Next normal for business post pandemics: COVID-19

The world's supply chains are changing as both expected and unexpected environmental pandemics occur. Even though some may be expected, the full extent and ramifications a pandemic will have is an estimate at best. Thus, both flexibility and resiliency are becoming crucial to efficient supply chain systems. This study analyses the recent COVID-19 phenomenon and uses it to gauge reactions, best practices, resilience-based issues and operational performance metrics in order to assist with potential future pandemics. Education, as seen, plays a pivotal role in effectively offering options to combat uncertainty and fluid situations. Such dynamic environments have historically posed a serious problem to operations;however, with proper preparation and care taken options are available today that help marginalize harm of future pandemics.

Impact of COVID-19-Related Travel Restrictions on the Environment and Travel Time Reliability

This study analyzes the effect of the restrictions in traffic movement enforced in order to combat the spread of coronavirus on air quality and travel time reliability under heterogeneous and laneless traffic conditions. A comparative analysis was conducted to examine quantity of pollutants, average travel time distributions (TTD), and their associated travel time reliability (TTR) metrics during the COVID-19 pandemic, postpandemic, and during partial restrictions. Pollutants data (PM2.5, NO2, and NOX) and travel time data for selected locations from Chennai City in India were collected for a sample period of one week using Wi-Fi sensors and state-run air quality monitoring stations. It was observed that the average quantity of PM2.5, NO2, and NOX were increased by 433.1%, 681.4%, and 99.2%, respectively, during the postlockdown period. Correlation analysis also indicated that all considered air pollutants are moderately correlated to Wi-Fi hits, albeit to varied degrees. From the analysis, it was also found that average TTD mean and interquartile range values were increased by 47.2% and 105.2%. In addition, the buffer time index, planning time index, travel index, and capacity buffer index associated with these TTD metrics were increased by 148.1%, 63.7%, 42.8%, and 202.9%, respectively, soon after relaxing travel restrictions. © 2023 American Society of Civil Engineers.

Temporal Dynamics of User Engagement with U.S. News Sources on Facebook

Recently, researchers have modeled how reliability and political bias of news may affect Facebook users' engagement, as measured using interaction metrics such as the number of shares, likes, etc. However, the temporal dynamics of Facebook users' engagement with news of varying degrees of bias and reliability is less studied. In light of the COVID-19 pandemic, it is also important to quantify how the pandemic changed user engagement with various news. This paper presents the first temporal study of Facebook users' interaction dynamics, accounting for both the bias and reliability of the publishers. We consider a dataset of 992 U.S. publishers, and the study spans the period from Jan. 2018 to July 2022. This allows us to accurately assess the effect of the covid outbreak on the temporal dynamics of Facebook users' interactions with different classes of news. Our study examines these two parameters' effect on Facebook user engagement using both per-publisher and aggregated statistics. Several findings are revealed by our analysis, including that publishers in different bias and reliability classes experienced significantly different levels of engagement dynamics during and following the covid outbreak. For example, we show that the least reliable news exhibited the most considerable growth of followers during the covid period and the most reliable news sources exhibited the greatest growth rate of followers during the post-covid period. We also show that the interaction rate (number of interactions normalized over the number of followers) with Facebook news posts during the post-covid period is smaller than it was even before the outbreak. Furthermore, we demonstrate how the COVID-19 outbreak caused statistically significant structural breaks in the temporal dynamics of engagement with several types of news, and quantify this effect. With social media becoming a popular news source during crises, the observed temporal dynamics provide important insights into how information was consumed over the recent years, benefiting both researchers and public sectors. © 2022 IEEE.

