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Chemical fibers such as glass fiber and aramid cover a large proportion of sound-absorbing composite materials on the current commercial market. These materials possess superior mechanical properties but have the disadvantages of high production costs and energy consumption and difficult recovery and degradation. In this paper, jute fiber and polypropylene were selected as raw materials, and a series of jute fiber-reinforced polypropylene composite materials were prepared by a mixing-hot-pressing process. The acoustic and mechanical performances of the composites with different fiber contents and fiber residue ratios were discussed. The results showed that the sound absorption coefficient values increased with the increasing fiber content and decreasing residual gum ratio. The mechanical properties varied inversely with the residual gum rate. With the increase of fiber content, the tensile and bending strengths first increased and then decreased. Therefore, the jute fiber-reinforced polypropylene composites can possess favorable sound absorption performance with no mechanical property penalty by adjusting the parameters properly, demonstrating that the composite materials have promising applications in the acoustic field.

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.

The strain of fiber-reinforced polymer (FRP) bars at high temperatures is currently difficult to measure. To overcome this difficulty, a method of smart FRP bars embedded with optical fibers was proposed and studied, in which an ordinary single-mode optical fiber was applied as a distributed sensor. In this paper, both the distributed temperature and strain-sensing characteristics of optical fiber were studied based on pulse pre-pump Brillouin optical time-domain analysis (PPP-BOTDA) under high temperature. The temperature and strain coefficients were investigated under a thermomechanical coupling environment with consideration of large strain levels. The experimental results show that the temperature and strain coefficients decreased as the temperature increased, because the properties of silica and coating materials changed with temperature. Then, the formulas for determining the temperature and strain coefficients at high temperatures were introduced and discussed. The excellent sensing performance of the optical fiber indicated that smart FRP bars have the potential for use at high temperatures.

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.

Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real‐time functional MRI (rtfMRI)‐based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI‐based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post‐training behavior. Our analysis revealed an “ROI‐network‐behavior” correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self‐regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real‐time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks. Hum Brain Mapp 36:1705–1715, 2015. © 2014 Wiley Periodicals, Inc.