Gender Pay Gap in Sports on a Fan-Request Celebrity Video Site

The internet is often thought of as a democratizer, enabling equality in aspects such as pay, as well as a tool introducing novel communication and monetization opportunities. In this study we examine athletes on Cameo, a website that enables bi-directional fan-celebrity interactions, questioning whether the well-documented gender pay gaps in sports persist in this digital setting. Traditional studies into gender pay gaps in sports are mostly in a centralized setting where an organization decides the pay for the players, while Cameo facilitates grass-roots fan engagement where fans pay for video messages from their preferred athletes. The results showed that even on such a platform gender pay gaps persist, both in terms of cost-per-message, and in the number of requests, proxied by number of ratings. For instance, we find that female athletes have a median pay of 30$ per-video, while the same statistic is 40$ for men. The results also contribute to the study of parasocial relationships and personalized fan engagements over a distance. Something that has become more relevant during the ongoing COVID-19 pandemic, where in-person fan engagement has often been limited. © 2023 Owner/Author.

Post-COVID-19 e-learning acceptance and its impact on community college students in Kelantan, Malaysia: A quantitative analysis

The purpose of this study is to explore the relationship between student motivation, student mindset, computer competency, and behavioural intention to continue using e-learning in the post-COVID-19 era among students at the community colleges in Kelantan, Malaysia. This quantitative study used a self-administered online survey questionnaire, and a convenience sampling method was employed to reach the respondents. Partial least square structural equation modelling (SmartPLS) 4.0 was then used for data analysis. The results of the current study reveal that community college students have a high behavioural intention to continue using e-learning in the post-COVID-19 era, and that behavioural intention positively correlates with student motivation and computer competency. However, there is limited evidence to support the relationship between student mindset and their behavioural intention to continue using e-learning in the post-COVID-19 era. Practically, the findings from this study can be an essential landmark to the management of Community Colleges in determining the direction of future learning in community colleges. © 2023 by the authors.

Sentiment Analysis on Covid-19 Outbreak Awareness Using Naïve Bayes Algorithm

Sentiment analysis has gained much attention nowadays among the researchers especially during the Covid-19 pandemic. Due to the increasing volume of data coming from the social media platforms, researchers have been using sentiment analysis to analyse topics regarding commercial products, daily issues among the society and also to detect important events from the community. Since the social media users are consisting of the community, content that are shared could also be used to detect possible situational hazard such as the outbreak of Covid-19 in advanced. The result from the sentiment analysis could be beneficial to government organizations in order to contain the outbreaks and public health crisis related to Covid-19. The objective of this research is to explore Naive Bayes algorithm for the sentiment analysis on the Covid-19 outbreak awareness based on Twitter data. In this research, the data were collected during the Malaysia's second lock down, which was between the months of April to June 2021 using the Twitter API Tweepy. After the pre-processing and feature extraction stages, the data have been divided into the training and testing dataset for the Naive Bayes sentiment classification. The result has shown that Naive Bayes has been able to generate high performance with more than 90% accuracy for this classification problem. Future work would include the improvement of data preprocessing, more balance of dataset, enhancement of the algorithm and also comparing the performance with other well-known classification algorithms. © 2022 IEEE.

Knowledge, Attitude and Practice (KAP) towards COVID-19 preventive measures and symptoms: A cross-sectional study among individuals living in Kedah

Introduction: Coronavirus disease (COVID-19) is a serious global health problem that was first detected in Wuhan, China. Insufficient level of knowledge, attitude, and practice (KAP) towards the preventive measures of COVID-19 lead to the economic downturn, rise in the number of infected people daily and increase mortality rate. Objective(s): This study was conducted to evaluate KAP towards COVID-19 preventive measures and symptoms among individuals living in Kedah. Material(s) and Method(s): A cross-sectional study was conducted by distributing online questionnaires among 388 individuals living in different districts in Kedah. Descriptive frequency analysis was used to summarise the socio-demographic characteristics. Chi-Square Test and Fisher Exact Test were used to identify the association between demographic characteristics and level of KAP. Spearman's correlation was used to assess the relationship between the dependent variables. Result(s) and Conclusion(s): The mean score obtained for knowledge, attitude, and practice were 9.67 +/- 1.64, 20.89 +/- 3.03 and 10.13 +/- 1.12, respectively. A significant association (p< 0.05) was found between respondent's demographic characteristics with the level of knowledge (age, gender, level of education, marital status, occupation), and level of practice (gender, level of education). This study also reported no significant association between respondent's demographic characteristics with the level of attitude. There was a positive correlation between knowledge - attitude (rs =0.103, p<0.05), knowledge - practice (rs =0.111, p<0.05) and attitude - practice (rs =0.207, p<0.05). This study shows that the greater a person's level of knowledge, the higher the level of attitude and practice. This study will provide essential groundwork data to help health officials facilitate the implementation of effective policy regarding COVID-19 preventive measures in Kedah.

