ABSTRACT
We use unique data on the travel history of confirmed patients at a daily frequency across 31 provinces in China to study how spatial interactions influence the geographic spread of pandemic COVID-19. We develop and simultaneously estimate a structural model of dynamic disease transmission network formation and spatial interaction. This allows us to understand what externalities the disease risk associated with a single place may create for the entire country. We find a positive and significant spatial interaction effect that strongly influences the duration and severity of pandemic COVID-19. And there exists heterogeneity in this interaction effect: the spatial spillover effect from the source province is significantly higher than from other provinces. Further counterfactual policy analysis shows that targeting the key province can improve the effectiveness of policy interventions for containing the geographic spread of pandemic COVID-19, and the effect of such targeted policy decreases with an increase in the time of delay.
ABSTRACT
Health is the centre of human enlightenment. Due to the recent Covid outbreak and several environmental pollutions, checking one's vitals regularly has become a necessity. Ours is an IoT-based device that measures a user's heart rate, blood oxygen level and body temperature. The device is compact and portable, making it easy for users to wear. The readings are measured and shown on an OLED display with the help of sensors. The data is also available on the cloud. A webpage and a mobile application were developed to view the data from the cloud. Individual graphs of the vitals with time are available on the mobile application. This can be used for progress measurement and statistical analyses. Authorized personnel can access the patient's vitals. This creates a scope for Tele-medication in rural and underdeveloped regions. Besides, one can also view his/her vitals for personal health routine. © 2022 IEEE.
ABSTRACT
Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the process of classifying reviews many researchers have adopted machine learning approaches. Keeping in view, the rising demand for educational applications, especially during COVID-19, this research aims to automate Android application education reviews' classification and sentiment analysis using natural language processing and machine learning techniques. A baseline corpus comprising 13,000 records has been built by collecting reviews of more than 20 educational applications. The reviews were then manually labelled with respect to sentiment and issue types mentioned in each review. User reviews are classified into eight categories and various machine learning algorithms are applied to classify users' sentiments and issues of applications. The results demonstrate that our proposed framework achieved an accuracy of 97% for sentiment identification and an accuracy of 94% in classifying the most significant issues. Moreover, the interpretability of the model is verified by using the explainable artificial intelligence technique of local interpretable model-agnostic explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
Purpose: to analyze the ability of the National Health Insurance mobile service quality to build BPJS brand image and public trust to increase intention to use online services during the Covid period. The background of this research is based on the phenomenon in the form of complaints on the quality of online services and research gaps on the effect of service quality on the intention to use online services. Brand image and trust are offered as a mediation for gaps in previous research results. Design/ methodology/approach: The type of research is quantitative, using a pre-existing measurement scale related to mobile service quality, brand image, trust and intention. Involving a sample of 140 BPJS users during the Covid pandemic. It is difficult to identify the population size, the sample size is determined by the formulation of a constant value of 5 multiplied by 28 indicators. The technique of selecting respondents was carried out by means of non-probability random sampling. PLS SEM model as an analysis tool. Findings: The results of this study indicate that the direct relationship of mobile service quality on brand image, trust and intention shows significant positive results. Furthermore, the influence of brand image on trust shows significant results. The influence of brand image and trust on intention is also found to be significantly positive. Practical/implications: although management policies encourage customers to use mobile services more, the public still considers the trustworthy image of BPJS to develop their intention to use mobile application services. The government must remain consistent in ensuring that the quality of mobile service is not compromised because the implications for BPJS image and public trust are at stake. Through the person in charge at BPJS, the government must continue to consistently evaluate and improve the system and educate the public regarding this BPJS health mobile service system. Originality/value: This research offers new insights, filling gaps in studies on national health insurance mobile services during the Covid-19 Pandemic
ABSTRACT
The creation of beautiful literature and art is one of humanity's most essential endeavours. The importance of literature as a component of the language-teaching curriculum has fluctuated over the last century with the popularity of various language-teaching pedagogies. Notwithstanding, it has recently seen a resurrection of appreciation for its effective utility in language acquisition. Covid-19 lockdown combined with the further progress of computer-assisted language learning has led to a gradual shift in the provision of literature-based language education to an online setting. Under this trend, Sandra Stadler-Heer and Amos Paran's edited chapter book Taking Literature and Language Learning Online: New Perspectives on Teaching, Research and Technology concentrates on a particular component of this transfer process, namely the interaction between literature and language learning. This book review provides an overview of this volume.
