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Moodle-based Learning Management Systems have been widely used in learning during the Covid-19 pandemic, especially in Indonesia. In this regard, this study aims to identify and describe the implementation of the Whole Language Approach in improving the ability to write simple scientific papers for students at PGRI Yogyakarta University through the Moodle-based Learning Management System. The method in this research is descriptive qualitative with data in the form of descriptions of the results of research implementation supported by observations, interviews, theoretical studies, and relevant research. The results of this study indicate an increase in the ability to write student scientific papers in the experimental class after using the Whole Language Approach through Moodle-based LMS in learning to write. Several aspects that were assessed were structure, systematics, language use, content coherence, and references used. This increase is indicated by the test numbers before and after the use of the Whole Language Approach through the Moodle-based LMS with mean values of 6.5 to 8.8 in the experimental class, and 6.7 to 7.1 in.the control class. This shows that the use of the Whole Language Approach through Moodle-based LMS is proven to improve students' ability to write simple scientific papers. [ABSTRACT FROM AUTHOR]

In this paper, we describe the dynamics of the spread of the COVID-19 disease. In this paper, a mathematical model of the problem is rewritten. Mathematical models can be used to find strategies to prevent the Coronavirus disease spread. A Susceptible-Exposed-Infected-Recovered (SEIR) mathematical model in the spread of COVID-19 disease is solved using fourth and fifth-order Runge-Kutta methods. The simulation results of the two methods show the same pattern of solution behavior as the results of the reference solution obtained using the ODE45 algorithm in the MATLAB software. Theoretically, the accuracy of the fourth-order Runge-Kutta method is not better than that of the fifth-order Runge-Kutta method. We show the correctness of this theory through simulations in this paper by solving the SEIR mathematical model for the COVID-19 disease. [ABSTRACT FROM AUTHOR]

This concept paper explores the challenges faced by employees from various industries as the world faces the COVID-19 pandemic at this point. The current situation finds that employees cannot go out to work face to face or are not allowed to come to the office physically as usual as a result of this COVID-19 pandemic outbreak. Furthermore, all citizens from almost all countries are involved in the implementation of the movement control order (MCO). However, jobs that involve day-to-day operations in the organisation still need to carry out their activities as usual for the sake of the survival of their respective organisations. Thus, there are various online platforms that are used to enable activities in the organisation to continue in the interest of stakeholders. To continue daily work activities there are several online platforms that are often used as communication media in organisations such as 'Google Meet', 'Google Classroom', 'Zoom', 'Webex', 'Microsoft Team' and 'Skype'. When the COVID-19 pandemic situation was announced to have subsided, there were organisations that adopted hybrid methods where online and face-to-face communication media were practiced for the purpose of discussions and meetings. This concept paper aims to identify the challenges faced by employees throughout working from home (WFH) when faced with this COVID-19 pandemic. Researchers have proposed three (3) recommendations in facing employee challenges throughout WFH with reference to Sociotechnical Systems Theory (STS) and Motivation-Hygiene Theory based on reference highlights of papers reviewed from 15 journals in previous studies. The results of this study can contribute to knowledge and in addition to the highlights of the work for this topic, especially to employers and employees who practice the concept of WFH. [ABSTRACT FROM AUTHOR]

For more than a year and a half, educators and students have been navigating the stormy seas of remote teaching-learning. After months of the coronavirus epidemic and countless research on its impacts on university teaching and learning, the repercussions of this unparalleled state of affairs are only partially known. This paper adds to the current research on how university teachers have dealt with it in an effort to understand the impacts on teaching the course of "design studio" that has never been taught online before. However, many are returning to in-person classes or planning to use a blended teaching approach. As a result, the second purpose of this study is to learn more about how teachers plan to teach in the future, which is an innovative topic that has piqued our attention. Six professors agreed to let us observe their design studio classes for the total period of 8 months spread over three semesters from 2020 and 2021. The majority of students had favorable experiences with online teaching and examinations, and most would welcome this format in the future, according to an analysis of these programs. The same cannot be said for teachers, who see this method of instruction as time-consuming and demanding. Simultaneously, this paper aims to highlight the advantages of remote teaching-learning models in teaching design studios, suiting students' educational needs, and aiding in their professional growth as architects. The research stems from the hypothesis that the pandemic will have a long-term impact on lesson planning, and opens a fresh discussion about multi-mode educational implications that learners, teachers, and other education actors should consider in the next phase of post-COVID-19 era before deciding on going back to the pre-COVID-19 pandemic classroom teaching-learning. [ABSTRACT FROM AUTHOR]

