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The COVID-19 pandemic has caused an unprecedented human and health crisis. And the shortage of protective equipment, especially the personal protective disposable surgical masks, has been a great challenge. Here, we developed an effective and simple scheme to prolong the lifetime of disposable surgical masks without changing their current structure, which is beneficial to solve the shortage of personal masks. After electrifying the meltblown PP filter by the new-developed single-electrode-based sliding triboelectric nanogenerator (TENG) charge replenishment (NGCR) technology, the processed filter is bipolar charged and has a filtration efficiency beyond 95% for the particulate matter (PM) ranging from PM 0.3 to PM 10.0 . Further, we demonstrate the 80 °C dry heating is an effective decontamination method. This method is compatible with single-electrode-based sliding TENG charge replenishment technology. The 80 °C dry heating and the NGCR technology can make up an effective regeneration procedure for the mask. Even after ten cycles of simulated 4-hour wearing process and such regeneration procedure, the filtration efficiency of the disposable surgical masks PM 0.3 is still higher than 95%.

In this paper, we propose a novel buffer-state-based relaying selection scheme in the context of cooperative cognitive radio networks (CRNs), supporting the primary and secondary networks. In the proposed scheme, both the effects of inter-network interference and fading are successfully suppressed by introducing a flexible link selection algorithm in the secondary network. More specifically, by relying on the broadcast nature of wireless communication channels between a source node and relay nodes in the secondary network, the associated source packet is shared among multiple relay nodes. This allows us to benefit from the additional degree of freedom. Furthermore, we consider the priority for link selection based on the buffer state of each relay node in the secondary network. This contributes to the avoidance of detrimental empty and full buffer states. Moreover, analytical bounds of the outage probability and the average packet delay are derived for the proposed scheme based on the Markov chain model, in order to verify the numerical results. The overhead required for a central coordinator of the secondary network to monitor channel state information and buffer states are also investigated. Our numerical results demonstrate that the proposed scheme achieves better outage and packet-delay performance than the conventional maxratio-based scheme in the buffer-aided CRN. INDEX TERMS Broadcast, buffer-aided relay, cooperative communication, cognitive radio network, Markov chain, outage probability, packet delay.

Bearing is the core component of rotating machinery, which directly affects its reliability and operational efficiency. The effective evaluation of its operation state is the key to ensure the safe operation of the equipment. In this paper, a novel prediction method of bearing performance trend based on elastic net broad learning system (ENBLS) and the grey wolf optimization (GWO) algorithm is proposed. The proposed method combines the advantages of ENBLS and GWO algorithm to achieve better prediction results. In order to solve the problems that traditional regression prediction algorithms may lead to unsatisfactory prediction results and long training time, we propose a performance trend prediction method based on ENBLS. To further improve the prediction accuracy, we utilize GWO algorithm to optimize various parameters present in the model to improve the performance of the model. The bearing data of whole life cycle from the 2012 IEEE PHM challenge are selected to verify the effectiveness of the proposed method. The results show that the proposed method has high prediction accuracy and stability.

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