ABSTRACT
Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.
ABSTRACT
Background: The 2019 novel coronavirus disease (COVID-19) has been reported as pandemy and the number of patients continues to rise. Based on recent data, cardiac injury is a prominent feature of the disease, leading to increased morbidity and mortality. In the present study we aimed to evaluate myocardial dysfunction using transthoracic echocardiography (TTE) and tissue Doppler imaging (TDI) in hospitalized COVID-19 patients. Methods and Results: We recruited 30 patients (56.7% male, 55.80 +/- 14.949 years) who were hospitalized with the diagnosis COVID-19 infection. We analyzed left ventricular (LV) and right ventricular (RV) conventional and TDI parameters at the time of hospitalization and during the course of the disease. Patients without any cardiac disease and with preserved LV ejection fraction (EF) were included. TTE examination was performed and all the variables were recorded and analyzed retrospectively. We observed that both LV and RV conventional echocardiographic parameters were similar when the day of admission to the hospital was compared to the 5th day of the disease. Regarding TDI analysis, we demonstrated significant impairment in LV septal and lateral deformation (P < 0.001). In the correlation analysis no marked correlation was observed between impairment in LV deformation and inflammation biomarkers. Conclusion(s): Cardiac involvement is an important feature of the COVID-19 infection but the exact mechanism is still undefined. Echocardiography is an essential technique to describe myocardial injury and provide new concepts for the possible definitions of cardiac dysfunction.Copyright © 2023 The Author(s).
ABSTRACT
A new coronavirus infection caused by the SARS-CoV-2 virus is characterized by a systemic hyperinflammatory response with a pronounced increase in the content of pro-inflammatory cytokines. Materials and methods. The study was conducted on the basis of the Samara Regional Children's Infectious Diseases Hospital from 2021 to 2022. 40 patients with moderate (n = 20, group I) and severe forms (n = 20, group II) COVID-19 were studied, the comparison group consisted of patients with viral pneumonia of another etiology (n = 35, group III). Results. The infectious agent SARS-CoV-2 induces high levels of cytokines IL-6 (p < 0.005), IL-8 (p < 0.05) and a slight increase in TNF-alpha (p < 0.05). IL-8 was significantly associated with disease duration (p < 0.01). We assume that the value of this interleukin will increase in the post-COVID period. Conclusions. Changes in IL-6 and IL-8 levels in patients with COVID-19, along with clinical features, are important biomarkers for predicting the severity and duration of the disease.Copyright © Children Infections.All rights reserved
ABSTRACT
During the covid-19 pandemic, students' online learning quality is imbued with teachers' support strategies while students' learning engagement is another great indicator underlies their learning experiences. Through a questionnaire survey of 500 freshmen who have had their college English class online in 2022 fall, an investigation using exploratory factor analysis, Pearson correlation analysis, stepwise regression analysis and parallel mediator model reveals the impact of teachers' support strategies (the six dimensions of challenge, authentic context, curiosity, autonomy, recognition and feedback) on the learners' online college English learning engagement (the four dimensions of cognitive engagement, behavioral engagement, emotional engagement, social engagement), thus particular concern is also given to the correlation with students' online learning experiences. It was found that even under diversified and comprehensive guiding strategies from teachers, university students' online college English learning engagement is at the medium level, among which the cognitive engagement should be devoted more. The experimental data also shows that teachers' support strategies have significant influence on learners' engagement, especially teachers' feedback and challenge setting will stimulate students to involve more in their study. In addition, both teachers' support strategies and students' learning engagement involves significant reflection of learning experiences accordingly. Based on this learning concept, related proposals see different degrees of prominence reflected in online instructional design, teachers' and students' feedback literacy, and technology-enabled innovative teaching practice are put forward, in order to effectively play the role of teacher scaffolding, learning experiences enrichment and students' engagement enhancement of online English learning. © 2023 IEEE.
ABSTRACT
As a critical influencing factor of learning engagement, teacher expectation plays a vital role in ensuring the quality of online teaching under COVID-19. This paper investigates the relationship between teacher expectations (three dimensions of teacher support, teaching interaction, and academic feedback) on students' online English learning engagement (three dimensions of cognitive engagement, behavioral engagement, and emotional engagement) in e-learning through a questionnaire survey of 513 college students. Pearson correlation analysis and multiple regression analysis were applied as research methods. The results manifest that college students' online English learning engagement was above average, but emotional engagement needs improvement. In addition, teacher expectations of teaching interaction positively and significantly predict English e-learning engagement. Based on this, the article puts forward suggestions on the future of online teaching from the aspects of online teaching design, feedback quality of teachers and students, innovative teaching practice of technology empowerment to effectively play the role of teachers as scaffolding and improve the effectiveness of online English teaching. © 2023 IEEE.
