We study the spontaneous emission of an atom embedded in a one-dimensional photonic crystal or superlattice using a classical electrodynamic theory of radiation. The rate of emission is a function of the frequency of the emitted photon, the dipole's position and orientation, as well as the geometric and material parameters of the superlattice. The emission spectrum shows an oscillatory behavior which follows the photonic band structure. For TE modes, there are frequency regions where radiative emission is completely prohibited due to the absence of modes with k//>omega/c; the radiation is then TM polarized. In addition to the radiative modes, there are always evanescent modes with k//>omega/c which are waveguided by the dielectric layers. The evanescent contribution to the spontaneous emission is dominant if a dielectric layer is in the near field region of the dipole. For TM modes, emission rates greatly vary for parallel and perpendicular dipole moments. In a photonic crystal with a high filling fraction of the dielectric and perpendicular dipoles located in the low-index layer, the decay rate can be as much as 76 times the free space value for a single atom and 50 times for a gas of atoms. We also find that the rate of emission presents a strong dependence on the atom's position.

As in a recent paper [I. Alvarado-Rodríguez, P. Halevi, and Adán S. Sánchez, Phys. Rev. E 63, 056613 (2001); 65, 039901(E) (2002)], we study the power emitted by an oscillating dipole in a superlattice (SL) modeled by means of a periodic distribution of Dirac-delta functions (Dirac-comb SL). However, while in the aforementioned paper the radiation was restricted to the transverse electric (TE) polarization mode, here we focus our attention on the transverse magnetic (TM) mode. Employing the same methodology, again we find that the power spectra are dominated by slope discontinuities. These occur - if at all - at the band edges for on-axis propagation, depending on the dipole's position and orientation. The largest enhancement or inhibition is present for normalized frequencies such that (omegad/c) less, similar 2pi; here, omega is the dipole frequency, c is the speed of light in vacuum, and d is the distance between the barriers. For substantial values of the grating strength considerable enhancement or suppression of the radiated power (in comparison to the free-space value) is obtained. We also find that the power emitted by a gas of randomly oriented dipoles exhibits slope discontinuities at all band edges for on-axis propagation. In comparison with the TE polarization case, the TM polarization exhibits several different qualitative features.

We simulate the tuning of the optical reflectance in one-dimensional photonic crystals. Two monolithic superlattices are considered: Intrinsic InSb/air and extrinsic n-type Si/air, both with high densities of free-electron plasmas. The tuning is achieved, respectively, by varying the temperature and the donor concentration. In our modeling, we have taken into account both dispersion and absorption for the electrons, the holes, and the phonons. Our realistic simulation demonstrates that very strong sensitivity of the optical response is achievable.

The novel coronavirus SARS-CoV-2 that causes the disease COVID-19 has forced us to go into our homes and limit our physical interactions with others. Economies around the world have come to a halt, with non-essential businesses being forced to close in order to prevent further propagation of the virus. Developing countries are having more difficulties due to their lack of access to diagnostic resources. In this study, we present an approach for detecting COVID-19 infections exclusively on the basis of self-reported symptoms. Such an approach is of great interest because it is relatively inexpensive and easy to deploy at either an individual or population scale. Our best model delivers a sensitivity score of 0.752, a specificity score of 0.609, and an area under the curve for the receiver operating characteristic of 0.728. These are promising results that justify continuing research efforts towards a machine learning test for detecting COVID-19.

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