Optimizing process parameters for the production of intercalated melt-blown nonwoven materials for face masks based on machine learning algorithms

Currently a new type of coronavirus is raging around the world, and many countries have relaxed the control of the epidemic. Wearing a mask has become the best self-protection measure for people to travel. Intercalated melt-blown nonwoven materials are in short supply as filter layers for daily-worn masks. This paper studies the relationship between the process parameters and structural variables of intercalated melt-blown nonwoven materials, and creatively uses machine learning-related algorithms to solve its nonlinear relationship. The optimized back propagation neural network model is the most suitable in this field, and the goodness of fit can reach more than 99.99%. Based on various limitations of actual industrial production, this model is used to traverse the process parameters, and the intercalated melt-blown nonwoven material is obtained. The best process parameters, in which the receiving distance is 27 cm, and the hot air velocity is 890 r/min, in this case, the thickness and porosity of the material produced are very low, while the compression resilience is very high, considering the filtration efficiency of the mask and comfort. © The Author(s) 2023.

A Data-Driven Context-Aware Health Inference System for Children during School Closures

Many countries have implemented school closures due to the outbreak of the COVID-19 pandemic, which has inevitably affected children's physical and mental health. It is vital for parents to pay special attention to their children's health status during school closures. However, it is difficult for parents to recognize the changes in their children's health, especially without visible symptoms, such as psychosocial functioning in mental health. Moreover, healthcare resources and understanding of the health and societal impact of COVID-19 are quite limited during the pandemic. Against this background, we collected real-world datasets from 1,172 children in Hong Kong during four time periods under different pandemic and school closure conditions from September 2019 to January 2022. Based on these data, we first perform exploratory data analysis to explore the impact of school closures on six health indicators, including physical activity intensity, physical functioning, self-rated health, psychosocial functioning, resilience, and connectedness. We further study the correlation between children's contextual characteristics (i.e., demographics, socioeconomic status, electronic device usage patterns, financial satisfaction, academic performance, sleep pattern, exercise habits, and dietary patterns) and the six health indicators. Subsequently, a health inference system is designed and developed to infer children's health status based on their contextual features to derive the risk factors of the six health indicators. The evaluation and case studies on real-world datasets show that this health inference system can help parents and authorities better understand key factors correlated with children's health status during school closures. © 2023 ACM.

Non-Academic Factors Analysis Impacting Students' Online Learning During the COVID-19 Pandemic in Universitas Logistic and Bisnis Internasional

In education, online learning with an e-learning system is an irreplaceable need. Many argue that online learning is the current educational crisis. Several studies show how complicated the handling of COVID-19 for universities is, especially in online learning (e-learning) outcomes. The variables influencing online learning during the COVID-19 epidemic have been shown in numerous studies. However, the influence of several other factors still needs to be investigated. Therefore, this study aims to determine non-academic factors that affect online learning during the COVID-19 pandemic. With data collected from the International University of Logistics and Business (ULBI), this study uses Cronbach's-Alpha analysis, Bayesian Exploratory Factor Analysis (BEFA), Principal Component Analysis (PCA), and Multivariate Regression Analysis. The evaluation of the research scale shows 20 observed variables. The test results prove that three non-academic factors influence students' online learning outcomes during the COVID-19 pandemic: education cost policy (H1), communication quality (H2), and student support (H3). Each factor has p - value < 0.001, p - value = 0.029, and p - value = 0.004, respectively. Meanwhile, family circumstances do not affect students' online learning outcomes during the COVID-19 pandemic (H4 rejected) because the p-value is 0.152. An example case in the questionnaire shows that most students say family income can adapt to changes during the COVID-19 pandemic. © 2022 IEEE.

