ABSTRACT
Improving the process of strategic management in hospitals preparation and equipping the intensive care units (ICUs) and the availability of medical devices plays an important role for knowing consumer behaviour and need. This cross-sectional study was performed in the ICU of Farhikhtegan Hospital, Tehran, Iran for a period of six months. During these months, ten medical devices have been used 5,497 times. These devices include: ventilator, oxygen cylinder, infusion pump, electrocardiography machine, vital signs monitor, oxygen flowmeter, wavy mattress, ultrasound sonography machine, ultrasound echocardiography machine, and dialysis machine. The Apriori algorithm showed that four devices: ventilator, oxygen cylinder, vital signs monitoring device, oxygen flowmeter are the most used ones by patients. These devices are positively correlated with each other and their confidence is over 80% and their support is 73%. For validating the results, we have used equivalence class clustering and bottom-up lattice traversal (ECLAT) algorithm in our dataset.
ABSTRACT
Mining frequent itemsets is an attractive research activity in data mining whose main aim is to provide useful relationships among data. Consequently, several open-source development platforms are continuously developed to facilitate the users' exploitation of new data mining tasks. Among these platforms, the R language is one of the most popular tools. In this paper, we propose an extension of arules package by adding the option of mining frequent generator itemsets. We discuss in detail how generators can be used for a classification task through an application example in relation with COVID-19.
ABSTRACT
This article, in order to address impreciseness, initiated the notion of dual hesitant fermatean fuzzy sets (DHFFSs), as a generalization of the combination of dual hesitant fuzzy set (DHFS), dual hesitant Pythagorean fuzzy set (DHPFS) and Fermatean fuzzy set (FFS). The authors defined the fundamental set of operations for DHFFS. Additionally, the authors have also proposed two ranking functions and an accuracy function for the ordering of this novel set. In order to facilitate the pragmatic implementation of DHFFS in optimization, the authors formulated three types of transportation problem with dual hesitant Fermatean fuzzy (DHFF) parameters. To optimize the DHFF-TP, an algorithm was proposed with the help of one of the proposed ranking functions. Artificial neural network is also applied to the transportation problems in DHFF environment. A numerical example based on the transportation of COVID-19 vaccine with DHFF cost has also been carried out to validate out to validate our technique.
ABSTRACT
The outbreak of COVID-19 led to rapid development of the mobile healthcare services. Given that user satisfaction is of great significance in inducing marketing success in competition markets, this research explores and predicts user satisfaction with mobile healthcare services. Specifically, the current research aimed to design a machine learning model that predicts user satisfaction with healthcare services using big data from Google Play Store reviews and satisfaction ratings. By dealing with the sentimental features in online reviews with five classifiers, the authors find that logistic regression with term frequency-inverse document frequency (TF-IDF) and XGBoost with bag of words (BoW) have superior performances in predicting user satisfaction for healthcare services. Based on these results, the authors conclude that such user-generated texts as online reviews can be used to predict user satisfaction, and logistic regression with TF-IDF and XGBoost with BoW can be prioritized for developing online review analysis platforms for healthcare service providers.
ABSTRACT
In response to the COVID-19 outbreak, the governments of different countries adopted, such as locking down cities and restricting travel and social contact. Online health communities (OHCs) with specialized physicians have become an important way for the elderly to access health information and social support, which has expanded their use since the outbreak. This paper examines the factors influencing elderly people's behavior in terms of the continuous use of OHCs from a social support perspective, to understand the impact of public health emergencies. Research collected data from March to April 2019, February 2020, and August 2021, in China. A total of 189 samples were collected and analyzed by using SmartPLS. The results show that (1) social support to the elderly during different stages has different influences on their sense of community and (2) the influence of the sense of community on the intention to continuously use OHCs also seems to change over time. The results of this study provide important implications for research and practice related to both OHCs and COVID-19.
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To reveal the influence mechanism of e-banking channel selection of elderly customers, according to the analysis of elderly customers'decision-making process, a threshold model is proposed by using small world customer relationship network and variable setting in this study. The multi-agent simulation of e-banking channel selection behavior of elderly customers is carried out from the perspectives of channel diffusion speed and customer channel selection proportion in the context of Covid-19 pandemic. The research shows that channel performance and individual differences of customers affect the adoption of e-banking by elderly customers. This study also has found that network size and network density can regulate the impact of channel performance on the selection behavior of elderly groups. However, they could play a regulatory role under certain conditions. Finally, this study puts forward some suggestions to improve the channel diffusion efficiency, such as building an elderly friendly e-financial service channel and construction of elderly business market culture.
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Based on the perspectives of social risk amplification and the knowledge-attitudes-practice model, this study aimed to test how the level of knowledge about COVID-19 and information sources can predict people's behavioral changes and to examine the effect mechanisms through the mediating roles of attitude, risk perception, and negative emotions in a survey of 498 older Chinese adults. The results showed that (1) older people had a lower level of factual knowledge regarding the variant strains and vaccines;(2) in the information sources-behavior, information sources had a critical influence on elderly individuals' coping behaviors;and (3) in the knowledge-behavior, factual knowledge had a significant effect on elderly individuals' coping behaviors. Specifically, for prevention behaviors, both risk perception and negative emotions played full mediating roles. The findings have significant implications for the development of an effective COVID-19 prevention program to older adults coping with pandemic conditions.
