THE IMPACT OF COVID-19 ON RUSSIAN ECONOMY

This article examines the impact of Covid-19 on the Russian economy. The pandemic has caused global changes in both our daily lives and economies. Enterprises were forced to close, resulting in decreased production volumes and oil prices. Capital outflows also occurred as a result of the pandemic, and the consequences of quarantine and measures taken in the country were significant, although not as extensive as those experienced by Europe or the United States. Furthermore, the method of correlation-regression analysis was employed to determine the mutual influence of various factors on the development of the Russian economy, including the exchange rate of the Russian ruble, world oil prices, and the level of unemployment. Based on the results, it is essential to monitor and control the level of unemployment in the country, especially under the conditions of Covid-19 restrictions. © 2022, Procedia Environmental Science, Engineering and Management. All Rights Reserved.

Rethinking North–South Research Partnerships Amidst Global Uncertainties: Leveraging Lessons Learned from UK GCRF Projects during COVID-19

International research and development projects (or grand challenge projects) consist of multicultural, multi-country, multi-sectoral, and multi-stakeholder initiatives aimed at poverty reduction. They are usually conceived as partnerships between actors in the global north–south. The COVID-19 pandemic was a major unexpected disruption to ongoing projects and challenged their already complex management. The aim of this paper is to present evidence on how international development projects were impacted by COVID-19 with a particular focus on the relationship between research institutions in the north and south. We conducted a mixed-methods research study, combining a reflective exercise with the co-author team and a survey with principal investigators, project managers, and capacity development leads drawn from 31 Global Challenges Research Fund (GCRF) projects funded through the UK government's Official Development Assistance (ODA) and focused on social–ecological system research. The survey contained closed- and open-ended questions in order to (i) demonstrate how those involved in managing projects adapted to risks, including both threats and opportunities, presented by the COVID-19 pandemic, and (ii) consider the implications for tailoring adaptive management approaches in international research projects amidst uncertainties, with a special focus on enhancing equities in global north–south partnerships. The paper offers the following recommendations on designing, planning, and implementing international research and development projects: (i) devolve project management in order to enhance project resilience and improve north–south equities;(ii) allocate dedicated resources to enable equitable north–south research partnerships;(iii) rely more on hybrid and agile approaches for managing a project's life cycle;and (iv) improve resource flexibility, transparency, and communication through enhanced funder–implementer collaboration.

The Drivers and Inhibitors of COVID-19 Vaccinations: A Descriptive Approach

The SARS-COV-2/ COVID-19 pandemic is a global challenge affecting hundreds of millions globally. The COVID-19 pandemic that began in Wuhan in China in 2019 has continued to pose a health concern and economic meltdown across the globe. Globally, numerous vaccines have been successfully rolled out against many vaccine-preventable diseases at all stages of human development. Despite the number of approved SARS-COV-2 vaccines, the seeming success in the global rollout, and the inoculation of billions globally, COVID-19 vaccination interventions still need improvement. However, for total control and eradication, there is a need to review the campaign methodologies to identify the drivers and inhibitors of COVID-19 vaccinations to promote booster uptake. Questions concerning the acceptability of the covid-19 vaccination were posed to respondents using a convenience sample method. This study contributes to the African vaccination literature and descriptively shows the drivers and inhibitors of COVID-19 vaccination. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Sustainable Development of Employee Lifecycle Management in the Age of Global Challenges: Evidence from China, Russia, and Indonesia

The COVID-19 pandemic has significantly affected the employee lifecycle management (ELM) sphere, leading to the adoption of new human resource (HR) technologies and policies. This study investigates the impact of megatrends, artificial intelligence, digital technologies, and innovation on ELM and human resource management (HRM) policies in China, Russia, and Indonesia. Data were collected through structured interviews and publicly available information from companies in these countries between 2021 and 2022. The study evaluates the effects of artificial intelligence (AI), digital transformation (DT), and innovations on the sustainable development of ELM and identifies differences in technological responses to ELM in companies depending on their level of digital maturity. The results show that the majority of companies have continued the process of ELM digital transformation, but the percentage varies based on the scope of activity, labor, and readiness of the country to implement new technologies. The study reveals that large companies in each analyzed country with over 10,000 employees have a greater need and opportunity to implement HR digital transformation, whereas small companies with up to 100 people can operate without automation. In addition, the findings of this study provide propositions for designing how AI and innovations contribute to ELM. This article contributes to the current debate in the literature by substantiating the positive impact of AI, digital technology, and innovation on ELM and HRM strategies, offering practical applications for companies to improve productivity. Overall, this study highlights the importance of adopting innovative HR technologies in response to global challenges and workplace trends. © 2023 by the authors.

