Developing Business Model Competences in the Enterprise

Despite the COVID-19 pandemic, 2021 saw a growing interest in starting own business: as per the Census Bureau's Business Formation Statistics, the number of applications to form new businesses filed in the U.S. was the highest compared to any other year on record, reaching the total of 5.4 million (Economic Innovation Group, 2022), while in the EU, after an initial downward trend recorded in the first and second quarters of 2020, the number of new business registrations grew again in the third quarter of that year, and this upward trend continued throughout 2021 (Eurostat, 2022). Of course, as a result of Russia's invasion on Ukraine and related economic crisis, a downward tendency could be observed, but business registration levels in the EU in the first quarter of 2022 were still higher than during the pre-COVID 19 pandemic period (2015-2019) (Eurostat, 2022) and online searches indicating and intent to open a business spiked by 76% from 2018 to 2022 (Search Engine Journal, 2022). This shows that despite many external impediments, people are still tempted to start their own business, and many influencers, motivational speakers and coaches, as well as various popular TV shows broadcast worldwide (like the Apprentice, Dragons' Den, Shark Tank or Planet of the Apps) encourage them to do so. Becoming an entrepreneur has become a goal many people, especially 20-, 30- and 40-year-olds, strive to achieve. However, many of those people fail to realise that the very entry in the business register does not automatically make them entrepreneurs or their business successful. Neither does a good (or even excellent and innovative) business idea that attracts customers, as it was in Kodak's, Blockbuster's, or Ask Jeeves' case. What is required, is the ability to stay attractive to existing and prospective customers, i.e., the ability to win and retain customers, and to adapt to the changing demands, trends and economic conditions. All this can be achieved thanks to a meticulously designed and regularly reviewed and updated business model. The aim of this paper is to present and analyse the learning process of acquiring and building competences in the area of business models with the use of different innovative tools. The results presented and discussed in this article come from surveys as well as face-to-face and on-line meetings conducted in the ProBM 2 ERASMUS+ project (Understanding and Developing Business Models in the Era of Globalisation), in which the total of 261 respondents from seven (7) European countries, i.e. Poland, Italy, Greece, Romania, Portugal, Malta, and Switzerland, took part between 2019 and 2022. From the meetings and surveys it follows that much more awareness of business models needs to be encouraged and developed, particularly as regards improving competences helping future business owners and their employees assess profitability and efficiency of their operations and ensure that the business will be a going concern. © 2022 Authors. All rights reserved.

In different worlds: The contributions of polarization and platforms to partisan (mis)perceptions

Partisanship, polarization, and platforms are foundational to how people perceive contentious issues. Using a probability sample (n = 825), we examine these factors in tandem across four political claims concerning US presidential elections and the COVID-19 pandemic. We find Democrats and Republicans differ in their belief in true and false claims, with each party believing more in pro-attitudinal claims than in counter-attitudinal claims. These results are especially pronounced for affectively polarized partisans. We also find interactions between partisanship and platform use where Republicans who use Google or Twitter are more likely to believe in false claims about COVID-19 than Republicans who do not use these platforms. Our findings highlight that Americans' beliefs in political claims are associated with their political identity through both partisanship and polarization, and the use of search and social platforms appears critical to these relationships. These findings have implications for understanding why realities are malleable to voter preferences in liberal democracies. [ FROM AUTHOR] Copyright of New Media & Society is the property of Sage Publications, Ltd. 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.)

Design Principles and a Software Reference Architecture for Big Data Question Answering Systems

Companies continuously produce several documents containing valuable information for users. However, querying these documents is challenging, mainly because of the heterogeneity and volume of documents available. In this work, we investigate the challenge of developing a Big Data Question Answering system, i.e., a system that provides a unified, reliable, and accurate way to query documents through naturally asked questions. We define a set of design principles and introduce BigQA, the first software reference architecture to meet these design principles. The architecture consists of high-level layers and is independent of programming language, technology, querying and answering algorithms. BigQA was validated through a pharmaceutical case study managing over 18k documents from Wikipedia articles and FAQ about Coronavirus. The results demonstrated the applicability of BigQA to real-world applications. In addition, we conducted 27 experiments on three open-domain datasets and compared the recall results of the well-established BM25, TF-IDF, and Dense Passage Retriever algorithms to find the most appropriate generic querying algorithm. According to the experiments, BM25 provided the highest overall performance. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

CovRelex-SE: Adding Semantic Information for Relation Search via Sequence Embedding

In recent years, COVID-19 has impacted all aspects of human life. As a result, numerous publications relating to this disease have been issued. Due to the massive volume of publications, some retrieval systems have been developed to provide researchers with useful information. In these systems, lexical searching methods are widely used, which raises many issues related to acronyms, synonyms, and rare keywrds. In this paper, we present a hybrid relation retrieval system, CovRelex-SE, based on embeddings to provide high-quality search results. Our system can be accessed through the following URL: https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/. © 2023 Association for Computational Linguistics.

