Scarcity nationalism during COVID-19: Identifying the impact on trade costs.

During the COVID-19 pandemic, many countries used export and import policy as a tool to expand the availability of scarce critical medical products in the domestic market (scarcity nationalism). This paper assesses the direct and indirect (via trade in intermediates) increases in trade costs of critical medical goods resulting from these uncooperative policies. The results show that scarcity nationalism led to substantial increases in trade costs between February 2020 and December 2021 for most COVID-19 critical medical products, particularly garments (for example, face masks) and ventilators. The exception is vaccines, which saw a reduction in trade costs, which, however, was driven by the reduction in indirect trade costs for high-income countries, consistent with the view of a COVID-19 vaccine production club.

Osmotic Cloud-Edge Intelligence for IoT-Based Cyber-Physical Systems.

Artificial Intelligence (AI) in Cyber-Physical Systems allows machine learning inference on acquired data with ever greater accuracy, thanks to models trained with massive amounts of information generated by Internet of Things devices. Edge Intelligence is increasingly adopted to execute inference on data at the border of local networks, exploiting models trained in the Cloud. However, the training tasks on Edge nodes are not supported yet with flexible dynamic migration between Edge and Cloud. This paper proposes a Cloud-Edge AI microservice architecture, based on Osmotic Computing principles. Notable features include: (i) containerized architecture enabling training and inference on the Edge, Cloud, or both, exploiting computational resources opportunistically to reach the best prediction accuracy; and (ii) microservice encapsulation of each architectural module, allowing a direct mapping with Commercial-Off-The-Shelf (COTS) components. Grounding on the proposed architecture: (i) a prototype has been realized with commodity hardware leveraging open-source software technologies; and (ii) it has been then used in a small-scale intelligent manufacturing case study, carrying out experiments. The obtained results validate the feasibility and key benefits of the approach.

Trade policy responses to the COVID-19 pandemic crisis: Evidence from a new data set.

This paper presents new high-frequency data on trade policy changes targeting medical and food products since the beginning of the COVID-19 pandemic, documenting how countries used trade policy instruments in response to the health crisis on a week-by-week basis. The data set reveals a rapid increase in trade policy activism in February and March 2020 in tandem with the rise in COVID-19 cases but also uncovers extensive heterogeneity across countries in both their use of trade policy and the types of measures used. Some countries acted to restrict exports and facilitate imports, others targeted only one of these margins, and many did not use trade policy at all. The observed heterogeneity suggests numerous research questions on the drivers of trade policy responses to COVID-19, on the effects of these measures on trade and prices of critical products, and on the role of trade agreements in influencing the use of trade policy.

Pandemic trade: COVID-19, remote work and global value chains.

This paper studies the trade effects of COVID-19 using monthly disaggregated trade data for 28 countries and multiple trading partners from the beginning of the pandemic to June 2020. Regression results based on a sector-level gravity model show that the negative trade effects induced by COVID-19 shocks varied widely across sectors. Sectors more amenable to remote work contracted less throughout the pandemic. Importantly, participation in global value chains increased traders' vulnerability to shocks suffered by trading partners, but it also reduced their vulnerability to domestic shocks.

Knowledge-Based Decision Support in Healthcare via Near Field Communication.

The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient's case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care.