Promotional activities are among the most significant aspects in ensuring continual visits and engagements from visitors to heritage sites and institutions, including museums. An ineffective marketing technique will make it challenging to attract visitors and communities. This leads to the decrease number of visitors, undermining the fundamental mission of museums as an informal educational establishment for the public. However, few studies have been done on a promotional strategy for museum management in Malaysia. This paper seeks to bridge the gap by exploring the current museum's marketing technique, using the Kelantan State Museum as a case study. It aims to look further into the museum's promotional strategy, the involvement of responsible bodies, and the challenges in promoting the museum, specifically during the covid-19 pandemic. The research employed a qualitative design with purposeful approaches and snowballing techniques by interviewing the personnel at the Kelantan State Museum. The collected data were then analysed by applying Braun and Clarke's six-step thematic analysis methodology. The results identified three main themes: marketing strategies and activities, collaboration between stakeholders and challenges going forward. The overarching findings highlighted major stakeholders and their degree of participation in museum promotion. This paper also discussed the challenges posed by the museum management in promoting their establishment to the public. This include issues such as visitor expectations, funding cuts and digital adaptation; and the cessation of many museum hubs for physical visitations due to the Covid-19, leading to new rules being presented to adapt to the recent changes in ensuring visitors' safety. It is believed that the new norm has impacted the museum management's efforts to promote their institution, necessitating the development of new strategies to increase public participation. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2021 or COVID-19 has affected almost all individuals around the globe. Consequently, Malaysia is one of the nations that has been facing tremendous changes in various sectors due to COVID-19. As the number of positive cases is on the rise, the Malaysian government has executed its National COVID-19 Immunization Program to reduce the spread of COVID-19 and the severity of disease in case a vaccinated person is infected. However, there are also individuals who have contributed to vaccine refusal due to many factors including misinformation about vaccines, religious beliefs, lack of knowledge, and awareness. Therefore, this paper presents a combination of instructional design and cognitive load theory named CoLoMo model, to increase the awareness of COVID-19 among parents. The CoLoMo model is a theory for mobile application development involving three elements in mobile instruction design which include platform, design, accessibility, and design page. These elements have been chosen prior to the target users which are parents or adults. The three cognitive load theory types in this model are intrinsic, extraneous, and germane cognitive load. The combination of the mobile interaction design and cognitive load theory types will affect the user's cognitive performance which are concentration, learning capabilities and attributes. This paper also discusses the previous research on the implementation of cognitive load theory through mobile applications. Several strategies that emphasize different relevant cognitive processes which are useful to consider when developing mobile applications are also discussed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has forced many higher education institutions (HEIs) to implement online learning. Various courses including entrepreneurship were conducted by using online methods, such as synchronous and asynchronous. However, the achievement of course outcomes through these two methods has yet to be examined. As such, this paper presented the level of entrepreneurial intention exhibited by university students after they have completed an entrepreneurship course through online learning method. It further compared the entrepreneurial intention level between students who attended the course through synchronous and asynchronous online learning modes. The data were collected through questionnaire survey method, in which only quantitative data pertaining to students' background and entrepreneurial intention were gathered. A total of 315 students were randomly selected from a population of full-time students who have completed an entrepreneurship course in a local university. They were required to respond to an online self-administered questionnaire. Informed consent was obtained prior to the process of data collection and responses were treated anonymously. As for data analysis, descriptive and inferential statistical analyses were performed. Specifically, mean and standard deviation were used to determine the level of entrepreneurial intention. Internal consistency was used to confirm the reliability of data. While independent sample t-test was carried out to compare the entrepreneurial intention level between students from synchronous and asynchronous learning modes. The data demonstrated that differences were found in entrepreneurial intention between the two groups of students. Therefore, it could be concluded that synchronous and asynchronous modes yielded different level of entrepreneurial intention among students. This paper provided new insight on online learning for entrepreneurship course, especially in the COVID-19 pandemic period in which most courses were conducted online. It was also useful for HEIs to assess the effectiveness of online learning in delivering entrepreneurship course. [ABSTRACT FROM AUTHOR]

Since the Covid-19 pandemic, videotelephony has become a popular communication method. Some communication, like business meetings, school classroom activities, market transactions, etc, should be replaced with online ways because of the social distancing policy. But not all people can easily use the online meeting platform. Some still struggle with bandwidth consumption of online meeting (video conference) activities. They need more low bandwidth consumption in doing video conferences because high bandwidth consumption means high costs. This paper discusses lowering video conferences based on turning on or off cameras. Because of this case, some platforms create their video conference applications. One of the popular video conference applications is Zoom Meeting. The data used in this paper are collected by using 30 computers in a laboratory. Firstly, the total time every user turns on his camera and his bandwidth consumption is investigated, every computer is set to turn on its camera for 1, 2, 3..., 30 minutes. The result shows there is no relation between them. Secondly, the number of people who turn on their cameras and the bandwidth consumption of every user is collected. All computer cameras are turned on in the first observation, and then a computer turns off its camera every three minutes. The result shows a relation with decreasing trend between both variables, but there is heteroscedasticity between them. The data is reduced until only fifteen computers turn on its camera until one computer. The result is a strong connection between both variables. It is caused by the gallery view method is using in the observation. Lastly, we recommended only speaker view in doing Zoom Meetings. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak impacted drastically to education and most of educational institutions started preferring online education for students. However, after the settlement of the pandemic there is uncertainty among people about whether they should prefer online education for furthermore or start in offline mode to make it more interactive, so this paper is about an analysis of people's sentiments and emotions through Tweets about COVID-19 Education. This paper aims to study the reaction of people around the world toward online education during COVID-19. This study is conducted on the basis of the responses of students, teachers, parents, college professors, etc. We started with labeling the data into three sentiments namely positive, neutral, and negative and for validation then we used Machine learning (ML) classifiers namely, Logistic regression, Decision tree, Random Forest, Multilayer Perceptron (MLP), Naïve Bayes, Support vector machine (SVM), K-nearest neighbors (KNN), and XG-Boost. Then we performed emotion detection by considering 5 emotions namely happy, surprise, sad, fear, and angry and for validation we used ML classifiers. After applying all these ML approaches, the XG Boost ML classifier achieved the highest accuracy of 94% in classifying the tweets as positive, neutral, or negative, and 96% accuracy in classifying the tweets as happy, surprised, sad, fearful, or angry. [ABSTRACT FROM AUTHOR]

COVID 19 disease has spread worldwide, disrupting the economy, society, education, and health. It became crucial to understand the transmission curve and identify the best action in combating this highly transmissible epidemic. Epidemiological parameters of the deterministic Susceptible-Infected-Removed (SIR) model are widely used in explaining the characteristics and behavior of the disease spreading. Disease like COVID-19 is highly influenced by the uncontrolled factors of randomness, hence in this paper, the deterministic susceptible-infected-recovered-death (SIRD) model is extended to the stochastic SIRD (SSIRD) model. SSIRD model takes into consideration the noisy behavior of the process which explains the uncontrolled effects of the COVID-19 outbreak. The epidemiological parameter of the model changes throughout the epidemic due to external factors such as enforcement and public obedience to the control measures as well as the changes in healthcare facilities. These parameters need to be estimated. This paper estimates the epidemiological parameters of the SSIRD model using the Markov Chain Monte Carlo (MCMC) method of the Metropolis Hasting algorithm. The COVID-19 data from Malaysia and Indonesia are used and the dynamical behavior of the COVID-19 outbreak in both countries is simulated. The results show the parameter changing due to the uncontrolled factors influencing the trend of the pandemic curve. [ABSTRACT FROM AUTHOR]

