Flexible work arrangements and gender differences in research during the COVID-19 period in Zimbabwean higher learning institutions

This study sought to investigate the impact of COVID-19-induced flexible work arrangements (FWAs) on gender differences in research outputs during COVID-19. A mixed research methodology was used, focusing on higher learning institutions in Zimbabwe. Purposive sampling was applied to select 250 researchers from the 21 registered universities in Zimbabwe. The study's findings revealed that institutions of higher learning in Zimbabwe did not provide the necessary affordances to enable both male and female academics to work from home effectively. The study also established that FWAs were preferred and appreciated by both male and female academics. However, whilst both male and female academics performed their teaching responsibilities without incident, unlike males, females struggled to find time for research, thus affecting professional growth and development negatively for female academics. Cultural traditions were found to subordinate females to domestic and caregiving responsibilities unrelated to their professions. The findings raise questions on the feasibility of the much-recommended FWAs for future work on female academics' research careers. Thus, without the necessary systems and processes to support female researchers, FWAs can only widen the gender gap in research outputs. This study contributes to the Zimbabwean higher learning institutions' perspective on how FWAs' policies and practices could be re-configured to assist female researchers in enhancing their research outputs as well as their career growth.

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model

Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients' suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP;(2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.

Design and Demonstration of a Portable Mobile Cloud Learning Platform for Post Pandemic Synchronous Learning

Post Covid-19 education posed an equally challenging task among teachers and learners. During these times limitation on face-to-face learning and gradual emigration from full online means of instruction had become an issue worth solving. Schools opted to adapt hybrid learning modalities as a means to cope with the learning demands in this era. However, most schools are put on a disadvantage because of the required technologies to support this mode of learning. This research describes an initial design and demonstration of a portable mobile cloud network to support synchronous learning. The system was installed and tested on both a schoolwide and classroom setting. Initial results showed that the proposed system was favorable as an alternative means to hybrid learning. © 2022 IEEE.

Predicting Student's Satisfaction towards Hybrid Learning in Informatics

This paper used regression and moderation approaches to evaluate the student's satisfaction with informatics towards the hybrid learning in their study. Multiple Linear Regression (MLR) identified student satisfaction based on hybrid learning difficulty and benefit ($p < 0.001$). Linear Regression (LR) found hybrid learning benefits impacted the student's satis-faction significantly $(p < 0.001$). Student's $t$-test also revealed that Overall Satisfaction (OS) significantly affected hybrid learning's satisfaction ($p < 0.001$). Analysis of Co-variants (ANCOVA) also proved that hybrid learning's benefit ($p < 0.001$) and OS ($p < 0.05$) significantly influenced student satisfaction. The paper also proved that hybrid learning's benefits positively correlate with student satisfaction (0.596). The slopes of 'Yes' and 'No' are substantially different from one another when the probability value of 0.22 $(p > 0.05$). Hence, no moderator (OS) affects the relationship's strength between the benefit and satisfaction of hybrid learning. The paper also revealed that hybrid learning's difficulty has a negative correlation (-.18), and the benefit of hybrid learning is positively associated with student satisfaction (.66). Implementing a hybrid learning mode during Covid-19 periods significantly impacted student satisfaction and the decision taken by the administration was also meaningful. © 2023 IEEE.

Dual Role of Leadership in ‘Janus-Faced' Telework from Home

The ‘forced' telework from home during the pandemic changed the practices, routines, and especially the working contexts of many employees in a leadership position as leaders themselves became teleworkers in addition to those they were expected to lead. This chapter looks at the challenges and resources of working from home (WFH)-and their ambivalences-among teleworkers and teleworking leaders during the first phase of the COVID-19 pandemic. Survey data was collected immediately after the lockdown. From this data, two subsets were filtered. First, the responses of teleworkers (N = 228) and, second, of teleworking leaders (N = 195) were identified and analysed in regard to the ‘the most challenging' and ‘the most rewarding' issues when working from home. The study shows that telework from home is ‘Janus-faced': telework is simultaneously challenging and rewarding in several respects. In addition, teleworking leaders have a dual role, as they must both adapt to working at home as teleworkers themselves and to being leaders of homeworkers. The findings can be used for designing, organizing, performing, and leading hybrid work in the future. In this evolving ʼnew normal, ' leaders need to adapt to their dual role, learn new leadership competencies, and encourage their employees to lead themselves. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Shaping Hybrid Collaborating Organizations