A Framework of a User Interface Covid-19 Diagnosis Model

The world is facing the global challenge of COVID-19 pandemics, which is a topic of great concern.It is a contagious disease and infects others very fast.Artificial intelligence (AI) can assist healthcare professionals in assessing disease risks, assisting in diagnosis, prescribing medication, forecasting future well, and may be helpful in the current situation.Designing, a user-friendly Web application-based diagnosis model framework, is more useful in health care.The study focuses on a Web-based model for diagnosing the COVID-19 patients without direct contact with the patient.Chest CT scans have been important for the testing and diagnosing of COVID-19 disease.The Web-based model would take inputs, CT scan images, and users' symptoms and display classification results: NON-COVID-19 or COVID-19 infected. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

RISK AND RETURNS: Large-scale Funding for Urban Research

The nature of research funding shapes knowledge outcomes, especially for urban research that is conducted in multiple sites and over multiple years. Recent unplanned cuts in the Global Challenges Research Fund (GCRF) grants, alongside the rupture caused by Covid-19, created ethical and procedural issues for completing the PEAK Urban programme. Building on durable partnerships, setting principles for the reduced fund distribution and adjusting modes of working enabled PEAK Urban to navigate the fiscal disruption—but the difficult episode highlights lessons for the ethical organization of global urban research under conditions of uncertainty. © 2023 The Authors. Internati onal Journal of Urban and Regional Research published by John Wiley & Sons Ltd on behalf of Urban Research Publicati ons Limited.

RISK AND RETURNS: Large‐scale Funding for Urban Research

The nature of research funding shapes knowledge outcomes, especially for urban research that is conducted in multiple sites and over multiple years. Recent unplanned cuts in the Global Challenges Research Fund (GCRF) grants, alongside the rupture caused by Covid‐19, created ethical and procedural issues for completing the PEAK Urban programme. Building on durable partnerships, setting principles for the reduced fund distribution and adjusting modes of working enabled PEAK Urban to navigate the fiscal disruption—but the difficult episode highlights lessons for the ethical organization of global urban research under conditions of uncertainty. [ FROM AUTHOR]

Matching International Business Teaching with the UN Suslainable Development Goals: Introducing Bi-directional Reflective Learning

Transboundary challenges such as climate change, loss of biodiversity, energy transformation and the Covid-19 pandemic put vast pressures on generating solutions. They also call for updated teaching providing the required capabilities for international business (IB) and -entrepreneurship (1E) students. This paper presents a teaching initiative supporting master's students to develop an overview of such contemporary and timely challenges and global concerns. The course, developed jointly by two universities and first administered in 2020 at LUT University, combines economic, social, and environmental sustainability aspects with managerial and entrepreneurial issues on IB, triggering the students to rethink and critically address ways forward. Students develop skills and competences to tackle complex real-life problems in collaboration with others, facilitating their entrepreneurial, global mind-set and sensitivity to cultural issues in IB. Thus, the presented teaching approach and course initiative contributes to theory and practice of teaching 1B, by presenting how key challenges in contemporary IB can be incorporated in international business education of universities.

