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In order to endow cotton fabric with the electromagnetic shielding property while preserving comfort and softness, carbon nanotubes (CNTs) were coated onto NaOH pretreated fabrics via a binder-free dip-coating approach. Scanning electron microscopy (SEM) and Infrared spectroscopy were utilised to investigate the surface morphology and modification of the CNT functionalised fabrics. The effects of the number of dip-coatings, the concentration of carbon nanotubes, and the impregnation temperature on electrical conductivity, electromagnetic (EM) shielding effectiveness (SE), and wave absorbing efficiency of cotton fabrics were evaluated, respectively. The SE value of the CNT functionalised cotton fabrics increased with the dip-coating time and reached 16.5 dB after CNT dip-coating ten times, which indicates that 97.76% of the electromagnetic wave was shielded. Meanwhile, by adding layers of stacking fabrics, the SE of CNT coated fabrics was further improved to 26.4 dB. The shielding mechanism was also studied by comparing its reflection and absorption behaviour, which demonstrates that 65.7% of the electromagnetic wave was absorbed.

Modern high-dimensional methods often adopt the ‘bet on sparsity’ principle, while in supervised multivariate learning statisticians may face ‘dense’ problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced-rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL is more interpretable than low-rank modelling and relaxes the stringent sparsity assumption in variable selection. In this paper, new information-theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning. Moreover, an efficient optimization algorithm is developed, which performs subspace learning and clustering with guaranteed convergence. The obtained fixed-point estimators, although not necessarily globally optimal, enjoy the desired statistical accuracy beyond the standard likelihood setup under some regularity conditions. Moreover, a new kind of information criterion, as well as its scale-free form, is proposed for cluster and rank selection, and has a rigorous theoretical support without assuming an infinite sample size. Extensive simulations and real-data experiments demonstrate the statistical accuracy and interpretability of the proposed method.

A new cis-caffeoyl coumarin glycoside, cichorigenin-5-O-(4′-cis-caffeoyl)-β-D-glucopyranoside (1), was isolated from the stem barks of Fraxinus depauperata, together with two known coumarins, cichorigenin (2) and esculin (3). The chemical structures of compounds 1–3 were elucidated through spectroscopic and chemical analysis, as well as by comparison with the literature. Compounds 2 and 3 are reported from this species for the first time.