Brain-Computer Interfaces (BCIs) have significantly improved the patients’ quality of life by restoring damaged hearing, sight, and movement capabilities. After evolving their application scenarios, the current trend of BCI is to enable new innovative brain-to-brain and brain-to-the-Internet communication paradigms. This technological advancement generates opportunities for attackers, since users’ personal information and physical integrity could be under tremendous risk. This work presents the existing versions of the BCI life-cycle and homogenizes them in a new approach that overcomes current limitations. After that, we offer a qualitative characterization of the security attacks affecting each phase of the BCI cycle to analyze their impacts and countermeasures documented in the literature. Finally, we reflect on lessons learned, highlighting research trends and future challenges concerning security on BCIs.

Respiratory viruses including Respiratory syncytial virus (RSV), influenza virus and cornaviruses such as Middle Eastern respiratory virus (MERS) and SARS-CoV-2 infect and cause serious and sometimes fatal disease in thousands of people annually. It is critical to understand virus propagation dynamics within the respiratory system because new insights will increase our understanding of virus pathogenesis and enable infection patterns to be more predictable in vivo, which will enhance targeting of vaccines and drug delivery. This study presents a computational model of virus propagation within the respiratory tract network. The model includes the generation network branch structure of the respiratory tract, biophysical and infectivity properties of the virus, as well as air flow models that aid the circulation of the virus particles. The model can also consider the impact of the immune response aim to inhibit virus replication and spread. The model was applied to the SARS-CoV-2 virus by integrating data on its life-cycle, as well as density of Angiotensin Converting Enzyme (ACE2) expressing cells along the respiratory tract network. Using physiological data associated with the respiratory rate and virus load that is inhaled, the model can improve our understanding of the concentration and spatiotemporal dynamics of virus

No abstract

While metasurface-based intelligent reflecting surfaces (IRS) are an important emerging technology for future generations of wireless connectivity in its own right, plans for the mass deployment of these surfaces motivate the question of their integration with other new and emerging technologies that would require such widespread deployment. This question of integration and the vision of future communication systems as an invaluable component for public health motivated our new concept of Intelligent Reflector-Viral Detectors (IR-VD). In this novel scheme, we propose deployment of intelligent reflectors with strips of receptor-based viral detectors placed between the reflective surface tiles. Our proposed approach encodes information of the presence of the virus by flicking the angle of the reflected beams, using time variations between the beam deviations to represent the messages. This information includes the presence of the virus, its location and load size. The paper presents simulations to demonstrate the encoding process that represents the number of virus particles that have bound to the IR-VD.