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We propose a boron–rhodamine-containing carboxylic acid (BRhoC) substance as a new sugar chemosensor. BRhoC was obtained by the Friedel–Crafts reaction of 4-formylbenzoic acid and N,N-dimethylphenylboronic acid, followed by chloranil oxidation. In an aqueous buffer solution at pH 7.4, BRhoC exhibited an absorption maximum (Absmax) at 621 nm. Its molar absorption coefficient at Absmax was calculated to be 1.4 × 105 M−1 cm−1, and it exhibited an emission maximum (Emmax) at 644 nm for the excitation at 621 nm. The quantum yield of BRhoC in CH3OH was calculated to be 0.16. The borinate group of BRhoC reacted with a diol moiety of sugar to form a cyclic ester, which induced a change in the absorbance and fluorescence spectra. An increase in the D-fructose (Fru) concentration resulted in the red shift of the Absmax (621 nm without sugar and 637 nm with 100 mM Fru) and Emmax (644 nm without sugar and 658 nm with 100 mM Fru) peaks. From the curve fitting of the plots of the fluorescence intensity ratio at 644 nm and 658 nm, the binding constants (K) were determined to be 2.3 × 102 M−1 and 3.1 M−1 for Fru and D-glucose, respectively. The sugar-binding ability and presence of a carboxyl group render BRhoC a suitable building block for the fabrication of highly advanced chemosensors.

For complex large scale networks, like social networks, it is usually impossible to observe complete information about their topology structure or link weight directly. A recent proposal, the network resonance method, can estimate the eigenvalues and eigenvectors of the Laplacian matrix for representing network structure, by using the resonance phenomena of oscillation dynamics on networks. However, it is generally not possible to observe all the eigenvalues and eigenvectors. This paper uses compressed sensing to create a new method of reconstructing the original Laplacian matrix from a partial set of its eigenvalues and eigenvectors. Since very few node pairs in social networks have links, we can expect that compressed sensing will be effective. The estimated Laplacian matrix of a social network enables to us to determine its structure and link weights.