A critical review of public–private partnerships in the COVID-19 pandemic: key themes and future research agenda

PurposeUndoubtedly, coronavirus (COVID-19) pandemic has released unprecedented disruptions and health crisis on people and activities everywhere. The impacts extend to public–private partnership (PPP) arrangements in the construction industry. Concomitantly, PPP pacts are contributing to combat the pandemic. However, literature on the PPP concept in the COVID-19 era remain under-researched. This study aims to review the current literature on PPPs in the COVID-19 pandemic and present the key themes, research gaps and future research directions.Design/methodology/approachIn this study, 29 highly relevant literature were sourced from Web of Science, Scopus and PubMed search engines within the systematic literature review (SLR) methodology. With the aid of qualitative content analysis, the 29 articles were critically analysed leading to the extraction of hot research themes on PPPs in the coronavirus pandemic.FindingsThe results of the SLR produced eight themes such as major changes in PPP contracts, development of the COVID-19 vaccines, economic recession, facemasks and testing kits, governance and sustainability of PPPs. In addition, the study reveals seven research gaps that need further investigations among the scientific research community on mental health and post-pandemic recovery plans.Research limitations/implicationsThe articles selected for this review were limited to only peer-reviewed journal papers written in English excluding conference papers. This restriction may have taken out some relevant literature but they had insignificant impact on the overall outcome of this research.Practical implicationsTo improve the understanding of practitioners in the construction industry on key issues on PPPs in the COVID-19 pandemic, the study provides them a checklist of relevant themes.Originality/valueAs a novel literature review relating PPPs to the coronavirus, it sets the foundation for further research and contributes to practical measures to control the virus.

Googling in Russian Abroad: How Kremlin-Affiliated Websites Contribute to the Visibility of COVID-19 Conspiracy Theories in Search Results

Research that audited search algorithms typically deployed queries in one language fielded from within only one country. In contrast, this study scrutinized 8,800 Google results retrieved in November 2020 from 5 countries (Russia, the United States, Germany, Ukraine, and Belarus) in response to queries on COVID-19 conspiracy theories in Russian and English. We found that the pandemic appeared similar to people who googled in Russian independent of their geolocation. The only exception was Ukraine, which had implemented rigorous media policies to limit the reach of websites affiliated with Russia within its national public sphere. Conspiracy narratives varied with input language. In response to Russian-language queries, 35.5% of the conspiratorial results suspected U.S. plotters to be behind the pandemic (English language: 5.8%). All source pages that blamed U.S. plotters showed connections with Russia's elites. These findings raise important theoretical questions for today's multilingual societies, where the practice of searching in nonlocal languages is increasing.

Different platforms, different plots? The Kremlin-controlled search engine Yandex as a resource for Russia's informational influence in Belarus during the COVID-19 pandemic

Extant research demonstrated that the algorithms of the Kremlin-controlled search engine Yandex, compared to those of its US-based counterpart Google, frequently produce results that are biased toward the interests of Russia's ruling elites. Prior research, however, audited Yandex's algorithms largely within Russia. In contrast, this study is the first to assess the role of Yandex's web search algorithms as a resource for Russia's informational influence abroad. To do so, we conduct a comparative algorithm audit of Google and Yandex in Belarus, examining the visibility and narratives of COVID-19-related conspiracy theories in their search results. By manually analysing the content of 1320 search results collected in mid-April to mid-May 2020, we find that, compared with Google, (1) Yandex retrieves significantly more conspiratorial content (2) that close to exclusively suspects US plotters to be behind the pandemic, even though the virus spread from the Chinese city of Wuhan across the globe.

