ABSTRACT
Social distancing is of key importance during the current pandemic. It helps limit the spread of COVID by observing distance between disease spreading individuals. Now it is not possible to station a person 24x7 at each queue to monitor social distancing violations. Banks, Public Offices, Malls, Schools, Theatre, etc.., usually see long queues for hours every day. To ensure social distancing in queues we hereby design a social distancing monitoring robot. The robot consists of a four wheel design system used to drive the robotic vehicle. It makes use of a line following principle to constantly move along with the queue and monitor for social distancing violations. The robot use IR sensor to travel along with the queue to and front in order to detect violations. The robot is now equipped with the obstacle detecting ultrasonic sensor in order to detect obstacles in the vehicle path. The robotic vehicle uses other ultrasonic sensor for detecting distance between two individuals in a queue. It any two individuals are found having less than three feet distance between them, the robot instantly sounds a buzzer and alert to inform about the violation, also it sends alerts of these violations along with a camera picture using WiFi over IoT to inform the higher authorities or head office to update them about violations with proof so instant disciplinary action can be taken. Thus this work allows for automatic maintaining social distancing in queues help to prevent the spread of the Corona virus
ABSTRACT
The concept of e-learning is not new to the education sector. However, in the recent years, with the outbreak of COVID-19, the importance of e-learning has grown globally. It has completely transformed the way in which learning is imparted to students. Unlike traditional chalk and board method of teaching, e-learning makes learning simpler, easier, and more effective. Earlier the access to knowledge was not accessible to all. Students with economic constraints, geographical boundaries or physical disabilities had scarce opportunities in the academic province. The concept of online education has broken all these barriers. Today's learners are engaged in appropriate, mobile, self-paced, and customized content. This need is satisfied by the online style of learning, where students can study at their own convenience and demand. This research work has proposed an E-Learning Website for Beginners especially designed keeping in mind the students of rural areas and backward classes who may not have access to correct and sufficient resources to learn the tools which are required in day-to-day life. The web-application will provide the user a complete basic knowledge on the various Microsoft Office Tools through videos, images and text materials provided on the platform. There is a quiz at the end of every course, so that students can examine themselves. Students can also download the course material from the platform. Being an open-source platform, this e-learning website can be used by any school or college students. © 2022 IEEE.
ABSTRACT
Purpose>This paper aims to focus on developing a theoretical framework for the analysis of factors influencing additive manufacturing (AM) in the health-care domain.Design/methodology/approach>A total of 18 factors are considered through extensive literature review and the relationship between each factor is studied using total interpretive structural modeling (TISM) and the model is logically developed. TISM model is developed using appropriate expert inputs. In addition, cross-impact matrix multiplication applied to classification (MICMAC) analysis is conducted to group the factors.Findings>It was found that “ease of design” and “research and development” are the two most important factors with the highest driving power and dependencies. Through MICMAC analysis, the significance of factors is studied.Practical implications>The study has been done based on inputs from academic experts and industry practitioners. The inferences from the study have practical relevance.Originality/value>The development of a structural model for the analysis of factors influencing AM in the health-care domain is the original contribution of the authors.