A literature survey of the robotic technologies during the COVID-19 pandemic

Artificial Intelligence in Nursing: Technological Benefits to Nurse's Mental Health and Patient Care Quality

Nurses are frontline caregivers who handle heavy workloads and high-stakes activities. They face several mental health issues, including stress, burnout, anxiety, and depression. The welfare of nurses and the standard of patient treatment depends on resolving this problem. Artificial intelligence is revolutionising healthcare, and its integration provides many possibilities in addressing these concerns. This review examines literature published over the past 40 years, concentrating on AI integration in nursing for mental health support, improved patient care, and ethical issues. Using databases such as PubMed and Google Scholar, a thorough search was conducted with Boolean operators, narrowing results for relevance. Critically examined were publications on artificial intelligence applications in patient care ethics, mental health, and nursing and mental health. The literature examination revealed that, by automating repetitive chores and improving workload management, artificial intelligence (AI) can relieve mental health challenges faced by nurses and improve patient care. Practical implications highlight the requirement of using rigorous implementation strategies that address ethical issues, data privacy, and human-centred decision-making. All changes must direct the integration of artificial intelligence in nursing to guarantee its sustained and significant influence on healthcare.

Construction Method of a Digital-Twin Simulation System for SCARA Robots Based on Modular Communication

Due to the high cost of robots, the algorithm testing cost for physical robots is high, and the construction of motion control programs is complex, with low operation fault tolerance. To address this issue, this paper proposes a low-cost, cross-platform SCARA robot digital-twin simulation system based on the concept of digital twins. This method establishes a 5D architecture based on the characteristics of different platforms, classifies data and integrates functions, and designs a data-processing layer for motion trajectory calculation and data storage for a virtual-reality robot. To address the complexity of data interaction under different cross-platform communication forms, an editable, modular, cross-platform communication system is constructed, and various control commands are encapsulated into simple programming statements for easy invocation. Experimental results showed that, based on modular communication control, users can accurately control data communication and synchronous motion between virtual and physical models using simple command statements, reducing the development cost of control algorithms. Meanwhile, the virtual-robot simulation system, as a data mapping of the real experimental platform, accurately simulated the physical robot's operating state and spatial environment. The robot algorithms tested using the virtual simulation system can be successfully applied to real robot platforms, accurately reproducing the operating results of the virtual system.

Design and analysis of a mobile robot with novel caster mechanism for high step-overcoming capability

The mobile robot market is experiencing rapid growth, playing a pivotal role in various human-centric environments like restaurants, offices, hotels, hospitals, apartments, and factories. However, current differential-driven mobile robots, employing conventional casters and wheel motors, encounter limitations in surmounting uneven surfaces and high steps due to constraints caused by wheel and caster dimensions. While some robots address these challenges by incorporating optimized wheel shapes and additional motors, this invariably leads to an increase in both size and cost. This research introduces an innovative solution; a novel caster-wheel mechanism designed to enhance the high-step overcoming capability of mobile robots without necessitating alterations to their overall size and structure. By incorporating a sub-wheel linked to a passive joint, the driving force is effeciently converted into a vertical force, thereby empowering the mobile robot to navigate obstacles 85% larger than its caster-wheel radius. Crucially, this innovative caster can be seamlessly manufactured and integrated, offering the potential for widespread adoption as a replacement for conventional casters. Validation through comprehensive simulations and experiments conducted on a prototype robot has been presented in this article, demonstrating its effectiveness even at a robot velocity of 0.1 m/s. This pioneering solution holds significant promise for diverse applications across various mobile robot configurations, presenting a compelling avenue for further exploration and implementation in the field.

A Retrospective Analysis of Indoor CO2 Measurements Obtained with a Mobile Robot during the COVID-19 Pandemic

This work presents a retrospective analysis of indoor CO2 measurements obtained with a mobile robot in an educational building after the COVID-19 lockdown (May 2021), at a time when public activities resumed with mandatory local pandemic restrictions. The robot-based CO2 measurement system was assessed as an alternative to the deployment of a net of sensors in a building in the pandemic period, in which there was a global stock outage of CO2 sensors. The analysis of the obtained measurements confirms that a mobile system can be used to obtain interpretable information on the CO2 levels inside the rooms of a building during a pandemic outbreak.

Is the sustainability of exposure to non-ionizing electromagnetic radiation possible?

Technological advances imply an increase in artificially generating sources of electromagnetic fields (EMF), therefore, resulting in a permanent exposure of people and the environment (electromagnetic pollution). Inconsistent results have been published considering the evaluated health effects. The purpose of this study was to review scientific literature on EMF to provide a global and retrospective perspective, on the association between human exposure to non-ionizing radiation (NIR, mainly radiofrequency-EMF) and health and environmental effects. Studies on the health effects of 5G radiation exposure have not yet been performed with sufficient statistical power, as the exposure time is still relatively short and also the latency and intensity of exposure to 5G. The safety standards only consider thermal effects, do not contemplate non-thermal effects. We consider relevant to communicate this knowledge to the general public to improve education in this field, and to healthcare professionals to prevent diseases that may result from RF-EMF exposures.

Exploring the influence of anthropomorphic appearance on usage intention on online medical service robots (OMSRs): A neurophysiological study

Online medical service robots (OMSRs) are becoming increasingly important in the medical industry, and their design has become a highly focused issue. This study investigated the neuroeconomics underlying the formation of usage intention, specifically evaluating the impact of anthropomorphic appearance and age on users' intentions to use OMSRs. Event-related potentials were used to analyze electroencephalography signals recorded from participants. This study found that OMSRs with a low anthropomorphic appearance induced larger P200 and P300 amplitudes, resulting in increased attentional resources compared to OMSRs with a moderate or high anthropomorphic appearance. OMSRs with moderate anthropomorphic appearances captured more attention and elicited larger P200 and P300 than those with high anthropomorphic appearances. Regarding age characteristics, OMSRs with senior features attracted more attention and induced larger P200 and P300 amplitudes. In terms of usage intention, compared to the others, users demonstrate a stronger usage intention towards the low anthropomorphism of OMSRs. Additionally, compared to the senior ones, users also exhibit a stronger usage intention toward a young appearance of OMSRs. These findings provide valuable insights for robot designers and practitioners to improve the appearance of OMSRs.

Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects

The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.

Assessment of needle-tissue force models based on ex vivo measurements

Needle insertion is one of the most common procedures in clinical practice. Existing statistics reveal that success rates of needle insertions can be low, leading to potential complications and patient discomfort. Real-time imaging techniques like ultrasound and X-ray can assist in improving precision, but even experienced practitioners may face challenges in visualizing the needle tip. Researchers have proposed models of force interactions during needle insertions into biological tissue to enhance accuracy. This article presents an evaluation of the forces acting on intravenous needles during insertion into skin. The aim was to explore mathematical models, compare them with data from tests on animal specimens, and select the most suitable model for future research. The experimental setup involved conducting needle insertion tests on animal-originated cadavers, using the Brucker Universal Mechanical Tester device, which measured the force response during vertical movement of the needle. The research was divided into 2 stages. In Stage I, force measurements were recorded for both the insertion and extraction phases of the hypodermic needles. The measurements were conducted for several different needle sizes, speed and insertion angles. In Stage II, five different models were examined to determine how well they matched the experimental data. Based on the analysis of fit quality coefficients, the Gordon's exponential model was identified as the best fit to the measured data. The influence of needle size, insertion angle, and insertion speed on the measured force values was confirmed. Different insertion speeds revealed the viscoelastic properties of the tested samples. The presence of the skin layer affected the puncture force and force values for subsequent layers.

Experiences of health and social professionals using care technologies with older adults during the COVID-19 pandemic: A qualitative study

OBJECTIVE

To investigate the perceptions and experiences of health and social care professionals concerning the use of technology for the care of older adults during the COVID-19 pandemic.

DESIGN AND MEASURES

A phenomenological qualitative, exploratory, and descriptive design using semi-structured interviews.

SAMPLE

Twenty Spanish health and social care workers in six Spanish cities between February and July 2021, during the COVID-19 pandemic.

RESULTS

During the COVID-19 pandemic care workers have become more familiar with technology devices, but they also recognize certain barriers for the implementation of technology, mainly in nursing homes and homecare, related to concerns of lack of humanization and difficulties in accessing and using these devices.

CONCLUSION

Politicians and social and healthcare managers should be aware of the benefits of techno-care, reducing the difficulties in implementing it and making more funding and further training available to care providers.

Effect of social influence, environmental awareness, and safety affordance on actual use of 5G technologies among Chinese students

5G technology continues to evolve, and its potential to revolutionize various aspects of society is becoming increasingly evident. However, the successful adoption and utilization of 5G technology depends on various factors, particularly among consumers expected to be early adopters and critical drivers of technological advancements. This study investigates the effect of social influence, environmental awareness, and safety affordance on Chinese university students' actual use of 5G (AU5G) technologies. It also analyzed the mediating role of trustworthiness and intention to use 5G (ITU5G) between them and the moderating role of facilitation conditions between trustworthiness and ITU5G. Data was collected from university students (n = 364) in Beijing and was examined employing the structural equation modelling (SEM) approach. The outcomes confirm that trustworthiness and ITU5G significantly mediate the relationship between social influence, environmental awareness, and safety affordance on AU5G technologies among Chinese students. Further, the supportive facilitation conditions strengthen the relationship between trustworthiness and ITU5G. These findings are backed by UTAUT2 models that support the technology acceptance and adoption among the users. The study outcomes can help policymakers design strategies to address potential barriers and encourage greater uptake of 5G services among university students.

Comparison of the safety, effectiveness, and usability of swab robot vs. manual nasopharyngeal specimen collection

Background

Healthcare workers face a risk of infection during aerosol-generating procedures, such as nasal swabbing. Robot-assisted nasopharyngeal sampling aims to minimize this risk and reduce stress for healthcare providers. However, its effectiveness and safety require validation.

Methods

We conducted a controlled trial with 80 subjects at two teaching hospitals and compared robot-collected vs manually-collected nasopharyngeal swabs. The primary outcomes included specimen quality and success rate of nasopharyngeal swab collection. We also recorded the pain index, duration of the collection, and psychological stress using a post-collection questionnaire.

Results

During the study period, from September 23 to October 27, 2020, 40 subjects were enrolled in both the robotic and manual groups. The cycle threshold (Ct) value for nasopharyngeal specimens was statistically higher in the robotic group compared to the manual group (30.9 vs 28.0, p < 0.01). Both groups had Ct values under 35, indicating good quality specimens. In the robotic group, 3 out of 40 subjects required a second attempt at specimen collection, resulting in a success rate of 92.5 %. Further, although the pain levels were lower in the robotic group, the difference was not statistically significant (2.8 vs 3.6, p = 0.07). The manual group had a shorter sampling time, which was 29 s (201 vs 29, p < 0.05). However, when factoring in the time needed to put on personal protective equipment, the average time for the manual group increased to 251 s (201 vs 251, p < 0.05). Participants' questionnaire results show comparable psychological stress in both groups. Medical staff expressed that using a robot would reduce their psychological stress.

Conclusions

We propose a safe and effective robotic technology for collecting nasopharyngeal specimens without face-to-face contact, which may reduce the stress of physicians and nurses. This technology can also be optimized for efficiency, making it useful in situations where droplet-transmitted infectious diseases are a concern.