CoviFL: Edge-Assisted Federated Learning for Remote COVID-19 Detection in an AIoMT Framework

Detection of COVID-19 has been a global challenge due to the lack of proper resources across all regions. Recently, research has been conducted for non-invasive testing of COVID-19 using an individual's cough audio as input to deep learning models. However, these methods do not pay sufficient attention to resource and infrastructure constraints for real-life practical deployment and the lack of focus on maintaining user data privacy makes these solutions unsuitable for large-scale use. We propose a resource-efficient CoviFL framework using an AIoMT approach for remote COVID-19 detection while maintaining user data privacy. Federated learning has been used to decentralize the CoviFL CNN model training and test the COVID-19 status of users with an accuracy of 93.01 % on portable AIoMT edge devices. Experiments on real-world datasets suggest that the proposed CoviFL solution is promising for large-scale deployment even in resource and infrastructure-constrained environments making it suitable for remote COVID-19 detection. © 2022 IEEE.

The role of sustainability assessment tools in realizing bioenergy and bioproduct systems

The pressing global challenges, including global warming and climate change, the Russia-Ukraine war, and the Covid-19 pandemic, all are indicative of the necessity of a transition from fossil-based systems toward bioenergy and bioproduct to ensure our plans for sustainable development. Such a transition, however, should be thoroughly engineered, considering the sustainability of the different elements of these systems. Advanced sustainability tools are instrumental in realizing this important objective. The present work critically reviews these tools, including techno-economic, life cycle assessment, emergy, energy, and exergy analyses, within the context of the bioenergy and bioproduct systems. The principles behind these methods are briefly explained, and then their pros and cons in designing, analyzing, and optimizing bioenergy and bioproduct systems are highlighted. Overall, it can be concluded that despite the promises held by these tools, they cannot be regarded as perfect solutions to address all the issues involved in realizing bioenergy and bioproduct systems, and integration of these tools can provide more reliable and accurate results than single approaches. © 2022 BRTeam. All rights reserved.

From the EHEA to the EEA: Renewed State-Making Ambitions in the Regional Governance of Education in Europe

The launch of the Bologna Process in 1999 supported by the European University Association was widely seen as an ambitious intergovernmental project to reshape national higher education institutions across Europe. Over time, however, the Bologna Process framework has not only been taken up in other parts of the world, but the European Commission has incorporated it into its European Higher Education Area, and most recently the creation of a European Education Area by 2025. In our chapter we explore the framing of this expanded agenda for the European Commission for education more generally in the face of rising national populisms across European, the new challenges posed by COVID-19 and institutional lockdowns, and the geo-strategic challenges to the East with the rise of China and its Belt and Road Initiative. We note the continuing dependence in techniques of governing such as mobility and ask about the ongoing challenges facing this state-making project. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Revising the Requirements of a Cross-Departmental Project-Centric Undergraduate Engineering Program and Launching a new Sustainability and Climate-themed Track

Present-day higher education institutions offering undergraduate engineering programs need to prepare their students for a world filled with complex global challenges. Such preparation requires the acquisition of multidisciplinary knowledge and the application of multidisciplinary methodologies. A pilot initiative was launched in Fall 2017 for an elective three-year (sophomore to senior year) cross-departmental multi-disciplinary undergraduate engineering program at the Massachusetts Institute of Technology (MIT) named New Engineering Education Transformation (NEET). The program has three cross-departmental pathways across STEM disciplines and technical domains. The program has completed its fourth year of operation and has grown to become the fourth-largest undergraduate academic cohort at MIT. This paper is divided into two parts: the first part describes the revision of program requirements and their implementation during Fall 2019-Fall 2020. The second part describes the launching of a new single-themed program titled Climate & Sustainability Systems, which took place and was implemented during Summer-Fall 2021. Both initiatives responded to issues and changing circumstances raised by students, faculty, and instructional staff, with the aim of affording students more flexibility, reducing the additional workload beyond their chosen majors, enhancing their educational experience, and increasing their engagement with the program-wide community. In January 2020, following feedback collected from MIT students, faculty, instructional staff, and senior administration, we began a systematic process of reviewing the program's academic requirements. Data collected includes student questionnaires and specifications of program requirements throughout the study period. The revised requirements were published toward the end of the Spring 2020 semester, serendipitously around the same time as the COVID-19 mandated university-wide pivot from in-person on-campus teaching to emergency remote teaching and were implemented in Fall 2020. Since the publication of these new requirements, enrollment in the program has increased substantially year-on-year across all program threads. Subsequent data collection during Spring 2020 and Spring 2021 showed that word-of-mouth about the program has grown stronger, with 'current students' and 'other first-years' being two of the most-cited sources as to how first-years get to know about NEET. This paper explains the impetus for changing the program requirements, describes how the new requirements were formulated and implemented, and outlines what we have learned from implementing the revised requirements. We also describe how we collaborated with various stakeholders in the planning, design, and implementation of the revised requirements. For the second part of the paper, we describe how we launched a new climate and sustainability pathway based on our three-year experience of introducing pathways connected to energy, manufacturing ands materials, and sustainable development of cities, and on the growing interest amongst students in combating climate change in a sustainable manner. We describe how the process of consolidation was planned out and designed, how we collaborated with various stakeholders and how initial implementation has undergone. It should be emphasized that the approach we have taken here is largely qualitative and based primarily on how students and other key stakeholders responded to, engaged with the NEET program, and helped to evolve it. NEET leadership commissioned a systematic programmatic evaluation starting from Spring 2021, and we will be guided by their assessment of the changes as we look to the future. This paper is intended for institutional leadership, departmental leadership, faculty, and academic staff seized by the need to create and implement relevant and engaging cross-departmental multi-disciplinary undergraduate engineering programs. © American Society for Engineering Education, 2022.