Validity and reliability of sustainable supply chain management frameworks in Indian smart manufacturing industries

PurposeSustainable supply chain management (SSCM) ensures integration of socially, environmentally and economically feasible practices in entire supply chain. SSCM principles can be implemented to improve efficiency and productivity of a system by different attributes of the system. The purpose of this article is to identify the most appropriate existing (SSCM) framework that can be implemented suitably in Indian smart manufacturing industries.Design/methodology/approachValidity and reliability analysis on the existing SSCM frameworks was carried out with the help of empirical data collected using questionnaire survey methodology from various Indian smart manufacturing organizations. The empirical data were gathered from various experts from top- and middle-level management in different smart manufacturing organizations across the country. Further, factor analysis was carried on the collected data to estimate the unidimensionality of each SSCM frameworks. Cronbach's alpha value was used to assess reliability of each framework. Subsequently, the frequency distribution analysis was done to obtain familiar elements in the segregated frameworks based on validity and reliability analysis.FindingsThe work observed that only five SSCM frameworks have shown unidimensionality in terms of the elements or constructs. The work further found that these segregated frameworks have not shown sufficiently high level of reliability. Additionally, this work attempted frequency distribution analysis and observed that there were very few elements which were being repeatedly used in numerous frameworks proposed by researchers. Based on the findings of this work, the work concluded that there is acute need of a new SSCM framework for Indian smart manufacturing industries.Research limitations/implicationsThis study gathered empirical data from 388 Indian smart manufacturing organizations. Thus, before generalizing the findings of the study across the sectors, there is a possibility of some more explication.Originality/valueThe main purpose of this article is to explore the feasibility of the existing SSCM frameworks in Indian smart manufacturing sector. The study also assumes that the manufacturing managers and executives may have the complete understanding on the existing sustainable manufacturing frameworks and a chance to executing proper suitable framework in the respective manufacturing organization.

Sentiment Analysis on Medical Personal Protective Equipment (PPE) Shops Customer Reviews

Online shopping through e-commerce sites is becoming more prevalent with the expansion towards a more digital age in our society together with recent factors such that of the COVID-19 Pandemic. Through Machine Learning and the concept of Sentiment Analysis, algorithms would be able to identify the sentiment of reviewers by processing the words used in the sentence. The research aims to determine the reliability of star ratings compared to sentiment analysis and which classification algorithm suits best for text classification by Filipino customer reviews in Shopee for Medical Personal Protective Equipment or PPEs. It also aims to identify the best classifier model to use in terms of its performance. The study was divided into two models: star ratings and sentiment analysis. Both data sets performed different preprocessing techniques and tested for Naive Bayes and Support Vector Machine classification models, and their performance measures were obtained. The findings of the study show that star ratings and annotated reviews present high similarity in terms of the sentiment and polarity classified per review. In terms of the best performing model, Support Vector Machine achieved the best scores for the performance measures among the tests. © 2022 IEEE.

Analysis of the Potential of Tourism Restorative Destinations to Overcome Health Disorders Due to Work Fatigue: (Study on Perceived Destination Restorative Qualities (PDRQ) in the Context of Tourism)

The tourism industry is greatly affected by environmental changes. Especially after the world went through the Covid pandemic era, people prefer doing healthy tourism. The daily workload, the increase in education and welfare have resulted in a shift in the trend of tourism choices. People are increasingly considering the fulfillment of work-life balance. This study is designed to examine the elements contained in the measurement of the restorative quality of destinations known as "Perceived destination restorative quality (PDRQ)”. PDRQ consists of four elements, namely being away, extent, fascination, and compatibility. These four elements will be tested for their effect on the overall restorative impact and visitor satisfaction. A quantitative study will be conducted in Indonesia with tourism objects in Jogjakarta and Central Java covering rural and urban areas. SEM statistical analysis tools will be used to test the validity and reliability of the data collected. This research will enrich the literature review in the field of health tourism and for managers will be useful to further broaden the perspective of offering tourism program that has more restorative impacts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mobile learning determinants that influence Indian university students' learning satisfaction during the COVID-19 pandemic

Due to the COVID-19 pandemic, the whole world went under strict lockdown, including educational institutions. This led to the quick reshaping of educational systems to provide uninterrupted education to the students. Preferably, both teachers and students switched from physical classrooms to online classrooms. This overnight change brought numerous challenges for a country like India. But the authors of this study see it as an opportunity and aim to explore mobile learning (m-learning) determinants that influence Indian university students' learning needs during the COVID-19. For this, the data were gathered using a web-based questionnaire from 557 students of seven different universities (both public and private) in India. Next, the data were quantitatively analysed using reliability analysis, confirmatory factor analysis, and multiple regression analysis. The results show that out of three first-order m-learning variables, only two (system and service quality items) have a positive impact on students' learning satisfaction in the Indian context. In the end, the implications of the study in the adoption of m-learning at different Indian universities have been discussed. Copyright © 2023 Inderscience Enterprises Ltd.