Human resource management (HRM) strategies of medical staff during the COVID-19 pandemic

Healthcare workers are at the forefront of fight against COVID-19 and the managers of medical centers should develop coping strategies for the challenges caused by COVID-19, especially for health human resources in order to improve the performance of healthcare organizations. Hence, the purpose of this study is to investigate the human resource management strategies of medical staff during the COVID-19 to help them cope with the new strains of COVID-19 or epidemics of viral diseases that may occur in the future. In this study, a search was performed in the international Web of Science electronic database, using keywords such as human resource management and COVID-19. As a result, a total of 1884 articles published between January 1st, 2020 and October 22nd, 2021 were extracted. After screening the articles based on inclusion and exclusion criteria, 24 articles were selected to enter the study. Then, a scientometric analysis was performed on the content of selected articles and the results were presented in the form of tables and conceptual models. In total, 9 strategies were extracted from the selected articles including development of organizational culture, staff screening, policy-making, infection control training and monitoring the implementation of learned materials, patient management, human resource management, psychological and motivational support, communication and coordination, and digital health services. Employing comprehensive strategies to maintain the health of healthcare workers during the COVID-19 can play an effective role in reducing burnout, improving productivity and employee satisfaction, and in increasing the resilience of healthcare workers. It also has a positive effect on the patient's safety. Revision and reengineering of human resource management strategies in health and treatment organizations according to different cultures and contexts require research and investment in creative and innovative strategies.

The promise of digital healthcare technologies

Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.

Stressors, Resources, and Strain Associated with Digitization Processes of Medical Staff Working in Neurosurgical and Vascular Surgical Hospital Wards: A Multimethod Study

The digitization of German hospitals is proceeding continuously, leading to the implementation of new digital technologies, such as electronic health records (EHRs) or other technologies, used for the purpose of medical documentation tasks. Even though the replacement of paper documentation through digitized documentation in general promises to come along with plenty of benefits, the daily utilization of technologies might also lead to stresses and strains among the medical staff, eventually possibly leading to the development of different negative work and health-related outcomes. This study, therefore, aims at identifying persisting digitization-associated stressors and resources among medical hospital staff, examining their influences on different work and health-related outcomes, and finally, identifying potential needs for preventive measures. A quantitative study in the form of an online questionnaire survey was conducted among physicians working in the medical field of neuro- and vascular surgery in German hospitals. The study was carried out between June and October 2022 utilizing an online questionnaire based on several standardized scales, such as the technology acceptance model (TAM) and the technostress model, as well as on several scales from the Copenhagen Psychosocial Questionnaire (COPSOQ). The study found medium levels of technostress among the participating physicians (n = 114), as well as low to medium levels of persisting resources. The queried physicians, on average, reported low levels of burnout symptoms, generally described their health status as good, and were mostly satisfied with their job. Despite the prevalence of technostress and the low levels of resources among the surveyed physicians, there is little awareness of the problem of digital stress, and preventive measures have not been widely implemented yet in the clinics, indicating a needs gap and the necessity for the strategic and quality-guided implementation of measures to effectively prevent digital stress from developing.

Impact of Pandemic SARS COVID-19 on Different Construction Project Management: Problems and Solutions

This study discusses risk management strategies caused by pandemic-related (Covid-19) suspensions in thirty-six engineering projects of different types and sizes selected from countries in the middle east and especially Iraq. The primary data collection method was a survey and questionnaire completed by selected project crew and laborers. Data were processed using Microsoft Excel to construct models to help decision-makers find solutions to the scheduling problems that may be expected to occur during a pandemic. A theoretical and practical concept for project risk management that addresses a range of global and local issues that affect schedule and cost is presented and results indicate that the most significant delays are due to a lack of good project risk management skills and remote project management capability which is exacerbated by shortfalls in technical development and information technology.

Role of academia in enhancing technology and innovation for a post COVID-19 recovery and growth

The COVID-19 pandemic represents one of the greatest challenges of this century with wide ranging impacts not only on health but practically every sector of the human society. The pandemic stretched our resources and coping capacities to almost breaking points even in wealthy economies and further exposed crucial weaknesses in infrastructure, human resources and emergency preparedness of most nations. This review article explored the role of technology and innovation in post COVID-19 growth and recovery. The academia has facilitated better understanding of the COVID-19 pandemic by increasing the body of knowledge on the disease. Better understanding of the disease informed technology and innovations which has made it possible to end the pandemic lockdown, and chart the course for recovery and growth. Relevant articles from a search of electronic databases were reviewed and the role of academia as well as some of the innovations that opened the pathway for recovery were highlighted. Recovery and growth after the COVID-19 pandemic will require synergistic efforts between the academia and the industry, more like taking the "Gown" to "Town". Research and development in academia, and industrial technology and innovation are veritable tools for a post COVID-19 recovery.

Trends in Robotics Research in Occupational Safety and Health: A Scientometric Analysis and Review

Robots have been deployed in workplaces to assist, work alongside, or collaborate with human workers on various tasks, which introduces new occupational safety and health hazards and requires research efforts to address these issues. This study investigated the research trends for robotic applications in occupational safety and health. The scientometric method was applied to quantitatively analyze the relationships between robotics applications in the literature. The keywords "robot", "occupational safety and health", and their variants were used to find relevant articles. A total of 137 relevant articles published during 2012-2022 were collected from the Scopus database for this analysis. Keyword co-occurrence, cluster, bibliographic coupling, and co-citation analyses were conducted using VOSviewer to determine the major research topics, keywords, co-authorship, and key publications. Robot safety, exoskeletons and work-related musculoskeletal disorders, human-robot collaboration, and monitoring were four popular research topics in the field. Finally, research gaps and future research directions were identified based on the analysis results, including additional efforts regarding warehousing, agriculture, mining, and construction robots research; personal protective equipment; and multi-robot collaboration. The major contributions of the study include identifying the current trends in the application of robotics in the occupational safety and health discipline and providing pathways for future research in this discipline.