Safety Profile of Molnupiravir with Significant Effect on COVID-19: A Review

Background: As the COVID era unfolds, researchers reveal that rapid changes in viral genetic material allow viruses to circumvent challenges triggered by the host immune system and resist anti-viral drugs, potentially leading to persistent viral manifestations in host cells. Molnupiravir (RNA-dependent RNA polymerase inhibitor) is a novel anti-viral medicine promising a vital role in coming setbacks.Objectives: This review aims to clarify the safety and efficacy of the molnupiravir molecule in light of existing case studies. As a result, it is intended to explore and discuss the molecular structure, mechanism of action, discovery and development process, preclinical research, clinical investigations, and other subtopics.Methods: A total of 75 publications were searched using multiple engines, such as Google Scholar, PubMed, Web of Science, Embase, Cochrane Library, ClinicalTrials.gov, and others, with a constraint applied to exclude publications published over 11 years ago. Molnupiravir, safety, efficacy, COVID- 19, RdRp, PK-PD, and clinical study were utilized as keywords.Results: Clinical results on molnupiravir are supported by investigations that were recently disclosed in a study on both sex volunteers (male and female) with an age restriction of 19 to 60 years, followed by a Phase-3 Clinical Trial (NCT04575584) with 775 randomly assigned participants and no fatalities reported due to treatment.Conclusion: Molnupiravir proved a high level of safety, allowing it to be tested further. This review supports the safety and efficacy of this molecule based on the established evidence, which claims the most anticipated employment of molnupiravir in COVID protocol.

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

Analysis of the effects of store image, trust and perceived quality on consumption habits of textile products in the COVID-19 pandemic

Consumer clothing presents behaviors defined by pre-established trends and patterns in contemporary societies, and in general the consumption of textile products follows this trend. However, as a result of the COVID-19 pandemic and the restrictions perpetuated as a consequence of it, the consumption of textile products has been affected throughout the world. Under this premise, the objective of this research is to analyze the effect of store images, trust and perceived quality on the habits of the textile consumer in the context of the COVID-19 pandemic, for which, firstly, a review of the literature was carried out regarding the variables of the habits of the textile consumer and their relationship with the store image, trust and perceived quality, for which documents from academic search engines were taken into account, such as Scopus, Web of Science, ResearchGate and Google Scholar. On the other hand, a survey was conducted among textile consumers in Ecuador. The measurement tool was completed by 500 participants. In this way, the survey was conducted virtually through Google Forms and through the use of IBM SPSS software. The sampling technique consisted of convenience sampling. For the specific case of this investigation, it was decided to opt for the use of 500 valid questionnaires. This allowed one to propose a model of structural equations based on constructs associated with reference investigations. The main results of this research confirmed that there is a positive impact of the image of the trusted establishment on the product, as well as a positive impact on the general perceived quality of consumption habits (comparison) and on the effect of the quality of perceived service in consumption habits (planning).

Unobtrusive and Acceptable Ways to Gather Participant Perceptions of Community-Based Interventions and Their Effectiveness at Improving Mental Health and Wellbeing: A Literature Review of Peer Reviewed and Grey Literature

In England, approximately 1 in 6 people have a common mental health condition, with certain groups experiencing worsening mental health since the start of the COVID-19 pandemic. Therefore, improving mental health remains a key priority for policy makers and practitioners. Community-based interventions are increasingly used to improve health and reduce inequalities. Evaluation of such interventions is important to ensure they are effective and to maintain financial support for continued delivery. Hesitation to complete administratively demanding evaluative measures by service users, which may not be suited to evaluating low intensity activities, point to the need to identify acceptable, unobtrusive methods of data collection. This review focuses on identifying unobtrusive methods that have previously been used to examine service user's perceptions of community-based interventions and their effectiveness, and the acceptability of the methods. A review of peer reviewed, and grey literature was undertaken in July 2022. Literature was identified via six databases, Google searches, and by contacting experts. Literature was included if it described unobtrusive methods to gather service users' perceptions of an intervention and/or reported the acceptability of such methods. Literature was excluded if it described traditional methods to gather service users' perceptions of an intervention. Our search identified 930 citations from searching databases (n = 886), Google (n = 40), and from contacting 15 experts directly, and over 300 experts indirectly via three e-mail lists (n = 4). No literature met our inclusion criteria. We report an empty review. There is no peer reviewed or grey literature that describes unobtrusive methods of data collection for mental health and wellbeing focused community-based interventions, or their perceived acceptability. The findings from this review indicate the need to develop unobtrusive methods of data collection in the field of public mental health, suitable for low intensity activities, and examine the acceptability and feasibility of such methods.