ABSTRACT
The COVID-19 pandemic has given people much free time. With this, the researchers want to encourage these people to read instead of scrolling through social media. A barrier to reading for many people is not knowing what to read and disinterest in popular books that they would find when they search online. The existing websites that encourage book reading rely on social networking for their recommendations, while the collaborative filtering algorithms applied to books do not exist in the mobile application form. Readwell is a book recommender Android app with a Point-of-Sales System created using Java, Python, and SQLite databases. The information regarding the books was web scraped from the Goodreads website. It aims to apply the more efficient collaborative filtering algorithm to an accessible mobile application that allows users to directly buy the books they are interested in, thus encouraging the reading and buying of books. The researchers created unit test cases to validate the different functionalities of the application. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has caused disruption to the economy due to the increasing infection that affects the workforce in different sectors. The Philippine government has imposed lockdowns to control the spread of infection. This urged the different sectors to implement flexible work schedules or work from home setup. A work-from-home (WFH) setup burdens both the employee and employer by installing different equipment set-ups such as WiFi-equipped laptops, computers, tablets, or smartphones. However, the internet stability in some of the areas in the Philippines is not yet reliable. In this study, an application is used collect survey information and provide an estimate of the telework internet cost requirement of a given government employee or a given government employee implementing a work-from-home set up in their respective household. This involves survey results from different respondents who are currently on a work-from-home setup and significant factors from the survey have been analyzed using machine learning (ML) algorithms. Among the machine learning algorithms used, the ensemble bagged trees model outperformed the other ML models. This work can be extended by incorporating a wider scope of datasets from different industry doing work from home set-up. In addition, in terms of education, it is also recommended to determine the WFH set up not just with the government employee and employer but to also extend this into the education side. © 2022 IEEE.
ABSTRACT
By the beginning of 2020, the illness had been named as COVID-19, which had spread due to its extreme severity affecting multiple industries and sectors throughout the world. To protect the public's health and safety, the Philippine government has established a number of quarantine regulations and travel restrictions in reaction to the current COVID-19 outbreak. Nonetheless, the ILO predicted that the pandemic would initially disrupt the economy and labor markets, affecting 11 million employees, or around 25% of the workforce in the Philippines. Therefore, the government continues to urge employers of local companies and enterprises to use alternative work plans, such as a WFH - work-from-home operation in accordance with the established policies. In line with the concept of telework, several studies have already been carried out, though some were declared inconclusive and require additional study. Hence, in this research, a mobile application was created to evaluate the employee's telework capability assessment using a Fuzzy-based model which utilizes Google AppSheet, Apps Script, and Sheets. The developed mobile application is able to provide capacity evaluation utilizing the four key input variables, which are also reasonably characterized for potential telecommuting cost evaluation. © 2022 IEEE.
ABSTRACT
By early 2020, COVID-19 has caused a global pandemic which led to an enormous number of challenges worldwide in various sectors. The Philippine government has implemented multiple quarantine guidelines and travel restrictions to ensure the people's health and safety. However, the International Labour Organization projected an initial economic and labor market disruption affecting 11 million workers, or about 25% of the Philippine workforce, due to the pandemic. Therefore, the government, thru the concerned agencies continues to encourage employers to implement alternative work plans such as a work-from-home (WFH) operation in compliance with the established regulations in line with existing laws and policies. In line with the telecommuting concept, various research has already been performed, however, some were regarded inconclusive and require further study. Hence, in this study, a Web application was developed along with an embedded fuzzy model to evaluate the telecommuting capability assessment of employees. The proposed web application with embedded fuzzy model is capable of providing capability assessment using the four main input variables which are also relatively characterized for possible telecommuting cost assessment. © 2022 IEEE.
ABSTRACT
After the outbreak of corona virus, all counties are paying special attention to their healthcare infrastructure. During second phase of covid-19, entire world has seen health care crisis. Large number of people died globally. Entire world was affected mentally or physically. There is a great need to strengthen the healthcare infrastructure, to vaccinate the population against covid virus infection and to take proper precaution to avoid spread of the virus, so that the world will not see such deadly days again. This paper discusses how technologies like Internet of Things (IoT), Artificial Intelligence (AI), Drones etc can help in remote monitoring of patients, judicious hospital admission, conscious distribution of lifesaving drugs etc. Investment in technology with not only help in the reduction of spread of the virus but will also help in fighting with all other future pandemics. All the countries must have to invest more on latest technologies in their healthcare to make themselves ready for such future pandemics. When the things will improve, the new normal will be very much different from the life that was before pandemic. IoT, AI and other technologies will become the non-separatable part of our life. © 2023 Bharati Vidyapeeth, New Delhi.