Design and Implementation of an Integrated Control System for Omnidirectional Mobile Robots in Industrial Logistics

The integration of intelligent robots in industrial production processes has the potential to significantly enhance efficiency and reduce human adversity. However, for such robots to effectively operate within human environments, it is critical that they possess an adequate understanding of their surroundings and are able to navigate through narrow aisles while avoiding both stationary and moving obstacles. In this research study, an omnidirectional automotive mobile robot has been designed for the purpose of performing industrial logistics tasks within heavy traffic and dynamic environments. A control system has been developed, which incorporates both high-level and low-level algorithms, and a graphical interface has been introduced for each control system. A highly efficient micro-controller, namely myRIO, has been utilized as the low-level computer to control the motors with an appropriate level of accuracy and robustness. Additionally, a Raspberry Pi 4, in conjunction with a remote PC, has been utilized for high-level decision making, such as mapping the experimental environment, path planning, and localization, through the utilization of multiple Lidar sensors, IMU, and odometry data generated by wheel encoders. In terms of software programming, LabVIEW has been employed for the low-level computer, and the Robot Operating System (ROS) has been utilized for the design of the higher-level software architecture. The proposed techniques discussed in this paper provide a solution for the development of medium- and large-category omnidirectional mobile robots with autonomous navigation and mapping capabilities.

Visual Positioning of Nasal Swab Robot Based on Hierarchical Decision

This study focuses on a robot vision localization method for coping with the operational task of automatic nasal swab sampling. The application is important in the detection and epidemic prevention of Corona Virus Disease 2019 (COVID-19) to alleviate the large-scale negative impact of individuals suffering from pneumonia owing to COVID-19. In this method, the idea of a hierarchical decision network is used to consider the strong infectious characteristics of the COVID-19, which is followed by processing the robot behavior constraint condition. The visual navigation and positioning method using a single-arm robot for sampling is also planned, which considers the operation characteristics of medical staff. In the decision network, the risk factor for potential contact infection caused by swab sampling operations is established to avoid the spread among personnel. A robot visual servo control with artificial intelligence characteristics is developed to achieve a stable and safe nasal swab sampling operation. Experiments demonstrate that the proposed method can achieve good vision positioning for the robots and provide technical support for managing new major public health situations.

Application of big data and artificial intelligence in epidemic surveillance and containment

Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems have resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about their use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, and develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment. These are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarized the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis on epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research would be on methods that promise value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.

Employees' perception of robots and robot-induced unemployment in hospitality industry under COVID-19 pandemic

The impact of the pandemic is driving the recent upsurge in service automation and the adoption of service robots in the hospitality industry. As service paradigm and customer expectations shift from conventional customized and personalized services towards a digitalized service environment, such customer orientation may favor using service robots at scales that could render service employees redundant. This study aims to answer the above question by investigating service employees' perceptions of service robots. Data solicited from 405 service employees in the United States of America via Amazon's MTurk were analyzed using structural equation modeling. The result revealed that employees' awareness of adopting and using service robots significantly impacts their perception of robot-induced unemployment. Further, results indicated that the perception of robots' social skills significantly influences service employees' perception of robot-induced unemployment. Employee status was found to moderate the relationships mentioned above. Specifically, entry-level employees perceive the unemployment risk more than managers.

A Multi-Criteria Decision-Making Model with Interval-Valued Intuitionistic Fuzzy Sets for Evaluating Digital Technology Strategies in COVID-19 Pandemic Under Uncertainty

Coronavirus diseases 2019 (COVID-19) pandemic is an essential challenge to the health and safety of people, medical members, and treatment systems worldwide. Digital technologies (DTs) have been universally introduced to improve the treatment of patients during the pandemic. Nevertheless, only a few governments have been partly successful in executing the DT strategies. In this regard, it is critical to demonstrate a suitable strategy for the governments. This problem is built based on the experts' opinions with some conflicting criteria to evaluate various types of alternatives. Hence, this research presents a new multi-criteria decision-making (MCDM) model under uncertain conditions. For this reason, interval-valued intuitionistic fuzzy sets (IVIFSs) are employed to help decision-makers (DMs) evaluate in a broader area and cope with uncertain information. Moreover, a new extended weighting method based on weighted distance-based approximation (WDBA) and a new combined ranking approach are proposed to determine the DMs' weights and rank the alternatives under IVIF conditions. The developed weighting method is constructed based on computing the DMs' weights with objective criteria weights. Furthermore, a new ranking approach is proposed by obtaining two ranking indexes separately: The first and second ranking indexes are calculated according to the positive and negative ideal solutions distances and the nature of criteria weights, respectively. Afterward, the final values of rankings are computed by considering a new aggregating procedure. The results of the proposed model represent the first alternative as the best strategy. Comparisons between the IVIF-TOPSIS and IVIF-VIKOR methods are also provided to investigate the proposed model to determine the rankings of main alternatives. Sensitivity analyses are conducted to check the reliability and the robustness of the model. For this purpose, criteria weights are analyzed to compute the dependencies' degree of the new extended weighting method. The dependencies of the ranking model are discussed on the criteria weights as well.

Performance analysis of autonomous UV disinfecting robot (UV bot) using Taguchi method

As a result of the COVID-19 epidemic, there is a growing demand for robots to perform various operations which include service bots, cleaning, and disinfection bots. Viral contamination has been one of the major causes of human fatality which has abruptly increased in this situation. Availability of existing technologies is always surpassed by an effective one so as is the UV-Bot developed in this project. This bot aims for a highly accurate percentage of up to 96.8% of germ clearance at pre-defined conditions which are user-friendly. Also, the robot is designed in a compact size and effective shape to achieve maximum efficiency. The robot is deployed in hospital pathway and rooms for disinfection whereas human detection and obstacle avoidance has been included with a custom-developed algorithm that supports autonomous navigation and corner tracking facility. The robot also supports live streaming of the disinfecting site with an emergency alarm and stop in human detection. This type of robot is highly capable of destroying viral infections at a particular point which is validated using Taguchi analysis and also the robot is 3D modelled and tested using static and dynamic obstacles. Thus UV-Bot is manually controllable or autonomous which uses the A* algorithm to store or retrieve the disinfecting site map which is recorded if used frequently.

Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead

Artificial Intelligence (AI) has played a substantial role in the response to the challenges posed by the current pandemic. The growing interest in using AI to handle Covid-19 issues has accelerated the pace of AI research and resulted in an exponential increase in articles and review studies within a very short period of time. Hence, it is becoming challenging to explore the large corpus of academic publications dedicated to the global health crisis. Even with the presence of systematic review studies, given their number and diversity, identifying trends and research avenues beyond the pandemic should be an arduous task. We conclude therefore that after the one-year mark of the declaration of Covid-19 as a pandemic, the accumulated scientific contribution lacks two fundamental aspects: Knowledge synthesis and Future projections. In contribution to fill this void, this paper is a (i) synthesis study and (ii) foresight exercise. The synthesis study aims to provide the scholars a consolidation of findings and a knowledge synthesis through a systematic review of the reviews (umbrella review) studying AI applications against Covid-19. Following the PRISMA guidelines, we systematically searched PubMed, Scopus, and other preprint sources from 1st December 2019 to 1st June 2021 for eligible reviews. The literature search and screening process resulted in 45 included reviews. Our findings reveal patterns, relationships, and trends in the AI research community response to the pandemic. We found that in the space of few months, the research objectives of the literature have developed rapidly from identifying potential AI applications to evaluating current uses of intelligent systems. Only few reviews have adopted the meta-analysis as a study design. Moreover, a clear dominance of the medical theme and the DNN methods has been observed in the reported AI applications. Based on its constructive systematic umbrella review, this work conducts a foresight exercise that tries to envision the post-Covid-19 research landscape of the AI field. We see seven key themes of research that may be an outcome of the present crisis and which advocate a more sustainable and responsible form of intelligent systems. We set accordingly a post-pandemic research agenda articulated around these seven drivers. The results of this study can be useful for the AI research community to obtain a holistic view of the current literature and to help prioritize research needs as we are heading toward the new normal.

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region's cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

The evolving roles and impacts of 5G enabled technologies in healthcare: The world epidemic COVID-19 issues

The latest 5G technology is being introduced the Internet of Things (IoT) Era. The study aims to focus the 5G technology and the current healthcare challenges as well as to highlight 5G based solutions that can handle the COVID-19 issues in different arenas. This paper provides a comprehensive review of 5G technology with the integration of other digital technologies (like AI and machine learning, IoT objects, big data analytics, cloud computing, robotic technology, and other digital platforms) in emerging healthcare applications. From the literature, it is clear that the promising aspects of 5G (such as super-high speed, high throughput, low latency) have a prospect in healthcare advancement. Now healthcare is being adopted 5G-based technologies to aid improved health services, more effective medical research, enhanced quality of life, better experiences of medical professionals and patients in anywhere-anytime. This paper emphasizes the evolving roles of 5G technology for handling the epidemiological challenges. The study also discusses various technological challenges and prospective for developing 5G powered healthcare solutions. Further works will incorporate more studies on how to expand 5G-based digital society as well as to resolve the issues of safety-security-privacy and availability-accessibility-integrity in future health crises.

Digital Twin and web services for robotic deburring in intelligent manufacturing

The development of modern manufacturing requires key solutions to enhance the intelligence of manufacturing such as digitalization, real-time monitoring, or simulation techniques. For smart robotic manufacturing, the modern approach regarding robot programming and process planning aims for both high efficiency and energy-awareness. During the design and manufacturing stages, optimization becomes crucial and can be fulfilled by means of appropriate digital manufacturing tools. This paper presents the development of a Digital Twin for a robotic deburring workcell along with the process planning and robot programming. Considering a large size workpiece, a new robot programming solution was implemented, based on image processing to safely re-machine only areas where burrs could not be completely removed in the main deburring routine. The work also covers the development of a new web platform to remotely monitor the robotic workcell, to trigger alerts for unexpected events and to allow the control to authorized personnel enabled by the employment of robot web services following an architectural RESTful style which establishes a communication link to the robot virtual controller. The aim of this research is to integrate the Digital Twin with the innovative proposals of Industry 4.0, offering a project-based model of smart robotic manufacturing and experience concepts such as Cyber-Physical System, digitalization, data acquisition, continuous monitoring, and intelligent solutions in a novel approach. Furthermore, the work covers energy consumption strategies for energy-aware robotic manufacturing. Finally, the results of an energy-efficient motion planning along with signal-based scheduling optimization of the robotic deburring cell are discussed.

Analysis of the COVID-19 pandemic’s impacts on manufacturing: a systematic literature review and future research agenda

The COVID-19 pandemic has affected manufacturing companies and necessitated adaptations of firms’ operations. Despite the increasing interest in this subject, a scarcity of systematic analysis can be observed. The present study systematically reviews the existing research on the COVID-19 pandemic concerning the manufacturing industry. This paper aims to highlight the main impacts of the COVID-19 pandemic on the manufacturing sector from the operations management perspective, the practical adaptation actions, and future research opportunities. Open research questions and directions for further investigation are articulated and triangulated across organisational, process and technology perspectives.

