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Ice formation and accretion on surfaces is a serious economic issue in energy supply and transportation. Recent strategies for developing icephobic surfaces are intimately associated with superwettability. Commonly, the superwettability of icephobic materials depends on their surface roughness and chemical composition. This article critically categorizes the possible strategies to mitigate icing problems from daily life. The wettability and classical nucleation theories are used to characterize the icephobic surfaces. Thermodynamically, the advantages/disadvantages of superhydrophobic surfaces are discussed to explain icephobic behavior. The importance of elasticity, slippery liquid-infused porous surfaces (SLIPSs), amphiphilicity, antifreezing protein, organogels, and stimuli-responsive materials has been highlighted to induce icephobic performance. In addition, the design principles and mechanism to fabricate icephobic surfaces with superwettability are explored and summarized.

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Pati and Krishnaprasad (1990) first studied the connection between neural networks and wavelet transforms. Zhang and Benveniste (1992) gave a different treatment of this connection. However, the problem of constructing multidimensional wavelet frames for use in neural networks has not been satisfactorily studied. In this paper, one-dimensional wavelet frame is generalized to the multidimensional case by using single-scaling and multiscaling parameters. The construction of multidimensional wavelet frames is also discussed. These results provide more insight on the use of wavelets in neural networks.