A Digital Twins Model for Analyzing and Simulating Cold Chain Risks

Due to the outbreak of COVID-19, increasing attention has been paid to designing a cold chain logistics mechanism to ensure the quality of vaccine delivery. In this study, a cold chain digital twins-based risk analysis model is constructed to handle and monitor the vaccine delivery process with a high level of reliability and traceability. The model integrates the Internet of Things (IoT) and digital twins to acquire data on environmental conditions and shipment movements and connect physical cold chain logistics to the digital world. Through the simulation of cold chain logistics in a virtual environment, the risk levels relating to physical operations at a certain forecast horizon can be predicted beforehand, to prevent a 'broken' cold chain. The result of this investigation will reshape the cold chain in the digital age, benefit society in terms of sustainability and environmental impact, and hence contribute to the development of cold chain logistics in Hong Kong. © 2023 IEEE.

Reliability analysis of body sensor networks with correlated isolation groups.

Body sensor networks (BSNs) are playing a crucial role in tackling arising challenges during the COVID-19 pandemic. This work contributes by modeling and analyzing the BSN reliability considering the effects of correlated functional dependence (FDEP) and random isolation time behavior. Particularly, the FDEP exists in BSNs where a relay is utilized to assist the communication between some biosensors and the sink device. When the relay malfunctions, the dependent biosensors may communicate directly with the sink for a limited, uncertain time. These biosensors then become isolated from the rest of the BSN when their remaining power depletes to the level insufficient to support the direct communication. Moreover, multiple biosensors sharing the same relay and a biosensor communicating with the sink via several alternative relays create correlations among different FDEP groups. In addition, the competition in the time domain exists between the local failure of the relay and the propagated failures of dependent biosensors. Both the correlation and competition complicate the reliability modeling and analysis of BSNs. This work proposes a combinatorial and analytical methodology to address both effects in the BSN reliability analysis. The proposed method is demonstrated using a detailed case study and verified using a continuous-time Markov chain method.

Discrete Erlang-2 distribution and its application to leukemia and COVID-19

Via the survival discretization method, this research revealed a novel discrete one-parameter distribution known as the discrete Erlang-2 distribution (DE2). The new distribution has numerous surprising improvements over many conventional discrete distributions, particularly when analyzing excessively dispersed count data. Moments and moments-generating functions, a few descriptive measures (central tendency and dispersion), monotonicity of the probability mass function, and the hazard rate function are just a few of the statistical aspects of the postulated distribution that have been developed. The single parameter of the DE2 distribution was estimated via the maximum likelihood technique. Real-world datasets, leukemia and COVID-19, were applied to analyze the effectiveness of the recommended distribution. © 2023 the Author(s), licensee AIMS Press.

Research on Cold Chain Logistics Service Quality Evaluation Considering Product Freshness under Community Group Buying

In 2019, The outbreak of Corona Virus Disease 2019 (named COVID-19) has caused great changes in the living habits of residents, and the community group buying model has re-emerged. Under the background of community group buying mode, combined with the characteristics of fresh products, and based on the SEVRQUAL model and the national standard of "Logistics Enterprise Cold Chain Service Requirements and Capability Evaluation Indicators", an evaluation index system of cold chain logistics service quality for community group purchase of fresh products with 5 dimensions and 29 indicators is constructed from the perspective of users. Then a 5-level Likert scale was used to design relevant questionnaires, and Xingsheng Youxuan and Meituan Youxuan were used as empirical cases for sample research. Combined collected sample data, the validity and rationality of the index system were tested through reliability, validity testing and factor analysis. The data analysis also shows the problems and influencing factors of Xingsheng Youxuan and Meituan Youxuan in terms of fresh food cold chain logistics service quality, and further suggestions for the development of cold chain logistics services considering product freshness under community group buying is also provided. © 2022 ACM.