Implications of COVID-19 policies on the sustainable corporate practices of European firms

Purpose: This study aims to analyze how mixes of COVID-19 policy responses are shaping the context in which companies will compete in the following years, defining how the crisis might impact firms' ability to keep their commitments to sustainable practices. Design/methodology/approach: European country-performance data for the years 2019 and 2020 were grouped into indicators of macro sustainability, then cross-analyzed against the policies adopted during the period (also grouped based on their impacts on sustainability pillars), using correlations, factor analysis and clustering. Findings: The influence of traditional sustainability determinants was reframed according to the novel context shaped by the policy responses to the pandemic crisis. The social and digitalization aspects gained the most relevance and appeared interconnected, with digitalization of employment attaining overall more traction. Moreover, changes in the leadership within sustainability domains were observed for each identified country-cluster, due to newly implemented emergency policies. In fact, environmental innovation, digitalization and social support policies appeared to be the main variables to be impacted by the intensity of the policy efforts. Practical implications: Businesses monitoring the developments of sustainability policies closely, will observe novel trends in technological applications. Social implications: Policymakers and researchers may gauge the efficacy of policies against the COVID-19 crisis in the domain of sustainable development and resilience. Originality/value: This paper provides a cross-analysis of quantitative macroeconomic and quantified policy responses to the 2020 pandemic crisis, linking each indicator to the pillars of sustainability that were relevant for companies between the crucial pandemic outbreak years 2019 and 2020. © 2023, Emerald Publishing Limited.

Becoming Holy Grail of Health on the Move: Identifying and Prioritizing Factors Affecting the Commercialization of m-Health

The need for mobile-based solutions for healthcare after COVID pandemic is more obvious than ever as mobile itself is an integral part of everyday life. m-Health is not an unfamiliar phenomenon, but despite the progress that has been made in this area, it is still difficult for m-health platforms to enter and stabilize in the market, especially in developing countries. So, in this study, we tried to prioritize the factors affecting the commercialization of m-Health and platforms. By reviewing related researches to the field of mobile health commercialization, 30 main effective indicators in mobile health commercialization were identified. After surveying experts and conducting exploratory factor analysis, these 30 indicators have been prioritized in 6 dimensions of efficiency and effectiveness, market, organizational and legal, technology and infrastructure, property and project management, and macro contexts. According to experts, the most important indicator is the timeliness of technology, and least important factor is the index of technology convergence with the laws and regulations in the field of health and treatment of the country. © 2022 IEEE.

Effects of Covid-19 Pandemic on Supply Chain Management in the Clothing Sector and Possible Solutions

The clothing sector is one which possesses significance in global trade. The sector has been negatively affected by the pandemic due to its labor-intensive structure and possession of a relatively long and global supply chain. At this point, the Turkish clothing sector, which is the sixth biggest clothing supplier in the world, the third biggest clothing supplier in the European Union, and comprised 10% of Turkey's general exports in 2019, should be investigated. In this context, this research aims to determine the effects of the COVID-19 pandemic on supply chain management in the Turkish clothing sector. Also, it aims to specify possible solutions against the negative effects of the COVID-19 pandemic. In accordance with the aim of the research, a survey was conducted in clothing enterprises. 391 survey questionnaires were incorporated into the research. According to the research results, it is determined that supply chain management in the Turkish clothing sector has been negatively affected by the COVID-19 pandemic. Order disruptions and cessations have ruined employment, production, procurement and investment processes resulting in financial disruptions. The most significant possible precautions that can be taken by enterprises that can be indicated as follows: benefiting from government support, heading towards online trade and an omni-channel strategy, actualising necessary alterations in product ranges, giving essential importance to innovation, efficient use of occupational health and safety systems, shortening the supply chain as far as possible, integrating digitalisation into all processes of the supply chain, and increasing the efficiency of marketing activities. © 2022 Seher Kanat et al., published by Sciendo.

Research on the Spread and Influencing Factors of the COVID-19 Variant in the UK

This paper first establishes a clustering algorithm to cluster the data in the attachment, dividing the data into three categories, and determines the infection rate and transmission time of each virus. On this basis, the cities in four regions are selected for analysis, and a multiple regression fitting model is established to determine their corresponding transmission in different stages and regions. In order to better analyze, this paper takes Aberdeen City as an example. According to the data corresponding to the original three viruses, a grey correlation model is established to analyze the data. Finally, according to the number of confirmed cases of Omicron virus transmission, this paper estimates the duration of Omicron virus transmission by establishing a neural network prediction model. © 2022 IEEE.