ABSTRACT
Background: During the Covid-19 period, the government made policies dealing with it. Policies issued by the government invited public opinion as a form of public reaction to these policies. The easiest way to find out the public's response is through Twitter's social media. However, Twitter data have limitations. There is a mix between facts and personal opinions. It is necessary to distinguish between these. Opinions expressed by the public can be both positive and negative, so correlation is needed to link opinions and their emotions. Objective: This study discusses sentiment and emotion detection to understand public opinion accurately. Sentiment and emotion are analyzed using Pearson correlation to determine the correlation. Methods: The datasets were about public opinion of Covid-19 retrieved from Twitter. The data were annotated into sentiment and emotion using Pearson correlation. After the annotation process, the data were preprocessed. Afterward, single model classification was carried out using machine learning methods (Support Vector Machine, Random Forest, Naïve Bayes) and deep learning method (Bidirectional Encoder Representation from Transformers). The classification process was focused on accuracy and F1-score evaluation. Results: There were three scenarios for determining sentiment and emotion, namely the factor of aspect-based and correlation- based, without those factors, and aspect-based sentiment only. The scenario using the two aforementioned factors obtained an accuracy value of 97%, while an accuracy of 96% was acquired without them. Conclusion: The use of aspect and correlation with Pearson correlation has helped better understand public opinion regarding sentiment and emotion more accurately © 2023 The Authors. Published by Universitas Airlangga.
ABSTRACT
This study examined psychological health and coping strategies among faculty and staff at a Saudi Arabian university. A web-based self-administered survey was used to assess probable anxiety, depression, post-traumatic stress disorder (PTSD), and coping strategies by using the Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Impact of Event Scale-Revised (IES-R), and Brief-COPE scale, respectively. Of 502 participants (mean age 36.04 ± 10.32 years, male: 66.3%), 24.1% (GAD-7 ≥ 10) had probable anxiety. Anxiety score was significantly higher in females (p < 0.001), those with a history of COVID-19 infection (p = 0.036), and participants with less work experience (p = 0.019). Approximately 40% of participants met the criteria of probable depression, with females (p < 0.001) and participants with less experience having more depressive symptoms. Around one-fourth (27.7%) of study participants indicated probable PTSD (score ≥ 33), with higher symptoms in females (p <0.001), less experienced staff (p < 0.001), and academic staff (p = 0.006). Correlation analysis indicated a significant positive correlation between anxiety and depression (r = 0.844, p < 0.001), anxiety and PTSD (r = 0.650, p < 0.001), and depression and PTSD (r = 0.676, p < 0.001). Active coping, religious/spiritual coping, and acceptance were common coping strategies, while substance use was the least adopted coping method among the study participants. This study indicated a high prevalence of probable psychological ailments among university staff.
ABSTRACT
Giving the COVID-19 vaccine has many benefits, including increasing immunity from exposure to COVID-19 and preventing new mutations from COVID-19. In addition, the COVID-19 vaccine that has been injected into the community has gone through a series of strict tests, so that it is guaranteed to be safe, quality and efficacious. The research aims to cluster the spread of the corona virus in DKI Jakarta province which is displayed on a visual map using ArcGIS Technology. Based on the data on the spread of the corona virus which has been grouped using K-means clustering, it is hoped that it can help make the right decisions in vaccination and the priority of COVID-19 assistance that is determined and directed based on information cluster, so this research is expected to help the government in tackling the COVID-19 pandemic in Indonesia, especially DKI Jakarta. In addition, this research also aims to see the correlation between the COVID-19 vaccine and the number of positive cases of Covid-19. © 2022 IEEE.
