insights derived from simulated experimentation,especially in resource-constrained scenarios where time,financial constraints,and limited resources necessitate innovative and efficient approaches.As public health faces increasing challenges,incorporating the Monte Carlo method presents an opportunity for enhanced system construction,analysis,and evaluation.

Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction

Rainfall prediction becomes popular in real time environment due to the developments of recent technologies.Accurate and fast rainfall predictive models can be designed by the use of machine learning(ML),statistical models,etc.Besides,feature selection approaches can be derived for eliminating the curse of dimensionality problems.In this aspect,this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression(CSMO-OKRR)model for accurate rainfall prediction.The goal of the CSMO-OKRR technique is to properly predict the rainfall using the weather data.The proposed CSMO-OKRR technique encompasses three major processes namely feature selection,prediction,and parameter tuning.Initially,the CSMO algorithm is employed to derive a useful subset of features and reduce the computational complexity.In addition,the KRR model is used for the prediction of rainfall based on weather data.Lastly,the symbiotic organism search(SOS)algorithm is employed to properly tune the parameters involved in it.A series of simulations are performed to demonstrate the better performance of the CSMO-OKRR technique with respect to different measures.The simulation results reported the enhanced outcomes of the CSMO-OKRR technique with existing techniques.

The role of graveyards in species conservation and beta diversity:a vegetation appraisal of sacred habitats from Bannu,Pakistan

Graveyards or sacred groves are often places of natural vegetation protected by spiritual believers because of their sacred beliefs and indigenous culture.A study of graveyards was conducted to determine their role in species conservation,community formation,and associated indicators and species composition using multivariate statistical approaches.It was hypothesized that variations in the age of graveyards would give rise to diverse plant communities under the impact of various edaphic and climatic factors.Quantitative ecological techniques were applied to determine various phytosociological attributes.All the data were put in MS Excel for analysis in PCORD and CANOCO softwares for cluster analysis(CA),two-way cluster analysis(TWCA),indicator species analysis and canonical correspondence analysis.CA and TWCA through Sorenson distance measurements identified five major graveyard plant communities:(1)FicusBougainvillea-Chenopodium;(2)Acacia-Datura-Convolvulus;(3)Ziziphus-Vitex-Abutilon;(4)Acacia-Lantana-Salsola;and(5)Melia-Rhazya-Peganum.Species such as Capparis decidua,Herniaria hirsuta,Salvadora oliedes and Populus euphratica were only present inside graveyards rather than outside and advocate the role of graveyards in species conservation.The impact of different environmental and climatic variables plus the age of the graveyards were also assessed for comparison of plant communities and their respective indicator species.The results indicate that higher chlorine concentration,age of graveyards,low soil electrical conductivity,lower anthropogenic activities,higher nitrogen,calcium and magnesium concentrations in the soil,and sandy soils were the strong environmental variables playing a significant role in the formation of graveyard plant communities,their associated indicators and species distribution patterns.These results could further be utilized to evaluate the role of edaphic and climatic factors,indicator species and conservation management practices at a greater scale.

ALTERNATIVE METHODS FOR CONDUCTING COMPARATIVE ANALYSES OF CADASTRAL SYSTEMS

The conception of an efficient cadastral system is an important element in the development of each coun-try.It is crucial for the efficient operation of the real estate market-the security and liberty of making tr ansactions,register-ing a property,planning operations,the introduction of an ad valorem tax on property and more rational use of space.In Europe there are different types of c adastral systems,because the count ries in Europe have different cultur al back-grounds,different economical and s ocial backgrounds.Through the cent uries,many types of cadastral syste ms evolved and their differences often depend u pon local cultural heritage,physic al geography,land use,technology,etc.Compara-tive analyses of cadastral systems h ave been the subjects of many publica tions and studies in world literatur e.It was as-sessed that the useful tools in conducting comparative analyses of vario us cadastral systems include the pro cedures of statisti-cal inference.This paper presents t he results of a project to compare the performance of ten cadastral system s international-ly by creating appropriate integrated indicators of a cadastral system u sing statistical technique.Such in dicators will make it possible to compare differen t cadastral systems and present them hierarchically in relation to their quality,struc-ture,as well as legal,organization al and technological solutions.Fro m a good number of methods available,techniques originating from two spheres of statistic inference were selected:distribution free methods and multivariate analysis meth-ods.For analyses with the distribut ion free methods,FRIEDMAN’s test(FRIENDMAN’s non-parametric varian ce analy-sis)as well as KENDALL’s test(KENDALL’s compatibility ratio)were selected.For analyses with the multivariate analy-sis methods,factor analysis was selected.

Utilizing Statistical Semantic Similarity Techniques for Ontology Mapping——with Applications to AEC Standard Models

The objective of this paper is to introduce three semi-automated approaches for ontology mapping using relatedness analysis techniques. In the architecture, engineering, and construction (AEC) industry, there exist a number of ontological standards to describe the semantics of building models. Although the standards share similar scopes of interest, the task of comparing and mapping concepts among standards is challenging due to their differences in terminologies and perspectives. Ontology mapping is therefore necessary to achieve information interoperability, which allows two or more information sources to exchange data and to re-use the data for further purposes. The attribute-based approach, corpus-based approach, and name-based approach presented in this paper adopt the statistical relatedness analysis techniques to discover related concepts from heterogeneous ontologies. A pilot study is conducted on IFC and CIS/2 ontologies to evaluate the approaches. Preliminary results show that the attribute-based approach outperforms the other two approaches in terms of precision and F-measure.

Modeling and process optimization for microbial desulfurization of coal by using a two-level full factorial design

The microbial sulfur removal was investigated on high sulfur content (1.9%) coal concentrate from Tabas coal preparation plant. A mixed culture of ferrooxidans microorganisms was isolated from the tailing dam of the plant. Full factorial method was used to design laboratory test and to evaluate the effects of pH, particle size, iron sulfate concentration, pulp density, and bioleaching time on sulfur reduction. Statistical analyses of experimental data were considered and showed increases of pH and particle size had negative effects on sulfur reduction, whereas increases of pulp density and bioleaching time raised microbial desulfurization rate. According to results of designing, and regarding statistical factors, the optimum values for maximum sulfur reduction were obtained; pH (1.5), particle size (-180 μm), iron sulfate concentration (2.7 mmol/L), pulp density (10%) and bioleaching time (14 d), which leaded to 51.5% reduction from the total sulfur of sample.

Photometric redshift estimation for quasars by integration of KNN and SVM

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches （such as the k-nearest neighbor （KNN） algorithm）. In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine （SVM） methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding Zphot. Our analysis has found two dense regions with catastrophic failure, one in the range of Zphot E [0.3, 1.2] and the other in the range of Zphot E [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey （SDSS） quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different /△z/ ranges and root mean square （rms） error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN （71.96%, 83.78%, 89.73% and 0.204）.

A fast Stokes inversion technique based on quadratic regression

Stokes inversion calculation is a key process in resolving polarization information on radiation from the Sun and obtaining the associated vector magnetic fields. Even in the cases of simple local thermo- dynamic equilibrium （LTE） and where the Milne-Eddington approximation is valid, the inversion problem may not be easy to solve. The initial values for the iterations are important in handling the case with mul- tiple minima. In this paper, we develop a fast inversion technique without iterations. The time taken for computation is only 1/100 the time that the iterative algorithm takes. In addition, it can provide available initial values even in cases with lower spectral resolutions. This strategy is useful for a filter-type Stokes spectrograph, such as SDO/HMI and the developed two-dimensional real-time spectrograph （2DS）.

Multivariate analysis of surface water quality in the Three Gorges area of China and implications for water management

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.