The prognostic and health management (PHM) of lithium-ion batteries has received increasing attention in recent years. The remaining useful life (RUL) prediction and state of health (SOH) monitoring are two important parts in PHM of the lithium-ion battery. Nowadays, the development of signal processing technology and neural network technology introduces new data-driven methods to RUL prediction and SOH monitoring of the lithium-ion battery. This paper presents a neural-network-based method that combines long short-term memory (LSTM) network with particle swarm optimization and attention mechanism for RUL prediction and SOH monitoring of the lithium-ion battery. Before predicting RUL of the lithium-ion battery, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized for the raw data denoising, which can improve the accuracy of prediction. A real-life cycle dataset of lithium-ion batteries from NASA is used to evaluate the proposed method, and the experiment results show that when compared with traditional methods, the proposed method has higher accuracy. INDEX TERMS Lithium-ion battery, prognostic and health management (PHM), long short-term memory (LSTM), attention mechanism.

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Background: Activated astrocytes release matrix metalloproteinase-2/9 (MMP-2/9) to induce central sensitization and maintain neuropathic pain. However, the mechanisms involved in the activation of MMP-2/9 on astrocytes during pain remain poorly understood. Meanwhile, there is a lack of effective treatment to inhibit the activation of MMP-2/9 on astrocytes. In this study, we aim to investigate the effect of tetramethylpyrazine (TMP), a natural compound with analgesic effects but unknown mechanisms, on MMP-2/9 in neuropathic pain. Methods: The nociception was assessed by measuring the incidence of foot withdrawal in response to mechanical indentation in rats (n = 6). Cell signaling was assayed using western blotting (n = 6) and immunohistochemistry (n = 5). The astrocyte cell line C8-D1A was cultured to investigate the in vitro effects. Results: TMP significantly attenuated the maintenance of chronic constrictive injury (CCI)-induced neuropathic pain, inhibited the activation of astrocytes, and decreased the expression of MMP-2/9. Furthermore, our results indicated that TMP could selectively suppress JNK activity but had no notable effects on ERK and p38. Our study also revealed that the effect of TMP may be dependent on the inhibition of TAK1.

In the wake of developments in remote sensing, the application of target detection of remote sensing is of increasing interest. Unfortunately, unlike natural image processing, remote sensing image processing involves dealing with large variations in object size, which poses a great challenge to researchers. Although traditional multi-scale detection networks have been successful in solving problems with such large variations, they still have certain limitations: (1) The traditional multi-scale detection methods note the scale of features but ignore the correlation between feature levels. Each feature map is represented by a single layer of the backbone network, and the extracted features are not comprehensive enough. For example, the SSD network uses the features extracted from the backbone network at different scales directly for detection, resulting in the loss of a large amount of contextual information. (2) These methods combine with inherent backbone classification networks to perform detection tasks. RetinaNet is just a combination of the ResNet-101 classification network and FPN network to perform the detection tasks; however, there are differences in object classification and detection tasks. To address these issues, a cross-scale feature fusion pyramid network (CF2PN) is proposed. First and foremost, a cross-scale fusion module (CSFM) is introduced to extract sufficiently comprehensive semantic information from features for performing multi-scale fusion. Moreover, a feature pyramid for target detection utilizing thinning U-shaped modules (TUMs) performs the multi-level fusion of the features. Eventually, a focal loss in the prediction section is used to control the large number of negative samples generated during the feature fusion process. The new architecture of the network proposed in this paper is verified by DIOR and RSOD dataset. The experimental results show that the performance of this method is improved by 2%-12% in the DIOR dataset and RSOD dataset compared with the current SOTA target detection methods.