Music and Stress During COVID-19 Lockdown: Influence of Locus of Control and Coping Styles on Musical Preferences

The imposition of strict lockdown by the government of India during the first outbreak of COVID-19 had a remarkable impact on the well-being/wellness of the citizens. Studies around the globe demonstrated music as one of the effective strategies to enhance well-being during the lockdown. However, response to stressful events is modulated by individual characteristics like coping styles and locus of control (internal/external dependence) which have received little attention. The present chapter examined the use of music to cope with COVID-19 lockdown by these individual traits and their musical preferences during this period. A factor analysis yielded four music dimensions preferred by the participants during the lockdown: intense and electronic;cultural, emotional, and melodious;Indian contemporary and popular;and devotional music. Among the music genres, new and old Bollywood music were the most preferred genres. Participants with a higher internal locus of control, emotion, and problem-focused coping style demonstrated greater use of music in coping with stress. Problem-focused coping showed significant positive correlations with all the music dimensions, and emotion-focused coping style correlated with intense and electronic music;cultural, emotional, and melodious music;and devotional music. Internals showed no correlation with the different music genres. Externals showed a preference for intense and electronic and Indian contemporary and popular music. Listening to music had a significant positive effect on people high in emotion-focused and problem-focused coping styles and internal locus of control. However, it was not necessarily effective for people endorsing high external locus of control and avoidant coping. It implies that it can be used as a self-administered tool and therapeutically for people who engage in these coping styles and locus of control. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

CHANGES IN HEALTH LITERACY ASSESSMENT OF VIDZEME STATISTICAL REGION IN LATVIA

The ability to find, understand, evaluate and use information about an individual's health during the Covid-19 pandemic has become crucial. Therefore, health literacy (HL) skills development in Latvia, also in other OECD countries, is a priority area. Insufficient HL information base in Latvia is a fundamental basis for the research goal: to determine factors influencing HL and their changes among the population of Vidzeme statistical region (LV008) in Latvia. The study compares the authors' 2020 study based on the European Health Literacy Questionnaire (HLS-EU-Q47). The study includes survey of respondents (n = 383) using pen-and-paper interviewing (PAPI) and telephone interview approach. Various methods and tests were used: Principal axis factor analysis (PFA) with varimax rotation, Confirmatory factor analysis (CFA), Compare means Independent-sample T-test, Anova, Kaiser-MayerOlkin (KMO), Bartlett's test and Chi-squared test, Cronbach's and Spearman-Brown methods, correlations (Pearson and Spearman's) and Multiple linear regression (MLR) with Assumption Testing analysis, and Cronbach's test. The study identified four factors influencing health literacy: access, understanding, evaluation, and use. Compared to the study conducted by the authors in 2020, which determined that education has a positive impact on factor – access, the HL has increased more strongly in the age groups 18-19, 20-29 and 30-39. However, among women the HL level has decreased compared to the previous study by authors and HLS-EU, the proportion of people with sufficient and excellent HL in the European Union has increased overall by 13.5%, while the proportion of people with limited HL decreased by 24.4%. © 2022, Latvia University of Life Sciences and Technologies. All Rights Reserved.

D-Dimer Elevation Matters to Predict COVID-19 Severity: A Machine Learning Approach

Although previous studies using limited data have documented an association of D-dimer levels with COVID-19 severity, the role of D-dimer in the progression of COVID-19 remains unclear and requires further investigation using data from larger cohorts. We used traditional statistical modeling and machine learning methods to examine critical factors influencing the D-dimer elevation and to characterize associated risk factors of D-dimer elevation over the course of inpatient admission. We identified 20 important features to predict D-dimer levels, some of which could be used to predict and prevent the D-dimer elevation. Laboratory monitoring of D-dimer level and its risk factors at early stage can mitigate severe or death cases in COVID-19. © 2022 IEEE.

