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Rechargeable aqueous Zn-ion batteries (ZIBs) are deemed as powerful candidates for large-scale energy-storage systems because of their intrinsic safety, inexpensive cost, and environment friendliness. However, the performance degeneration of ZIBs occurs by virtue of structural instability and uncompetitive conductivity of cathode materials. In this work, a vanadium oxide of Li0.21Mn1.44V8O20·0.55H2O (4-MLVO) cathode was successfully synthesized for the first time by optimizing the ratio of Mn/V. Intriguingly, the as-assembled traditional aqueous hybrid Li+/Zn2+ batteries based on the Li0.21Mn1.44V8O20·0.55H2O cathode, 1 M Li2SO4 + 2 M ZnSO4 electrolyte, and Super P@Zn anode possess superior performance of 350.0 mA h g–1 at the current density of 0.1 A g–1 and can accomplish 6000 cycles with 147.5 mA h g–1 at 10.0 A g–1. Moreover, the outstanding flexibility of sandwiched quasi-solid-state Zn//4-MLVO batteries with the CMC-Na+/PAM hydrogel electrolyte is demonstrated by multifarious deformation tests. The quasi-solid-state batteries at a bending state can achieve a high open voltage of 1.026 V and a specific capacity of 184.8 mA h g–1 at 0.1 A g–1. This work offers thoughts for the devisal of aqueous hybrid ZIB cathode materials and lays a foundation for advanced flexible energy devices.

Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users’ indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of Wi-Fi signals. When pedestrians block the signals between the transceivers, the non-line-of-sight (NLOS) transmission occurs. It should be noted that NLOS has been a significant factor restricting the device-free positioning accuracy due to signal reduction and abnormalities during multipath propagation. For this problem, we analyzed the NLOS effect in an indoor environment and found that the position error in the LOS condition is different from the NLOS condition. Then, two empirical models, namely, a CSI passive positioning model and a CSI NLOS/LOS detection model, have been derived empirically with extensive study, which can obtain better robustness identified results in the case of NLOS and LOS conditions. An algorithm called SVM-NB (Support Vector Machine-Naive Bayes) is proposed to integrate the SVM NLOS detection model with the Naive Bayes fingerprint method to narrow the matching area and improve position accuracy. The NLOS identification precision is better than 97%. The proposed method achieves localization accuracy of 0.82 and 0.73 m in laboratory and corridor scenes, respectively. Compared to the Bayes method, our tests showed that the positioning accuracy of the NLOS condition is improved by 28.7% and that of the LOS condition by 26.2%.