Mental health and life satisfaction among Canadian paramedics during the COVID-19 pandemic: an update

IntroductionPrevious results suggest that COVID-19 adversely impacted a number of health and coping measures among Canadian paramedics, particularly females. Estimated prevalence for meeting screening criteria for mental health disorders and suicidal thoughts were higher than previously reported.ObjectivesTo provide an update on the impact of the COVID-19 pandemic on the wellbeing of Canadian paramedics with the inclusion of an additional year of participant data.MethodsSelf-reported questionnaire data was collected from paramedics across five Canadian provinces as part of the COVID-19 Occupational Risks, Seroprevalence and Immunity among Paramedics (CORSIP) project. Validated psychological assessment tools were used to screen for major depressive disorder (MDD, PHQ-9 questionnaire) and probable post-traumatic stress disorder (PTSD, PC-PTSD-5 questionnaire). Satisfaction with life (SWL) scores were adapted from validated Canadian Census questions and confirmed by reliability analysis. All measures were compared before versus during the pandemic using Wilcoxon signed-ranked, Cliff's d, and differences in proportions tests where appropriate.ResultsQuestionnaires from an additional 1662 recruited paramedics were included, now totaling 3568 participants. Prevalence meeting screening criteria remained similar for MDD (31.6%) and PTSD (41.4%), with PTSD risk continuing to not be impacted by COVID-19. Paramedics continued to report higher median SWL scores (20 vs. 17, p<.001) prior to the pandemic, with a large effect size (d=0.58) that suggests a greater probability of reporting higher SWL prior to COVID-19. Suicidal ideation (i.e., ‘thoughts that you would be better off dead, or of hurting yourself in some way') was reported by 9.0% of paramedics, which was consistent with original findings.ConclusionOriginal findings appear stable with the addition of another year of participant data. Future analyses will be employed to investigate whether health and satisfaction measures differed between the original cohort and added participants by adjusting for questionnaire responses with respect to the pandemic timeline.

Structural transformation of fuzzy analytical hierarchy process: a relevant case for Covid-19

Covid-19 has posed difficult and challenging situations to the supply chains and companies are in fix how to choose the vendors under the uncertainty and complexity in recent years. Therefore, this research aims to incorporate structural transformation of the fuzzy analytical hierarchy process (FAHP) that is most appropriate for the uncertainty and disruption caused by Covid-19 like situation for ensuring supplies from vendors. The conventional approaches for vendor selection and evaluation use numerous multi-criteria decision-making tools that may not ensure reliability in a dynamic situation caused due to Covid-19. In this research, Fleiss' Kappa method ensures the reliability of responses from eight respondents by using pairwise comparisons and assigning weights as envisaged in FAHP. In addition to determine the reliability of responses, a step under FAHP has been altered. This alteration is demonstrated in the vendor selection case in the Covid-19 scenario. The research suggests a plausible system required to address the uncertainties associated with Covid-19 to select and evaluate vendors by modifying a FAHP. The proposed altered mechanism can be incorporated in a similar type of other decision-making circumstances such as Covid-19, where the decision-makers are more than one, and the situation is very dynamic. The study is likely to facilitate information management, algorithmic development in decision making, or machine-driven decisions in uncertain conditions. The study offers managerial implications to purchase managers to accommodate and combine multiple factors and responses concerning the vendor performances for their evaluation, thus making a process more reliable.

