Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software pack...Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.展开更多
The contamination of the environment by organic pollutants is a major risk factor,particularly for developing countries.Selected organic pollutants(SOPs)like the phenolic compounds,polyaromatic hydrocarbons(PAHs),pest...The contamination of the environment by organic pollutants is a major risk factor,particularly for developing countries.Selected organic pollutants(SOPs)like the phenolic compounds,polyaromatic hydrocarbons(PAHs),pesticides,and herbicides pose serious environmental and health issues owing to their toxic characteristics and poor degradability.Apart from their potential mutagenicity,carcinogenicity,tetragenicity and high body accumulation,these pollutants have become an increase concern worldwide.Biosorption is a promising alternative strategy for removing organic pollutants during water purification processes.Biosorbents have several advantages such as simplicity of operation,good sorption capacity,high recoverability and modifiability.As a result,the focus and novelty of this review is on recent trends in the use of biosorbents,with a particular emphasis on the removal of SOPs from wastewater.It also cover use of bacteria biosorbents,fungal,algae and chitosan/chitin biosorbents.Apart from that,we have also reviewed various classes of SOPs,their levels in the environment,classification and available characteristics techniques suitable for the adsorption experiments of these nanocomposites materials.In addition,we have provided comprehensive explanations and conclusions on possible future application of biosorbents and the mechanism of adsorption of these materials for removal of these SOPs from wastewater during water purification processes.展开更多
基金supported by the Yonsei University Research Fund of 2021(2021-22-0060).
文摘Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.
基金the UJ Global Excellence and Stature(GES)for Postdoctoral Fellowship Award offered him as well as the University of Ilorin,Ilorin,Nigeria,for the one-year study leave granted the main author.The co-author(Prof J.C.Ngila)also thanks UJ Global Excellence and Stature Scholarship for the running cost paid by Water Research Commission(WRC)Project No.K5/2365.
文摘The contamination of the environment by organic pollutants is a major risk factor,particularly for developing countries.Selected organic pollutants(SOPs)like the phenolic compounds,polyaromatic hydrocarbons(PAHs),pesticides,and herbicides pose serious environmental and health issues owing to their toxic characteristics and poor degradability.Apart from their potential mutagenicity,carcinogenicity,tetragenicity and high body accumulation,these pollutants have become an increase concern worldwide.Biosorption is a promising alternative strategy for removing organic pollutants during water purification processes.Biosorbents have several advantages such as simplicity of operation,good sorption capacity,high recoverability and modifiability.As a result,the focus and novelty of this review is on recent trends in the use of biosorbents,with a particular emphasis on the removal of SOPs from wastewater.It also cover use of bacteria biosorbents,fungal,algae and chitosan/chitin biosorbents.Apart from that,we have also reviewed various classes of SOPs,their levels in the environment,classification and available characteristics techniques suitable for the adsorption experiments of these nanocomposites materials.In addition,we have provided comprehensive explanations and conclusions on possible future application of biosorbents and the mechanism of adsorption of these materials for removal of these SOPs from wastewater during water purification processes.