Sentiment Analysis on Acceptance of New Normal in COVID-19 Pandemic using Naïve Bayes Algorithm

The COVID-19 pandemic has such a significant impact and causes difficulties in many aspects that the new normal rules should be implemented to reduce the effects. New normal rules have been implemented by governments worldwide to break the virus chain and stop its transmission among the society. Even if the COVID-19 outbreak is under control, governments still need to know whether society could adapt and adjust to their new daily lifestyles. Many precautions still must be addressed as the transition to endemic status does not mean that COVID-19 will naturally eventually disappear. The World Health Organization also has warned that it is too early to treat COVID-19 as an endemic disease. Since the pandemic, many interactions have been done online, leading to the increasing social media usage to express opinions about COVID-19. The objective of the study is to explore the capability of the Naïve Bayes algorithm in the sentiment classification of the public’s acceptance on the new normal in the COVID-19 pandemic. Naïve Bayes has been chosen for its good performance in solving various other classification problems. In this study, Twitter data were used for the analysis and were collected between March and June 2022. The evaluation results have shown that Naïve Bayes could generate excellent and acceptable performance in the classification with an accuracy of 83%. According to the findings of this research, many people have accepted the new normal in their daily lives. The future works would include scrapping more data based on geolocation, improving the feature extraction technique, balancing the dataset and comparing Naïve Bayes performance with other well-known classifiers. The subsequent study could also focus on detecting the emotions of the public and processing non-English tweets. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Major Obstetric Trauma Simulation -Learning from Experience

Objective: Despite advances in the care of the multiply injured trauma patient, clear understanding and guidance for the management of major obstetric trauma is lacking. Explanations may include the altered physiology of pregnancy, as well as the relative infrequent occurrence of obstetric trauma. Following a recent serious incident, it was recognised that greater trust-wide awareness and understanding of the management of an injured parturient was needed. University Hospitals Coventry & Warwickshire is one England's busiest trauma centres. Therefore, it was of paramount importance that an intervention was introduced to improve staff confidence and competence. The COVID-19 pandemic necessitated socially distanced multidisciplinary teaching. Although trainees engage in PROMPT and MOET courses to focus on inter-specialty team working, we felt a greater emphasis on major obstetric trauma management would be beneficial. Design: A high-fidelity trauma simulation video was developed following a multidisciplinary meeting with representatives from Emergency Medicine, Anaesthesia and Obstetrics, enabling specialty specific learning points to be highlighted. Method: The video outlined the management sequence of an obstetric trauma. Starting with the trauma call and paramedic handover, a rapid primary survey identified multiple injuries including an abdomino-pelvic injury. The scenario demonstrated a collaborative team approach, culminating in a resuscitative hysterotomy. Visual annotations highlighted important learning points. The video was used as a departmental induction teaching session for rotating trainees in Anaesthetics, Obstetrics and Emergency Medicine. The video encouraged facilitated group discussion of learning points. We plan for the session to be included in future departmental teaching. Results: Feedback forms were completed before and after the session. Only 35.7% of trainees reported prior involvement in the management of major obstetric trauma or obstetric cardiac arrest. Prior to the session, 42.9% of trainees did not feel confident to manage a major obstetric trauma. Encouragingly, this reduced to only 7.1% post-session. Overall 85.7% of candidates agreed or strongly agreed the session had improved their understanding of the management of major obstetric trauma. Conclusion: Creating a high performing team takes significant work and motivation. A serious event provided the impetus to develop a cross specialty teaching session aimed at improving trainees confidence and ability in major obstetric trauma management. The video facilitated the discussion of important learning points, and feedback showed improved trainee confidence and understanding. We aim to tackle low trainee confidence and overcome the challenges caused by frequently changing trauma teams through ensuring continued use and development of this standardised multi-specialty teaching session.

THE ERA OF COVID-19: WILL CONVALESCENT PLASMA AND INTRAVENOUS IMMUNOGLOBULINS BE THE ANSWER

Since the World Health Organization (WHO) declared severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) infection a pandemic in December 2019, observational and interventional studies have been underway to investigate potential therapeutic options to treat and prevent the progression of coronavirus disease (COVID-19). Most COVID-19 patients develop mild to moderate symptoms. However, elderly patients suffering from chronic comorbidities and immunocompromised patients are susceptible to more severe life-threatening presentations. Convalescent plasma and intravenous immunoglobulins (IVIg) are two attractive options for managing and preventing severe COVID-19. However, current literature does not confirm nor deny the efficacy of the convalescent plasma and IVIg against COVID-19. Moreover, there is much concern considering the safety of blood-derived immune products. For these reasons, the current clinical guidelines do not recommend for or against the use of blood-derived immune products for managing COVID-19 cases. This article summarizes recent evidence on the safety and efficacy of the convalescent plasma and IVIg in COVID-19 patients.