The Impact of COVID-19 on Human Mobility: A Case Study on New York

COVID-19 has dramatically changed people's mobility patterns. This report aims to analyze the impact of COVID-19 on people's mobility through statistics and comparing the visits of POIs (Point-Of-Interests) in New York State in 2019 and 2020. The report uses data from SafeGraph, which is a data company. The raw data contains POI visits across the United States in 2019 and 2020. Considering the analysis size and difficulty of the data, POI visits from New York State are extracted for analysis, and POI locations are classified according to the tags provided by the source data. The scale of analysis is from macro to micro, and they are the total POI visits data of New York State based on different ways in 2019 and 2020, the POI visits of CBG (Census Block Group) division in New York City, and three representative POI samples to do individual analysis. The analysis methods are: (1) use line plot and bar plot statistics to compare the trends of POI visits data from 2019 to 2020, and (2) make the spatial visualization comparison, which includes grid map, scatter map, heatmap, and OD map, between the first peak of epidemic impact in the first full week of April 2019 and April 2020, and the scope is narrowed to New York City. Wherein the OD maps are drawn based on the CBG division. Compared to related work, the analysis object includes CBG, categories, and individual POI. In addition, the analysis method combines statistical graphs and spatial visualizations and explores the policy impact of the New York City government. This report adopts more multidimensional analysis methods and objects to improve the comprehensiveness and reliability of the analysis content. © 2022 IEEE.

Impact of Remote Audit, Computer Literacy, and Audit Software Skill to Audit Quality during Pandemic of Covid-19

In order to face industrial revolution 4.0 auditors must start developing their skills. In the past, auditors have used remote audit only to reach remote places. However, currently Covid-19 comes and encourages even more the use of technology and provides an opportunity to rethink the way audits are conducted. In this study, researchers wanted to know how remote audit, computer literacy and audit software skill has affected audit quality. This research is quantitative in nature, by processing data using primary data obtained from distributing questionnaires to auditors who work at public accounting firms in Jabodetabek. Statistical analysis used multiple linear regression, previously carried out a feasibility test through validity, reliability and classical assumption tests. The results showed that the variables of remote audit, computer literacy and audit software skill had a significant effect on the audit quality. © 2022 ACM.

Analysis of Impact of Competency, Professionalism, and Audit Deadline on Remote Audit Effectiveness

Auditors must begin to develop their skills to face industry 4.0. The spread of Covid-19 has further encouraged auditors to conduct remote audits and provides an opportunity to rethink the way audits are conducted. In this study, the researcher wanted to find out how the influence of competence, professionalism, and audit deadlines on the effectiveness of remote audits. This research is quantitative, with data processing using primary data obtained from distributing questionnaires to auditors who work at Public Accounting Firms in Jakarta. Statistical analysis using multiple linear regression, before conducting a feasibility test through validity, reliability and classical assumption tests. The results showed that the variables of competence, professionalism, and audit time limit had a significant effect on the effectiveness of remote auditing. © 2022 ACM.

Ease of use and perceived usefulness and its influence on motivation, collaboration and behavioral intention in university students in times of Covid-19

The purpose of this study was to test the relationship of influence of ease of use and perceived usefulness on motivation, collaboration, and behavioral intention in university students in times of Covid-19. The methodology used for the study corresponds to a non-experimental investigation, a questionnaire was applied to a convenience sample of 530 university students (n=19;α=0.944 ω=0.946), using Factor Exploratory and Confirmatory Analysis as tests of validity and reliability, through the Modeling of Structural Equations of Partial Least Squares PLS-SEM. The results have shown that there is a positive effect of causality of perceived ease of use on student collaboration, student motivation and perceived usefulness;in the same way, there exists a causal relationship between the perceived usefulness and the student's collaboration, the intention of the behavior and the student's motivation. Contrarily, there would not be an influence relationship between perceived ease of use on behavioral intention in university students in a context of Covid-19. © 2022 IEEE.

Evaluation of Online Teaching based on SPSS Data Analysis

In order to study the overall situation of college English online teaching and students' online learning satisfaction during the COVID-19 epidemic, this paper made a questionnaire from four aspects: learning environment, teachers' teaching activities, students' learning activities, and online learning effect. Through SPSS23.0 software, the questionnaire data were analyzed by reliability analysis, validity analysis, principal component analysis, regression analysis and other methods, and the impacts of learning environment, teaching activities, learning activities, learning evaluation on students' learning satisfaction were studied. © 2022 IEEE.

