Due to the COVID-19 epidemic, ordering food online has become very popular. This study used a structural equation model to analyze the indicators that influence the decision to order food through a food-delivery platform. The theory of planned behavior and the technology acceptance model were both used, along with a new factor, the task–technology fit (TTF) model, to study platform suitability. Data were collected using a questionnaire given to a group of 1320 consumers. The results showed that attitudes toward on-line delivery most significantly affected the behavioral intentions of the consumers, followed by subjective norms. Among attitudes, perceived ease of use was the most significant, followed by perceived usefulness and trust. The study’s results revealed that TTF had the most significant impact on perceived ease of use, followed by perceived usefulness. This means that, if a food-ordering platform is deemed appropriate, consumers will continue to use it, and business sustainability will be enhanced.

This document proposes a statistical classification model using k-means cluster analysis to classify partial discharge (PD) patterns into four categories listed as corona at high voltage side in air, corona at low voltage side in air, surface in air, and internal discharge. The independent variables in this k-means cluster analysis model are skewness, kurtosis, asymmetry, and cross correlation following the (phi)-q-n PD patterns obtained from the fingerprint analysis which is a digital signal processing technique for PD measurement. The experiments were set to simulate all four PD patterns to obtain statistical parameters into 10 independent variables from the fingerprint analysis. This document recommends using 8 final clusters instead of using 4 final clusters to achieve the accuracy of using this k-means cluster approach to 100 percent

This document proposes a statistical classification model using principal component analysis (PCA) for a data reduction approach combined with linear discriminant analysis (LDA) for a classification purpose, so called, PCA-LDA to classify PD patterns into four categories listed as corona at high voltage side in air, corona at low voltage side in air, surface in air, and internal discharge. The original independent variables in this classification model are skewness, kurtosis, asymmetry, and cross correlation following the phi - q - n PD patterns obtained from the fingerprint analysis which is a digital signal processing technique for PD measurement. The experiments were set to simulate all four PD patterns to obtain statistical parameters into 10 independent variables from the fingerprint analysis. With PCA, the original 10 independent variables can be reduced to 3 independent variables and using PCA can retains the accuracy of this classification model to 97.5 percent

This article presents a study of the use of ferrocement and expanded metal in the columns, beams, and walls of a reinforced concrete building structure to increase its seismic resistance. This study focused on a concrete building with three stories, located in Thailand. In the design of the reinforcement structure, a calculation and analysis of seismic resistance were conducted in a comparison of the original concrete building structure with the reinforced concrete building structure using the nonlinear static force method (pushover analysis method). The results showed that the seismic resistance of the reinforced concrete building structure was higher than the design value (DPT), with different seismic resistance values for the reinforced frames, columns, and walls of approximately 14%, 81%, and 19%. In particular, it had increased stiffness values for the frames, columns, and walls and increased ductility values for the walls. Moreover, the seismic resistance values originating from the reinforcement were significantly higher than those of the concrete building structure. Therefore, reinforcement should be applied to all concrete building structures with the implementation of the damage index, ensuring that it is reduced to the allowable level.

This document develops two asymptotic regression models to estimate the value of sphere gap breakdown voltage from any value of gap diameter and gap spacing under IEC 60052 table 2 and table 3 to facilitate those two IEC tables that provide limited values consisting of 12 sizes in sphere diameter ranging from 20 mm to 2000 mm and sphere gap spacing range from 0.5 mm to 1500 mm. In those two IEC tables, the relationship of sphere gap voltages from sphere gap diameter and gap spacing is nonlinear in which asymptotic regression models are proposed is in the form Voltage = b₁+ b₂e^b ₃Spacing + b₄Diameter that follows the law of diminishing returns that imitates the relationship between sphere gap voltages versus sphere gap diameter and gap spacing. By using this asymptotic regression, this document provides expressions of standard sphere gap voltages as functions of the sphere gap diameter and gap spacing. The expressions are in high accuracy measured by r² values. These proposed asymptotic regression equations can be used to estimate sphere gap voltage breakdown from any values of sphere gap diameter and gap spacing that are not specified by IEC 60052 tables.