ABSTRACT
The COVID-19 pandemic has challenged many of the healthcare systems around the world. Many patients who have been hospitalized due to this disease develop lung damage. In low and middle-income countries, people living in rural and remote areas have very limited access to adequate health care. Ultrasound is a safe, portable and accessible alternative;however, it has limitations such as being operator-dependent and requiring a trained professional. The use of lung ultrasound volume sweep imaging is a potential solution for this lack of physicians. In order to support this protocol, image processing together with machine learning is a potential methodology for an automatic lung damage screening system. In this paper we present an automatic detection of lung ultrasound artifacts using a Deep Neural Network, identifying clinical relevant artifacts such as pleural and A-lines contained in the ultrasound examination taken as part of the clinical screening in patients with suspected lung damage. The model achieved encouraging preliminary results such as sensitivity of 94%, specificity of 81%, and accuracy of 89% to identify the presence of A-lines. Finally, the present study could result in an alternative solution for an operator-independent lung damage screening in rural areas, leading to the integration of AI-based technology as a complementary tool for healthcare professionals. © 2023 SPIE.
ABSTRACT
Este artigo aborda conflitos socioambientais decorrentes da construção de complexos portuários no estado do Pará a partir de três parâmetros de análise: ameaças às comunidades tradicionais, agentes envolvidos e formas de resistências daquelas comunidades. Em função da pandemia do novo coronavírus (SARS-COV-2), os procedimentos metodológicos foram redefinidos, utilizando-se da técnica de Revisão Sistemática de Literatura (RSL) agregada à pesquisa documental, observação in loco e entrevistas realizadas na comunidade de Guajará de Beja, município de Abaetetuba, um dos lócus da pesquisa. Da aplicação do Protocolo de Pesquisa (PP) da RSL resultaram dez estudos selecionados, os quais, após análise agregada aos demais procedimentos metodológicos, indicaram que as disputas por recursos naturais/locacionais, em especial para instalação de complexos portuários no estado do Pará, têm produzido conflitos socioambientais entre agentes econômicos, agentes públicos e comunidades tradicionais na Amazônia paraense.Alternate :This article addresses socio-environmental conflicts arising from the construction of port complexes in the state of Pará from 03 (communities) parameters of analysis - threats to traditional communities, agents involved and forms of complex resistance. Due to the new coronavirus (SARS-COV-2) pandemic, the methodological procedures were redefined, using the Systematic Literature Review (RSL) technique combined with documentary research, on-site observation and interviews carried out in the community of Guajará de research Beja, municipality of Abaetetuba, one of the locus of the research. The application of the RSL Research Protocol/PP resulted in 10 (ten) selected studies which, after being added to the other methodological procedures, indicating that as natural/locational resources, especially for the analysis of port complexes in the state of Pará, companies socio-environmental producers between local agents for audiences and traditional communities in the Amazon.
ABSTRACT
PurposeIn order to ensure effectiveness of staff's performance using online meetings applications during coronavirus disease (COVID-19), having the behavioural intention is mandatory for staff to measure, test, and manage the staff's data. Understanding of Public Higher Education Institution (PHEI) staffs' intention and behaviour toward online meetings platforms is needed to develop and implement effective and efficient strategies. The objectives of this paper to identify the factors that affect staff to use online meetings applications, to develop a model that examining the factors that affect PHEI staff to online meetings applications and to validate the proposed model. This study used a cross-sectional quantitative correlational study with using UTAUT2 model by validating the model and mediating variables to enhance the model's explanatory power and to make the model more applicable to PHEI staff's behavioural intention.Design/methodology/approachThe data were collected in Malaysia from March to May 2021. The survey took place using Google form and was send to PHEI staff for answer. This research particularly chooses PHEI as the location to carry out the research due to two main factors. Statistical analysis and hypotheses were tested using structural equation modelling based on the optimisation technique of partial least squares. SmartPLS software, Version 3.0 (Hair et al., 2010) was used to conduct the analysis. A conceptualised estimation model was "drawn in” the partial least squares structural equation modeling (PLS-SEM) to analyse the consequences of the variables' relationships. In essence, the PLS-SEM simulation was carried out in a model by assessing and computing various parameters that included elements like validity, durability, and item loading. Henseler et al. (2009) suggested a two-step method that includes PLS model parameter computing. This is accomplished by first solving the estimation model in the structural model independently before calculating