Humidifying membranes with ultrafast water transport and evaporation play a vital role in indoor humidification that improves personal comfort and industrial productivity in daily life. However, commercial nonwoven (NW) humidifying membranes show mediocre humidification capability owing to limited wicking capacity, low water absorption, and relatively less water evaporation. Herein, we report a biomimetic micro-/nanofibrous composite membrane with a highly aligned fibrous structure using a humidity-induced electrospinning technique for high-efficiency indoor humidification. Surface wettability and roughness are also tailored to achieve a high degree of superhydrophilicity by embedding hydrophilic silicon dioxide nanoparticles (SiO 2 NPs) into the fiber matrix. The synergistic effect of the highly aligned fibrous structure and surface wettability endows composite membranes with ultrafast water transport and evaporation. Strikingly, the composite membrane exhibits an outstanding wicking height of 19.5 cm, a superior water absorption of 497.7%, a fast evaporation rate of 0.34 mL h −1 , and a relatively low air pressure drop of 14.4 Pa, thereby achieving a remarkable humidification capacity of 514 mL h −1 (57% higher than the commercial NW humidifying membrane). The successful synthesis of this biomimetic micro-/nanofibrous composite membrane provides new insights into the development of micro-/nanofibrous humidifying membranes for personal health and comfort as well as industrial production.
In this paper, two different types of neural networks are investigated and employed for the online solution of strictlyconvex quadratic minimization; i.e., a two-layer back-propagation neural network (BPNN) and a discrete-time Hopfield-type neural network (HNN). As simplified models, their error-functions could be defined directly as the quadratic objective function, from which we further derive the weight-updating formula of such a BPNN and the state-transition equation of such an HNN. It is shown creatively that the two derived learning-expressions turn out to be the same (in mathematics), although the presented neural-networks are evidently different from each other a great deal, in terms of network architecture, physical meaning and training patterns. Computer-simulations further substantiate the efficacy of both BPNN and HNN models on convex quadratic minimization and, more importantly, their common nature of learning.Index Terms-Convex quadratic minimization, BP neural networks, Hopjield networks, common nature of learning.
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