The implementation of Community Activities Restrictions Enforcement (PPKM) during the Covid-19 pandemic reduced activities in the transportation sector. This paper presents the measurement of Carbon Monoxide (CO) concentrations during the PPKM in Makassar City, Indonesia. The samples were collected weekly from August to September 2021 from two traffic-heavy roads using Tedlar bags and a vacuum pump and then analyzed using the Non-Dispersive Infrared method (NDIR). Results showed that the average concentrations of CO on the first street in the morning, midday, and afternoon were 826.63±83.34 µg/m3, 685.65±57.43 µg/m3, 861.6±59.79 µg/m3, respectively; and on the second street were 875.46±77.80 µg/m3, 741.80±62.95 µg/m3, 833.04±44.80 µg/m3, respectively. These CO concentrations measured during the PPKM are lower than those measured before the PPKM on the same sites, reported in another paper. The concentrations are also much lower than those of the Air Quality Standard in Makassar, 30,000 µg/m3. This finding implies that similar policies to reduce traffic and pollution, such as car-free-day, should be enacted to improve air quality. [ABSTRACT FROM AUTHOR]

Today, the world is exposed to many widespread health crises. One of these crises is the emergence and spread of the covid-19 virus, where any person becomes infected with this disease through the rapid transmission of infection between people, either by palpation or by inhaling droplets of carriers of this virus. Hence, there is a need to build a tracking system to respond quickly to problems. This paper proposes using one of the unsupervised machine learning algorithms, the K-Means Clustering Data, to determine the areas with the most significant percentage of infections by dividing them into infected and uninfected human groups. Where the infected persons are divided into three high groups (C1), medium (C2), and low (C3), and follow the path of the infected persons using Google Maps. The contribution to this paper is to support appropriate decision-making by public health authorities by predicting endemic areas and how to address problems that occur as they can use this system to reduce other infectious diseases. [ABSTRACT FROM AUTHOR]

In this proposed system we have achieved the benefit of online voting as we all know that internet and gadgets are very good. In this pandemic situation we can prefer Online Voting System instead of offline voting system. By using the Online Voting System, we can avoid the crowd, gathering and to overcome the Covid cases. For Example, nowadays people are not maintaining social distance that can causes more Covid cases. In order to save the time and life's of the citizens of nation we can prefer Online Voting System project. This paper presents an effort to leverage benefits to achieve an effective scheme for online voting. The election can be conducted in two ways: By Paper Polling form and Mechanized Voting Form. Web-based voting refers to the decisions made by robotized polling forms. The internet casting a ballot framework is profoundly evolved and the web-based surveying framework can be supplanted by precisely and legitimately casting a ballot on the web and prompt outcomes to provide the security implementation. User-based voting will benefit from this system. Both the implementation and its implementation are made up of two parts: There are two sections: one for voters and one for administrators. In these protocols, the voter segment can be found at home or work, or through a device that allows the voter to access the information and grant data authentication. It is up to the administrator section to perform or allow the functions of a voter based on validation of voter ID, candidate registration, verification and the database result. A One Time Password module is the mainly used for the security implementation. In this research, each time a user logs in to the system, a unique password is generated. Every time a user logs on to complete an operation, an OTP is sent to their registered mobile number. This OTP is made up of a series of random digital numbers, and it changes over time. [ABSTRACT FROM AUTHOR]

On data that are not normally distributed, classical linear regression is developed into the Generalized Linear Model (GLM). One of them is logistic regression which is used for binary discrete data. A different strategy is to consider all variables (response and predictors) to be random observations from the joint probability distribution. A model with dependencies should be used because some factors are dependent. The copula model, one of which is the Gaussian copula, is a popular technique for creating multivariate models with dependencies. The Gaussian copula regression model (GCMR) is a general structure that can be used to model any form of dependent response. The Gaussian copula blends the ease of interpretation of marginal modeling with the flexibility of the dependency structure specification. In this case, the estimated parameter is the regression coefficient and the error is expressed in the dependency structure. The model estimation uses the maximum likelihood method. This paper aims to apply the GCMR model to time series data where the dependent variable is discrete with a binomial distribution. A case study was conducted to see the effect of weather factors on Covid cases in DKI Jakarta. The regression model used is binomial data logistic regression with the dependency structure expressed in the correlation matrix with the assumption that the error is obtained from the ARMA(p,q) process. The dependent variable is the number of Covid-19 cases and the independent variable is the maximum temperature, average temperature and humidity. The data are a daily time series with a range of March 1, 2020 to April 30, 2022. Based on the GCMR model, Maximum temperature, average temperature and Humidity have a significant influence on the number of Covid-19 cases in Jakarta. For this case study, the results show that the GCMR model is better than logistic regression analysis. [ABSTRACT FROM AUTHOR]

Covid-19 is a severe pandemic with a high mortality rate caused by the infectious respiratory virus SARS-CoV-2. Viruses associated with respiratory tract infections will use epithelial cells and airway mucosa as initial targets and cause respiratory tract infections or organ damage. This paper develops a model for the spread of Covid-19 with the effect of vaccination and quarantine. This model has seven variables, namely Susceptible, Vaccinated, Exposed, Symptomatic, Asymptomatic, Quarantined, and Recovered. From this model, two equilibrium points are obtained, non-endemic and endemic equilibrium points. Further, we analyze the global stability using the Lyapunov method. If basic reproduction number (ℛ0) less than one, then the non-endemic equilibrium point is globally asymptotically stable, while if basic reproduction number more than one, then the endemic equilibrium point is globally asymptotically stable. Numerical simulations are given to describe the behavior of the spread of Covid-19 in Banten Province on February 1, 2022 until May 1, 2022 with the result that increasing vaccination rates can reduce susceptible subpopulations. From the simulation results, it is obtained with a value of ℛ0 = 0.0000103<1, meaning that the Covid-19 spread model is asymptotically stable at a non-endemic equilibrium point. This indicates that the spread Covid-19 will decrease over time. [ABSTRACT FROM AUTHOR]