Adoption of innovative strategies to mitigate supply chain disruption: COVID-19 pandemic

COVID-19 pandemic outbreak caused supply chain (SC) disruption and threatened human life across the world, which could be mitigated through innovative strategies. Based on this scenario, this study examines the impact of COVID-19 on green practices, SC crisis mitigation strategies, smart technologies, and sustainable supply chain performance in the Pakistani manufacturing industry. Data was collected from Pakistani firms and employed structural equation modeling for testing hypotheses. The empirical results found that the COVID-19 pandemic is statistically related to green practices, SC crisis mitigation strategies, and smart technologies, while it harms sustainable supply chain performance. Moreover, green practices, SC crisis mitigation strategies, and smart technologies positively contribute to sustainable supply chain performance. The results of this study also confirmed the mediating role of green practices, SC crisis mitigation strategies, and smart technologies and moderating role organizational commitment in the context of a developing economy’s manufacturing industry. This study enhances awareness and understanding and contributes to the existing literature on verifying the link between COVID-19 pandemic and green practices, SC crisis mitigation strategies, and smart technologies to increase sustainable supply chain performance during a pandemic disruption in the Pakistani context. This study supports the managers of supply chain and manufacturing firms in adopting green practices and smart technologies. Also, it helps in the formation and successful implementation of SC crisis mitigation strategies during the crisis.

Anthropomorphic Robotic Eyes: Structural Design and Non-Verbal Communication Effectiveness

This paper shows the structure of a mechanical system with 9 DOFs for driving robot eyes, as well as the system's ability to produce facial expressions. It consists of three subsystems which enable the motion of the eyeballs, eyelids, and eyebrows independently to the rest of the face. Due to its structure, the mechanical system of the eyeballs is able to reproduce all of the motions human eyes are capable of, which is an important condition for the realization of binocular function of the artificial robot eyes, as well as stereovision. From a kinematic standpoint, the mechanical systems of the eyeballs, eyelids, and eyebrows are highly capable of generating the movements of the human eye. The structure of a control system is proposed with the goal of realizing the desired motion of the output links of the mechanical systems. The success of the mechanical system is also rated on how well it enables the robot to generate non-verbal emotional content, which is why an experiment was conducted. Due to this, the face of the human-like robot MARKO was used, covered with a face mask to aid in focusing the participants on the eye region. The participants evaluated the efficiency of the robot's non-verbal communication, with certain emotions achieving a high rate of recognition.

Healthcare Workers’ Point of View on Medical Robotics During COVID-19 Pandemic – A Scoping Review

COVID-19 affected how healthcare workers interact with patients. Medical technology and robotics are developed in hospital settings to limit human contact. The aim of this review is to elucidate what kind of medical robotics is required for healthcare workers during COVID-19 pandemic. This review was obtained from electronic databases such as Google Scholar, PubMed, EBSCO, and Cochrane reviews were searched for articles using keywords such as “healthcare professional” OR “health worker” AND “COVID-19” AND “robot application” OR “robotics” OR “health technology” AND “needs assessment” OR “expectation” OR “perception” published during 2020 to 2021. Inclusion criteria were full-text articles related to assessment of healthcare workers’ need for medical robotics during COVID-19 pandemics. Exclusion criteria included abstracts, duplicate articles, blogs, news articles, promotional brochures, and conference proceedings. A total of 13,692 articles were identified through the search engines (PubMed 179, Cochrane Library 1300, EBSCO 13, Google Scholar 12,200). Five full-text articles fulfilled the inclusion criteria. Determining robotic functions is important to healthcare workers who will be user of such medical technology. This review divided robotic functions into medical, operational, movement, and social functions. Healthcare workers’ demands for robotics were also influenced by the types of robots, such as examination robots, robot-based sample test and medicine production, surgery and rehabilitation robots, disinfection and cleaning robots, delivery and logistic robot, telemedicine, and telepresence robots. Medical robotics is required for healthcare workers during the COVID-19 pandemic. The highest demands for medical robotics functions include cardiac measurements and oxygen saturation monitoring (medical functions); examination record delivery, video and image play, and medical information delivery (operational functions); and the ability to recognize and avoid obstacles (movement functions). Disinfection and cleaning robots were the type of robots with the highest demand among healthcare workers.

Remote Ultrasound Scan Procedures with Medical Robots: Towards New Perspectives between Medicine and Engineering

Background

This review explores state-of-the-art teleoperated robots for medical ultrasound scan procedures, providing a comprehensive look including the recent trends arising from the COVID-19 pandemic.

Methods

Physicians' experience is included to indicate the importance of their role in the design of improved medical robots. From this perspective, novel classes of equipment for remote diagnostics based on medical robotics are discussed in terms of innovative engineering technologies.

Results

Relevant literature is reviewed under the system engineering point of view, organizing the discussion on the basis of the main technological focus of each contribution.

Conclusions

This contribution is aimed at stimulating new research to obtain faster results on teleoperated robotics for ultrasound diagnostics in response to the high demand raised by the ongoing pandemic.

Design and development of an IoT based intelligent multi parameter screening system

The SARS-CoV-2 or shortly COVID-19, is a viral disease which causes serious lung fever and hugely impacts different body parts from mild to critical depending on tolerant immune system. As the virus multiplies through human-to-human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Viral spreading of this virus can only be prevented by early detection of positive cases and to treat infected patients as quickly as possible. As many businesses, banks, gymnasiums, and stores etc., are using temperature screening as the primary step to assess for possible COVID-19 infection. Moreover, the proper hand sanitization is the very effective method to limit the outspread of this virus. This paper proposes the design and development of a fully automated low-cost portable electronic system in the form of a robot named CovBot that can be installed in the above-mentioned places by incorporating the mechanisms to automatically detect the body temperature, store the details directly to cloud so as to get the data latter by the authorities, to control/restrict the entry, a hand sanitization dispenser unit, auto alert to refill the sanitizer, a mobile display unit etc. Whole system can be managed by a mobile application. The system is controlled using an Arduino-Uno development board. The mobile and the microcontroller system is wirelessly communicated and that to cloud is done by IoT facility. Once this system is implemented, the primary concern and the initial screening associated to COVID-19 can be fully resolved. Comparing to other systems CovBot is cost effective and can be easily installed and operated.

