Modulus of Elasticity for Grain-Supported Carbonates—Determination and Estimation for Preliminary Engineering Purposes

The determination and estimation of elastic behaviour are essential in engineering practise, especially in quarrying, mining, construction, and all engineering professions that perform operations dealing with rock materials. Young’s modulus, or modulus of elasticity, is the most important property describing the deformability of rock material. In this paper, grain-supported carbonates from Croatia are described and their elastic modulus and significant physical and mechanical properties are determined. The analysis of the collected data was performed in the R statistical environment. Estimation models based on multiple linear regression and the regression tree model were created. The methodology of model development and evaluation in R environment is described in detail. According to the more stringent coefficients (RMSECV and adjusted R2) used to evaluate the success of the estimation, simple regression tree models were found to perform well for the preliminary estimation, while more complex models based on Bagging performed very well.

Relationship between neighborhood land use structure and the spatiotemporal pattern of PM2.5 at the microscale: Evidence from the central area of Guangzhou, China

Fine particulate matter (PM2.5) is harmful to human health. Although the relationship between urban land use and PM2.5 has been studied in recent years, there has been little consideration of the relationship between land use structure and PM2.5 spatiotemporal patterns at the microscale. Based on mobile monitoring PM2.5 data and point of interest data, this paper explored their relationship with a classification and regression tree model. The results showed that PM2.5 exhibits spatiotemporal heterogeneity at the microscale. The neighborhoods’ land use structure can explain 60.4% of the PM2.5 spatiotemporal patterns. Transportation and ecology are the two most significant land use types that correlated with PM2.5 spatiotemporal patterns. Fourteen rules of neighborhood land use structures with different land use types are identified land use structure which leads to different spatiotemporal patterns of PM2.5. The higher the PM2.5 risk, the stronger the correlation with neighborhood land use structure is. The classification and regression tree model can be effectively used to judge the relationship between neighborhood land use structure and PM2.5 spatiotemporal patterns. The results provide a basis for developing appropriate measures, based on local conditions, to predict PM2.5 pollution levels at the microscale, and reduce the risk of neighborhood exposure to PM2.5.

Modeling and parameter optimization of the papermaking processes by using regression tree model and full factorial design

One of the major challenges in the pulp and paper industry is taking advantage of the large amount of data generated through its processes in order to develop models for optimization purposes, mainly in the paper-making, where the current practice for solving optimization problems is the error-proofing method. First, the multi-ple linear regression technique is applied to find the variables that affect the output pressure controlling the gap of the paper sheet between the rod sizer and spooner sections, which is the main cause of paper breaks. As a measure to determine the predictive capacity of the adjusted model, the coefficient of determination (R2) and s values for the output pressure were considered, while the variance inflation factor was used to identify and elimi-nate the collinearity problem. Considering the same amount of data available by using machine learning, the regres-sion tree was the best model based on the root mean square error (RSME) and R2. To find the optimal operating con-ditions using the regression tree model as source of output pressure measurement, a full factorial design was developed. Using an alpha level of 5%, findings show that linear regression and the regression tree model found only four independent variables as significant; thus, the regression tree model demonstrated a clear advantage over the linear regression model alone by improving operating conditions and demonstrating less variability in output pressure. Furthermore, in the present work, it was demonstrated that the adjusted models with good predictive capacity can be used to design noninvasive experiments and obtain.

Differences in Cloud Radar Phase and Power in Co- and Cross-Channel—Indicator of Lightning

Thunderstorms and especially induced lightning discharges have still not been fully understood, although they are known to cause many casualties yearly worldwide. This study aims at filling the gap of knowledge by investigating the potential of phase and power of the co- and cross-channels of a vertical cloud radar to indicate lightning close to the radar site. We performed statistical and correlation analyses of vertical profiles of phase and power spectra in the co- and the cross-channel for 38 days of thunderstorms producing lightning up to 20 km from the radar in 2018–2019. Specifically, we divided the dataset into “near” and “far” data according to the observed distance of lightning to the radar and analyzed it separately. Although the results are quite initial given the limited number of “near” data, they clearly showed different structures of “near” and “far” data, thus confirming the potential of radar data to indicate lightning. Moreover, for the first time in this study the predictability of lightning using cloud radar quantities was evaluated. We applied a Regression Tree Model to diagnose lightning and verified it using Receiver Operating Characteristic (ROC) and Critical Success Index (CSI). ROC provided surprisingly good results, while CSI was not that good but considering the very rare nature of lightning its values are high as well.