We describe an existing co-dynamics model of the spread of malaria and COVID-19. Mathematical model can be used to find strategies to control the spread of malaria and COVID-19, so that their spread can be minimised in a population. The mathematical model we use is of the type of the SEI (Susceptible-Exposed-Infected) model for malaria and COVID-19 co-dynamics. For this model, we have three compartments of the human population and three compartments of the vector population. The compartments are based on S (Susceptible subpopulation), E (Exposed subpopulation), and I (Infected subpopulation). Solutions to this mathematical model can be obtained using numerical methods. We use the third-order and the fourth-order Runge-Kutta methods to solve the co-dynamics model. From our simulation results, both methods perform quite well. The fourth-order Runge-Kutta method theoretically has a higher accuracy than the third-order one. Our simulations in this paper confirm that, when solving the SEI model of the spread of malaria and COVID-19, the fourth-order Runge-Kutta method performs better than the third-order Runge-Kutta method. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic problem has had a negative impact on many individuals, and as a result, there have been fatalities since there are no artificial ventilation systems. In this regard, this paper offers a suggestion for the strong electromechanical ventilator design and operation of a non-invasive, inexpensive ventilator, In this work, a strong mechanical motion control system is proposed, ensuring control of the tracking of the corresponding path of Patients' necessary breathing patterns. Results from experiments and simulations show the efficiency of the prototype's suggested design using a program ANSYS, however, the prototype is awaiting testing and approval for use in health assistance. [ABSTRACT FROM AUTHOR]

In this paper, the population was divided into seven subpopulations, namely susceptible (S), vaccination (V), exposed (L), infected not reported (I1), infected reported (I2), quarantine (Q) and recovered (R) subpopulations. Analytical study on the COVID-19 model, to determine the non-endemic equilibrium point, reproduction number, and system stability. The model solution was determined by numerical analysis, and model sensitivity analysis by changing the values of the vaccination rate and treatment rate parameters. Based on numerical simulation results, if the value of the vaccination parameter or the value of the treatment parameter increases, the number of individuals in the subpopulation infected with COVID-19 decreases, and the number of individuals recovered from the subpopulation increases. [ABSTRACT FROM AUTHOR]

The outbreak of the novel Coronavirus has taken the lives of many and has increased the demand for new and innovative medical and testing equipment. In addition to that, the longing to retain normalcy, during these difficult times, is also required. Industry 4.0 is the contemporary phase of the Industrial revolution whose backbone is 'cyber-physical' systems. It is automation of the conventional industrial and manufacturing processes using the latest technology. This paper aims to show an in-depth evaluation done on the technologies of industry 4.0 and their implementation during the Covid-19 pandemic. It also gives an overview of the major technologies used and their range of operation, towards making societies better and saving lives. [ABSTRACT FROM AUTHOR]

The paper describes the methods and tools for realizing the electronic information and educational environment of an educational institution. The paper describes the requirements for systems for monitoring and supporting the educational process. The indicators requiring attention during the functioning of the electronic learning environment are considered. The paper presented aspects of the increased system load because of covid-19 pandemic [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze students' chemical literacy through implementing an Ethical Dilemma-STEAM teaching model in chemistry acid and base lessons. The model is designed to engage students in values-based learning through dilemma stories that pose environmental problems to be solved through the development of an interdisciplinary project. The study involved 40 grade 11 students from a high school in Jakarta, Indonesia, during the 2021/2022 academic year. A qualitative research method was employed to investigate students' learning experiences through classroom observations, semi-structured interviews, reflective journals, and a chemical literacy test. The model engaged students through five steps of learning: value reflection, problem-solving and decision-making, content integration, STEAM integration, and transformation. In this study students were challenged to develop a jet-powered boat as a solution to an inter-island mobility dilemma. The study found that the Ethical Dilemma-STEAM teaching model supported students' chemical literacy development. The study demonstrated that students were encouraged to think critically by applying their conceptual understanding to practice. They were motivated to look closely at chemistry issues in everyday life, an indication that this model could be used as an alternative way to engage students to see the relevance of chemistry concepts and their application to the local environment. The paper also addresses the difficulties researchers faced in implementing an innovative approach to chemistry education during multiple COVID-19 outbreaks. [ABSTRACT FROM AUTHOR]

The technology breakthroughs have opened up unlimited potential to re-imagine and create innovations and applications that can better support human life. Due to the characteristics of the new technologies, the new innovation often disrupts the communication manner, standard development, production, trading and the entire logistics chain. The disruption is compounded by impacts caused by other global development including the climate change and COVID-19 pandemic. Social and economic structures of society have changed and the future is filled with uncertainties. Rapid adoption of technologies to achieve full digital transformation is deemed to be the solution to curb the disruption of existing structures and practices especially after the outbreak of the virus pandemic in 2019. Users and markets are demanding for new innovations to be created fast to revolutionise applications and user experiences in all sectors and aspects of human activities. This paper quoted some successful applications created using 21st century frontier technologies for better human life and its potential economic impacts. Based on the macro-level surveys done by the United Nation, consultancy firms and some industry intelligence platforms, this paper examined the level of technological adoption in digital transformation across the world, development and users' requirement of digital systems in this new adaptation era. This paper highlighted that adoption of technology for digital transformation is not only about technology, but new characteristics of the systems, policies and strategies adopted are equally important to ensure the success in the utilisation of technology for the betterment of human life. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 pandemic, engineering professions are adapting to the new conditions of working at home. For clothing patternmakers, it means the necessity to purchase both CAD/CAM systems and machinery to print markers and patterns. These devices take a lot of place at home. The same troubles suffer small clothing manufacturers and experimental departments of the large ones. Besides that, the printers and plotters must be mandatorily disposed of. They need a lot of toners, and they are expensive. Paper patterns produced in this way are used only once; after that, they become paper junk that is rarely correctly disposed of. Thus, the article aims to analyze and select a fabric cutting technique that would allow a user to avoid printing patterns. We considered different options, such as augmented reality applications and projection techniques, which have been popularized recently due to the increased numbers of available projectors on the market. Though AR is a promising method of transmitting patterns on the fabric, AR techniques are not developed enough for fabric cutting in apparel manufacturing. Thus, the projection technique was selected as a prospective one. The authors tested the technique. [ABSTRACT FROM AUTHOR]