Reliability-Oriented Route Generation Algorithm for Multimodal Transport: A Perspective from Supply Chain Reliability Enhancement

Sudden infectious diseases and other malignant events cause excessive costs in the supply chain, particularly in the transportation sector. This issue, along with the uncertainty of the development of global epidemics and the frequency of extreme natural disaster events, continues to provoke discussion and reflection. However, transport systems involve interactions between different modes, which are further complicated by the reliable coupling of multiple modes. Therefore, for the vital subsystem of the supply chain-multimodal transport, in this paper, a heuristic algorithm considering node topology and transport characteristics in a multimodal transport network (MTN): the Reliability Oriented Routing Algorithm (RORA), is proposed based on the super-network and improved k-shell (IKS) algorithm. An empirical case based on the Yangtze River Delta region of China demonstrates that RORA enables a 16% reduction in the boundary value for route failure and a reduction of about 60.58% in the route cost increase compared to the typical cost-optimal algorithm, which means that RORA results in a more reliable routing solution. The analysis of network reliability also shows that the IKS values of the nodes are positively correlated with the reliability of the MTN, and nodes with different modes may have different transport reliabilities (highest for highways and lowest for inland waterways). These findings inform a reliability-based scheme and network design for multimodal transportation. Practical Applications: Recently, the COVID-19 epidemic and the frequency of natural disasters such as floods have prompted scholars to consider transport reliability. Therefore, efficient and reliable cargo transportation solutions are crucial for the sustainable development of multimodal transport in a country or region. In this paper, a new algorithm is designed to obtain a reliability-oriented optimal routing scheme for multimodal transport. Using actual data from the Yangtze River Delta region of China as an example for experimental analysis, we obtain that: (1) the proposed algorithm is superior in terms of efficiency, accuracy, and route reliability, which means that the new algorithm can quickly find more reliable routing solutions in the event of urban transport infrastructure failures;and (2) highway hubs have the greatest transport reliability. Conversely, inland waterway hubs are the least reliable. The influence of national highways and railways on the multimodal transport system is unbalanced. These findings provide decision support to transport policymakers on reliability. For example, transport investments should be focused on building large infrastructure and increasing transport capacity, strengthening the connectivity of inland waterway hubs to hubs with higher transport advantages, and leveraging the role of large hubs. © 2023 American Society of Civil Engineers.

The Concurrent Validity and Test-Retest Reliability of Possible Remote Assessments for Measuring Countermovement Jump: My Jump 2, HomeCourt & Takei Vertical Jump Meter

Featured ApplicationThe same tool could be used repeatedly to track the changes in CMJ performance. Average jump heights should be analyzed. Practitioners and sports professionals without extensive knowledge of assessment could self-administer CMJ tests using these devices.Mobile applications and portable assessments make remote self-assessment of the countermovement jump (CMJ) test possible. This study aimed to investigate the concurrent validity and test–retest reliability of three portable measurement systems for CMJ. Thirty physically active college students visited the laboratory twice, with two days in between, and performed three jumps each day. All jumps were recorded by My Jump 2, HomeCourt, and the Takei Vertical Jump Meter (TVJM) simultaneously. Results indicated significant differences among the three systems (p < 0.01). HomeCourt tended to present the highest jump height mean value (46.10 ± 7.57 cm) compared with TVJM (42.02 ± 8.11 cm) and My Jump 2 (40.85 ± 7.86 cm). High concurrent validities among assessments were found (r = 0.85–0.93). Good to excellent reliability of jump assessments was demonstrated (ICC3,1 = 0.80–0.96). Reliable coefficients of variation were shown in all measurements (2.58–5.92%). Significant differences were revealed among the three apparatuses while they demonstrated high intra-device test–retest reliability. TVJM was the most reliable, and average jump heights were recommended for analysis.