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The rapid development of technology and innovation has made the performance of the new-generation machinery generally better than that of retired machinery. Simply using remanufacturing technology to bring retired machinery back into specifications of previous life will lead to reduced demands from consumers because of the out-of-date functioning remanufactured products. This technology gap (TG) is rarely considered in existing researches. This paper proposes an emergy based sustainability assessment model for retired machinery to solve this problem. Firstly, the TG between retired and new-generation machinery is quantified as functional devaluation from the perspectives of energy, environment and society through emergy theory. Secondly, the added value of retired machinery is derived from the evaluation of remanufacturing cost. Among them, the remanufacturing cost is predicted based on the BP Neural Networks. Then, this paper combines the functional devaluation and added value of retired mechanery to calculate the sustainability indicator. Finally, the feasibility of this study was verified by the sustainability evaluation of the WD615.50 diesel engine, and the results show that the WD615.50 diesel engine has no potential for re-service. • The functional devaluation of retired machinery is proposed by emergy theory. • The added value and sustainability of retired machinery are analyzed. • The intrinsic connection between technological progress and remanufacturing behavior is revealed. [ABSTRACT FROM AUTHOR]

• An efficient sampling strategy optimization method with high-quality FSPI reconstruction is implemented. • Numerical Simulations and experiments proved the effectiveness, which has advantages over deterministic sampling strategies. • The geometric topologies of the optimal sampling strategy for different categories of datasets are analyzed. • The geometric topologies of the optimal sampling strategy for different categories of datasets are summarized. • It provides subsequent for sampling strategy choice of Fourier single pixel imaging. It is necessary to artificially set the sampling strategy of Fourier single-pixel imaging (FSPI) to obtain more useful information of the image under the assumption that the image information is mainly concentrated in the low-frequency part of the spatial frequency domain. However, due to the empiricism sampling strategy, it is easy to sample the spatial frequencies with low contribution to image reconstruction, resulting in a waste of sampling resources. To solve this problem, a one-stage FSPI reconstruction network with sampling strategy optimization is designed to obtain the efficient sampling strategy and improve the reconstruction quality. The proposed one-stage FSPI reconstruction network contains the sampling strategy optimization module and reconstruction module, which are jointly trained to obtain high-quality reconstructed results and efficient sampling strategy simultaneously. Experiments and numerical simulations demonstrate the effectiveness of the proposed method. Moreover, the geometric topology of the optimal sampling strategy for FSPI reconstruction trained on various categories of datasets is summarized. It is found that the dataset with similar categories have the similar geometric topology of the optimal sampling strategy, which provides support for sampling strategy selection in subsequent Fourier single-pixel imaging studies. [ABSTRACT FROM AUTHOR]