This chapter explores the major lessons learned from the COVID-19 pandemic, which has strongly influenced collaboration in almost all private and public organizations. Hybrid collaboration refers to the balance between onsite and remote collaboration in such a way that organizational performance, employee involvement and innovativeness can be optimized. When we focus on different levels of aggregation, it is proposed that different balances of hybrid work collaboration are needed at the level of teams, the internal organization, and the organization in relation to its external stakeholders (ecosystem). Such a hybrid collaborating organization requires a multidisciplinary understanding and effort in which (top) management, employees, and other internal and external stakeholders share knowledge, interact, and work together to generate benefits, both tangible and intangible, that an organization can provide to what relevant stakeholders actually value. In conclusion, some dilemmas that most organizations have to deal with during their journey toward shaping hybrid collaboration organizations will be discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Enhancing the feasibility of airport PPP projects with hybrid funding

In transport infrastructure concessions, the sources of revenue to the private partner (or concessionaire) may include (i) the infrastructure users (e.g., landing fees, in the case of airports), (ii) the government (e.g. through availability payments), and (iii) both users and government, which might be called a hybrid concession. An example of the latter is a highway concession where the concessionaire charges tolls to the road users but, because of relatively low revenues, the government agency complements the toll revenue with availability payments. Focusing on airports, this paper summarizes the cases where it may be justified for the government to complement users' revenues and describes a model developed for the financial assessment of airport concessions involving payments by both the government and airport users, through the collection of several charges. The methodology described in the paper is also used to review the flexibility in new or ongoing airport concessions to mitigate traffic risks, which have been aggravated by the COVID-19 pandemic. The methodology can also be applied to other forms of transport infrastructure. A practical application of the model is demonstrated in the paper, using publicly available information, as well as basic assumptions, to build case studies for the Larnaca and Paphos airports in Cyprus. The model can also be used to carry out sensitivity analyses of the impact of key input parameters on outputs such as the investor's return on equity and annual debt service cover ratio. © 2023 The Authors. Published by ELSEVIER B.V.

DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

A quantitative study on how covid-19 has impacted the relationship between employees and the hybrid employment model in the information technology field

The purpose of this case study was to provide a better understanding on how COVID-19 has impacted the relationship between employees and the hybrid employment model in the Information Technology (IT) field. A review of the literature regarding the general background of a hybrid employment model and the relationship it has with employees in the IT field and related technologies is provided. Hypotheses were tested using a research model. Data was collected through a survey that was conducted on individuals that worked in the IT field. A total of 102 participants completed the survey. The findings indicated that before COVID-19, individuals of an older age who worked in the Information Technology industry perceived that working remotely is not a better place for them to be productive while in a post COVID-19 society, individuals of an older age who worked from home did not perceive a negative impact on their career due to a lack of face-to-face interaction with colleagues and managers. © 2022 Authors. All rights reserved.

The Impact of Cloud Computing on the Development of Accounting Education in Light of the Corona Pandemic