The majority of individuals on the earth have been affected by COVID-19 as a result of which researchers from across the psychological sciences are trying to document and examine the influence of COVID-19 in.a number of ways. This paper offers a rapid and comprehensive summary of high-quality studies that are available through early 2021 that examine the mental-health issues during COVID-19 pandemic. This is required because policy and practice aimed at assisting people live healthier and happier lives requires an insight into robust patterns of evidence. After the careful analysis of the available literature it was revealed that there was an increase in anxiety, depression, despair, and distress in the early stages of the epidemic. Throughout the first year of the epidemic, however, the rates of suicide, life satisfaction, and loneliness remained relatively steady. This paper also offers suggestions to support mental health both during and after the epidemic in response to these discoveries. [ABSTRACT FROM AUTHOR]

The spread of the Corona Virus epidemic (COVID-19)arouses the human normal life and seen a million of death around the world. Researchers are designing machine learning models to predict and judge the spread of COVID-19 epidemic. The significance of this paper is to predict the number of people who will be infected with COVID19 using machine learning algorithms. In this context, four models such as Autoregressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), Bayesian Ridge Regression(BRR), and Polynomial Regression (PR) have been employed to anticipate COVID-19 prediction in this paper. All models predict newly infected case's location wise including countries, states, cities etc. for the next 30 days. Furthermore, all models have applied on the time series dataset taken from WHO's official website and build a comparative study on five evaluation metrics such as RMSE, MAE, MSE, MAPE, and R2. The feature selection has been performed on the raw datasets via. Time series properties and demographic attributes. The results showed SVM outperformed the other models, while in the available dataset the Polynomial regression performs poorly in all the predictions. This model also scales on different geography, country and city. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic had a severe impact on various services across the world. This paper analyzes the barriers and challenges in the integration of Information and communication technology (ICT) in the legal education and justice delivery system. The recent pandemic highlighted the lacunae in the traditional legal education sector and court proceedings. Thus, this paper extensively explores and scrutinizes how ICT can be seamlessly integrated in the legal education and judicial processes, such as virtual courts, e-filing systems, digital tools etc. It analyzes the various risks posed by the use of ICT including data and security concerns, algorithmic biases, access issues for marginalized and rural communities. Thus, the paper underscores the need for ICT-based solutions and aims to act as a catalyst in this transition from the traditional systems, by analyzing the different dimensions related to ICT and Artificial Intelligence. [ABSTRACT FROM AUTHOR]

Un-proctored online test/examination increases the occurrence of academic dishonesty which is a major factor in maintaining academic integrity. A major challenge for the course instructor is the lack of resources to conduct proctored test. To tackle the challenge posed by the COVID-19 pandemic, an alternative strategy to traditional invigilated test or examination is the conduction of online open book assessment. The focus of the online open book assessment is to gauge individual student's development of knowledge, learning and experience against established academic/professional standards. In the current work, quality of the examination is assessed based on the performance of the students. This is done to ensure that the students meet the adequate program outcomes to complete the requirements for higher studies or a work setting. The question paper setting pattern and analysis of the learner's performance are very critical to ensure learning according to Bloom's Taxonomy level. A case study of Engineering Chemistry open book quiz and test analysis is performed to improve the quality of the question paper. Question papers are prepared using Achievable Matrix focusing on Bloom's Taxonomy level. Preparing questions focusing on Bloom's Taxonomy level is an effective tool for potent learning that supports long term retention. The quantitative analysis is done using Normal Distribution plot and item analysis such as facilitation value and discrimination index. The mathematical analysis has given insight to improve the quality of question paper while preparing for deeper learning in higher education. In our current study we have observed the effect of quality of question paper on the distribution of marks obtained by the students. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused a sudden shift to online teaching in almost all countries of the world. This paper examines the preparedness and professionalism of teachers for online teaching at a university in India during Lockdown-1 (April 2020) and Lockdown-2 (May 2021). The paper attempts to understand the effect of the COVID-19 pandemic on teachers' preparedness and professionalism to adopt technology in classrooms. The data was collected through the semi-structured interviews of 12 teachers in April 2020 and May 2021. Online observation of teachers was also carried out to understand the preparedness and professionalism of teachers. The findings suggest that most teachers were not prepared for this rapid transition to an online mode of teaching both at the personal and contextual level. The paper finds a considerable improvement in the preparedness and professionalism of teachers after a year of online teaching. The results also highlight that universities need to devote a significant amount of resources for creating an enabling environment for effective online teaching. [ABSTRACT FROM AUTHOR]

In establishments and discrete person, the work/life balance's challenge is intensifying several employer's and employee's perception. In this accelerating society, human asset experts look for choices to emphatically affect the primary concern of their organizations, improve worker's confidence, hold them with significant organization statistics, and maintain peace in working environment. Earlier the pandemic, a common protest to remote working was the understanding that staff would untie and productivity levels would drip. But then recent survey suggests the contradictory is true – work from home efficiently means additional working. For instance, in the UK many employees are apparently putting in two spare hours per day. It's much more in the US. In near future overwork might be normalised and work-life balance becomes nothing more than an aspiration which cannot remain acceptable. This article gives human asset professionals with a recorded point of view, information and probable resolutions for organizations and employees on work/life balance. Also, this paper suggests that human resource professionals can help their organizations to exploit these variables by utilizing work/life motivations to acquire an upper hand in the financial market. Balance between work and life has consistently been a concern of those who fascinated by the idea of working life and its association with more personal satisfaction. The present paper aims to study the wok life balance of IT employees theoretical and empirical. [ABSTRACT FROM AUTHOR]

The Covid-19 epidemic has negatively impacted many industries around the world; the fashion industry is no exception. However, the business activities of the high-end fashion field have found virtual solutions to maintain operations by new technology platforms, which help improve the customer's access to the fashion brand. This paper identifies digital fashion as the most optimal solution not only in the present time but also further in the future of the fashion industry, by collecting the methods, software and tools that many fashion brands have used since 2020 and predicting that the technology industry will explode in the following years. Through analyzing and synthesizing the effectiveness of the software applications that fashion designers have used, the study presents design trends that use technology as a platform to reality clothes on computer screens, clothes that look real without the use of sewing machines and fabrics. These results provide an overview of how top brands communicate, operate and control the fashion industry in the context of the Covid-19 epidemic. On the other side, it helps expand design knowledge and thinking for countries with a growing fashion industry. In general, digital fashion is a fast, convenient, useful and effective transformation and connection between many fashion departments, design and business in the era of industry 4.0. [ABSTRACT FROM AUTHOR]

