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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]

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]

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]

This paper describes the detection of unusual event that occurs in ATM machine. ATM is a simple source for people to get their money in a faster way. Also, there are many thefts and crime happening in ATM. The COVID-19 outbreak is the toughest challenge we have faced. The main precautions to avoid the transmission of virus are to wear a mask and maintaining the social distance between us. Here we introduce a system to solve such problems by using a computer vision to keep track of activity and detect violations (Not Wearing Mask) and alerts a mail with violation photo attached to concerned authority. In addition to this object detection and weapon detection is also appended. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact on the transportation sector, affecting travel behaviours and modes of transportation all over the world. The study questionnaire is divided into three sections: a) socio-demographic parameters; b) mode choice factors prior to and during the COVID-19 epidemic; and c) post-COVID transportation planning. The perspectives and travel choices of people from various socioeconomic backgrounds were gathered through a web-based survey. The findings of descriptive statistical analyses and statistical choice of modes modelling both prior to and during the COVID-19 era are included in the paper. Binary logistic regression models have been created for determining the tendency of people to travel for various reasons, such as work, study, and other trips. The findings found that critical employees were about 2% more likely than the overall population to travel for work during the epidemic. Furthermore, having a car improves the likelihood of making a journey for primary needs during the pandemic. Multinomial logistic regression forecasts categorical position inside or the likelihood of belonging to a category for predictor variables by using a number of unique parameters as inputs. If the overall transport sector is perceived to be improperly transferring to post-pandemic circumstances, the perception regarding public transport being unsafe may gain traction and be sustained. While executing the survey, the questionnaire was addressed by public transport guidelines. The percentages of public acceptance show how much people are willing to accept these strategies. The study's findings may be beneficial for planning transport during epidemics and determining travel constraints by considering people's travel motives and mode choices. [ABSTRACT FROM AUTHOR]

The SARS-COV-2 virus had infected over 600 million people, and this global pandemic seems endless. The individual interaction affects the virus transmission but is ignored in most reported prediction models. In this paper, we build a Susceptible-Exposed-Infected-Recovered (SEIR) model to include the individual interactions with the direct simulation Monte Carlo (DSMC) method. The classical pandemic model is a series of ordinary differential equations with constant parameters. In the real world, as viruses proliferate in the human body, the critical parameter of the pandemic is time-varying. In this state, the result from the present DSMC method shows the time to reach the peak number of patients is linearly related to the infectious period, which is beyond the capability of the traditional deterministic model. This study provides several valuable results which are potentially useful to the centers for disease control and prevention (CDC) in the future. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has caused severe repercussions that has affected many lives in Malaysia especially university or college students who still struggle with studying in the ongoing pandemic which physical distancing and quarantine are in place. Most of the educational institutions in the world were closed and the teaching and learning activities were placed online. Students have been severely impacted both physically and mentally, as some students are unable to manage their time for academic work due to a lack of motivation caused by online learning and other factors. This study aims to investigate the impact of social media and time management during the pandemic for students and to develop a mobile app to assist students who are facing time management issues or social isolation during this pandemic. This paper presents the architecture and the tools being used to develop the mobile app. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the wind flow in a building that has been built and equipped with a Heating, Ventilation, and Air Conditioning (HVAC) system. With the number of COVID-19 cases still increasing every day, society and the environment must adapt to conditions that require high awareness of everyone's health. Based on previous research, it is proven that the possibility of airborne dispersal is higher in a full room, such as a classroom. One of the important health protocols is to emphasize good ventilation so that indoor air circulation can run perfectly. The notion of the passive design itself refers to the architectural design strategies used by designers to develop designs based on environmental conditions. The concept used to achieve passive design is to increase the comfort of building users. Observations were made on the 5th floor of the i-CELL building, an integrated engineering laboratory building in Universitas Indonesia, as a typical floor sample in a building to obtain data on the existing state of the building. Observations were made by measuring existing data and then simulating the recommendations for passive cooling optimization as a design intervention. As a result of this study, the recommendation to use sun shading to direct the wind into the building could potentially cause an increase of indoor airflow. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear mathematics model that is formulated and analyzed to assess the impacts of quarantine and intense medical intervention on the dynamics of Coronavirus Disease 2019 (COVID-19). An extended SEIR model with quarantine and the hospitalization compartment is proposed to simulate the COVID-19 spread. The model considers seven stages of infection: susceptible, exposed, asymptomatic, symptomatic, quarantined asymptomatic, hospitalized symptomatically, and recovered. A deterministic system of bilinear incidence nonlinear differential equations is used to model it. It was found that the model has two equilibrium points: the disease-free equilibrium point and the endemic equilibrium point. The next-generation matrix approach is used to calculate the basic reproduction numbers. The global asymptotical stability of the proposed model's equilibria is examined using the Lyapunov function and the LaSalle invariance principle. When the basic reproduction number is less than unity, the disease-free equilibrium point is globally stable, and when the basic reproduction number is more than unity, the endemic equilibrium point with a special case is globally stable, which takes the form of a deterministic system of nonlinear differential equations with bilinear incidence. Sensitivity analysis and numerical simulation results, using a reasonable set of parameter values. Finally, we investigate how the prevalence of COVID-19 is affected by the rates of transmission, quarantine, and hospitalization. [ABSTRACT FROM AUTHOR]