The study aims to evaluate the impact of the experience of using cloud computing on the development of accounting education in the Gulf Cooperation Council countries in light of the Corona pandemic. To achieve this goal, the researchers relied on reviewing previous literature and conducting interviews with a number of accounting professors and students in universities in the Gulf Cooperation Council countries in order to develop a proposed framework for developing accounting education programs using both traditional education and cloud-based education. During the Corona pandemic, educational institutions in the Gulf Cooperation Council countries relied on the interactive learning management system such as Microsoft Teams, Zoom, and Modal. to support the e-learning process, because these systems enable them to interact with their students and meet their needs, which made studying easier. These systems help Students acquire the skill of self-learning, organizing and managing time. In addition, improving the efficiency of the lecturer in managing his time, due to the decrease in the weekly time needed for lecture and preparation, and helping to social distance between students and the lecturer. On the other hand, students suffered from not accepting e-learning due to the difficulty of understanding lectures, and the lack of skills and experience of some professors and students in the field of e-learning due to the familiarity with traditional education. In addition, the professors suffered from the difficulty of evaluating students under this system. Through personal interviews with a number of accounting professors and students in universities in the Gulf Cooperation Council countries, the researchers found that the previous obstacles were at a great level at the beginning of the application of e-learning through cloud computing applications, but the level of these obstacles has decreased over time as professors and students gradually acquired teaching skills at the same time, many technical problems were solved. Despite some advantages achieved through the transition to accounting education based on cloud computing, the Gulf Cooperation Council countries decided to return completely to traditional education. Therefore, the GCC countries should draw on the advantages that have been achieved from e-learning with a return to traditional education and study the possibility of adopting hybrid accounting education. Therefore, the researchers tried to propose a framework for the development of hybrid accounting education programs, based on several basic components that represent the elements of the educational process represented in the material and technological capabilities, the preparation and preparation of human elements (professor, student, technicians and administrators) and the teaching process (commitment to international accounting education standards, the development of accounting curricula and educational aids, And the use of different teaching styles, and the development of methods of evaluating students). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Research on cold chain logistics optimization model considering low-carbon emissions

Cold chain logistics distribution orders have increased due to the impact of COVID-19. In view of the increasing difficulty of route optimization and the increase of carbon emissions in the process of cold chain logistics distribution, a mathematical model for route optimization of cold chain logistics distribution vehicles with minimum comprehensive cost is established by considering the cost of carbon emission intensity comprehensively in this paper. The main contributions of this paper are as follows: 1) An improved hybrid ant colony algorithm is proposed, which combined simulated annealing algorithm to get rid of the local optimal solution. 2) Chaotic mapping is introduced in pheromone update to accelerate convergence and improve search efficiency. The effectiveness of the proposed method in optimizing cold chain logistics distribution path and reducing costs is verified by simulation experiments and comparison with the existing classical algorithms. © 2023 The Author(s). Published by Oxford University Press.

HyFlip learning: A case for better online instruction post-COVID

This research offers a case study of the technology courses at a regional campus of an R1 institution. Student preferences of various learning modalities were measured via both quantitative and qualitative methods. The quantitative data was supplemented with the qualitative data in order to suggest a new mode of teaching that is highly preferred by students. This new mode of teaching, HyFlip, combines the best aspects of traditional in-person learning and online learning to create a method of offering courses that gives students the flexibility they desire without losing the in-person interactions that some students prefer. With this new method all students can learn in whatever way works for them and faculty have more time to help those students who need it most. © 2022 International Association for Computer Information Systems

VR-mediated storytelling of experience in destination branding: hybrid 4D transformations1

In COVID-19 era, destination branding faces the challenge of digitalization and virtual reality (VR) in particular. The fundamentals of VR-mediated storytelling in destination branding are in the process of being developed. There is a luck of research on immersive VR-mediated storytelling, scenarios, and messages in destination branding, especially realised with technologies of more complex – hybrid – immersivity (4D). The shift from 2D, 3D to 4D hybrid multisensory VR technologies is not only among the main technology developments – it provokes new research problems with VR-mediated destination branding and storytelling. The authors present the results of theoretical and empirical research of VR-mediated destination storytelling of a brand driven by the newest 4D hybrid multisensory technological approaches on the case of Switzerland. In Switzerland, VR-mediated projects in destination branding are developing actively last years but VR-mediated storytelling research in this field was not provided yet. In this regard, it was chosen 100 destination brand VR projects, presented in 2016–2022, to compare the parameters of VR-mediated storytelling of a brand. VR has to be included into brand storytelling paradigm, which must be rethought for this specific sphere. It was proved that it is more effective to combine different types of experience, virtual and physical both and make the VR-mediated brand storytelling hybrid. In terms of theoretical implications, this paper opened a specific research area by bridging theoretical and empirical ideas of destination branding, VR-mediated storytelling and digital media, technical and social communication. © Shilina M.G., Sokhn M., Wirth J., 2023.