The present study is about the detailed analysis of the pandemic Corona Virus 2019 which has rocked the entire world with a maximum impact with its structure, its appearance and nomenclature, Clinical presentation and transmission, diagnostic technique. It's a respiratory illness caused by the virus SARS-CoV2 (Severe Acute Respiratory Syndrome). It's a syndrome is different from other virus syndromes as this might act as Symptomatic also as Asymptomatic. The common symptoms would be cold, fever, cough, sneezing, running nose, breathing issues, Fatigue, etc. The paper may be a vivid picture on the COVID 19 cases in Tamilnadu which is within the Southern part of India and therefore the treatments like Ventilator, Plasma, etc and therefore the remedy offered to the patients like Naturopathy, Ayurvedic, Siddha, Unani, Homeopathy, etc,.for better recovery and immunity to fight against the virus. It analyses the detailed reports and statistics during a regression form with reference to daily new death rate and total death rate with the assistance of statistical tools of the general rectilinear regression equation. the entire confirmed cases and daily new confirmed cases are plotted using Normal P-P plot of regression Standardized Residual. The Pearson correlation value is 0.837 which features a strong direct correlation between active cases and total recoveries. [ABSTRACT FROM AUTHOR]

Financial markets were the first to react on COVID-19 pandemic and were also very sensitive on following lock-outs, support policies and restrictions. Though equally exposed to the threat and lack of information, different countries reacted to the disease in a different way. In this paper we focus on European financial markets and respective representative indices to analyze their characteristics and study their response to the economic changes that followed the outbreak of the pandemic. We use six European stock market indices (FTSE 100, CAC 40, DAX, IBEX 35, FTSE MIB and STOXX Europe 600) to compare their performance before and after 2020, assess their reaction on COVID-19 and discuss their recovery. Using MLE estimation and BFGS optimization we analyze return distribution and use them as proxy for pandemic impact. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study of the trends in banking services. The consequences of the severe post-Covid-19 crisis and the isolation have had a strong impact on all areas of business. This has led to a rapid digitization and implementation of smart bank branch technology, aiming at saving financial resources. Transactional models, representing the participants in a mobile payment transaction, as well as the connections, by means of which the communication takes place, are considered. The application of artificial intelligence in the bank activities and in the activities of the new bank branches is analyzed. The trends for future development of both the bank branches and the banking operations are outlined. [ABSTRACT FROM AUTHOR]

The goal of this study is to outline the outcomes of analysis on the creation of useful math learning resources during the COVID-19 pandemic. The issue is that goods from the creation of math learning resources are required to counteract an acceleration in learning following the COVID-19 epidemic. Many of the results of development research, however, are only kept as study materials and are only preserved in Study Program documents or on Google Drive. The study was conducted utilizing the survey methodology, questionnaire data collection strategies, and development research outcomes papers completed by students of the Mathematics Education Study Program FKIP UPGRI Palembang during the COVID-19 epidemic. strategies for analyzing quantitatively described data. The outcomes of the development research take the form of products for math learning devices, with PBL/PBM, RME, CTL, and mathematics software being the most widely utilized learning models and methods. E-modules, LKPD/LKS, learning media in the form of video, and PPT are the most common product kinds. Algebra, Geometry, Calculus, trigonometry, statistics, and Arithmetic are among the mathematics topics covered throughout the development. The development's validity yields a product quality of 87.19%, while its practicality comes in at 85.48% and its potential impact is at 83.48%. According to the development research's findings, it should be given some thought if mathematics instruction is to be changed following the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Mobile payments (M-payments) refer to the deployment of mobile technologies to perform transactions. This paper develops a conceptual model for shoppers' M-payment technology innovation readiness and adoption during the Covid-19 pandemic. The model incorporates the Technology Readiness Index (TRI) as a determinant of the Technology Acceptance Model (TAM) to create a framework that contains readiness characteristics (contributors and inhibitors) affecting users' adoption and acceptance of M-payment technologies. This unified model presents mobile payment adoption as a process progressing from the antecedents of the TAM to the adoption decision. [ABSTRACT FROM AUTHOR]

The devastating effects of the COVID-19 global pandemic has forced most business transactions to shift from the conventional approach to the online medium to ensure their sustainability during this critical period. Artists are also transforming their approach from physical exhibitions to virtual exhibitions to ensure their business sustainability. Conventional or physical exhibitions draw large audiences; however, due to the COVID-19 global pandemic, such gatherings are strictly prohibited. Thus, virtual museum exhibitions will continue to be organised in the future, therefore, knowledge about factors that affect virtual exhibitions will be in demand. This review paper intended to examine the predictors that influence viewers' satisfaction in Virtual Art Exhibition. A comprehensive literature survey was carried out from March to May 2021 and the reviews reveal that there are three factors that possibly influence viewers' satisfaction, which are the technology, aesthetic value, and individual factors. This review study has outlined the fundamental aspects that will serve as a guideline for artists by considering the technology, aesthetic value and individual factors in preparing for future virtual exhibitions as well as in improving viewers' satisfaction. [ABSTRACT FROM AUTHOR]

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]

The Indonesian government has taken several policies to break the chain of transmission of the COVID-19 virus infection since the spread of this virus variant was first discovered in March 2020 in Indonesia. One of them is to require everyone to get vaccinated. This national vaccination program has been started since early January 2021 until now and expected to be one of the solutions to control the spread of coronavirus in Indonesia. This paper presents robust k-means to show the cluster of the spread of COVID-19 in Indonesia after the implementation of the vaccine policy for Indonesian citizens. The k-means algorithm is an unsupervised machine learning method. This method is well known in clustering. This is a partition clustering algorithm which is widely used in practice but in general, this method is sensitive to the presence of outliers. The variables considered in this study are the number of deaths, the number of recovered patients and the number of patients. Experimental comparisons are made with the basic k-means and robust k-means algorithm. It seems that for data on May, 2021, the cluster with k=3 in the robust case will provide closer risk levels of the provinces in Indonesia with their risk in reality, namely provinces with high, mid and low-level risk. [ABSTRACT FROM AUTHOR]