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

Service robots are an option for contactless services due to the COVID-19 pandemic in the hotels

The development of robotics in the current COVID-19 pandemic scenario can change the face of the industries. Robots are becoming more prominent in the hospitality industry. In this scenario, the usage of service robots for hotels is the best option. This study is performed using TAM and TRI theories. The constructs selected for the study are perceived ease of use, perceived usefulness, attitudes, behavioural intention, discomfort, insecurity, and trust. Survey-based research is carried out with the help of a questionnaire. The target population are the employees working in the hotels. Ten hypotheses are proposed for the study. This study highlights the acceptance of service robots in the hotels of India. Out of ten proposed hypothesis, five hypotheses were accepted, and the rest were rejected. For data analysis, structural equation modelling in AMOS 20.0 was carried out. This study will help the managers and the top management in the adoption of service robots.

The Effect of COVID-19 on Airline Transportation Services: A Study on Service Robot Usage Intention

Robots have started to be used in the service sector as well as in the manufacturing industry. The use of service robots in airport services, tourism, and hospital services also brings many advantages. Because of the COVID-19 pandemic, reducing physical contact and providing hygiene are essential benefits of service robots. For this purpose, the effect of COVID-19 fear on perceived trust in service robots and the intention to use them at airports was investigated in this study. For analysis, data were collected from 730 participants from 64 different countries, by questionnaire. The data were analyzed with the SPSS and AMOS package programs. As a result of the structural equation model analysis, it was determined that COVID-19 fear affects perceived trust in service robots and the intention to use them positively, and perceived trust also affects the intention to use service robots positively. In addition, as a result of the Process Macro analysis, it was found that perceived trust has a high mediating effect on the effect of COVID-19 fear on the intention to use. The research results show that people’s attitudes towards COVID-19 shape the perception of robot usage in air transport services.

Socially Assistive Robots Helping Older Adults through the Pandemic and Life after COVID-19

The COVID-19 pandemic has critically impacted the health and safety of the population of the world, especially the health and well-being of older adults. Socially assistive robots (SARs) have been used to help to mitigate the effects of the pandemic including loneliness and isolation, and to alleviate the workload of both formal and informal caregivers. This paper presents the first extensive survey and discussion on just how socially assistive robots have specifically helped this population, as well as the overall impact on health and the acceptance of such robots during the pandemic. The goal of this review is to answer research questions with respect to which SARs were used during the pandemic and what specific tasks they were used for, and what the enablers and barriers were to the implementation of SARs during the pandemic. We will also discuss lessons learned from their use to inform future SAR design and applications, and increase their usefulness and adoption in a post-pandemic world. More research is still needed to investigate and appreciate the user experience of older adults with SARs during the pandemic, and we aim to provide a roadmap for researchers and stakeholders.

Samarakoon SMB, Muthugala MAVJ, Elara MR. Design of a Reconfigurable Wall Disinfection Robot

During a viral outbreak, such as COVID-19, autonomously operated robots are in high demand. Robots effectively improve the environmental concerns of contaminated surfaces in public spaces, such as airports, public transport areas and hospitals, that are considered high-risk areas. Indoor spaces walls made up most of the indoor areas in these public spaces and can be easily contaminated. Wall cleaning and disinfection processes are therefore critical for managing and mitigating the spread of viruses. Consequently, wall cleaning robots are preferred to address the demands. A wall cleaning robot needs to maintain a close and consistent distance away from a given wall during cleaning and disinfection processes. In this paper, a reconfigurable wall cleaning robot with autonomous wall following ability is proposed. The robot platform, Wasp, possess inter-reconfigurability, which enables it to be physically reconfigured into a wall-cleaning robot. The wall following ability has been implemented using a Fuzzy Logic System (FLS). The design of the robot and the FLS are presented in the paper. The platform and the FLS are tested and validated in several test cases. The experimental outcomes validate the real-world applicability of the proposed wall following method for a wall cleaning robot.

The Effect of Perceptions on Service Robot Usage Intention: A Survey Study in the Service Sector

The current age of artificial intelligence, along with the advent of robots, portends increased use of innovative technologies in the tourism industry, with higher levels of service innovation than in many other industries. In addition, factors such as an approaching worldwide pandemic have limited the amount of physical contact that people can have. So as a result, the use of service robots in service areas, such as tourism, has recently become controversial. In this study, accommodation customers’ perceptions of advantages and disadvantages about robots and the effect of the perceived value of their intention to use them were investigated. Within the scope of the research, data were collected from 1408 people living in various cities in Turkey through an online survey. The data were analyzed by structural equation modeling. As a result of the analyses, it was found that the perception of advantage and the perceived value affect the intention to use service robots positively and significantly. It has been determined that the perception of disadvantage affects the intention to use service robots negatively and significantly. The research results show that the accommodation companies should be innovative and rapidly transition to robotization, as in the manufacturing industry. Advanced technological innovation applications, such as service robots, will play an essential role in the revival of the tourism industry, especially during the global epidemic.

The Adoption of Robotic Process Automation Technology to Ensure Business Processes during the COVID-19 Pandemic

The study provides knowledge on the adoption of the Robotic Process Automation (RPA) technology during the COVID-19 pandemic in 110 Polish service companies. As this research was the first of its kind in Poland, the objectives of the CAWI survey were to identify the technology features of the RPA technology and the related determinants and barriers influencing the adoption of the RPA as well as to determine correlations between them. Moreover, the statistical analyses involved considering whether there were differences in the evaluation of individual RPA technology features, mainly in terms of perceived usefulness, ease of use, security and functionality. The results of the study show that almost 60% of the respondents indicated that robotization tools allowed maintaining continuity of business processes during the pandemic. The highest rated were features related to usefulness of the RPA technology. Furthermore, the analysis pointed to the most frequently indicated barriers to technology implementation that were related to nonoptimized, nonstandardized and non-digitized processes with a large number of exceptions. The study contributes to scientific knowledge and has practical implications for process automation decision-makers concerned with the adoption of the Robotic Process Automation technology. The obtained results can help them to understand the potential drivers of and barriers to the adoption of software robots by enterprises and may be an important determinant for companies’ managers in the field of implementing such solutions.

