The Internet of Energy (IoE) provides an effective networking technology for distributed green energy, which allows the connection of energy anywhere at any time. As an important part of the IoE, electric vehicles (EVs), and charging pile management are of great significance to the development of the IoE industry. Previous work has mainly focused on network performance optimization for its management, and few studies have considered the security of the management between EVs and charging piles. Therefore, this paper proposes a decentralized security model based on the lightning network and smart contract in the blockchain ecosystem; this proposed model is called the lightning network and smart contract (LNSC). The overall model involves registration, scheduling, authentication, and charging phases. The new proposed security model can be easily integrated with current scheduling mechanisms to enhance the security of trading between EVs and charging piles. Experimental results according to a realistic infrastructure are presented in this paper. These experimental results demonstrate that our scheme can effectively enhance vehicle security. Different performances of LNSC-based scheduling strategies are also presented.INDEX TERMS Blockchain, smart contract, vehicle charging, mutual authentication, Internet of Energy.
The precise role of nucleus pulposus cell proliferation in the pathogenesis of intervertebral disc degeneration remains to be elucidated. Recent findings have revealed that microRNAs, a class of small noncoding RNAs, may regulate cell proliferation in many pathological conditions. Here, we showed that miR-21 was significantly upregulated in degenerative nucleus pulposus tissues when compared with nucleus pulposus tissues that were isolated from patients with idiopathic scoliosis and that miR-10b levels were associated with disc degeneration grade. Moreover, bioinformatics target prediction identified PTEN as a putative target of miR-21. miR-21 inhibited PTEN expression by directly targeting the 3′UTR, and this inhibition was abolished through miR-21 binding site mutations. miR-21 overexpression stimulated cell proliferation and AKT signaling pathway activation, which led to cyclin D1 translation. Additionally, the increase in proliferation and cyclin D1 expression induced by miR-21 overexpression was almost completely blocked by Ly294002, an AKT inhibitor. Taken together, aberrant miR-21 upregulation in intervertebral disc degeneration could target PTEN, which would contribute to abnormal nucleus pulposus cell proliferation through derepressing the Akt pathway. Our study also underscores the potential of miR-21 and the PTEN/Akt pathway as novel therapeutic targets in intervertebral disc degeneration.
We researched the diagnostic capabilities of deep learning on chest radiographs and an image classifier based on the COVID-Net was presented to classify chest X-Ray images. In the case of a small amount of COVID-19 data, data enhancement was proposed to expanded COVID-19 data 17 times. Our model aims at transfer learning, model integration and classify chest X-Ray images according to three labels: normal, COVID-19 and viral pneumonia. According to the accuracy and loss value, choose the models ResNet-101 and ResNet-152 with good effect for fusion, and dynamically improve their weight ratio during the training process. After training, the model can achieve 96.1% of the types of chest X-Ray images accuracy on the test set. This technology has higher sensitivity than radiologists in the screening and diagnosis of lung nodules. As an auxiliary diagnostic technology, it can help radiologists improve work efficiency and diagnostic accuracy.
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