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In the last month of 2019, a new coronavirus has been discovered. The coronavirus (COVID-19) pandemic already has caused major human suffering as well as significant economic loss. The rapid spread of this disease all around the world has made every country to implement lockdown in order to break the chain of Covid-19. The imposed of lockdown in every country all around the world have given a major impact towards political, social, human suffering as well as significant economic disruption. The COVID-19 pandemic has flipped the world of work all over the sides. It having a significant impact on workers' and their families' jobs, lives, and well-being, as well as on businesses around the world, particularly small and medium-sized businesses. This paper aims in exposing the effect of COVID-19 on the economic as well as in the employment globally. As a result, there are a lot of effect from the movement restriction order that have been imposed by the government in each nation. The imposed on movement control order with the implementation of stay at home policy and social distancing have impacted all sectors of economic which has led impact the employment. [ABSTRACT FROM AUTHOR]

Corona virus (covid-19) – an infectious illness caused by SARS-COV-2. It can be transmitted from one person to another when an infected person cough, sneeze, speak. sing or breathe. Thus, masking your face in general places is an effective method of protection. Globally, governments implemented lockdowns following the covid-19 pandemic. other transmission off the virus. However, the long period the lockdown affects peoples live hood. In order to overcome this, the government has announced partial lockdown with some rules & regulations. An efficient & economic approach of using AI and do setup a device to create a safe environment. This paper will present a hybrid face mask detection algorithm that utilizes deep learning. A device to detect masks entail of a dataset of images with mask or without mask, and with the help of open-CV in real time, a webcam stream is used to detect whether or not a face mask is worn by the person. Persons without face mask has been indicated in with an alert massage and door will not open using Arduino controller. [ABSTRACT FROM AUTHOR]

In its simplest form, a pandemic like Covid-19 progresses roughly according to a logistical law – exponentially at first till a peak rate is reached and then the curve turns and finally levels off. However, this textbook feature is interrupted by lockdowns, other pandemic mitigating protocols, and the emergence of new variants and so the picture becomes complex with multiple wavelike interferences. In this paper, we identify two components – the death rate per million of population (N) and the fractional rate of change (f) at each stage. We propose a series of hierarchically ordered indicators with a final second-order indicator X = f2N. This serves as a single number metric summarising the criticality of the rate of progress of the pandemic at that stage. We use as examples, the progress of Covid-19 at the global level, and within India. This helps to reveal the complex features of the evolution of a pandemic in a region. [ABSTRACT FROM AUTHOR]

Amidst the imposition of lock down due to COVID-19 outbreak, the functioning of several sectors got stuck and very few sectors which had an option of functioning online survived to some extent. Educational sector is one such sector that somehow managed to function with the aid of technology and gadgets. But there is a common saying that when the students are exposed to gadgets, they tend to get deviated from their goals as they are assumed to consume a lot of time with gadgets and which is assumed to be a barrier for their education. To arrive at some conclusive inferences in this case, we have conducted a survey and the interpretations obtained out of it are presented in this paper. [ABSTRACT FROM AUTHOR]

With the rapid spread of the coronavirus (COVID-19), the Malaysian government implemented a series of lockdown measures to break the chain of COVID-19 within the community. The lockdown measures were separated into three phases, namely movement control order (MCO), conditional movement control order (CMCO), and recovery movement control order (RMCO). Due to limited research conducted, this paper aims to investigate the effect of COVID-19 lockdown on mobility and traffic accidents in Malaysia. Quantitative data were collected from the internet and the government agency. They were analysed through descriptive and inferential statistics. Descriptive statistics were used to analyse the changes in mobility and the number of traffic accidents in different aspects. Meanwhile, inferential statistics such as simple regression analysis were applied to validate the relationship between mobility and the number of accidents. The results were presented using graphical and tabular methods. The findings show that the mobility dropped by 64% on average during MCO compared to a baseline value computed during the pre-lockdown period. The reduction in mobility during CMCO and RMCO was 40% and 15%, respectively. Next, the regression analysis reveals a strong positive relationship between mobility and the number of traffic accidents. Hence, the reduction in traffic accidents can be concluded as an impact of COVID-19 lockdown. The reduction in the number of accidents during the three lockdown periods was: MCO (73%), CMCO (46%), and RMCO (19%), compared to the average of 2015-2019. Besides, it was found that the reduction in the number of traffic accidents was greater than the reduction in mobility during the lockdown periods. This suggests that traffic reduction has a multiplicative effect on road safety. Moreover, the findings reveal that the reduction in severe and fatal accidents was greater than the reduction in mobility. This suggests a positive effect of COVID-19 lockdown on the severity of traffic accidents. This study provides an insight into the effect of COVID-19 lockdown on traffic patterns in Malaysia. It also provides important implications for traffic management. [ABSTRACT FROM AUTHOR]

Due to the Covid-19 pandemic which leads to lockdown and social distancing demands, face-to-face classes in most educational institutions worldwide have been ceased. As an alternative, classes have been abruptly shifted to online learning mode. This educational restructuring has impacted almost all students across fields of study, especially those in the engineering quarters whose curricula incorporate both lectures on theories and laboratory experiments which involve essential robust hands-on but can only be carried out on campus. Previous research has reported that with the abrupt shift, engineering students experience setbacks, mental stress and negative emotions. Educational psychologists believe that positive emotions or affective components are essential to students' learning success. However, literature exploring the affective components that engineering students have exhibited during Covid-19 outbreak is limited. Thus, this narrative review presents the most recent research concerning the affective components adopted by engineering students across higher education institutions globally during the existing pandemic. The affective components are exercising readiness for change, exercising self-motivation, exercising self-efficacy, exercising self-directed learning, getting support and practising self-discipline. The main contribution of this paper is a presentation of a synthesis of research literature on engineering students' affective components which have been used in surviving online learning so that there are practical insights that engineering students, together with educators and policy makers, can leverage to substantially improve their learning during the pandemic and beyond. [ABSTRACT FROM AUTHOR]