Oral and Dental Needs and Teledentistry Applications in the Elderly: Real-Time Surveillance Using Google Trends

Featured ApplicationThe present cross-sectional analysis aimed to evaluate the level of interest in oral and dental needs and teledentistry applications among the elderly, as well as whether COVID-19 pandemic outbreaks were influenced by real-time surveillance, using Google Trends. As the number of elderly dental patients continues to increase, there is a growing need for specific interventions that address the biological and psychological issues of this population. Teledentistry represents a healthcare delivery system that can overcome these problems, although the oral and dental care provision methods involved are still unknown to most people. Indeed, there is a need to raise awareness of the indications for teledentistry, the available interventions, and the potential benefits for the oral and dental care of elderly patients.Considering the increasing need for oral and dental care in the elderly, teledentistry has been proposed to improve the education of elderly patients in oral health maintenance and risk factor control, identify patients' concerns in advance, facilitate monitoring, and save time and money. The present cross-sectional analysis of Google search data through real-time surveillance with Google Trends aimed to determine Google users' interest in oral and dental needs and teledentistry applications in the elderly, and to compare search volumes before and after the COVID-19 outbreak. Extracted CVS data were qualitatively analyzed. Pearson and Spearman correlation analyses were performed between searches for "elderly” and "teledentistry”, and all the oral and dental needs and teledentistry applications. The Mann–Whitney U test compared search volumes in the 36 months before and after the beginning of the COVID-19 pandemic. Google users' interest in the elderly and related oral and dental needs was diffusely medium–high, while teledentistry and its applications were of lower interest. Interest in teledentistry and its applications was strongly related to interest in the older population, which is consistent with the assumption that older adults represent the population segment that could benefit most from these tools. A positive correlation was also found between searches for "Elderly” and searches for almost all oral and dental needs typical of the geriatric population. Search volumes increased significantly after the outbreak of the COVID-19 pandemic. More information about teledentistry should be disseminated to increase knowledge and awareness, especially among older patients, about its indications, applications, and advantages.

A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula> <tex-math notation="LaTeX">$<$</tex-math> </inline-formula> 0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies. IEEE

The Adoption of Blended Learning by Rural-Based Institutions of Higher Learning in South Africa Amid Covid-19: Experiences and Challenges

Many institutions of higher learning were forced to adopt blended learning since the outbreak of coronavirus disease (Covid-19). The adoption of blended learning amid Covid-19 has delayed learning processes in most rural-based institutions of higher learning in South Africa. Thus, the study has adopted a non-empirical research design: a systematic review, and it was conducted to establish solutions to blended learning challenges faced by rural-based institutions of higher learning in South Africa amid Covid-19. Conversation theory was adopted in this study because it advocates that students should get the opportunity to interact with the lecturers, which could help to amend the digital divide and promote advanced blended learning in rural-based institutions of higher learning. Therefore, the data for the study was obtained by using scientific search engines for articles and books. The study's articles were obtained from the computer-based scientific search engines Google Scholar, EbscoHost, ResearchGate, ScienceDirect, and Scopus. Thus, purposive sampling was used to select relevant articles rather than using any articles that had no bearing on the study. The secondary data was then analysed using thematic analysis. It was found that the delay in advanced blended learning was caused by the digital divide and barriers to digital transformation in rural-based institutions, among other challenges. It was recommended that the government should provide digital equipment to rural-based institutions of higher learning and provide training to all students and lecturers on how to use different technologies to ensure the accessibility of blended learning.

True or false? Cognitive load when reading COVID-19 news headlines: An eye-tracking study

Misinformation is an important topic in the Information Retrieval (IR) context and has implications for both system-centered and user-centered IR. While it has been established that the performance in discerning misinformation is affected by a person's cognitive load, the variation in cognitive load in judging the veracity of news is less understood. To understand the variation in cognitive load imposed by reading news headlines related to COVID-19 claims, within the context of a fact-checking system, we conducted a within-subject, lab-based, quasi-experiment (N=40) with eye-tracking. Our results suggest that examining true claims imposed a higher cognitive load on participants when news headlines provided incorrect evidence for a claim and were inconsistent with the person's prior beliefs. In contrast, checking false claims imposed a higher cognitive load when the news headlines provided correct evidence for a claim and were consistent with the participants' prior beliefs. However, changing beliefs after examining a claim did not have a significant relationship with cognitive load while reading the news headlines. The results illustrate that reading news headlines related to true and false claims in the fact-checking context impose different levels of cognitive load. Our findings suggest that user engagement with tools for discerning misinformation needs to account for the possible variation in the mental effort involved in different information contexts. © 2023 ACM.

