Issue 12

Analysis Affect Factors of Smart Meter A PLS-SEM Neural Network

Minh Ly Duc; Que Nguyen Kieu Viet

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 12, Pages 288-301
DOI: 10.47392/irjash.2022.071

Smart electricity meters measure, control, analyze, and predict the amount of electricity used. Do the same for water and gas power. Automatically svae this monitoring data to the energy provider, for billing and tracking services. In developed countries, there has not been a consensus to accept the use of smart electricity meters, in addition to the benefits mentioned above, there are many possible risks when using smart meters. This paper examines information technology system (IS) related factors and engineering model related factors following technical readiness such as optimism, innovation insecurity, and discomfort. Accompanying that is the expectation of a smart meter, for the Vietnamese people’s intention to continuously use smart meters. The oriented approach is applied to evaluate the intention model of continuous use of smart meters, through the survey results of 500 answer samples of Vietnamese people. We propose to use a 2-layer research model to analyze the results of the user survey about the smart meter system. Most of the previous studies on smart meter systems focused on analyzing the impact of factors affecting applications, using single-step Structure Equation Modeling (SEM). The purpose of this study based on the Technology Acceptance Method (TAM) theory, describes the Artificial Neural Network (ANN) method to perform indepth analysis, yielding more accurate results than the SEM model. The study measures the relationship between the readiness for new technologies (optimision, innovation, discomfort, and insecurity). Technology acceptance (Perceived ease of use, Perceived usefulness). Expectations confirmed and Information systems acceptance (service quality, system quality, and information quality). This paper outlines the research model of the multi-analysis approach by combining Partial Least Squares Structure Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, the PLS-SEM model evaluates the factors affecting the intention to use the smart meter system. Second, ANN ranks the impact factors of important predictors from the PLS-SEM model. The findings from the PLS-SEM and ANN approach research model confirm the results obtained from PLS-SEM by ANN. In addition, ANN performs linear and non-linear relational modeling with high prediction accuracy compared with the SEM model. In addition, an Importance Performance Map Analysis (IPMA) analyzes the results accurately for factors’ important performance.

Analysis of Interpersonal Skill Learning Outcomes in Business English Students Class

Lely Novia; Muhammad Basri Wello

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 12, Pages 302-305
DOI: 10.47392/irjash.2022.072

The research examines the interpersonal competence of student communication in the Business English study program. Interpersonal competence refers to the ability of individuals to collaborate and communicate in groups, both verbally and nonverbally. People with effective interpersonal skills will be sensitive to the feelings and emotions of others around them. This ability is a way to mea- sure the quality of interpersonal communication, which includes knowledge of the rules of nonverbal communication, such as physical contact and intimacy, knowledge of interaction by context, attention to the person to whom to com- municate, and attention to the amount. This is evidenced by the test results data of class A students of the Class of 2020, the average score obtained in the listening skill aspect is 53.56, the emotional intelligence aspect is 57.65, and verbal communication is 45. 47, communication in groups is 53.27, and the average score of students totalling 45 is 53.11. There are eleven students who are at a level below average, this happens because of several factors. Then the average score in class B class of 2020, in the aspect of listening skills is 53.35, emotional intelligence is 57.67, verbal communication is 47.47, and communi- cation in the group is 53.28 while the total average number of res throughout is 53.44. There were 21 students who were in the below-average category out of a total of 43. Based on the data obtained, it shows that the scores obtained by class A and B Year 2020 are not too significantly different, this happens because the students experience the same difficulties

Comparison of multi-class motor imagery classification methods for EEG signals

Ms. Nikita; Sandeep Kumar; Prabhakar Agarwal; Manisha Bharti

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 12, Pages 306-311
DOI: 10.47392/irjash.2022.073

This paper presents a comparative study of EEG-based multiclass motor imagery classifiers based on Kullback-Leiber regularised Riemann Mean and support vector machine, hybrid one versus one classifier, linear discriminant analysis, and convolutional neural network. The paper is felt to be of inter- est to those researchers working in the motor imagery classification of EEG signals. The work presented in this paper helps to understand the basics of different multi-class motor imagery classifiers, their accuracy, and the number of channels involved.

Elastic properties of ferrite nanomaterials: A compilation and a review

Aniket Manash; Ratan Kumar; Rakesh Kumar; Pandey S C; Saurav Kumar

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 12, Pages 312-317
DOI: 10.47392/irjash.2022.074

The Poisson’s ratio, bulk modulus, Young’s modulus and modulus of rigidity are the elastic moduli that are frequently employed in engineering practice. In the industrial world, elastic data are utilised to assess material strength. When considering the polycrystalline material (such as spinel ferrites, superconduc- tors, perovskites and garnets) expose to mechanical stresses, information of the material’s magnetic, electric, elastic and dielectric properties aid in finding the material’s suitability for a given application. From the perspective of funda- mental research, understanding elastic moduli clarifies the nature of forces (i.e interatomic and interionic) in the nanomaterial. Studies on a material’s elasticity play a significant role because they are crucial for determining the strength of its binding force. The computation of elastic moduli plays a very important role in overcoming physical stresses especially in material fabrica- tion and its industrial use. The present review focuses on the determination of elastic modulli using X-ray diffractometry and infrared spectroscopy.

A Review on coating of steel with nanocomposite for industrial applications

Prabin Kumar; Rahul Kumar; Vivek Rai; Aniket Manash

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 12, Pages 318-323
DOI: 10.47392/irjash.2022.075

The current review goal to provide a thorough understanding of the nanocom- posite coating of steel for industrial applications. Based on findings from recent research, we will go into detail here about the several coating mate- rials, deposition methods, and the challenges associated with creating the best coating for steel. The two primary categories in which this article has been prepared are. In the first part, anti-corrosion coating, ceramic film, chemical vapour deposition, carbon nanotube, graphene has been presented with lat- est research. The second part focuses on industrial application of the current research including aerospace, automotive and petrochemical industry.