Coronavirus Disease (COVID-19) pandemic has emerged as one of the biggest challenge faced by researchers, especially in public health system. Since the virus spread and mutates quickly, strategies are needed so that the public health system doesn't collapse due to overcapacity. Modeling and forecasting accurately the COVID-19 daily new cases is very important to understand and help carry out risk management for the outbreak control. To obtain the estimation of the Indonesian daily cases of COVID-19 based on daily confirmed cases, we present a comparative study between three deep learning methods in this paper: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Long Short-Term Memory (Conv-LSTM). The data used is daily confirmed cases from September 1st to September 7th, 2021. The result is Conv-LSTM model produces better performance than other methods. Here we also provide 7 day out-of-sample forecast for daily cases from September 1st to September 7th, 2021 using each model. [ABSTRACT FROM AUTHOR]

The global crisis brought on by COVID-19 is unparalleled. Tourism is one of the many industries that have been made public by the recent COVID-19 epidemic. Indonesia itself was affected by the impact that caused a decrease in both foreign and domestic tourists. Smart tourism through the metaverse concept is one of the proposed solutions to deal with this problem, considering that metaverse has been booming lately, so it can attract the attention of tourists who cannot come directly to the tourist attractions. This paper aims to provide new insights regarding how to improve the Indonesian economy through tourism, so people realize the importance of the metaverse in the future. Moreover, we believe that the future of this metaverse will be very bright because the advantages of technology used in the metaverse are very beneficial for users (Blockchain, Crypto, Non-Fungible Token). [ABSTRACT FROM AUTHOR]

The global Covid-19 pandemic has significantly impacted various sectors in Indonesia. One sector that has a significant impact is the tourism industry, especially amusement parks and theme parks. Not much can be expected to attract visitors during a pandemic with just a regular touring video, even though the amusement park maintenance must still exist. This paper aims to develop an Augmented Reality (AR)-based Dino Park application with Unity and the SCRUM development method. This AR application was created aiming to increase user interest and experience to come to this place, such as virtual touring with AR features, providing a tourist sensation with 3D visuals because it can be moved according to the user's wishes and it is as if they have a virtual guide with information in the form of audio contained in each dinosaur fossil. The results of this study are Based on the results of the user acceptance test, an average of 83% user satisfaction levels with details (85% stated that the application is interactive to use, 83% stated the display is very attractive, 81% of the Dino Park application is very useful for people in in the field of theme park tourism, and 84% state they were satisfied with this application compared to similar applications in terms of features and appearance). [ABSTRACT FROM AUTHOR]

Recently, researchers and clinicians have been searching for new technologies to slow down or stop COVID-19 pandemic. The utility of Data Mining (DM) algorithms to suggests new opportunities to combat the spread of the new Coronavirus. This paper suggests a comparative study on data mining approaches to predict COVID19. We used common classification algorithms like the Support Vector Machines, Random Forest, Logistic Regression, K-Nearest Neighbor and Artificial Neural Network with Python simulation to compare it in metrics accuracy, recall, precision and AUC; results showed that Random Forest model had a 98.43% accuracy – which is a higher accuracy than many other previous studies known COVID-19 data mining algorithms. [ABSTRACT FROM AUTHOR]

Exponential smoothing models are used to smooth time series data and treat the impact of Trend or Seasonal in the time series. There are several models including of smoothing: Simple Exponential Smoothing, Double Smoothing and Holt-Winters smoothing. Robust statistical methods in time series models or in linear models (Regression, Experiment Design) they are a suitable to treat the problem of outlier values in data in order to obtain more reliable forecast results. In this paper, we applied the Robust Exponential smoothing method Compared with some anther exponential as :(Double smoothing, Holt-Winters Additive/Multiplicative smoothing, EST method) for estimation model parameters and forecasting are presented. and application of all these methods in Data of corona Pandemic (COVID-19) from Iraq in period (Mar. 2020 – Mar 2022), the robust exponential method is presented that applies the standard technique to preclean data from the outlier values in time series.We depended on R-packages functions as [ses(),EST, robiest(), hw(),...],it use to obtain smoothing parameters are robust. In this research we found Robust method have a good forecast performance for time series with an outlier values. [ABSTRACT FROM AUTHOR]

Data analytic approach has been widely used in combining with the traditional method in order to improve the accuracy of forecasting, especially in tourism industry. Currently, the common used approach is known as data mining technology, which can dramatically accelerate the recovery process of the tourism industry after the outbreak of COVID-19. The purpose of this paper is to review the implementation of data mining technology in tourism industry based on different case studies that were collected through selected digital databases. Based on the literature reviews, there is no doubt that data mining technology has the potential to be extremely important in tourism research. This technology can help the industrial practitioners make wise decision in future since it provides an efficient approach for identifying and comprehending customer behavior's pattern. It is hoped that this study can contribute as a reference for the future studies and are able to help destinations and tourism businesses maintain continuous supplies of tourism products and services in order to fulfill the customer's demand. [ABSTRACT FROM AUTHOR]

COVID-19 is a global public health problem that causes severe acute respiratory syndrome (SARS), and it is highly infectious with increasing mortality and often changing transmission forces. In this paper, we employ a mathematical model with SEIR-type (Susceptible–Exposed-Infected–Recovered) kinetics that takes into consideration the governmental actions and public sentiments. This model is analyzed to comprehend how the combined effects of several epidemiological factors (e.g., recovery and transmission rates) and governmental-individual reactions determine the transmission dynamics of COVID-19. The governmental-individual reactions are crucial to be examined in this epidemiological system due to: (i) to better understand the feasibility of certain intervention strategies before (and after) these measures are being implemented by the government; (ii) to comprehend the impacts of these distinct intervention measures to local communities; (iii) to create awareness and facilitate the public adherence to different preventive measures imposed by the government. Using bifurcation analysis techniques, we discover qualitatively different dynamical behaviours such as disease-free and endemic equilibria as the intensity of these epidemiological factors change. The occurrence of in-phase oscillatory dynamics, which would lead to recurrent pattern of endemic outbreaks is also evident in our findings. Our further analysis also proposes some of the intervention measures that can be enforced by the government and practiced by individuals to avoid the possibility of COVID-19 seasonal outbreaks throughout the years. Overall, the use of mathematical modelling approaches is important in order to better understand the interplay between different epidemiological and external factors in the transmission of infectious diseases. The knowledge extracted from the modelling frameworks can help to support authorities in making informed decisions so as to control the spread of the COVID-19 effectively. [ABSTRACT FROM AUTHOR]