The Effects of COVID-19 On The Food Supply Chain As A Result Of Living In Close Quarters

An efficient and functional supply chain is essential for economies to prosper. Pandemics, however, have proven to be a global challenge that has disrupted the supply chain's routine operations. Inspired by the impact of the COVID-19 pandemic, this paper is studies the effects of COVID-19 on the food supply chain as a result of living in close quarters. To determine how much the food supply chain has been impacted by the COVID-19 pandemic, we employ an agent-based simulation model, combined with an SEISR (Susceptible, Exposed, Infectious, Symptomatic, and Recovered) disease model, to quantify the impact on the food supply chain in terms of productivity, disruption time, and the number of sick workers. In relation to how many contacts workers have in a day, five social distance metrics were varied taking into account infection probabilities. A key finding is that social distance practices and the level of contacts that occur at a time along with the level of infection probability define the level of impact the pandemic has on the food supply chain. Essentially it is seen that the pandemic indeed has a disruptive effect on the food supply chain and workers living in close quarters. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Development of the Digital Economy in Terms of New Global Challenges

The article defines the essence of the economic category “digital economy” and establishes its close relationship with the traditional economy. The presence of the influence of the digital economy on the level of economic growth and development has been noted. The existence of a relationship between integration into global added value chains and the concept of ecosystem development and smart specialization has been revealed. The trends and features of the global digital development, as well as its drivers, have been studied. New global challenges of digital development and their impact on the economy and society in Russia and abroad have been identified and analyzed. It is shown that over the past decades Russia has transformed into a center for the creation of digital products and services and has a number of powerful competitive advantages. The state and possible prospects for the development of the Industry 4.0 in the context of such a global challenge as the coronavirus pandemic are analyzed. The main threats and new opportunities for socio-economic and digital transformations caused by COVID-19 were identified. At the same time, the main emphasis is placed on Russia’s opportunities to be included in new value chains or develop existing ones. The need to ensure the complementarity of digital and socio-economic transformations is indicated. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Labor Market Change Trends Under Global Challenges: Problems and Prospects

This article deals with the research of the factors impacting current trends and changes in the labor market, such as the drop in global oil prices, the COVID-19 pandemic, and the digital transformation of the economy. The results of the analysis confirm that these factors have similar impacts on threats like cutting previously created jobs in various sectors and areas of activity, intensifying gender and age inequalities (with young people, senior employees, and working women facing the highest risks). At the same time, the impacts of these factors on the structural elements of the global and Russian labor markets are diverse and ambivalent. The authors show that global digital transformation does not only threaten to eliminate low-skilled jobs and routines that can be easily automated but also helps create new jobs. Besides, it leads to changes in working conditions and the establishment of new sets and combinations of skills required to succeed in the labor market. The development of digital qualifications and skills must become one of the prioritized aspects in the development of the global and national education systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Stochastic Epidemic Model of Covid-19 via the Reservoir-People Transmission Network

