RESUMO
Inspired by nature, superhydrophobic surfaces have been widely studied. Usually the wettability of a superhydrophobic surface is quantified by the macroscopic contact angle. However, this method has various limitations, especially for precision micro devices with superhydrophobic surfaces, such as biomimetic artificial compound eyes and biomimetic water strider robots. These precision micro devices with superhydrophobic surfaces proposed a higher demand for the quantification of contact angles, requiring contact angle quantification technology to have micrometer-scale measurement capabilities. In this review, it is proposed to achieve micrometer-scale quantification of superhydrophobic surface contact angles through droplet adhesion characteristics (adhesion force and contact radius). Existing contact angle quantification techniques and droplet characteristics' measurement methods were described in detail. The advancement of micrometer-scale quantification technology for the contact angle of superhydrophobic surfaces will enhance our understanding of superhydrophobic surfaces.
RESUMO
Tissue biopsy can be wildly used in cancer diagnosis. However, manually classifying the cancerous status of biopsies and tissue origin of tumors for cancerous ones requires skilled specialists and sophisticated equipment. As a result, a data-based model is urgently needed. In this paper, we propose a data-based ensemble model for tumor type identification and cancer origins classification. Our model is an ensemble model that combines different models based on mRNA groups which serve distinct functions. The experiment on the TCGA dataset exhibits a promising result on both tasks - 98% on tumor type identification and 96.1% on cancer origin classification. We also test our model on external validation datasets, which prove the robustness of our model.
