Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

/pdf/industry-4-0-disruption-and-its-neologisms-in-major-4316cu9gbs.pdf

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Background: Since the beginning of the coronavirus disease 2019 (COVID-19) outbreak, healthcare workers (HCWs) have been the workers most likely to contract the disease. Intensive focus is therefore needed on hospital strategies that minimize exposure and diffusion, confer protection and facilitate early detection and isolation of infected personnel.

Methods: To evaluate the early impact of a structured risk-management for exposed COVID-19 HCWs and describe how their characteristics contributed to infection and diffusion. Socio-demographic and clinical data, aspects of the event-exposure (date, place, length and distance of exposure, use of PPE) and details of the contact person were collected.

Findings: The 2411 HCWs reported 2924 COVID-19 contacts. Among 830 HCWs who were at ‘high or medium risk’, 80 tested positive (9.6%). Physicians (OR=2.03), and non-medical services resulted in an increased risk (OR=4.23). Patient care did not increase the risk but sharing the work environment did (OR=2.63). There was a significant time reduction between exposure and warning, exposure and test, and warning and test since protocol implementation. Hospital managers were the main source of infection due to the high number of interactions.

Interpretation: A proactive system that includes prompt detection of contagious staff and identification of sources of exposure helps to lower the intra-hospital spread of infection. A speedier return to work of staff who would otherwise have had to self-isolate as a precautionary measure improves staff morale and patient care by reducing the stress imposed by excessive workloads arising from staff shortages.

Funding Statement: This research was funded by University of Torino.

Declaration of Interests: All the authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Ethics Approval Statement: All data were anonymized, and the hospital directorate approved data collection and analysis. The research followed ethical principles for medical research involving human subjects expressed by the Declaration of Helsinki.

/pdf/covid-19-infection-and-diffusion-among-the-healthcare-1665usdcfl.pdf

COVID-19 Infection and Diffusion Among the Healthcare Workforce in a Large University-Hospital in Northwest Italy

Backgroud: Since the beginning of the coronavirus disease 2019 (COVID-19) outbreak, healthcare workers (HCWs) have been the workers most likely to contract the disease. Intensive focus is therefore needed on hospital strategies that minimize exposure and diffusion, confer protection and facilitate early detection and isolation of infected personnel. Methods To evaluate the early impact of a structured risk-management for exposed COVID-19 HCWs and describe how their characteristics contributed to infection and diffusion. Socio-demographic and clinical data, aspects of the event-exposure (date, place, length and distance of exposure, use of PPE) and details of the contact person were collected. Results The 2411 HCWs reported 2924 COVID-19 contacts. Among 830 HCWs who were at 'high or medium risk', 80 tested positive (9.6%). Physicians (OR=2.03), and non-medical services -resulted in an increased risk (OR=4.23). Patient care did not increase the risk but sharing the work environment did (OR=2.63). There was a significant time reduction between exposure and warning, exposure and test, and warning and test since protocol implementation. HCWs with management postitions were the main source of infection due to the high number of interactions. Discussion A proactive system that includes prompt detection of contagious staff and identification of sources of exposure helps to lower the intra-hospital spread of infection. A speedier return to work of staff who would otherwise have had to self-isolate as a precautionary measure improves staff morale and patient care by reducing the stress imposed by excessive workloads arising from staff shortages.

COVID-19 infection and diffusion among the healthcare workforce in a large university-hospital in northwest Italy.

Non-occupational and occupational factors associated with specific SARS-CoV-2 antibodies among hospital workers - A multicentre cross-sectional study.

Very well into the dawn of the fourth industrial revolution (industry 4.0), we hardly distinguish between what is artificial and what is natural (e.g. man-made virus and natural virus). Thus, the level of discombobulation among people, companies or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector, makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption in the industrial sectors remains ill-defined in both academic and non-academic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technologies convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in Education industry (Education 4.0), Energy industry (Energy 4.0), Agriculture industry (Agriculture 4.0), Healthcare industry (Healthcare 4.0), and Logistics industry (Logistics 4.0) are described. The convergence of 12 disruptive technologies including 3D printing, Artificial intelligence, Augmented reality, Big Data, Blockchain, Cloud computing, Drones, Internet of things, Nanotechnology, Robotics, Simulation and Synthetic biology in agriculture, healthcare and logistics industries are illustrated.

/pdf/the-hype-and-disruptive-technologies-of-industry-4-0-in-3khci6ruqo.pdf

The Hype and Disruptive Technologies of Industry 4.0 in Major Industrial Sectors: A State of the Art

Sources of viral respiratory infections in Canadian acute care hospital healthcare personnel.

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Sources of viral respiratory infections in Canadian acute care hospital healthcare personnel.