Recent economic, political and behavioral changes have put both traditional and modern management methods under stress. When facing supply shortages, high volatility and uncertainty about near future, it becomes crucial to be able to decide and act quickly. Unfortunately, the information that we need to decide is rarely available and requires substantial efforts and time to be collected and preprocessed. In this paper we discuss an automated system that can track and verify different information sources in order to extract valuable details and support the decision-making process. To demonstrate the benefits of such approach we analyze it the context of migration tracking in terms of crises – such like recent COVID pandemic. This process impacts not only the disease development, but it also influences industrial managers by depriving them of resources and forcing them to plan for peaks in number of sick leaves, unexpected resource shortages and delivery problems. [ABSTRACT FROM AUTHOR]

The Non-Homogeneous Poisson Process has been used to determine the occurrence of COVID-19 in an area. Being a Stochastic Process of the Poisson distribution, it deals with the probability of rare events. Unlike the other distributions, including the Poisson distribution and the Homogeneous Poisson Process, the Non-Homogeneous Poisson Process incorporates the idea of having non-homogeneous intensities. In this paper, we apply the process of modelling the trend of COVID-19 cumulative data in Baguio City during Alert Level 1 from March 1, 2022 to May 31, 2022. As part of model fitting, the Linear Intensity Function and the Power Law Process were incorporated. Applying the Non-Homogeneous Poisson Process in determining the best fit for the data through the Markov Chain Monte Carlo algorithm, it concluded that the daily cumulative cases and daily cumulative recoveries follow the Linear Intensity Function. On the other hand, the daily cumulative death follows the Power Law Process. [ABSTRACT FROM AUTHOR]

In response to the outbreak of COVID-19 pandemic, vaccination programmes have emerged as the top priority for many countries over the world. In spite of various strategic measures in accelerating vaccination, the implementation of vaccination planning is greatly driven by the vaccine supply and demand which are uncertain (stochastic). Thus, a properly-developed vaccination planning model is essentially needed to carry out the vaccination programme so that the residents can get vaccinated as soon as possible. Correspondingly, this paper aims to integrate both aspects of stochastic vaccine supply and demand in vaccination planning. Besides, the set-up of numerous types of operational vaccination centres, with varying capacities, would be determined appropriately under uncertainties. By analyzing an illustrative case study, the results revealed that stochastic supply and demand would highly affect the vaccination planning. In particular, the total vaccination demand met by the vaccine supply could reach up to 115% in relation to the initial targeted level. In average, the vaccination programme would require 711 additional vaccination centres (which comprises mobile, small, semi-mega and mega centres) throughout the planning horizon. This would result in a total of 36,710,900 vaccinations within 24 weeks. Concisely, it is anticipated that the developed study is advantageous to assist the relevant authorities to implement the vaccination planning appropriately in achieving herd immunity rapidly. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has opened doors to the academic community to explore more possibilities for innovations. The present paper discusses three prototypes developed for the user's safety during the pandemic lifestyle. The modules offer alert and hygiene practices to helps users not to get affected by the Covid-19 Virus. The first module intended for user alerts to maintain social distance concerning nearest humans. The module prompts the buzzer if a person comes near within one-meter vicinity. The Second module is an automatic hand sanitiser dispenser for self-clean hands with disinfectant liquid. It uses IR sensors for hand proximity detection to trigger a pump to deliver sanitiser to the hands. The last module dispenses water into a glass. The sensors incorporated are ultrasound and IR sensors for glass detection, automatic water level and user water level control. These modules components, methodology and operation, are discussed. The cost-effective modules offer a long-standing performance with an Embedded board. [ABSTRACT FROM AUTHOR]

