This article introduces a novel computer-aided procedure to design optimised coated structures with precise shell thickness control using the Smallest Univalue Segment Assimilating Nucleus operator and a novel augmentation-projection technique. Structures with heterogeneous sections, or coated structures, combine two different materials for the nucleus and the shell, which are generally chosen so that the material in the infill is lighter and the material in the coating is stiffer, which in this work are supposed homogeneous. Solving the interface problem requires material properties interpolation equations that consider three material phases, accurate placement of the coating over the base material, and precise control over the coating's thickness. The formation of the coating is controlled by the Smallest Univalue Segment Assimilating Nucleus, an edge detection operator developed in Digital Image Processing. The coating's thickness is controlled by an innovative methodology consisting of the projection of an augmented contour field, which is shown to create a constant thickness coating around the material domain. The optimisation problem is solved with the Sequential Element Rejection and Admission method. The validity of the procedure has been verified by solving various numerical application examples.

Summary: GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods. Availability and Implementation: GENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.