US County-level Risk Factors Associated with COVID-19 Exacerbation During the Vaccination Era

The COVID-19 pandemic has caused unprecedented challenges to public health and disruption to everyday life. The news in 2020 was dominated by the worldwide spread of COVID-19, overwhelming healthcare providers and drastically changing people's lives. In 2021, the release of vaccines from multiple pharmaceutical companies changed the focus to ending the pandemic through mass inoculation. Nevertheless, the vaccine acceptance rate differs significantly across US counties, ranging from 99% to 0.1%. Our study investigates the principal risk factors in predicting COVID-19 infection and mortality rates at the county level during the early vaccination era. We are particularly interested in the role of vaccination in curbing the exacerbation of COVID-19. To this end, we first compare the efficacy of six established machine learning algorithms to predict county-level infection and mortality rates. Next, we perform risk factor analysis by identifying common principal predictors revealed by the models. Our experimental results suggest that vaccination plays an essential role in limiting COVID-19 infection and mortality. Furthermore, socioeconomic factors (e.g., severe housing problems and median household income) are more predictive of county-level mortality rate than intuitive features such as availability of healthcare resources (e.g., total numbers of hospitals/ICU beds/MDs). Our findings could provide additional insights to assist in COVID-19 resource allocation and priority setting. © 2022 IEEE.

Influential factors in knowledge management in the acceptance of M-learning in university students

The purpose of the research was to establish the influence of the factors in knowledge management in the acceptance of M-learning in university students in times of covid-19. The study consisted of two stages;the first was an exploratory factor analysis, which allowed the instrument to be validated and homologated. The second stage consisted of carrying out a confirmatory analysis by validating a structural model based on variances called modelling of structural equations of partial minimum squares PLS-SEM, whose results determined that the most influential factors in the application of knowledge are perceived ease of use, knowledge sharing, and perceived utility of the model (TAM);not like this, the intention of behavioural use, that does not influence the application of knowledge. This hypothesis would not be tested regarding the level of influence that the application of knowledge would have on the actual use of the system. The research was carried out with 150 students from a private university in Peru. © 2022 IEEE Computer Society. All rights reserved.

Developing The Student's Attitude Assessment Instrument in The Learning Process During Covid-19 Pandemic

This research was intended to produce: 1) a product in form of the instrument for assessing student attitude in the e-learning process during Covid-19 pandemic, and 2) producing an instrument that fits the requirements to measure student attitudes through: a) proving validity of the expert judgment and construct validity by using exploratory factor analysis and confirmatory factor analysis, and b) estimating the reliability by using Cronbach’s Alph formula. The approach used in this research was Research and Development adopted from Dick et al. which was divided into five stages, they were: introduction, planning, development, limited trial, and broad trial. The research subjects were students of the Islamic Education Major, IAIN Ambon, which were selected using a simple random sampling technique. The data was collected using a questionnaire. Furthermore, the results of the research were: 1) The instrument for assessing student attitudes in the e-learning process during Covid-19 pandemic. 2) The assessment instrument that had met the requirements and was able to measure student attitudes in terms of: a) proving the validity of the instrument using expert judgment with an average result of the Aiken’s V index for each item was > 0.80 (high validity category) and proving construct validity through exploratory factor analysis showed a KMO value was 0.981 while Bartlett’s Test was Sig. 0.000. The results of the confirmatory factor analysis showed the Chi-Square value was 480;p-value was <0.001;RMSEA was 0.0251;SMRM was 0.0298;CFI was 0.983;TLI was 0.981, and b) reliability estimation showed that Cronbach’s Alpha coefficient was 0.959. © 2021 ACM.