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This study focuses on the restorative effects of immersive virtual reality (VR) forest experiences on elderly people during the COVID-19 lockdown. A field experiment with 63 elderly participants was conducted in an elderly care institution in China. The results showed that a five-minute VR forest experience with three minutes of subsequent reliving can bring immediate psychological improvements (i.e., increased positive affect, decreased negative affect, and enhanced stress recovery) to elderly individuals. The negative affect decrease and stress recovery enhancement were more obvious among introverted individuals. Furthermore, participating in three VR forest experiences over 3 consecutive days can bring continuous psychological improvements. Moreover, short VR forest experiences were unable to significantly decrease the blood pressure of participants. The effects of three VR experiences over 3 days on blood pressure improvement were also nonsignificant. Additionally, VR forest experiences can increase elderly participants' intentions to undertake real forest therapy.
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The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin/destination (O/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease. © 2023 by the authors.
ABSTRACT
Recent investigations show how the pandemic has affected learners' behavioral traits. The results of three semi-structured surveys carried out in a major Italian university: 2020, 1st sem. (n=102);2022, 1st sem. (n=235);and 2022, 2nd sem. (n=61) under COVID-19 containment measures, manifest deviations in students' perceptions about social patterns, learning routines, and expectations. During the two-year emergency remote learning, students revealed a progressive downsize of social expectancy and increasing self-management behaviors in relation to a higher degree of independence. The Community of Inquiry principles were adopted to observe student motivation and self-direction in a Moodle-based learning environment. Conversely, the focus on English as a Foreign Language as the main subject represents an uncharted perspective in the research contexts around the Community of Inquiry. Future expansions may enlarge the sample to further education bodies and broaden the range of e-learning tools.
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In the post-COVID-19 era, promoting the healthy development of social e-commerce platforms, taking into account the coordinated development of online social networking and online consumption, enhancing the shopping and sharing atmosphere of the platform, increasing the enthusiasm of distribution members, driving consumption traffic with social traffic, and achieving low-cost publicity and promotion increase the exposure of products. The key to solving the problem is to promote consumer participation, improve user conversion, and improve customer acquisition capabilities. The study is based on the stimulus-organism-response (S-O-R) theory. It introduces the technology acceptance model (TAM) and, according to the member life cycle theory and member value mining theory, will affect consumers' social presence factors in social e-commerce shopping situations. As independent variables, perceived trust and perceived usefulness are used as mediating variables to construct a research model that shows social e-commerce affects user conversion.
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Entrepreneurship research is paying increasing attention to big data. However, there is only a fragmented understanding on how big data influences entrepreneurial activities. To review previous research systematically and quantitatively, the authors use bibliometrics method to analyze 164 research articles on big data in entrepreneurship. They visualize the landscape of these studies, such as publication year, country, and research area. They then use VOSviewer to conduct theme clustering analysis, finding four themes, namely the COVID-19 pandemic and small medium enterprise (SME) digitization, application of big data analytics to decision making, application of big data in platform, and the effects of big data on enterprises. In addition, they construct an integrated framework that integrates the antecedents of big data adoption and influence mechanism of big data on entrepreneurial activities.
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The lockdown down of countries has fueled the alternative option of open distance e-learning education because one of the precautions of the coronaviruses is maintaining social distance from one another. Since higher education institutions could no longer function as expected where teaching and learning, research, and other activities could take place, the suggestion of open distance e-learning where students could work from home was adopted. Many educational institutions have leverage online learning platforms in providing vital resources to promote the culture of learning as the world continues to combat the dreaded coronavirus. Open distance e-learning as a strategy for higher education institutions has rescued the academic environment from total paralysis. This paper explores this phenomenon.
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The COVID-19 pandemic is expeditiously stirring the global economy. The impact of this pandemic has implications on the sustenance of industries worldwide. This study investigates the influence of various endogenous and exogenous factors affecting e-wallet adoption among micro entrepreneurs in India. A sample of 287 micro enterprises were identified in NCR (National Capital Region) region on the basis of random sampling. Structured questionnaires were administered to the respondents. Structural equation modelling was used to analyse the data with the help of Smart PLS 3. The main findings of the study show that self-belief, personal innovativeness, and satisfaction are the key indicators affecting the e-wallet adoption among the microentrepreneurs. Microentrepreneurs contribute greatly to economic development in developed and developing nations. Digitalisation of this segment of industry can turn India into a cashless country, thereby reducing the cash burden of the economy. Microentrepreneurs can also act as a catalyst for financial inclusion.
