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Modelling of multi-wavelength metasurfaces relies on adjusting the phase of indi-vidual nanoresonators at several wavelengths.The traditional procedure neglects thenear-field coupling between the nanoresonators, which dramatically reduces the over-all diffraction efficiency, bandwidth, numerical aperture and device diameter.Anotheralternative design strategy is to combine a numerical optimization technique with full-wave simulations to mitigate this problem and optimize the entire metasurface at once.Here, we present a global multiobjective optimization technique that utilizes statisticallearning method to optimize RGB spherical metalenses at the visible wavelengths. Theoptimization procedure, coupled to a high-order full-wave solver, accounts for the nearfield coupling between the resonators. High numerical aperture RGB lenses(NA= 0.47and NA= 0.56) of 8μm and 10μm diameters are optimized with numerical average1focusing efficiencies of 55% and 45%, with an average focusing error smaller than 6%for the RGB colors. The fabricated and experimentally characterized devices present44.16% and 31.5% respective efficiencies. The reported performances represent thehighest focusing efficiencies for highNA >0.5 RGB metalenses obtained so far. Theintegration of multi-wavelength metasurfaces in portable and wearable electronic de-vices requires high performances to offer a variety of applications ranging from classicalimaging to virtual and augmented reality.

We clarify some analytical expressions existing in the literature, • Within the correct formulae we conclude that there is no need for an ad hoc improvement on the opposite to the title paper, • We highlight the symmetry properties of the function to be integrated in order to agree with the usual assumptions made to derive the Kramers-Kronig relations, • The analytical formula we provide may be used to increase the accuracy of the "Poor Man's Kramers-Kronig analysis" method and the "Kramers-Kronig constrained variational analysis" method.

Water transport in plant roots is of vital importance: it is a necessary transport to feed the rest of the organism in most vascular plants. To reach the xylem vessels, which ensure the long-distance transport to the aerial parts of the plant, water has first to flow across the root tissues surrounding the xylem. This flow, denoted to as radial transport, is not easily amenable to the experimentation, and has been studied mostly by measurements at a larger scale, and by models that poorly take into account cells and roots geometries. We adopted a continuous description of stationary root radial water transport to investigate how the geometry and the permeability contrasts between root compartments affect the transport of water. We experimentally modeled the root radial section as a two-dimensional and composite porous material with variable water permeabilities. It mimics the most salient water transport features of the root anatomy and allows a direct isualization of the water pathways. We also present 2D continuous numerical simulations of the water flow, in which we systematically varied the permeabilities of the different tissues. Our approach provides the physical premises to explain preferential sub-cellular radial routes from one cell to another and look for the subcellular pattern of structures or molecules involved in water transport.