Developing the Teacher Self-Efficacy Scale in the Use of ICT at Home for Pre-school Distance Education During Covid-19

Distance education has added a different dimension to the change in the use of ICT (information and communication technologies) in the early childhood education paradigms in recent years due to the COVID-19 pandemic. These changes before and during the pandemic will also affect education in the post-pandemic period and this is why research in teachers’ ICT use, competencies, and self-efficacy has become a priority. The theoretical foundations of the scale developed to measure preschool teachers’ ICT self-efficacy in distance education were based on Bandura’s social cognitive theory, Bronfenbrenner’s ecological systems theory, and Vygotsky’s socio-cultural theory. Accordingly, this study employed a survey method based on quantitative research. This study utilized two different study groups: the first was the Exploratory Factor Analysis (EFA) study group (N = 270), and the second was the Confirmatory Factor Analysis (CFA) study group (N = 275). A total of 555 preschool teachers volunteered to participate in the study. Exploratory Factor Analysis of the data set showed that the scale comprised three factors: information, communication, and technology. Confirmatory Factor Analysis confirmed the scale’s factors according to the fit index values on a different sample. The reliability analysis of the scale showed all factors to be reliable. After scale development phases and statistical analyses, it was determined that the teacher self-efficacy scale for the use of ICT at home is a valid and reliable measurement tool. The structure and results of the scale that was developed were compared with the results of different studies and discussed. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Tourist Behaviour in the COVID-19 Pandemic Context

The tourism industry is being severely affected by the coronavirus (COVID-19) pandemic. This paper aims to analyse how the COVID-19 pandemic affects the behaviour of Portuguese tourists towards travels and how the likelihood of their intention to deeply change behaviour is different according to their sociodemographic characteristics, the pattern of past trips, and the level that it has affected their life and work. With the results of a survey, factor analysis was made and with the scores of the extracted factors, logistic models were estimated. The results suggest that the Portuguese tourists are mainly concerned about health and safety in travel and the likelihood of having a major concern is greater for women and for those most affected by the pandemic, varying also with age. The chance of reducing more their travel plans is also higher for individuals who have been deeply affected by the pandemic. This research highlights the potential changes in tourists behaviour due to the pandemic and recommends some policies and practices for the sector. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Post-pandemic Era Study on the Design of High-speed Railway Seats

The COVID-19 has led to people’s increased concern about health issues. In this paper, we investigate the needs of users traveling by high-speed rail in the post-pandemic era and optimize the design of high-speed rail seats, and evaluate the feasibility. Methodology: Using INPD combined with AHP and QFD to guide the design of high-speed railway seats, we use INPD as the main line of research and SET factor analysis to find the product opportunity gaps;using questionnaires and user interviews to research different high-speed railway travelers and derive various needs of users for high-speed railway seats;AHP was used to calculate and prioritize the target user requirements, and then QFD was used to determine the weights of each design requirement point. Conclusion: This paper aims to provide design ideas and future development trends for the design of high-speed railway seats in the post-pandemic era by using INPD, AHP and QFD methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Food Image Generation and Appetite Evaluation Based on Appetite Factor Analysis by Image Features

In recent years, due to the COVID-19 pandemic and the widespread use of technology, the Internet and food and beverage websites are often used for take-out and food and beverage reservations, and information such as reviews and photos on these platforms has a significant impact on revenue. In this study, to develop an appetite-enhancing application, we focus on food images that strongly influences appetite and analyze what image features stimulate appetite. Then, based on the results of the analysis, we generate appetizing images using GAN (Generative Adversarial Network). © 2022 SPIE.

Korean Economic Growth Factor Analysis with Total Factor Productivity

Korea has recorded negative real GDP growth only three times in the last 60 years: the oil crisis in 1980, the financial crisis in 1998, and the COVID-19 crisis in 2020. While the economic recession for the first time in 22 years may be attributed to COVID-19, it is more noteworthy is that the growth rate of the Korean economy has been continuously declining. To find counter measures for the decline in the economic growth rate, it is necessary to analyze the cause of the decline in the growth rate. The factors of economic growth can generally be divided into changes in input factors such as labor and capital and productivity. We analyzed the relative contribution of each input factor from 1982 to 2020. Our result suggests that the contributions of capital and labor to economic growth are decreasing over time, and the contribution of TFP is gradually increasing. This study is employing annual time series data to provide up-to-date estimates of TFP and exploring the determinants of TFP to help detect future growth engines for the long-run sustainable development in Korea. © 2022 IEEE.