The recent world crisis caused by the rapid spreading of the COVID-19 variants elicited how urgent it is to develop a robust and reliable epidemiological model for effective policy decision-making. The aim of this paper is to investigate the impact of con-trol measures, e.g. face mask use, on the transmission dynamics of infectious diseases pandemic using two different approaches of mathematical modeling: deterministic (which is suited for homogeneous population) and agent-based (that can capture het-erogeneous attributes across individuals and social network) modeling. We discuss and compare the ability of agent-based and deterministic modeling in describing the transmission dynamics of infectious diseases using numerical simulation of the models with face mask use in order to study its impact on the epidemic curve. For the deterministic model, we employ an optimal control method where the objective function is the minimization of the number of infected individuals and the associated cost of appli-cation of the control measures. On the other hand, the control measures of the developed agent-based model are optimized using a derivative-free optimization approach. We present the numerical resolution of the study for both the deterministic and agent-based models. The aim is to reduce the transmission of infectious diseases to keep the healthcare services below full capacity and reduce the total number of infected individuals. The numerical results of the optimal control problem show the effectiveness of agent-based and deterministic modeling in flattening the infection curve with rather different strategies of face mask use. Solutions using the agent-based models tend to be more realistic, i.e. based on cycles of stricter and less strict control measures, whereas the optimal control ones follow a full policy-enforcing with subsequent alleviation style. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected various aspects of global society's livelihood, including changes in human and forest relations as a result of social restrictions. This paper presents the challenges faced by local communities in managing and utilizing forest resources during the Covid-19 pandemic. The research sample was on Sumba, Flores, and Timor islands. Observations, interviews, and secondary data collection were carried out and analyzed descriptively-qualitatively to understand the challenges faced by local communities that manage state and community forests. The results of the research showed that even though farmers have experience in conventional farming, they are not yet supported by adequate educational and expertise capacities. This has implications for the ability to innovate in the use of forest resources during the pandemic. Institutional farmers experience a decline in the function of coordination, and this has implications for the aspects of farmers' livelihoods from the forest areas. The biggest changes occur to farmers in state forest areas and have implications for the potential for a higher poverty increase compared to community forest farmers. The Covid-19 pandemic is a lesson to build assistance strategies to empower communities to be more resilient in dealing with changes caused by disasters and pandemics. [ABSTRACT FROM AUTHOR]

During COVID-19 pandemic from 2019 onwards emergent demand of online classes has become worldwide prevalent. In the present era of pandemics along with advanced information and communication technology (ICT), institutions have shifted their pedagogy and are providing effective class room learning using web. Students are also joyful getting rid of class room constraints like modality, rigidity and inconveniency. The main objective of this research was to determine quality learning and students career growth as educational outcome via massive open online courses (MOOCS) as compared to that of traditional classes going physically offline and face to face (F2F). A total of 92 students of UG and PG level from different Indian universities and 8 countries including India were interviewed through questionnaire shared to achieve the objective. No significant difference was observed. In many research questions MOOCS were better proved than F2F. This paper will enrich the domain of distance learning, management and promote online learning using ICT. [ABSTRACT FROM AUTHOR]

Nowadays the use of Artificial Intelligence (AI) around the globe is constantly growing. These processes were accelerated even more due to the COVID-19 pandemic. In the cybersecurity realm, the real implementations of AI solutions are also constantly growing. For some enterprises, AI is considered more as a risk for overall cybersecurity while others see AI as the way forward to achieve cyber resilience. That kind of uncertainty points out that cybersecurity leaders are still not united around AI and do not fully understand the effects of utilizing it. That lack of understanding is emphasizing again the need for more research into AI for cybersecurity and in addition to that the urgency of attracting young talent to the cybersecurity career path. On the other hand, cyber threat actors are getting more and more sophisticated and may already be on the way to integrating AI into their future attacks. In this paper, we are analyzing the artificial intelligence methods for cybersecurity in Industry 4.0. In addition, we detail the challenges, motivations, and pre-conditions to be taken into consideration when using these AI methods for improving overall cybersecurity in Industry 4.0. [ABSTRACT FROM AUTHOR]

In 2020, the Covid-19 virus has become a global pandemic, which started in the Chinese province of Wuhan and spread around the world, The aim of the present study is to discuss the most important and latest methods of tracking Covid-19 patients and the strategies followed by developed countries in integrating digital technology to control the epidemic, it also covers the most important issues and discusses the difficulties these technologies as well as how people interact with and feel about the use of this type of technology. Among these methods used, are those developed by technology companies in cooperation with telecom companies, such as Covid-19 patient tracking applications that rely on raising case information. These applications have been effective (GPS, Wi-Fi, and Bluetooth), as also the most important prevention measures to be followed and symptoms that are likely to appear on infected or carriers of the virus have been announced by global health agencies, which have been accepted all over the world. In this paper we present. A progressive of digital solutions technologies from the methods used to confront COVID-19 since the appearance of the first case used to control and stop the spread of the pandemic and track infected people, to the contemporary technologies in use, depending on the country used for each of these strategies as well, in addition to the most prominent issues that impede these preventive methods. Then there are the advantage and disadvantages of these technologies, by analyzing the different features of contemporary technology in order to combat the COVID-19 pandemic, we provide a window of ideas for reviewing technological advances used to reduce and control the spread of the epidemic. Despite the emergence of various studies related to modern technology towards COVID-19, there are still limited applications and contributions of technology in this battle. On-going progress in modern technology has contributed to improving people's lives and hence there is a solid conviction that validated research plans including artificial intelligence will be of significant advantage in helping people to fight this infection. [ABSTRACT FROM AUTHOR]