ABSTRACT
In recent years, the concentration levels of various air pollutants have been constantly increasing, primarily due to the high vehicle flow. In 2020, however, severe lockdowns in Greece were imposed to reduce the spread of the COVID-19 pandemic, which led to a rapid reduction in the concentration levels of air pollutants such as PM2.5 and PM10 in the atmosphere. Initially, this paper seeks to identify the correlation between the concentration levels of PM10 and the traffic flow by acquiring data from low-cost IoT devices which were placed in Thessaloniki, Greece, from March to August 2020. The correlation and the linearity between the two parameters were further investigated by applying descriptive analytics, regression techniques, Pearson correlation, and independent T-testing. The obtained results indicate that the concentration levels of PM10 are strongly correlated to the vehicle flow. Therefore, the results hint that the decrease in the vehicle flow could result in improving the quality of environmental air. Finally, the acquired results point out that the temperature and humidity are weakly correlated with the concentration levels of PM10 present in the atmosphere.
ABSTRACT
Recent advancements in nanotechnology have resulted in improved medicine delivery to the target site. Nanosponges are three-dimensional drug delivery systems that are nanoscale in size and created by cross-linking polymers. The introduction of Nanosponges has been a significant step toward overcoming issues such as drug toxicity, low bioavailability, and predictable medication release. Using a new way of nanotechnology, nanosponges, which are porous with small sponges (below one microm) flowing throughout the body, have demonstrated excellent results in delivering drugs. As a result, they reach the target place, attach to the skin's surface, and slowly release the medicine. Nanosponges can be used to encapsulate a wide range of medicines, including both hydrophilic and lipophilic pharmaceuticals. The medication delivery method using nanosponges is one of the most promising fields in pharmacy. It can be used as a biocatalyst carrier for vaccines, antibodies, enzymes, and proteins to be released. The existing study enlightens on the preparation method, evaluation, and prospective application in a medication delivery system and also focuses on patents filed in the field of nanosponges.Copyright © 2023 The Authors.
ABSTRACT
Background: Mobile phones have become a widely accepted learning mode due to the impact of COVID-19. This study explores the mobile technology acceptance, among nursing students at selected educational institutions in South India. Materials and Methods: Quantitative cross-sectional descriptive design. First-year 176 B.Sc. nursing students who underwent blended learning were selected by the purposive sampling method. The tool "Technology Acceptance Model" was used to collect responses. Bivariate analysis was used to determine the relationship between the demographic and study-related variables with the mobile technology acceptance using SPSS version 25.0. Results: The majority 73.9% of the students belonged to the age group of 18-19 years, females 76.7% and, 98.9% were unmarried. Among the constructs of TAM, a mean (SD) value of 22.08 (2.26) was found for material (mobile device audio/video) characteristics the mean (SD) value was 22.08 (2.26), attitude about use 17.58 (1.95), behavioral intention 17.46 (1.78) and system characteristics 17.21 (2.27). The mobile technology acceptance revealed that 126 (71.6%) strongly agreed, 49 (27.8%) agreed, and 1 (0.6%) was neutral with a mean (SD) of 105.19 (8.68), respectively. A positive correlation was found between the system characteristics, material characteristics, perceived ease to use, perceived usefulness, attitude about the use, behavioral intention with a P value <0.001. There was a statistically significant association between Mobile technology acceptance and time spent by the students for independent studies shown the Chi-square value of 12.7, with P value <0.05. Conclusion: Nursing students had a positive acceptance and behavior toward smartphone use.
ABSTRACT
The respiratory pump that provides pulmonary ventilation includes the respiratory center, peripheral nervous system, chest and respiratory muscles. The aim of this study was to evaluate the activity of the respiratory center and the respiratory muscles strength after COVID-19 (COronaVIrus Disease 2019). Methods. The observational retrospective cross-sectional study included 74 post-COVID-19 patients (56 (76%) men, median age - 48 years). Spirometry, body plethysmography, measurement of lung diffusing capacity (DLCO), maximal inspiratory and expiratory pressures (MIP and MEP), and airway occlusion pressure after 0.1 sec (P0.1) were performed. In addition, dyspnea was assessed in 31 patients using the mMRC scale and muscle strength was assessed in 27 of those patients using MRC Weakness scale. Results. The median time from the COVID-19 onset to pulmonary function tests (PFTs) was 120 days. The total sample was divided into 2 subgroups: 1 - P0.1 <= 0.15 kPa (norm), 2 - > 0.15 kPa. The lung volumes, airway resistance, MIP, and MEP were within normal values in most patients, whereas DLCO was reduced in 59% of cases in both the total sample and the subgroups. Mild dyspnea and a slight decrease in muscle strength were also detected. Statistically significant differences between the subgroups were found in the lung volumes (lower) and airway resistance (higher) in subgroup 2. Correlation analysis revealed moderate negative correlations between P0.1 and ventilation parameters. Conclusion. Measurement of P0.1 is a simple and non-invasive method for assessing pulmonary function. In our study, an increase in P0.1 was detected in 45% of post-COVID-19 cases, possibly due to impaired pulmonary mechanics despite the preserved pulmonary ventilation as well as normal MIP and MEP values.Copyright © Savushkina O.I. et al., 2023.
