ABSTRACT
The Covid-19 disease is a highly contagious disease that causes respiratory tracts and lung infections, where some of the cases can lead to fatalities. Malaysia recorded the first Covid-19 case on January 25th, 2020. Although the government at the time considered the disease was not a threat, as the days went by the cases started to increase. The total lockdown measure, also known as the movement control order (MCO) was declared by the new Government of Malaysia following the surge of Covid-19 cases in early March 2020. The new prime minister, following a change of government, announced that schools were to be closed, offices and non-essential business activities were told to cease operation, and people were ordered to stay in their homes. As a newly industrialized country, with little economic activities, there were significant drops in the energy demand in Malaysia. This paper analyzes the change in the power demand and energy consumption trend in Malaysia before, during and after the Covid-19 total lockdown. © 2022 IEEE.
ABSTRACT
The transmission of Covid-19 continues to be a serious concern of many institutions in industrialized countries. However, circumstances dictate that on-site work must continue in education and industry. How authorities address the pandemic is a subject of controversy, and enforcement of Covid-19 protocols locally by everyday workers and educators can be uncomfortable, if not dangerous. The Maskbot project aims to automate two of these protocols: people should wear facemasks in public areas to minimize exhalation of water droplets in the air, and people should quarantine if they are running a body temperature above nominal values. With Maskbot, enforcement of these protocols take the shape of indoor traffic control of incoming and outgoing movement. Incoming traffic is audited with a Raspberry Pi B+ loaded with a model that allows it to analyze faces for masks with the feed coming from a USB camera. Additionally, incoming traffic is checked for high body temperature with an infrared thermometer. If an unmasked or irregularly high temperature status is received, the entrance gate will block incoming traffic with barrier arms attached to two parallax servos, controlled by a linked Arduino Uno. Furthermore, these undesired states will result in an alarm going off, an LCD informing the passerby that they are not to proceed, and a picture being taken of the passerby to be sent to a building manager email. Desired states will result in a prompt to proceed and raise barrier arms. Outgoing traffic is managed by an exit gate, which raises its barrier arms on command when a leaving pedestrian waves their hand in front of an ultrasonic sensor. © American Society for Engineering Education, 2022.
ABSTRACT
The world is still following an unsustainable development pathway, facing great challenges in global CO2 emissions reduction in the coming decades. In this study, we used Kaya identity model to project CO2 emissions. We considered three main scenarios (business-as-usual, BAU;post-Copenhagen-Paris, PCP;deglobalization (COVID-19), DGC), which explored different pathways for CO2 emissions of fuel combustion to 2050 from global to national level. The results show that industrialized countries have been the largest contributors to global CO2 emissions, but some emerging economies are now among the top emitters in absolute terms. For countries at all stages of development, it's going to be important to reduce the trajectory of global greenhouse gas emissions and boost resilience to mounting climate impacts. Among the global, regional, the United States and China CO2 emissions reduction scenarios, the PCP scenario is the best CO2 emissions reduction pathway, in this scenario global CO2 emissions will reach a peak by 2029 (31.813 Gt). Renewable energy leads the transition to a lower-carbon energy mix. The COVID-19 pandemic resulted in the largest-ever decline in global emissions. © 2022 The Authors
ABSTRACT
Compared to the automotive sector, where automation is the rule, in many other less standardized sectors automation is still the exception. This could soon hurt the productivity of industrialized countries, where the unemployment is low and the population is aging. Phenomena like the recent downfall in productivity, due to lockdowns and social distancing for prevention of health hazards during the COVID19 pandemic, only add to the problem. For these reasons, the relevance, motivation and intention for more automation in less standardized sectors has probably never been higher. However, available statistics say that providers and users of technologies struggle to bring more automation into action in automation-unfriendly sectors. In this paper, we present a decision support method for investment in automation that tackles the problem: the STIC analysis. The method takes a holistic and quantitative approach tying together technological, context-related and economic input parameters and synthetizing them in a final economic indicator. Thanks to the modelling of such parameters, it is possible to gain sensibility on the technological and/or process adjustments that would have the highest impact on the efficiency of the automation, thereby delivering value for both technology users and technology providers. © 2021 IEEE.