Deep-learning models for Covid-19 Detection Using Chest X-Ray Images

Deep Convolutional Neural Networks are a form of neural network that can categorize, recognize, or separate images. The problem of COVID-19 detection has become the world's most complex challenge since 2019. In this research work, Chest X-Ray images are used to detect patients' Covid Positive or Negative with the help of pre-trained models: VGG16, InceptionV3, ResNet50, and InceptionResNetV2. In this paper, 821 samples are used for training, 186 samples for validation, and 184 samples are used for testing. Hybrid model InceptionResNetV2 has achieved overall maximum accuracy of 94.56% with a Recall value of 96% for normal CXR images, and a precision of 95.12% for Covid Positive images. The lowest accuracy was achieved by the ResNet50 model of 92.93% on the testing dataset, and a Recall of 93.93% was achieved for the normal images. Throughout the implementation process, it was discovered that factors like epoch had a considerable impact on the model's accuracy. Consequently, it is advised that the model be trained with a sufficient number of epochs to provide reliable classification results. The study's findings suggest that deep learning models have an excellent potential for correctly identifying the covid positive or covid negative using CXR images. © 2023 IEEE.

How Can Organizations Improve Virtual Onboarding? Key Learnings from the Pandemic

Onboarding, the process through which newcomers become organization's insiders, has gained increasing attention in recent years. Such attention is justified by the considerable costs that companies have to face when onboarding is not properly managed. The challenges to manage this process effectively have increased during the COVID-19 pandemic that forced many organizations to onboard newcomers remotely, while fully working from home. The purpose of this chapter is (1) to explore the main goals associated with the onboarding process, (2) analyse the challenges that the COVID-19 pandemic has generated on the onboarding of employees fully working remotely and (3) present some viable solutions to address these challenges. To do this, we developed a conceptual analysis that builds on literature resources and provides empirical illustrations. The chapter is structured as follows. We first summarize the general objectives of the onboarding process for newcomers and organizations. We then discuss the challenges and sustainable solutions for managing the onboarding remotely and help newcomers and organizations get attain their respective objectives. We conclude by reflecting on the post-pandemic scenario, highlighting opportunities for future research focused on the interplay between remote and in presence working domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Faculty Perceptions of Health Professional Students' Hybrid-Online Learning Strategies: A Multi-Center Qualitative Study

Purpose: The COVID-19 pandemic accelerated the utilization of hybrid-online and fully-online instruction in health professional education. Physical (PT) and occupational therapy (OT) programs have become increasingly reliant upon this mode of instruction. Therefore, it is important to understand advising strategies for this educational environment. Faculty advisors may endorse specific learning strategies over others. However, advising strategies of faculty are not well represented in the scientific literature. Methods: A qualitative phenomenological design used a six-item, open-ended questionnaire to purposefully survey faculty members teaching and advising students in hybrid-online PT and OT graduate programs during COVID-19. Dedoose® v.9.4 qualitative software (Los Angeles, CA;2021) was used to perform coding and thematic analysis. Three investigators performed data analysis to reach consensus on the organization of emerging codes and themes. Results: A sample of N = 36 participants was collected from three states: Florida 14 (38.9%);Texas 12 (33.3%);California 10 (27.8%). Total N (%) of PT and OT faculty enrolled were 26 (72%) and 10 (28%), respectively. Years teaching in hybridonline programs N (%) was: 1e4 years 20 (55.6%);5e9 years 8 (22.2%);10e14 years 5 (13.9%);15þ years 4 (11.1%). Thematic analysis revealed three major themes: Self-regulated Behaviors, Student Engagement, and Studying Strategies. Self-regulated Behaviors and Student Engagement were most prevalent among participant narratives. Coded responses such as " ‘time management', ‘preparedness', ‘chunking study time', ‘daily engagement with learning material', ‘work/ life balance', and ‘peer-to-peer teaching'” were positively associated with perceived student success. Conversely, "'procrastination/cramming', ‘poor work ethic', ‘lack of engagement', ‘lack of preparedness', and ‘rote memorization'” were negatively associated with perceived student success. Discussion: This study identified faculty perceptions of student strategies for success in hybrid-online health professional learning. The self-regulated behaviors of time management, preparedness, work/life balance, and the engagement behaviors of daily engagement with course materials, content application, class participation, and peer collaboration strongly emerged. These findings may help guide novice faculty advisors as hybrid-online instruction becomes more frequently leveraged across health professional education programs. © 2023 Association of Medical Education in the Eastern Mediterranean Region (AMEEMR).