ABSTRACT
Physical inactivity is becoming an important threat to public health in today's society. The COVID-19 pandemic has also reduced physical activity (PA) levels given all the restrictions imposed worldwide. In this work, physical activity interventions supported by mobile devices and relying on control engineering principles were proposed. The model was constructed relying on previous studies that consider a fluid analogy of Social Cognitive Theory (SCT), which is a psychological theory that describes how people acquire and maintain certain behaviors, including health-promoting behaviors, through the interplay of personal, environmental, and behavioral factors. The obtained model was validated using secondary data (collected earlier) from a real intervention with a group of male subjects in Great Britain. The present model was extended with new technology for a better understanding of behavior change interventions. This involved the use of applications, such as phone-based ecological momentary assessments, to collect behavioral data and the inclusion of simulations with logical reward conditions for reaching the behavioral threshold. A goal of 10,000 steps per day is recommended due to the significant link observed between higher daily step counts and lower mortality risk. The intervention was designed using a Model Predictive Control (MPC) algorithm configured to obtain a desired performance. The system was tested and validated using simulation scenarios that resemble different situations that may occur in a real setting.
ABSTRACT
Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) has sev-eral structural proteins including the spike (S) protein, which is the main target of current vaccines. There are various methodologies for the mea-surement of antibodies against it that provide information about the im-mune response to vaccination. The objective of this study was to determine the correlation between chemiluminescence (CLIA) and enzyme-linked im-munoassay (ELISA) for the measurement of IgG anti-S protein (IgG anti-S) antibodies. Serological results were collected from 169 individuals and antibody levels were determined by both methodologies. Out of the total samples, 106 were positive by both methodologies and 15 were discordant (CLIA+, ELISA-), with a Kappa index of 0.80. The correlation between both methodologies was good. This study could contribute to the management and follow-up of the vaccinated population, in order to obtain a cut-off value to evaluate the appli-cation of an additional dose.
ABSTRACT
The energy-efficient plate heat exchanger (PHE) and refrigerant R1234yf, which has a low global warming potential (GWP), can be used to realize an energy efficient heat pump (HP) system for electric vehicles (EV), extending their driving range. Therefore, the characteristics of R1234yf in an offset-fin strip (OSF) flowstructured PHE are critical for heat-exchanger design. This study investigates the condensation heat transfer coefficient (C-HTC) and two-phase frictional pressure drop (2P-FPD) of R1234yf during condensation in an OSF flow-structured PHE under various operating conditions. First, a modified Wilson plot method was used to determine the multiplier (C) and Reynolds number exponential (n) for the coolant side as -0.426 and 0.494, respectively. When the heat flux (q), average vapor quality (xa), and mass flux (G) increased, the C-HTC increased, whereas it decreased with saturation temperature (Tsat). Despite the force-convective condensation flow regime, the C-HTC increment was minimal with G at lower xa owing to the lesser significance of the shear effect. Additionally, the 2P-FPD was unaffected by q but increased considerably with an increase in xa and G and a decrease in Tsat. Based on the current experimental database, empirical correlations for forecasting friction factor and Nusselt number were developed with a 91% predictability.
ABSTRACT
Objective. To assess the T-cell immune status against SARS-CoV-2 in HIV patients with or without antiretroviral therapy. Patients and methods. The study included 21 HIV patients who had laboratory-confirmed COVID-19 between September and December 2021 without previous immunization against SARS-CoV-2. The characteristics of HIV infection (CD4-lymphocytes count, HIV viral load in blood plasma, the presence of antiretroviral therapy) and COVID-19 (the severity degree and duration of the disease) were analyzed, the T-cell immune response to SARS-CoV-2 was assessed using the ELISPOT method 1 month after COVID-19. Statistical analysis was carried out by non-parametric methods (Mann-Whitney U test, Spearman's rank correlation coefficient) using the IBM SPSS Statistics 22 software package. Results. The study showed a more favorable course of COVID-19 in HIV-infected persons who achieved HIV suppression in the blood: a mild form of the disease was significantly more common, and the virus was eliminated faster. T-cell immune response to SARS-CoV-2 was recorded more frequently in these patients. Significant correlation of T-cell immune status with the CD4-lymphocytes count and HIV suppression in the blood was revealed. Conclusion. Thus, T-cell immune response to SARS-CoV-2 as assessed using the ELISPOT method was registered significantl.Copyright © 2023, Dynasty Publishing House. All rights reserved.