ABSTRACT
During the pandemic outbreak of COVID-19 in Greece that coincided with the spring semester of the year 2020, conventional face-to-face lessons presented a threat to public health. As a result, house confinement measures were taken. Universities, due to their offering either directly or via their lifelong education centers, were partially prepared to offer distant learning solutions for their students during the pandemic. The lessons, in the general case, were delivered in an ad hoc manner utilizing teachers' personal experiences and preferences creating some pressure on existing infrastructures. In the case of the Department of Industrial Design & Production Engineering at the University of West Attica, things were more organized than in the general case: there was a, more or less, uniform practice of preferring synchronous lessons and some monitoring was planned in order to evaluate the application for future reference. While data collected in the process are still going through statistical analysis there are some preliminary results that can be reported here.
ABSTRACT
The coronavirus disease 2019 (COVID-19) epidemic that began in early 2020 quickly formed a global trend, bringing unprecedented shocks to many countries' and even the global trade economy. Big data is the main feature of the Internet era, which has transformed the industrial development pattern of modern society and has now flourished in the field of trade economy;therefore, it is of great significance to apply the big data analysis technology to study the impact of the COVID-19 epidemic on the global trade economy. On the basis of summarizing and analyzing previous research works, this paper, expounded the research status and significance of the impact of the COVID-19 epidemic on the global trade economy, elaborated the development background, The study results of this paper provide a reference for further researches on the impact of the impact of the COVID-19 epidemic on the global trade economy based on big data analysis.
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The coronavirus (COVID-19) has had severe global impacts in many aspects of education. Asian countries and regions have been the first responders to move entirely online since the epidemic started. The aim of this paper is two-folded. First, this study investigates the correlations in order to understand the compounded effects on presences in the participating synchronous learning environments. Second, this paper provide empirical evidence and insights for educators on the future trends of learning and instructional strategy in online teaching. This study investigated students' perception of synchronous e-learning during the COVID-19 pandemic for the better design of the e-learning teaching pedagogy and determines how the key factors of e-learning perception are inter-correlated enabling educators to focus on. The study has important implications that student readiness in educational technology is critical to assist the recent practice in implementing online learning.
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The spread of the COVID-19 pandemic had a huge impact on personal lives, society, and economies all over the world. Many countries are still struggling with the rising and falling numbers of COVID-19 cases. The drastic effects of the pandemic have brought sharp focus on healthcare and the need for rapid technology adoption and strong collaborative digital healthcare solutions for dealing with the health crisis. 5G networks can play a vital role in transforming the critical components of healthcare ecosystem by providing cost effective, high connectivity to the patients and healthcare workers. This research article investigates and highlights the technical aspects 5G technology, its effective utilization for collaborative e-health services, and the 5G-based solutions. It also presents a detailed discussion on challenges of 5G implementation and possible solutions. In the end, it discusses the future research directions for 5G-enabled e-collaboration in decreasing the health-based challenges and issues in future pandemic outbreaks.
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The paper analyzes the assembly process by the example of assembly-modular containers using building information modeling technologies. This paper simulates a 3D model of the Huoshenshan Hospital with a description of the assembly mechanism process based on information modeling of prefabricated buildings. The purpose of this paper is to analyze the sources on prefabricated houses and explore the concept of creating a digital prototype of a building based on Huoshenshan Hospital, using the Autodesk Revit software. The article describes the methodology of installing modular containers and assembly structures using building information modeling technologies to improve rapid construction technology. The study results showed that building object implementation directly depends on a proper model with a step-by-step mechanism for installation, which can reduce the initial project cost due to the supply of prefabricated structures on the construction site, as well as reduce the project time. The prefabricated house technology demonstrated the high efficiency of using information technology in the assembly of the Huoshenshan Hospital, with which the simulated facility was implemented in 10 days. The need for information modeling data exchange with modern gadgets and systems is investigated, which allows one to get acquainted with the object at the construction site before installation work start. © 2022 ICE Publishing: All rights reserved.
ABSTRACT
COVID-19 has wreaked unprecedented havoc in the world. Response efforts have also made huge evident gaps in preparedness and governments around the world's capacity to respond to a health crisis of this magnitude adequately. As a result, local communities have taken matters into their own hands and turned to technology platforms to coordinate mutual aid efforts, shed light on response gaps, and hold governments accountable. This paper explores the role of open data sharing platforms and collective intelligence in COVID-19 response efforts by studying two examples of community-led initiatives from Spain and Japan. Frena La Curva (Spain) and Safecast (Japan) utilized the Ushahidi platform, an open-source technology tool born out of Kenya's post-election violence that has been widely used in over 160 countries for crisis response since its inception in 2008. Research reports have been warning of pandemic breakouts for decades. However, the response to COVID-19 was inadequate, with healthcare systems buckling under the pressure of the spread of the disease. Moreover, existing social protection programs could not shield citizens despite having experienced similar economic impacts in the years that have passed. Data hugging and suppression of information regarding the pandemic outbreak led to significant delays in measures being put in place to curb the spread of COVID-19. This paper proposes that governments would benefit from leveraging open data and technology platforms to engage with ordinary citizens and eliminate data blind spots in the design of social protection programs. It also posits that we need to invest in interoperable data exchange systems to increase the speed of response and learning. Finally, it also proposes the need for internet freedom and access as a critical tool for preparedness by enabling the free flow of information. © 2022 Copyright held by the owner/author(s).