COVID-19 in children: an approach for multisystem inflammatory syndrome

Background and objectives: Children suffering from coronavirus disease (COVID-19) usually present with mild symptoms and show lower mortality rates than adults. However, there have been several recent reports of more severe hyperinflammatory presentation in pediatric COVID-19 patients. This review article aims to summarize the current literature available on the main clinical features and management approaches of multisystem inflammatory syndrome in children (MIS-C). Methods: The authors searched different indexing databases for observational and interventional studies using search terms including "Coronavirus, COVID-19, pediatric, MIS-C, Kawasaki, and inflammation." The retrieved publications were further assessed for relevance to the topic. Only relevant articles were included in writing this review article. Main body: Multisystem inflammatory syndrome in children (MIS-C) is a hyperinflammatory syndrome temporally related to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in pediatrics. It is characterized by persistent fever, rash, elevated inflammatory markers, and multiorgan failure with increasing rates of cardiovascular and gastrointestinal involvement. The exact pathophysiologic mechanisms of MIS-C are still unknown, but it is postulated to be due to an exaggerated immune response to SARS-CoV-2 infection. Multisystem inflammatory syndrome in children is diagnosed by exclusion of other underlying causes of organ failure. There is a lack of clinical evidence on the management of MIS-C. The current guidelines depend mainly on expert opinion based on the management of other hyper-inflammatory syndromes in children. Patients suffering from MIS-C are treated with intravenous immunoglobulin (IVIg), corticosteroids, infliximab, tocilizumab, and anakinra. Conclusions: Despite the growing reports on COVID-19 in children, there is still a lot to elucidate on the pathophysiology, diagnosis, and subsequent management of MIS-C. Further trials are needed to investigate new approaches to manage MIS-C. Specific evidence-based guideline for management of MIS-C should be tailored to the current available information on MIS-C.

Students’ learning habit factors during COVID-19 pandemic using multilayer perceptron (MLP)

Rapid dissemination of coronavirus disease 2019 (COVID-19) across the globe has necessitated the introduction of social distance interventions to slow the spread of the disease. Online learning has become essential, considering the implications of this virus to be spread among the students during physical classes. Hence, educational institutions have shifted the traditional physical classes to online classes. Due to this implementation worldwide, a study on student learning habits is crucial to analyse students learning habits as it is one of the main factors that affecting students’ performance in learning. Fifteen independent variables as inputs to one of the well-known Artificial Neural Network algorithms, Multilayer Perceptron (ANN-MLP) has been used to investigate the student’s learning habit factors during the COVID-19 pandemic. Through analysing original survey data from 420 secondary students (Grade 6-12) in Hanoi shows that the ANN-MLP model is stable for both ANN-MLP optimization algorithms which are for Adjusted Normalized, to be concise. The hours spend for self-learning before COVID-19 is observed to be the most influential factors of student’s learning habit during COVID-19 pandemic. Moreover, the promising Sum of Squares Error (SSE) and Relative Error (RE) values obtained signify that the ANN-MLP model is appropriate in identifying the student’s learning habit factors during COVID-19 pandemic. © 2021 Nur Nabilah Abu Mangshor et al.

Global geographical climate impacts on the spread and death of covid-19 in Asia and America

A viral infection which is named as Coronavirus disease 2019 (COVID-19) is triggered by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To date, almost two million cases and over 100,000 deaths from the disease caused by this virus were reported worldwide. The environmental and meteorological factors are claimed to stimulate the spread of the virus in which the transmissibility in terms of climatic fluctuations increases exponentially with high humidity and low temperature. In an attempt to understand this epidemic, there is a need to investigate the factors that could impact the spread and death of COVID-19. We, therefore, proposed to investigate global geographical climate impacts on the COVID-19 spread and death in Asia and America. The Artificial Neural Network (ANN) is a network that seeks to replicate neuronal functionality in the human brain. It is the preferred instrument for several predictive applications of data mining, due to its strength, versatility, and simplicity. A dataset of COVID-19 cases and deaths revealed from 49 states in America and 41 countries in Asia during April 2020 were tested. Nine covariates were used in the networks which are Cases, Death, High Temperature, Low Temperature, Average Temperature, Population, and Percentage of Cases over Population, Percentage of Death over Population, and Total Cases. Based on the analysis conducted, the global geographic climate is observed to have the least impacts on the COVID-19 spread and death in Asia and America particularly. However, different results could be reflected by different datasets used in the future. © 2021 Shafaf Ibrahim et al.

