The COVID-19 pandemic is an example of a temporary situation when critical infrastructure (CI) operators had to operate with continuously changing conditions. The role of cyber infrastructure during pandemics, for example for the remote work or access to critical systems, has also changed.

The fact than more and more Cyber-Physical or IoT systems are integrated in vehicle fleets, coupled with the growing connectivity of these devices, poses a higher risk of cyber attacks, due to the increased attack surface.

With the increased digitalization and adoption of cost-effective off-the-shelf components and cyber connectivity, Critical Infrastructures (CI) operators have benefited in many ways, but the attack surface has also become larger.

Data scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data).

In the field of plant disease protection, many approaches exist, but all acknowledge the necessity of fast and accurate identification of the source in order to make the most efficient applications.

In general, one of the complexities of large simulations is related to the usage of the heterogeneous computational resources that are needed to execute them. The definition of workflows, usually linked to concrete orchestrations solutions, has reduced most of that complexity.

Accurate digital twinning of the global challenges (GC) leads to computationally expensive coupled simulations.