A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings

Characterization of Communes with Quality Accredited Primary Healthcare Centers in Chile

The accreditation process of primary healthcare centers in Chile has not had the same progress as in hospitals, which show high levels of compliance. The purpose of this research is to characterize the communes that have accredited family healthcare centers (CESFAMs) through socio-economic, municipal management, clinical management, and population variables by performing a principal components analysis (PCA) with biplot analysis and a grouping of communes through a hierarchical analysis. The biplot analysis and hierarchical analysis yielded the formation of three large groups of communes with accredited CESFAMs, characterized mainly by population size, number of people registered in the municipal health system, socioeconomic indicators, and financial management and clinical management variables. It was found that the communes that have accredited CESFAMs are characterized by dissimilar behavior in relation to the variables analyzed. Through the model used, it was possible to establish at least three groups of communes according to their behavior against these variables. Of these, the variables of a municipal financial nature were not decisive in achieving the accreditation of the CESFAMs of these communes. Therefore, it is possible that there are other variables or factors that could be facilitating the achievement of accreditation processes.

An application of machine learning regression to feature selection: a study of logistics performance and economic attribute

This study demonstrates how to profit from up-to-date dynamic economic big data, which contributes to selecting economic attributes that indicate logistics performance as reflected by the Logistics Performance Index (LPI). The analytical technique employs a high degree of productivity in machine learning (ML) for prediction or regression using adequate economic features. The goal of this research is to determine the ideal collection of economic attributes that best characterize a particular anticipated variable for predicting a country’s logistics performance. In addition, several potential ML regression algorithms may be used to optimize prediction accuracy. The feature selection of filter techniques of correlation and principal component analysis (PCA), as well as the embedded technique of LASSO and Elastic-net regression, is utilized. Then, based on the selected features, the ML regression approaches artificial neural network (ANN), multi-layer perceptron (MLP), support vector regression (SVR), random forest regression (RFR), and Ridge regression are used to train and validate the data set. The findings demonstrate that the PCA and Elastic-net feature sets give the closest to adequate performance based on the error measurement criteria. A feature union and intersection procedure of an acceptable feature set are used to make a more precise decision. Finally, the union of feature sets yields the best results. The findings suggest that ML algorithms are capable of assisting in the selection of a proper set of economic factors that indicate a country's logistics performance. Furthermore, the ANN was shown to be the best effective prediction model in this investigation.

Simulation and Analysis of Indoor Air Quality in Florida Using Time Series Regression (TSR) and Artificial Neural Networks (ANN) Models

Exposures to air pollutants have been associated with various acute respiratory diseases and detrimental human health. Analysis and further interpretation of air pollutant patterns are correspondingly important as monitoring them. In the present study, the 24-h and four-month indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were measured simultaneously for a laboratory in Gainesville city, Florida. The indoor PM2.5, PM10, and NO2 concentrations were predicted using multiple linear regression (MLR), time series regression (TSR), and artificial neural networks (ANN) models. The modeling conducted in this study aims to perform a cross comparison study between these models in a symmetric environment. The value of root-mean-square error was improved by 18.33% in comparison with the MLR model. In addition, the value of the coefficient of determination was improved by 24.68%. The ANN model had the best performance and could predict the target air pollutants at 10-min intervals of the studied building with 90% accuracy levels. The TSR model showed slightly better performance compared to the MLR model. These results can be accordingly referred for studies analyzing indoor air quality in similar building types and climate zones.