This research aimed to determine the level of academic burnout on online learning for junior high school, high school, and college students in East Java. Online learning was one of the government's efforts to minimize the effect of the COVID-19 virus. The use of gadgets for too long could cause various emotional and physical disturbances for the user. Data was collected via a questionnaire asking about the effect of online learning on academic burnout for students in East Java. There were 334 respondents, consists of 102 junior high school students (31%), 104 high school students (31%), and 128 college students (38%). Academic burnout was measured using three dimensions, namely emotional exhaustion, cynicism, and reduced academic efficacy. This present study shows 67.66% of junior high school students, 71.12% of senior high school students, and 68.50% of collage students experienced academic burnout. Online learning increases academic burnout, where the rate of emotional exhaustion is high, however the rate of cynicism and reduced academic efficacy are moderate. Students who prefer offline learning compared to online learning have a higher rate of cynicism because they felt burdened with the assignments given, and the material taught in class was not clear. The analysis of academic burnout from these three dimensions is also concluded in this paper. [ABSTRACT FROM AUTHOR]

During the Covid-19 pandemic, it is important to have systems that support patient recovery, for example, a robot that can help in delivering various medical supplies to patients. This paper describes the process of designing a low-cost robot that can be used for helping patients during their self-isolation. The robot is composed of two main parts: a manipulator and a mobile base. The manipulator's joints were made using a 3D printer articulated by several DC servos, whereas the mobile base was constructed using three DC motors equipped with omni-wheels. Both parts were controlled using a single Raspberry Pi that provides all means for data acquisition, control, and communication. The manipulator was also designed with the ability to avoid fall over by adding a controllable beam that works as a balancer. On the other hand, the mobile base was also equipped with a proximity sensor that can be used to measure the distance of objects surrounding the robot. The manipulator can be controlled by using a PID or a Fuzzy controller. From the experiment, we observed that the Fuzzy controller has a slower response compared to the PID one with a difference between 0.23 and 1.07 seconds. For the mobile base, we used only an inverse kinematics proportional controller with odometry calculation that results in the smooth robot movement with error at about 9.5% on the x-axis and 8.1% on the y-axis. [ABSTRACT FROM AUTHOR]

This paper studied the hierarchy of end-of-use options for smartphones to support online learning during COVID-19 pandemic in Indonesia. The EOU options were first studied by reviewing relevant literature about e-waste in Indonesia and then a hierarchy of EOU options was drew up. Finally, integrating the result with the analysis from the literature review on the use of smartphones in online learning during the pandemic, we proposed a new hierarchy that could bridge the challenges in both aspects. As a result, it helps to reduce e-waste from used smartphones and at the same time helps underprivileged students to obtain smartphones for online learning. [ABSTRACT FROM AUTHOR]

This paper examines the effects of COVID-19 on the capital structure of publicly listed firms in Malaysia. Using panel data regressions and total debt as proxy for capital structure, we study the relation between the leverage and asset tangibility, firm size, market to book value, liquidity, profitability, growth opportunity together with crisis dummies where it is built based on a sample of 478 firms listed on Bursa Malaysia within industrial products & services, consumer products & services, property, construction, energy, health care, plantation, technology, telecommunications & media, and transportation & logistics industries before and during COVID-19 pandemic for the period of 2016-2020. [ABSTRACT FROM AUTHOR]

