Modeling Haze Problems in the North of Thailand using Logistic Regression

At present, air pollution is a major problem in the upper northern region of Thailand. Air pollutants have an effect on human health, the economy and the traveling industry. The severity of this problem clearly appears every year during the dry season, from February to April. In particular it becomes very serious in March, especially in Chiang Mai province where smoke haze is a major issue. This study looked into related data from 2005-2010 covering eight principal parameters: PM10 (particulate matter with a diameter smaller than 10 micrometer), CO (carbon monoxide), NO2 (nitrogen dioxide), SO2 (sulphur dioxide), RH (relative humidity), NO (nitrogen oxide), pressure, and rainfall. Overall haze problem occurrence was calculated from a logistic regression model. Its dependence on the eight parameters stated above was determined for design conditions using the correlation coefficients with PM10. The proposed overall haze problem modeling can be used as a quantitative assessment criterion for supporting decision making to protect human health. This study proposed to predict haze problem occurrence in 2011. The agreement of the results from the mathematical model with actual measured PM10 concentration data from the Pollution Control Department was quite satisfactory

Comparisons of SVM Kernels for Insurance Data Clustering

This paper will study insurance data clustering using Support Vector Machine (SVM) approaches. It investigates the optimum condition employing the three most popular kernels of SVM, i.e., linear, polynomial, and radial basis kernel. To explore sum insured datasets, kernel comparisons for Root Mean Square Error (RMSE) and density analysis have been provided. It employs these kernels to classify based on sum insured datasets. The objective of this research is to demonstrate to industrial researchers that data grouping may be accomplished in an organized, error-free, and efficient manner utilizing R programming and the SVM approach. In this study, we check the insurance data for the sum insured with statistical methods in the form of Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROC), Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds. Then, sum insured data are followed up to classify using SVM kernels. This paper finds new ideas for evaluating insurance data using the SVM approach with multiple kernels. This novel research emphasizes the statistical analysis methods for insurance data and uses the SVM method for more accurate data classification. Finally, it informs that this research is a pure finding, and there has never been any research on this subject. This research was conducted using the sum insured data as a sample from the Office of the Insurance Commission (OIC) in Thailand as an independent insurance institution providing actual data. Doi: 10.28991/ESJ-2022-06-04-014 Full Text: PD

Analytical analysis of the magnetic field, heat generation and absorption, viscous dissipation on couple stress casson hybrid nano fluid over a nonlinear stretching surface

The aim of this research paper is to study two-dimension flow of Casson hybrid nanofluid along with magnetic field, heat generation and absorption, and viscous dissipation on a nonlinear extending surface. The primary goal of this study is to improve the heat transfer relationship, which is in high demand in the manufacturing and engineering industries. The outputs of this study will be used to reduce the energy consumption in industry and other engineering fields,for example, the achievement of energy is not enough, but also to adjust the con-sumptions of energy and this is possible only to approve the development heat transmission liquids to mechanism the expenditures of energy and to improvement. The described similarity transformation is used to convert the non-dimensionless form of the nonlinear partial differential equation to the dimensionless form of the nonlinear ordinary differential equation. An approximate analytical method is used to solve the derived dimensionless form of nonlinear ordinary differential equations, one for velocity and the other for temperature. Graphs are used to highlight the most relevant results acquired from velocity and temperature. Tables are used to describe the skin friction coefficient and the Nusselt number.The work of U·F.G. was supported by the government of the Basque Country for the ELKARTEK21/10KK-2021/00014 and ELKARTEK22/85 research programs, respectively

Mathematical modeling of infectious disease transmission in macroalgae

Abstract Understanding the infectious diseases outbreak of algae can provide significant knowledge for disease control intervention and/or prevention. We consider here a disease caused by highly pathogenic organisms that can result in the death of algae. Even though a great deal of understanding about diseases of algae has been reached, studies concerning effects of the outbreak at the population level are still rare. For this reason, we computationally model an outbreak in the algae reservoir or container systems consisting of several patches or clusters of algae being infected with a contagious infectious disease. We computationally investigate the systems as well as make some predictions via the deterministic SEIR epidemic model. We consider the factors that could affect the spread of the disease including the number of patches, the size of initial infected population, the distance between patches or spatial range, and the basic reproduction number ( R 0 $R_{0}$ ). The results provide some information that may be beneficial to algae disease control, intervention or prevention