the direction coefficients. The results of data analysis using SmartPLS findings and interpretation of the data are addressed. The questionnaire was extensively examined to ensure that the data obtained were presented in a clear and intelligible manner, with the use of figures, and graphs.FindingsThis current study found that the usability of the material, the reliability of operating, the impact of the PHEI staff's views on its usage, and finally the familiarity with the online meetings platforms influenced PHEI staff's behavioural intention for adoption and long-term use of online meeting platforms using UTAUT2. The staff's behavioural intention for using online meeting platforms was significantly influenced by the effort expectancy, facilitating conditions and habit of online meeting platforms. There was a clear association between "Habit” and "Behavioural Intention” for the usage of information technology in learning in several studies (El-Masri and Tarhini, 2017;Uur and Turan, 2018;Mosunmola et al., 2018;Venkatesh et al., 2003). As a consequence of the utility of online meeting platforms in daily staff meetings and learning activities, this technology has been adopted.Originality/valueThis study used UTAUT2 and structural equations modelling in this study to assess respondents' perspectives on the use of online meetings platforms in PHEI, since users' perspective is a significant factor in the adoption and acceptance of online meeting applications. Staff's behavioural intention to use online meeting platforms was effectively enhanced by "Effort Expectancy,” "Facilitating Conditions” and "Habit” in this study. The study shows that identifying PHEI staff's perspectives will effectively increase the staff's aversion to utilising online meeting platforms for online meetings purposes.
ABSTRACT
During the health crisis caused by COVID-19, virtual reality (VR) proved to be useful for the tourism industry, allowing this industry to continue working despite the restrictions imposed. However, it remains to be seen if the impact of this sanitary crisis in the tourism industry influenced managers' intention to adopt this technology in the post-pandemic period. To fill this gap, a qualitative methodological approach was adopted, using the MAXQDA20 software and interviews with managers of tourism enterprises. The results show that the willingness to invest in technology, the perception of VR as a business strategy, and the perception of the impact of the pandemic are factors that regulate the intention of companies to adopt VR. In addition, prior experience with VR and the perception of technical support are also important for its adoption. Thus, it was concluded that VR can be a valuable sustainable strategy for tourism companies to address the challenges imposed by the pandemic. However, adopting the technology depends on factors such as financial availability, business strategy, and previous experience with VR. Furthermore, tourism companies must also receive adequate technical support to ensure its correct implementation.
ABSTRACT
There is a paucity of research on the role of food delivery apps (FDAs) in food waste generation. This gap needs to be addressed since FDAs represent a fast-growing segment of the hospitality sector, which is already considered to be a key food waste generator globally. Even more critically, FDAs have become a prominent source of ordering food during the COVID-19 pandemic. In addition, the growing usage of FDAs warrants an improved understanding of the complexities of consumer behavior toward them, particularly during a health crisis. The present study addresses this need by examining the antecedents of FDA users' food ordering behavior during the pandemic that can lead to food waste. The study theorizes that hygiene consciousness impacts the enablers and barriers to FDA usage, which, in turn, shape the attitude toward FDAs and the tendency to order more food than required, i.e., shopping routine. The conceptual model, based on behavioral reasoning theory, was tested using data collected from 440 users of FDAs during the pandemic. The results support a positive association of trust and price advantage with attitude, but only of trust with shopping routine. Perceived severity and moral norms did not moderate any associations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
Nanofibers are a type of nanomaterial with a diameter ranging from ten to a few hundred nanometers with a high surface-to-volume ratio and porosity. They can build a network of high-porosity material with excellent connectivity within the pores, making them a preferred option for numerous applications. This review explores nanofibers from the synthesis techniques to fabricate nanofibers, with an emphasis on the technological applications of nanofibers like water and air filtration, photovoltaics, batteries and fuel cells, gas sensing, photocatalysis, and biomedical applications like wound dressing and drug delivery. The nanofiber production market has an expected compound annual growth rate (CAGR) of 6% and should reach around 26 million US $ in 2026. The limitations and potential opportunities for large-scale applications of nano-fibrous membranes are also discussed. We expect this review could provide enriched information to better understand Electrospun Polymer Nanofiber Technology and recent advances in this field. © 2023 Bentham Science Publishers.
