In this study, nanocomposites with microwave absorption capability were synthesized based on cobalt-based MOF/SrFe10CoTiO19/carbon nanofibers. The microstructural, morphological, and magnetic characteristics were examined via X-ray diffraction, field emission scanning electron microscopy, and vibrating sample magnetometer, respectively. The microwave absorption properties of single-layer microwave absorbers were examined in the ku-band frequency range of 12.5–18 GHz. For the single-layer absorbers, the absorption characteristic of nanocomposite of all components is more efficient than that of sample containing just only cobalt-based MOF or doped strontium hexaferrite nanoparticles. The composite containing 70 wt.% cobalt-based MOF, 29 wt.% doped strontium hexaferrite, and 1 wt.% carbon nanofibers nanocomposite reached − 19 dB with 3.9 GHz bandwidth in the range of ku-band with the thicknesses of only 2.5 mm. The addition of carbon nanofibers (CNFs) has increased the interfacial polarization, while dipole polarization enhanced due to interactions between CNFs and Co/C. Capacitor-like structures will be formed due to the dielectric difference between components and so on generates great space-charge polarization. Enhanced in these parameters may be linked to increase in the magnetic and dielectric losses and increase the microwave absorption characteristic. Cobalt-based MOF/SrFe10CoTiO19/carbon nanofiber nanocomposite can be used as a potential candidate for microwave absorbers with strong absorption capability.

Person re-identification (re-ID) presents various applications in surveillance system, but most existing models are proposed under supervised framework. These methods require large amounts of annotated pedestrian data, which limits their scalability and flexibility in a new application scenario. Aiming to relax this limitation, this article exploits the attribute-invariant characteristics and domain correlations into cross-domain person re-ID, while most unsupervised methods only consider the identity features and ignore the different importance of each source image to the target domain. Specifically, this article proposes an Attribute Memory Transfer Network (AMTNet) with two major contributions of domain-balanced memory and attribute-invariant memory modules. The first domain balanced-memory integrates a domain correlation learning method to evaluate the importance of each source image to the target domain, which is involved into the transfer learning; The second attribute-invariant memory can transfer the source attribute knowledge into the target domain with preserving the identity information to conduct the re-ID process. Extensive evaluated experiments elaborate the superiority of AMTNet on two large datasets of Market-1501 and DukeMTMC-reID, compared with hand-crafted and deep learning feature-based methods.