Smart Cleaner: A New Autonomous Indoor Disinfection Robot for Combating the COVID-19 Pandemic

The COVID-19 pandemic imposes an increasing demand for service robots as a substitute for humans to conduct various types of work in contaminated areas. Such work includes logistics, patient care, and disinfection, which can reduce the risk of human exposure to the highly contagious and deadly virus. This paper presents the design and development of Smart Cleaner, which is a new cost-effective autonomous indoor disinfection robot. It integrates a wheeled mobile robot platform and a hydrogen peroxide atomization device for automated disinfection operation in the complex indoor environment. Through the system integration of various hardware components and software programming, a prototype of the disinfection robot has been fabricated for experimental investigation. A simulation study of the drymist hydrogen peroxide disinfection model was carried out to understand the diffusion of disinfectant in a room environment. The effectiveness of the developed robot was verified in practical scenarios, such as hospital, hotel, office, and laboratory. The effect of disinfection was validated by a qualified third-party testing agency. Results demonstrate the high efficiency of the developed disinfection robot dedicated to autonomous indoor disinfection work.

Evaluating Supply Chain Collaboration Barriers in Small- and Medium-Sized Enterprises

The disruption has a significant impact on supply chain collaboration (SCC) which is an important task to improve performance for many enterprises. This is especially critical for small- and medium-sized enterprises (SMEs). We developed a decision-modeling framework for analyzing SCC barriers in SMEs for the emerging economy in Bangladesh. Through literature review and expert opinion survey, we have identified a comprehensive list of SCC barriers under four main categories, namely, information-related, communication-related, intra-organizational, and inter-organizational barriers. Then we applied the Grey DEMATEL and Fuzzy Best-Worst methods to evaluate these SCC barriers and compared the results. We also conducted a sensitivity analysis to assess the robustness of the proposed approach. The study reveals that lack of communication is the most crucial barrier in SCC, providing a model for assessing barriers in other emerging economies. This study contributes to the literature by analyzing SCC barriers and by comparing the results obtained from two different MCDM methods. The findings of this study can help decision-makers to plan for overcoming the most prioritized SCC barriers which ultimately contribute to improving the resilience and sustainability performances of SMEs.

Applications of additive manufacturing (AM) in sustainable energy generation and battle against COVID-19 pandemic: The knowledge evolution of 3D printing

Sustainable and cleaner manufacturing systems have found broad applications in industrial processes, especially aerospace, automotive and power generation. Conventional manufacturing methods are highly unsustainable regarding carbon emissions, energy consumption, material wastage, costly shipment and complex supply management. Besides, during global COVID-19 pandemic, advanced fabrication and management strategies were extremely required to fulfill the shortfall of basic and medical emergency supplies. Three-dimensional printing (3DP) reduces global energy consumption and CO2 emissions related to industrial manufacturing. Various renewable energy harvesting mechanisms utilizing solar, wind, tidal and human potential have been fabricated through additive manufacturing. 3D printing aided the manufacturing companies in combating the deficiencies of medical healthcare devices for patients and professionals globally. In this regard, 3D printed medical face shields, respiratory masks, personal protective equipment, PLA-based recyclable air filtration masks, additively manufactured ideal tissue models and new information technology (IT) based rapid manufacturing are some significant contributions of 3DP. Furthermore, a bibliometric study of 3D printing research was conducted in CiteSpace. The most influential keywords and latest research frontiers were found and the 3DP knowledge was categorized into 10 diverse research themes. The potential challenges incurred by AM industry during the pandemic were categorized in terms of design, safety, manufacturing, certification and legal issues. Significantly, this study highlights the versatile role of 3DP in battle against COVID-19 pandemic and provides up-to-date research frontiers, leading the readers to focus on the current hurdles encountered by AM industry, henceforth conduct further investigations to enhance 3DP technology.

New IT driven rapid manufacturing for emergency response

COVID-19, which is rampant around the world, has seriously disrupted people's normal work and living. To respond to public urgent needs such as COVID-19, emergency supplies are essential. However, due to the special requirements of supplies, when an emergency occurs, the supply reserve mostly cannot cope with the high demand. Given the importance of emergency supplies in public emergencies, rapid response manufacturing of emergency supplies is a necessity. The faster emergency supplies and facilities are manufactured, the more likely the pandemic can be controlled and the more human lives are saved. Besides, new generation information technology represented by cloud computing, IoT, big data, AI, etc. is rapidly developing and can be widely used to address such situations. Therefore, rapid response manufacturing enabled by New IT is presented to quickly meet emergency demands. And some policy suggestions are presented.

Investigation of COVID-19 Impact on the Food and Beverages Industry: China and India Perspective

The sudden breakout of coronavirus disease (COVID-19) rapidly spread across the globe, leaving no country behind in being affected by the global pandemic in the year 2019-20. As COVID-19 commenced, within months two major Asian giants initiated the norms of social distancing and lockdowns in their societies. The indiscriminate nature of the current pandemic has not only impacted the health and quality of life of people but has also disrupted the global economy, supply chains, and countries all over the world. In food and beverage manufacturing industries, the unanticipated disruption has encumbered its lock on the global food supply chain and service sector as major cities shut down for several months in China and India. Human existence is dependent upon food, which renders energy for activity, growth, and all functions of the body. Although both China and India have shown eminent response to tackle the ongoing pandemic, the food supply chain remains vastly exposed to significant COVID-19 risks. This research primarily investigates the ongoing COVID-19 scenario in two major economies (China and India), delivering insight into the pandemic's impact within the food and beverage manufacturing sectors, and explores the policies adopted and strategies undertaken for sustainability in food supply chains.