ABSTRACT
During the COVID-19 pandemic, point-of-care genetic testing (POCT) devices were used for on-time and on-site detection of the virus, which helped to prevent and control the spread of the pandemic. Smartphones, which are widely used electronic devices with many functions, have the potential to be used as a molecular diagnostic platform for universal healthcare monitoring. Several integrated diagnostics platforms for the real-time and end-point detection of COVID-19 were developed using the functions of smartphones, such as the operating system, power, sound, camera, data storage, and display. These platforms use the 5V output power of smartphones, which can be amplified to power a micro-capillary electrophoresis system or a thin-film heater, and the CMOS camera of smartphones can capture the color change during a colorimetric loop-mediated isothermal amplification test and detect fluorescence signals. Smartphones can also be used with self-written web-based apps to enable automatic and remote pathogen analysis on POCT platforms. Our lab developed a handheld micro-capillary electrophoresis device for end-point detection of SARS-CoV-2, as well as an integrated smartphone-based genetic analyzer for the qualitative and quantitative colorimetric detection of foodborne pathogens with the help of a custom mobile app. © 2023 SPIE.
ABSTRACT
The main purpose of this paper was to classify if subject has a COVID-19 or not base on CT scan. CNN and resNet-101 neural network architectures are used to identify the coronavirus. The experimental results showed that the two models CNN and resNet-101 can identify accurately the patients have COVID-19 from others with an excellent accuracy of 83.97 % and 90.05 % respectively. The results demonstrates the best ability of the used models in the current application domain. © 2022 IEEE.
ABSTRACT
Center za pomoč uporabnikom rešitev eZdravja je ključna komponenta sistema eZdravje v Sloveniji, ki je namenjena vsem uporabnikom tega sistema. Center izvaja tri osnovne naloge. Splošna podpora vsem uporabnikom rešitev eZdravja je namenjena zdravstvenim delavcem, administrativnemu osebju, informatikom, ponudnikom programskih rešitev, pacientom in vsem drugim uporabnikom rešitev eZdravja, ki želijo prijaviti motnje v delovanju, potrebujejo pomoč ali zahtevajo informacije v zvezi z delovanjem rešitev eZdravja. Storitev elektronskega naročanja na zdravstvene storitve pomaga pacientom pri naročanju. Podpora pri priklopu v zNET je namenjena izvajalcem zdravstvene dejavnosti pri postopku vključitve v omrežje zNET. Dostopanje do pomoči je možno s spletnim obrazcem, preko elektronske pošte, pogosto zastavljenih vprašanj ali telefona. Na svoji spletni strani Center za pomoč uporabnikom objavlja obvestila, povezana z delovanjem rešitev eZdravja, in semafor o delovanju rešitev. V zadnjih dveh letih je bilo v delo Centra vključenih več novih rešitev. Uporaba je pospešeno narasla - v letu 2021 smo beležili več kot sedemkratno povečanje glede na leto 2020. Prispevek analizira delovanje Centra skozi dinamiko in vsebino obravnavanih zahtevkov in opravljenih storitev. Delovanje Centra je pomembna komponenta uspešne uporaba rešitev eZdravja v Sloveniji, kar se je še posebej izkazalo v času epidemije COVID-19.Alternate :The eHealth Service Desk is a key component of the eHealth system in Slovenia, which is intended for the all users of the system. The Service Desk performs three main tasks. General support for all users of eHealth solutions addresses health care professionals, administrative staff, information technology specialists, software solution providers, patients and all other users of eHealth solutions who wish to report malfunctions, need assistance or require information related to the functioning of the eHealth solutions. The electronic appointment for health care services helps patients to make an eAppointment for health care services. The zNET Connection Support offers help to health care providers in the process of joining the zNET network. Assistance can be accessed in several ways: via an online form, email, FAQs or phone. The Service Desk publishes notifications related to the eHealth solutions on its website and maintains a simple indicator that displays the status of eHealth solutions. During the last two years, several new eHealth solutions have been added to the Service Desk portfolio. The use has been growing rapidly in recent years, namely in 2021 we recorded a more than sevenfold increase compared to 2020. The paper analyses the operation of the eHealth Service Desk through the dynamics and content of the requests handled or services provided. The Service Desk is an important component for the successful use of eHealth solutions in Slovenia, which was particularly evident during the COVID-19 epidemic.
ABSTRACT
COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, based on 5434 records of COVID cases collected from a popular data science community and using Rapid Miner Studio software, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Employing the same clinical dataset, a linear regression model is also proposed having a moderately high coefficient of determination of 0.739 in accurately predicting the occurrence of COVID-19. A decision support system can also be developed using the association rules to ease out and automate early detection of other diseases. © 2023 by the authors.