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Graphical abstract 4D printing is inspired by embedded product designs to produce stimuli-responsive consumables fabricated by available commercial 3D printers. Although significant progress on smart material performance has been made and different studies have focused on new strategies and process improvements in typical additive manufacturing. Herein, the proposed review article discusses material arrangements for 4D printing, highlighting the structural evolvement mechanism, the behavior of deformation, and their prospective implementation with respect. Starting from a generalized idea, and fundamental workflow, together with a graphical manifestation of the 4D printing concept, and 4D printing for shape-memory materials (SMMs), self-fitting wearables based on shape memory alloys (SMAs) are reviewed exclusively. Furthermore, the capabilities of single and multiple materials mechanisms for shape-shifting behavior are summarized. Finally, we explored the future application potential under succeeding context: SMA-based knitted garments, transforming food, and relevant sectors wise development and proceedings with the advancement in smart materials. We determined our review by aiming our future directions such as the “dream it and make it feasible” technology.

One of the key challenges in the area of signal processing on graphs is to design transforms and dictionary methods to identify and exploit structure in signals on weighted graphs. In this paper, we first generalize graph Fourier transform (GFT) to spectral graph fractional Fourier transform (SGFRFT), which is then used to define a novel transform named spectral graph fractional wavelet transform (SGFRWT), which is a generalized and extended version of spectral graph wavelet transform (SGWT). A fast algorithm for SGFRWT is also derived and implemented based on Fourier series approximation. Some potential applications of SGFRWT are also presented.