ABSTRACT
A multitude of studies have investigated the opportunities and limitations of telemedicine pre- and post-COVID-19 pandemic. However, most of the research has focused on telemedicine's constraints in the context of international, regional, and developed nations, with few studies examining the specific challenges that may affect telemedicine's progress in developing countries where the pandemic may have exacerbated existing technological and geographical difficulties. This study takes the Philippines as a case study due to its archipelagic location, use of English as an official language, and other factors that influence its adaptability to international telemedicine. We assessed the barriers and challenges to the advancement of telemedicine from four viewpoints: policy, organization, individual, and collaboration between organizations. Therefore, the significance of this study is twofold: (a) to concentrate on international telemedicine education by contrasting domestic and international practices, and (b) to newly reveal connections between each component, as prior research highlighted barriers and difficulties but did not clarify relationships among different elements. We surveyed and interviewed 38 physicians, technicians, coordinators, and staff involved in telemedicine education in the Philippines. The study found that (1) public support yields favourable results, (2) a strong correlation exists between domestic and international telemedicine, (3) communication and technical obstacles are interconnected, (4) unity and cooperation in intra-hospital collaboration are critical, and (5) comprehending the "significance of work" has a positive impact. This study underscores the intersectionality of several barriers to telemedicine development. It also recommends providing greater support for telemedicine education in developing nations and promoting collaboration between developing and developed nations. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
ABSTRACT
Micro, small and medium enterprises (MSMEs) across the globe have been the major victims of the COVID-19 pandemic, which has plunged the business world into a serious crisis. However, Employee Engagement (EE) has been labeled as an essential nutrient that organizations need in order to survive in these turbulent times. This study was to examine the effect of EE on Employee Performance during the COVID-19 pandemic and how this relationship is moderated by job demands. The study used a sample size of 395 respondents who were chosen from a variety of MSMEs via a convenience sampling method. The study used a survey questionnaire approach as the main method for collecting primary data. The result of the correlation analysis showed that there was a significant relationship between EE and employee performance. It was revealed that job resources moderated the relationship between EE and employee performance. Finally, the result showed that job resources had a significant effect on EE. Thus, it can be concluded that, in the face of COVID-19, EE is a significant predictor of the performance of employees in the MSMEs sector. [ FROM AUTHOR] Copyright of Journal of Social Science (2720-9938) is the property of Ridwan Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Over the years, the gold prices have been increasing rapidly. Covid-19 and its impact leads to rise in the prices of Gold in the year 2020. So many variables are mindful for increasing the gold cost in India and it leads to investment decisions of individuals and enterprises. Autoregressive Integrated Moving Average (ARIMA) is useful method to gauge time series data. In the paper we mainly focus on daily costs of Gold from the year 2018 to 2020 to determine and forecast the daily gold prices in 2021. Also estimate the error (%) between the observed and estimated values through ARIMA model. This study will provide the estimates of suitable ARIMA model (0,1,2) along with Autocorrelation function (ACF) & Partial autocorrelation function (PACF) from the selected data, The auxiliary source information shows the positive patterns for getting effectiveness, For quantitative examination and speculation selections of financial backers. © 2023 Author(s).
ABSTRACT
We examined the evolution of cross-market linkages between four major precious metals and US stock returns, before (Phase I) and after (Phase II) the COVID-19 outbreak. Phase II was also extended to encompass the Ukrainian conflict, which prolonged the period of uncertainty in financial markets. Due to the increase in volatility observed in Phase II, we used a heteroskedasticity-adjusted correlation coefficient to examine the evolution of correlation changes since the COVID-19 outbreak. We also propose a relevant dissimilarity measure in multidimensional scaling analysis that can be used for depicting associations between financial returns in turbulent times. Our results suggest that (i) the correlation levels of gold, silver, platinum, and palladium returns with US stock returns have not changed substantially since the COVID-19 outbreak, and (ii) all precious metal returns exhibit movements that are less synchronized with US stock returns, with palladium and gold being the least synchronized. © 2023 by the author.