The health and working conditions of employees have become increasingly important issues in modern society. In recent years, there has been a continuous rise in problems related to the deterioration of workers’ alth, which seriously affects their safety and overall quality of life. Although existing research has investigated various factors affecting workers’ health and working conditions, there is still a lack of studies that scientifically analyze and identify key variables from the vast number of factors. This study employs the Lasso (Least Absolute Shrinkage and Selection Operator) technique to mathematically analyze the key variables influencing workers’ health status and satisfaction with their working environment. Lasso is a technique used in machine learning to identify a small number of variables that impact the dependent variable among a large set of variables, thereby reducing model complexity and improving predictive accuracy. The results of the study can be utilized in efficiently improving workers’ health and working environments by focusing on a smaller set of impactful variables.

It is the purpose of this study to document the decline of cosmopolitism in Lasso's music. Lasso and Palestrina were spoken of important masters of Franco-Netherland School, and in many ways, Lasso(1532-94) compares with Palestrina(1525-94). Whereas Palestrina spent his entire life in and around Rome, Lasso traveled widely. Both Lasso's career and his music contrast with those of the contemporary composer. He was a Netherlander by birth, but the mutual influence of Romans, Venetians, Netherlanders, Germans, and the rest had produced something approaching a pan-European style. Wherea Palestrina concentrated his attention almost on sacred music, Lasso displayed a virtuosity in every style and genre of his time. Lasso is one of the last of the great cosmopolitan. Lasso was the most prolific composer of the 16th century. He comprised about 60 masses, 500 motets, 170 madrigals, 150 chansons, 90 german lieders, and so on. His mass was drawn from secular sources, from madrigals and chansons, occasionally from motets by himself or others. Lasso was the supreme master of 16th century motet. His motets, which owe something to the lighter style of the madrigal, were a literature themselves. In the last quarter of the 16th century, the Italian madrigal became widely popular. Under the influence of the madrigal, the chanson lost some of the characteristics it had possessed during the second third of the period. For Lasso's german lieders, he lived and worked in Germany during the his mid-twenties, so he published no lied until 1567. In a comparison of Lasso with Palestrina, certain similarities could be cited: each possessed a flawless technique, and each revelled in its use; Both were conservative musicians, perhaps by treatment but certainly in accord with the demands of the positions they held.

The objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Quebec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters forcomparison.