In order to solve the optimization problem of emergency logistics system, this paper provides an environmental protection point of view and combines with the overall optimization idea of emergency logistics system, where a fuzzy low-carbon open location-routing problem (FLCOLRP) model in emergency logistics is constructed with the multi-objective function, which includes the minimum delivery time, total costs and carbon emissions. Taking into account the uncertainty of the needs of the disaster area, this article illustrates a triangular fuzzy function to gain fuzzy requirements. This model is tackled by a hybrid two-stage algorithm: Particle swarm optimization is adopted to obtain the initial optimal solution, which is further optimized by tabu search, due to its global optimization capability. The effectiveness of the proposed algorithm is verified by the classic database in LRP. What’s more, an example of a post-earthquake rescue is used in the model for acquiring reliable conclusions, and the application of the model is tested by setting different target weight values. According to these results, some constructive proposals are propounded for the government to manage emergency logistics and for the public to aware and measure environmental emergency after disasters.

Traditional digital camouflage is mainly designed for a single background and state. Its camouflage performance is appealing in the specified time and place, but with the change of place, season, and time, its camouflage performance is greatly weakened. Therefore, camouflage technology, which can change with the environment in real-time, is the inevitable development direction of the military camouflage field in the future. In this paper, a fast-self-adaptive digital camouflage design method based on deep learning is proposed for the new generation of adaptive optical camouflage. Firstly, we trained a YOLOv3 model that could identify four typical military targets with mean average precision (mAP) of 91.55%. Secondly, a pre-trained deepfillv1 model was used to design the preliminary camouflage texture. Finally, the preliminary camouflage texture was standardized by the k-means algorithm. The experimental results show that the camouflage pattern designed by our proposed method is consistent with the background in texture and semantics, and has excellent camouflage performance in optical camouflage. Meanwhile, the whole pattern generation process takes a short time, less than 0.4 s, which meets the camouflage design requirements of the near-real-time camouflage in the future.

A highly transparent polarization-insensitive metamaterial absorber with wideband microwave absorption is presented. The broadband absorption (6.0~16.7 GHz, absorptance > 85%) is achieved using three patterned resistive metasurfaces. The visible light transmittance of the absorber is as high as 85.7%. The thickness of the absorber is 4.42 mm, which is only 0.088 times of the upper-cutoff wavelength. A prototype sample is fabricated and measured to demonstrate its excellent performance. The experimental results agree well with the simulation results. In view of its wide band absorption, high transmittance, low profile, polarization insensitivity and wide incidence angle stability, the presented absorber has a wide range of potential applications.

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A transparent metamaterial absorber with broadband microwave absorption and polarization insensitiveness is presented in this paper. Consisting of a two-layer closed square ring, one-layer patch-shaped indium tin oxide films, and a three-layer soda-lime glass substrate, the proposed absorber has advantages of broadband absorption with absorptivity higher than 85% ranging from 4.6 to 18 GHz, transparency, good polarization insensitiveness, wide-incident-angle stability, and high shielding efficiency. A prototype sample is fabricated and measured to demonstrate its excellent performance. The experimental results agree well with the numerical simulations.