This paper studied the impact of COVID-19 on SME. We study the impact of COVID-19 in terms of demand, supply, employment, long term financing and short-term financing. Survival strategies for SME during COVID-19 are also incorporated in this study. To study the impact of COVID-19 on SMEs empirically, primary data will be collected using questionnaires and will be analysed by quantitative approach. The sample size of research is targeted to achieve 384 respondents. Methods used for data collection of this research are mainly adapted questionnaires which are sourced from the public documents of the US Bureau of Labour Statistics. [ABSTRACT FROM AUTHOR]

The global expansion of online education is a natural process in developing the information society. The purpose of this research is to study the factors that influence the stakeholders' attitudes towards online learning and their willingness to buy online courses. To achieve the purpose of the research, there was carried out a quantitative analysis of the results of a survey comprising 120 respondents regarding the factors that determine the demand for online courses, taking into account the expansion of user experience during the COVID-19 pandemic. The paper shows that quarantine during the COVID-19 pandemic is a social factor in the demand for online courses, which led to a significant improvement in the stakeholders' attitude to online education. Also, the results of inductive analysis of respondents' answers showed that these are students, self-employed and employees in the age group from 25 to 45 years old with sufficient income, motivation and the ability to study online who are more inclined to buy online courses. Most students and employees prefer short-term courses that suit their income and goals. The self-employed, being more demanding of the online education outcomes, prefer long-term and more expensive courses. The analysis of the survey did not reveal significant differences in these aspects by gender. These results are consistent with the results of a statistical study on the topic "Would you be willing to pay for an online course to obtain business-related knowledge?" held in early 2020 (at the start of the COVID-19 pandemic). The results of the study can be used as an informational and analytical base for evaluating trends in the development of online education. [ABSTRACT FROM AUTHOR]

Malaysia's standard operating procedure forbids the provision of Muslimah prayer outfits in public prayer facilities to minimize the risk of the COVID-19 virus spread among the shared-outfits users. The reuse of personal prayer outfits, overcrowding and spending longer in public prayer facilities can also increase the risk of fomite and airborne transmission by people with the virus. Thus, a Muslimah prayer outfit that forbids the spread of infectious diseases is crucially important. This paper presents a prayer gown that is made of disposable materials. Making efficient use of material, the gown is designed to be a single piece, comfortable and low cost. Besides the shari'ah-compliance features (reasonably thick fabric, black colour, elastic sleeves cuff and face opening), the prayer outfit provides a feasible solution to curb the transmission of contagious diseases amongst the prayers. It also serves as a gateway toward safe and easy travel for Muslim women. Introducing the disposable prayer gown helps achieve the perfect balance of the health, economic and social challenges of COVID-19 and other contagious illnesses. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) outbreak–first emerged in December 2019–has left a mark on how people throughout the world live, particularly students at educational institutions. Those students had to prepare for the rest of their studies emotionally and physically by adjusting to virtual teaching and learning methods, which could make a substantial impact on their academic achievement. As a result, this study was carriedout to investigate students' academic performance expectations depending on their various localities during the pandemic to forecast their academic success for the following semester. This research enables a variety of steps to be constructed to improve and maintain students' achievement in their learning activities. The research involved university students from the University of Education Sultan Idris' Faculty of Science and Mathematics. The Support Vector Machine model was used to predict a student's academic performance based on the respective locality in this paper. [ABSTRACT FROM AUTHOR]

Efficiency in the stock market is an essential component of the capital allocation system and is crucial in promoting economic growth. In recent years, there has been a growing number of publications focusing on the effect of Covid-19 on human norms. Although there are many studies on the impact of Covid-19 on human norms, the empirical implications of the Covid-19 pandemic on financial markets are still poorly understood. This paper adds to the literature by discussing the effects of a pandemic by studying the stock market efficiency before and after the first case of Covid-19 hit Malaysia. Several robust techniques are utilised to validate the efficient market hypothesis (EMH), namely Ljung-box, Hurst exponent, Chow-denning, and Variance ratio tests. Results indicate a strong influence of Covid-19 on the stock movement. The returns are efficient for the overall sample period. However inefficient after the global pandemic. The findings reported here suggest an increasing chance for market predictability in the Malaysian stock market during the global Covid-19 pandemic. [ABSTRACT FROM AUTHOR]