The novel Coronavirus COVID-19 emerged in Wuhan, China in December 2019. COVID-19 has rapidly spread among human populations and other mammals. The outbreak of COVID-19 has become a global challenge. Mathematical models of epidemiological systems enable studying and predicting the potential spread of disease. Modeling and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge, this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely random. In this paper, we develop and analyze a model to simulate the Coronavirus transmission dynamics based on Reservoir-People transmission network.When faced with a potential outbreak, decision-makers need to be able to trust mathematical models for their decision-making processes. One of the most considerable characteristics of COVID-19 is its different behaviors in various countries and regions, or even in different individuals, which can be a sign of uncertain and accidental behavior in the disease outbreak. This trait reflects the existence of the capacity of transmitting perturbations across its domains. We construct a stochastic environment because of parameters random essence and introduce a stochastic version of theReservoir-Peoplemodel. Then we prove the uniqueness and existence of the solution on the stochastic model. Moreover, the equilibria of the system are considered. Also, we establish the extinction of the disease under some suitable conditions. Finally, some numerical simulation and comparison are carried out to validate the theoretical results and the possibility of comparability of the stochastic model with the deterministic model. © 2022 Tech Science Press. All rights reserved.

Generalizable multi-vaccine distribution strategy based on demographic and behavioral heterogeneity

Vaccines have proved to be highly effective in preventing severe outcomes in COVID-19 patients. Despite swift vaccine development, the policymakers are still struggling to meet the global challenges in the availability, cost and distribution of vaccines. With the emergence of new vaccine types and boosters to beat the newer strains of the virus, it is necessary to design effective vaccine distribution strategies. In this paper, we present generalizable, multi-vaccine distribution measures that allocate vaccines based on the socio-economic, epidemiological and demographic profiles of different zones. The proposed approach incorporates myriad features, whereby it can assign vaccines based on a subset of the chosen criteria, minimize or fix the number of assigned vaccines and balance the trade-off between cost and criteria. Through simulation experiments, we demonstrate the ability of the optimizer to capture the variable vaccine adoption rates among zones and reward lower vaccine hesitancy with reduced contagion. © 2021 IEEE.

COVID-19 vaccinations: The unknowns, challenges, and hopes.

The entire world has been suffering from the coronavirus disease 2019 (COVID-19) pandemic since March 11, 2020. More than a year later, the COVID-19 vaccination brought hope to control this viral pandemic. Here, we review the unknowns of the COVID-19 vaccination, such as its longevity, asymptomatic spread, long-term side effects, and its efficacy on immunocompromised patients. In addition, we discuss challenges associated with the COVID-19 vaccination, such as the global access and distribution of vaccine doses, adherence to hygiene guidelines after vaccination, the emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, and vaccine resistance. Despite all these challenges and the fact that the end of the COVID-19 pandemic is still unclear, vaccines have brought great hope for the world, with several reports indicating a significant decline in the risk of COVID19-related infection and hospitalizations.

COVID-19 classification based on CNNs models in CT image datasets

The COVID-19 coronavirus pandemic was a global challenge to the whole society and at the same time created a unique situation for the development of science, scientific communication and open access to scientific information. At the beginning of 2019 the world has faced a pandemic of Covid-19 coronavirus. The coronavirus impacted dramatically lives of majority people around the globe. Deep learning methods allow automatic classification of the coronavirus disease from the computer tomography (CT) scans of the lung. In our work we test several popular convolutional neural network (CNN) models to classify slices of the CT scans. In this study we indicate that the VGG-19 model gives the best classification accuracy among the other tested models such as DenseNet201, MobileNetV2, Xception, VGG-16 and ResNet50v2. In particular, the model achieves the accuracy of 99.08% for CovidX CT Dataset and 98.44% for SARS-CoV-2 CT dataset and 92.30% for UCSD COVID-CT dataset. Additionally, our results include 3D heatmaps that explain classification results for each individual model, showing regions of the lung affected by the coronavirus. © 2021 IEEE.