COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long /Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.
In this work, near-zero-index material boundary properties have been exploited to achieve new electromagnetic functionalities. The extraordinary guiding properties of a cylindrical dielectric rod waveguide surrounded by a thin epsilon-mu-near-zero shell is analyzed and discussed. A closed-form solution for the dispersion equation has been developed, able to model and design such properties at will. Analytical and numerical results will confirm that the use of near-zero cover materials leads to extraordinary properties in terms of field configurations, low attenuation, and bandwidth. The dielectric wire acts as an efficient "waveguide" with great potentials for advance nanocircuit and electronics.
Artificial sheet materials, known as MetaSurfaces, have been applied to fully control both space and surface waves due to their exceptional abilities to dynamically tailor wave fronts and polarization states, while maintaining small footprints. However, previous and current designs and manufactured MetaSurfaces are limited to specific types of surfaces. There exists no general but rigorous design methodology for MetaSurfaces with generic curvature. The aim of this paper is to develop an analytical approach to characterize the wave behavior over arbitrary curvilinear MetaSurfaces. The proposed method allows us to fully characterize all propagating and evanescent wave modes from the MetaSurfaces. We will validate the proposed technique by designing, realizing and testing an ultrathin MetaSurface cloak for surface waves. Good results are obtained in terms of bandwidth, polarization independence and fabrication simplicity.
A modeling and design approach is proposed for nanoparticle-based electromagnetic devices. First, the structure properties were analytically studied using Maxwell’s equations. The method provides us a robust link between nanoparticles electromagnetic response (amplitude and phase) and their geometrical characteristics (shape, geometry, and dimensions). Secondly, new designs based on “metamaterial” concept are proposed, demonstrating great performances in terms of wide-angle range functionality and multi/wide behavior, compared to conventional devices working at the same frequencies. The approach offers potential applications to build-up new advanced platforms for sensing and medical diagnostics. Therefore, in the final part of the article, some practical examples are reported such as cancer detection, water content measurements, chemical analysis, glucose concentration measurements and blood diseases monitoring.
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