Influence of quality of work life on psychological capital of organizational leaders using artificial neural networks: a study on learning in hybrid workplace

PurposeThe COVID-19 pandemic has greatly impacted work, leading to the adoption of remote work practices and changes in power dynamics and trust. Although managing remote work has received much attention, the impact of the quality of work life on the effectiveness of hybrid workplaces has been less studied. This study aims to examine the relationship between quality of work life and psychological capital among organizational leaders using an artificial neural network (ANN) model. Design/methodology/approachThis study used a cross-sectional quantitative methodology. A structured questionnaire was used to collect 268 responses from organizational leaders using the convenience sampling method. The data collected were analyzed using the ANN model in the Python interface. FindingsThe ANN model training and testing revealed that there is a positive relationship between the quality of work life and psychological capital among organizational leaders. The R-squared values for hope, efficacy, resilience and optimism were 85.19%, 82.08%, 78.55% and 81.08%, respectively, in the training set, and 81.30%, 78.95%, 76.52% and 71.41% in the testing set. Originality/valueTo the best of the authors' knowledge, no previous research in the context of studying the relationship between quality of work life and psychological capital among organizational leaders using the machine learning approach - ANN model.

Towards a Metaverse Workspace: Opportunities, Challenges, and Design Implications

Both enterprises and their employees have globally experienced remote work at an unprecedented scale since the outbreak of COVID-19. As the pandemic becomes less of a threat, some companies have called their employees back to a physical office, citing issues related to working remotely, but many employees have refused to return. Thus, working in the metaverse has gained much attention as an alternative that could complement the weaknesses of completely remote work or even offline work. However, we do not know yet what benefits and drawbacks the metaverse has as a legitimate workspace, because there are few real cases of 1) working in the metaverse and 2) working remotely at such an unprecedented scale. Thus, this paper aims to identify real challenges and opportunities the metaverse workspace presents when compared to remote work by conducting semi-structured interviews and participatory workshops with various employees and company stakeholders (e.g., HR managers and CEOs) who have experienced at least two of three work types: working in a physical office, remotely, or in the metaverse. Consequently, we identified 1) advantages and disadvantages of remote work and 2) opportunities and challenges of the metaverse. We further discuss design implications that may overcome the identified challenges of working in the metaverse. © 2023 Owner/Author.

Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer

This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. In image-based diagnosis of viral pneumonia cases including COVID-19, both local and global image features are important because viral pneumonia cause many ground glass opacities and consolidations in large areas in the lung. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. MLP-Mixer is a recent method of image classification using Vision Transformer-like architecture. It performs classification using both local and global image features. To classify 3D CT volumes, we developed a hybrid classification model that consists of both a 3D convolutional neural network (CNN) and a 3D version of the MLP-Mixer. Classification accuracy of the proposed method was evaluated using a dataset that contains 1205 CT volumes and obtained 79.5% of classification accuracy. The accuracy was higher than that of conventional 3D CNN models consists of 3D CNN layers and simple MLP layers. © 2023 SPIE.