COVID-19 detection for chest X-ray images using local binary pattern

The increase in patients with COVID-19 is overwhelming in healthcare systems around the world. Due to the large number of people affected by this pandemic, the medical and healthcare departments are facing a delay in the detection of COVID-19. Besides, it is not an easy task to clarify the images from the radiograph on what types of infection between bacteria pneumonia and COVID-19. The automatic feature analysis can help physicians more precisely in the treatment and diagnosis of diseases. In this research, Local Binary Pattern (LBP) texture features algorithm has been proposed to automate the current manual approach. This process starts by extracting the intensity grayscale texture from the normal, bacteria pneumonia and COVID-19 chest x-ray images. To prove the accuracy of LBP, a simple classifier k-Nearest Neighbour (k-NN) has been implement to classify the chest x-ray images into normal, bacterial and pneumonia class. The 10-fold cross validation has been used to validate the chest x-ray images. From the evaluation, 96% accuracy can be achieved by using LBP as a feature extraction feature. It shows that LBP is a powerful texture features to detect COVID-19 from the x-ray images. More samples will be collected in the future and neural network approach is suggested as a classifier in the future due to its ability to imitate human respond. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

Visualizing communication of service providers reputation during COVID-19 pandemic: A conceptual model

This paper presents to fill the gap and proposes a new conceptual model in developing an application to visualizing the reputation of communication service providers (CSP) during the Covid-19 pandemic. The outbreak of the COVID-19 caused a significant increase in the usage of voice and data using CSP. Regardless of it is seems under a protective umbrella during the pandemic, the increasing demand for CSP in a pandemic may cause customers to switch for better service. CSP companies have an abundance of data about their customers;however, the social element mainly the pithy, real-time commentary express via networks such as Twitter is often overlooked. It is due to the widely used NPS (Net Promoter Score) to measure their customers' loyalty and satisfaction. Even some of the telecommunication has started venturing into social media data analytics, the improvements required in detecting the combination of many languages used in blogs and forums. This gap inclusive the short words, not enough sentiment analytics for non-English languages, and obviously, social media in non-English languages favoured comparing to English languages. Therefore, we proposed a comprehensive conceptual model that adapted from two existing conceptual models, Simulation in Modeling CM (2008) and Integrated Framework for CM (2016). We believed it could be a guideline in visualizing the reputation of CSP that involves extracting public tweets from twitter sentiment analysis. As a result, CSP companies can get a more unobstructed view of their reputation, insights about the products and services that their customers appreciate. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

Student emotion estimation based on facial application in E-learning during COVID-19 pandemic

The COVID-19 pandemic occurred in late 2019, and by the beginning of 2020 the entire education system has shifted from traditional teaching methods to online learning systems around the world. COVID-19 reinforces the need to explore online learning and learning opportunities. However, the ability of teachers to recognize and see how individual students engage in online learning is more challenging. Student emotions such as self-esteem, inspiration, dedication, and others that are assumed to be determinants of student success cannot be overlooked. The main objective of this research is to evaluate the emotion of student on e-learning during COVID-19 pandemic using a facial recognition application. This application able to interpretation of facial expressions into extracted emotional states. An image processing approach has been implements in 4 types of emotion which is happy, normal, sad and surprise. Next the image will go through the identifying of emotion type from the static frontal face image. It starts with image acquisition, grayscale conversion and contrast stretching for image pre-processing, Haar Cascade or also known as Viola-Jones technique for face detection, face model technique for eye and mouth localization, skin-color segmentation technique for image segmentation, and Grey-Level Co-Occurrence Matrix (GLCM) for feature extraction. The classification for emotion type is using SVM Regression. The accuracy percentage of emotion classification is calculated. The result showed that SVM Regression has a high accuracy rate of 99.16%. A real-time application will be developed to identify human face emotion instead of static image for future work with additional of speech recognition exploration. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

The influences of global geographical climate towards COVID-19 spread and death

As the world’s coronavirus disease 2019 (COVID-19) case total and death toll continue to climb, an increasing data collection and analysis are providing insights into the pandemic. Although outbreaks continue to develop rapidly, and researchers' understanding of the virus is increasing, a consensus is emerging on certain main aspects of the spread, symptoms, and deadliness of the virus. Enormous global data distribution on COVID-19 is made available online with a combination of global climate data, which creates an opening for further analysis to be conducted. To date, the global climate change has been studied widely, particularly regarding its influences on the distribution of species. This reflects the need for an analysis that is best suited to big data analysis which offers high performance and efficiency in understanding this pandemic issue. The state-of-art in data mining and statistics areas show that the adaptation of these methods could be the most suitable candidate for this purpose. We, therefore, proposed to investigate the influences of the global geographical climate towards the COVID-19 spread and death using a technique of Artificial Neural Network (ANN). It is believed that the proposed study could introduce a new suggestive strategy in improving the precaution measures, enhancing the new normal living activities, and to increase the performance scalability of big data processing comprehensively. © 2020, World Academy of Research in Science and Engineering. All rights reserved.