ABSTRACT
Recently, due to the coronavirus pandemic, we are experiencing a revolution that is transforming the way, the education has now shifted to an "physical plus digital” or "phygital” multimodal. This paper analyses the students' behavioral intention to the phygital learning, meaning how students use online learning platform (e.g. Moodle), collaboration application (e.g. Microsoft teams), chat application (e.g. Wechat) and device (e.g. smartphone, laptop) of a course. For the evaluation purpose is followed by using the Semantic Differential Technique to distinguish the usage attitude of computer and smartphone. The Usage Questionnaire is followed by the System Usability Scale (SUS), which is a Human Computer Interaction (HCI) based approach, and the Technology Acceptance Model (TAM), which is an Information Systems (IS) based approach. The sample size consisted of 68 participants completed the survey questionnaire measuring their responses to perceived usefulness (PU), perceived ease of use (PEOU) and attitudes towards usage (ATU). Through simultaneously both these instruments in one work for the purpose of usability evaluation. By doing so, this work attempts to streamline and unify the process of usability evaluation. Results that are obtained from a large-scale survey of university students show the attitudes towards usage on phygital learning. Moreover, this work also considers the digital-divide aspect (mobile v.s. web environment) whether it has any effect on the perceived usability. Results show that the multiple education modal could reduce the stress on the learning. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Because predictions of transportation system reliability can provide useful information for intelligent transportation systems (ITS), evaluation of them might be viewed as a beneficial activity for reducing traffic congestion. This evaluation procedure could include some alternatives and criteria in a discrete decision space. To handle this evaluation process in an uncertain environment, a novel uncertain multi-criteria decision-making (MCDM) method is put forward in this paper. Considering the validity of uncertainty theory as a measure of epistemic uncertainty, we first introduce it into analytic hierarchy process (AHP) and provide the whole calculation procedure of the approach. The proposed approach is employed to evaluate regional travel time belief reliability in a case study. Additionally, a comparison is performed between the results of uncertain AHP and other MCDM methods to examine the efficiency of this method. These analyses show that uncertainty theory is particularly suited to be employed combination with the AHP method.
ABSTRACT
BACKGROUND AND PURPOSE: Severe respiratory distress in patients with COVID-19 has been associated with higher rate of neurologic manifestations. Our aim was to investigate whether the severity of chest imaging findings among patients with coronavirus disease 2019 (COVID-19) correlates with the risk of acute neuroimaging findings. MATERIALS AND METHODS: This retrospective study included all patients with COVID-19 who received care at our hospital between March 3, 2020, and May 6, 2020, and underwent chest imaging within 10 days of neuroimaging. Chest radiographs were assessed using a previously validated automated neural network algorithm for COVID-19 (Pulmonary X-ray Severity score). Chest CTs were graded using a Chest CT Severity scoring system based on involvement of each lobe. Associations between chest imaging severity scores and acute neuroimaging findings were assessed using multivariable logistic regression. RESULTS: Twenty-four of 93 patients (26%) included in the study had positive acute neuroimaging findings, including intracranial hemorrhage (n = 7), infarction (n = 7), leukoencephalopathy (n = 6), or a combination of findings (n = 4). The average length of hospitalization, prevalence of intensive care unit admission, and proportion of patients requiring intubation were significantly greater in patients with acute neuroimaging findings than in patients without them (P < .05 for all). Compared with patients without acute neuroimaging findings, patients with acute neuroimaging findings had significantly higher mean Pulmonary X-ray Severity scores (5.0 [SD, 2.9] versus 9.2 [SD, 3.4], P < .001) and mean Chest CT Severity scores (9.0 [SD, 5.1] versus 12.1 [SD, 5.0], P = .041). The pulmonary x-ray severity score was a significant predictor of acute neuroimaging findings in patients with COVID-19. CONCLUSIONS: Patients with COVID-19 and acute neuroimaging findings had more severe findings on chest imaging on both radiographs and CT compared with patients with COVID-19 without acute neuroimaging findings. The severity of findings on chest radiography was a strong predictor of acute neuroimaging findings in patients with COVID-19.