Don't Be a Victim during a Pandemic! Analysing Security and Privacy Threats in Twitter during COVID-19

There has been a huge spike in the usage of social media platforms during the COVID-19 lockdowns. These lockdown periods have resulted in a set of new cybercrimes, thereby allowing attackers to victimise social media users with a range of threats. This paper performs a large-scale study to investigate the impact of a pandemic and the lockdown periods on the security and privacy of social media users. We analyse 10.6 Million COVID-related tweets from 533 days of data crawling and investigate users' security and privacy behaviour in three different periods (i.e., before, during, and after the lockdown). Our study shows that users unintentionally share more personal identifiable information when writing about the pandemic situation (e.g., sharing nearby coronavirus testing locations) in their tweets. The privacy risk reaches 100% if a user posts three or more sensitive tweets about the pandemic. We investigate the number of suspicious domains shared on social media during different phases of the pandemic. Our analysis reveals an increase in the number of suspicious domains during the lockdown compared to other lockdown phases. We observe that IT, Search Engines, and Businesses are the top three categories that contain suspicious domains. Our analysis reveals that adversaries' strategies to instigate malicious activities change with the country's pandemic situation. © 2013 IEEE.

A Secure Blockchain-Assisted Access Control Scheme for Smart Healthcare System in Fog Computing

IoT-based smart healthcare system allows doctors to monitor and diagnose patients remotely, which can greatly ease overcrowding in the hospitals and disequilibrium of medical resources, especially during the rage of COVID-19. However, the smart healthcare system generates enormous data which contains sensitive personal information. To protect patients’privacy, we propose a secure blockchain-assisted access control scheme for smart healthcare system in fog computing. All the operations of users are recorded on the blockchain by smart contract in order to ensure transparency and reliability of the system. We present a blockchain-assisted Multi-Authority Attribute-Based Encryption (MA-ABE) scheme with keyword search to ensure the confidentiality of the data, avoid single point of failure and implement fine-grained access control of the system. IoT devices are limited in resources, therefore it is not practical to apply the blockchain-assisted MA-ABE scheme directly. To reduce the burdens of IoT devices, We outsource most of the computational tasks to fog nodes. Finally, the security and performance analysis demonstrate that the proposed system is reliable, practical, and efficient. IEEE

Engagement with Facebook Advertisements among Senior Citizens as a Vehicle to Choose Assisted Living Facilities in the Local Area: An Observational Study

The widespread usage of social media as a vehicle to communicate and exchange information has been massively growing, especially in the times of the current COVID-19 pandemic. Billions of people around the world now can connect to the internet and transmit uninterrupted data via social media platforms. There are numerous papers available targeting teenagers and adults utilizing social media as a platform to make vital decisions. However, the adoption of modern communication technologies among senior citizens has been rarely investigated. In this paper, we propose to investigate a sample of 80 senior adults who were looking for assisted living facility services through a Facebook social media platform. We aim to understand what guided their decision to contact an assisted living facility for the service, and what options they prefer for their stay. This paper will be among pioneering papers since the investigation of senior citizens and social media utilization for assisted living is scarcely found on scientific search engines such as PubMed.

Information-Centric Networking based content collection and caching mechanism for network public opinion

With the global influence of the COVID epidemic, network public opinion control is particularly important especially for the purpose of stabilizing the panic at home and abroad. Effective public opinion collection and caching mechanism has a positive significance for the rapid spread of network public opinion. Therefore, by analyzing the accurate and rapid requirements of public opinion communication, this paper introduces the concept of Information-Centric Networking (ICN) to build a public opinion communication system. At the same time, the corresponding public opinion collection and caching mechanism is designed to optimize the dissemination process of the public opinion. The natural distributed structure of ICN makes the process of public opinion collection and caching distributed. Specifically, a suitable cache server is added between different public opinion collection servers via the distributed search engines. The experimental results show that the proposed distributed public opinion collection and caching mechanism can effectively deal with the spread of public opinion under the environment of the global COVID epidemic, including improving the accuracy of the public opinion transmission in time. © 2022 John Wiley & Sons, Ltd.