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Remaining useful life (RUL) prediction of lithium-ion batteries can reduce the risk of battery failure by predicting the end of life. In this paper, we propose novel RUL prediction techniques based on long short-term memory (LSTM). To estimate RUL even in the presence of capacity regeneration phenomenon, we consider multiple measurable data from battery management system such as voltage, current and temperature charging profiles whose patterns vary as aging. Unlike the traditional LSTM prediction that matches input layer with output layer as one-to-one structure, we leverage many-to-one structure to be flexible for various input types and to substantially reduce the number of parameters for better generalization. Using the NASA lithium-ion battery datasets, we verify the accuracy of the proposed LSTM-based RUL prediction. The experimental results show that the proposed single-channel LSTM model improves the mean absolute percentage error (MAPE) by 39.2% compared to the baseline LSTM model. Furthermore, the proposed multi-channel LSTM model significantly improves the MAPE, e.g., by 63.7% compared to the baseline; the proposed model achieves 0.47-1.88% of MAPE while the state-of-the-art baseline LSTM shows 0.6-6.45% of MAPE. INDEX TERMS Lithium-ion battery, long short-term memory, remaining useful life, capacity estimation.

Prognostics and health management is a promising methodology to cope with the risks of failure in advance and has been implemented in many well-known applications including battery systems. Since the estimation of battery capacity is critical for safe operation and decision making, battery capacity should be estimated precisely. In this regard, we leverage measurable data such as voltage, current, and temperature profiles from the battery management system whose patterns vary in cycles as aging. Based on these data, the relationship between capacity and charging profiles is learned by neural networks. Specifically, to estimate the state of health accurately we apply feedforward neural network, convolutional neural network, and long short-term memory. Our results show that the proposed multi-channel technique based on voltage, current, and temperature profiles outperforms the conventional method that uses only voltage profile by up to 25%-58% in terms of mean absolute percentage error. INDEX TERMS Lithium-ion battery, neural network, remaining useful life, capacity estimation, state of health.

For sensors that emulate human tactile perception, we suggest a simple method for fabricating a highly sensitive force sensor using a conductive polyurethane sponge where graphene flakes are self-assembled into the porous structure of the sponge. The complete sensor device shows a sensitive and reliable detection response for a broad range of pressure and dynamic pressure that correspond to human tactile perception. Sensitivity of the sensor to detect vibration is also confirmed with vertical actuations due to slipping over micro-scale ridge structures attached on the sensors. Based on the sensor's ability to detect both pressure and vibration, the sensor can be utilized as a flexible tactile sensor.

Background The neutrophil-lymphocyte ratio (NLR) and the prognostic nutritional index (PNI) are markers of systemic inflammation known to be useful prognostic indicators of malignancy. However, little evidence has defined the influence of inflammation on the tumor microenvironment. Methods Two hundred eighty-eight patients who underwent curative surgery for gastric cancer were included. Preoperative peripheral blood samples were used to analyze the NLR and PNI. The optimal cutoff levels for the NLR and PNI were defined by receiver operating characteristic curve analysis for survival (NLR = 2.7, PNI = 47.7). The densities of specific immune cells (CD3 ? , CD4 ? , CD8 ? ) within the tumor microenvironment were measured in tumor microarrays by immunohistochemical analysis.Results Two hundred thirty-five patients (81.6 %) had a low NLR and 53 patients (18.4 %) had a high NLR. One hundred seventeen patients (40.6 %) had a low PNI and 171 patients (59.4 %) had a high PNI. CD3? and CD8? immune cell density were not associated with the NLR and PNI. However, in the high-NLR group compared with the low-NLR group, CD4 ? immune cell density was significantly decreased (P \ 0.001). Similarly, the density of CD4? immune cells was also significantly decreased in the low-PNI group compared with the high-PNI group (P = 0.007). A high NLR and a low PNI were correlated with worse overall survival in multivariate analysis (P = 0.028 and P = 0.002 respectively). Conclusions The NLR and PNI are associated with the density of CD4? immune cells in the tumor microenvironment, which leads to prognostic values of systemic inflammation in gastric cancer.

A self-sustainable base station (BS) where renewable resources and energy storage system (ESS) are interoperably utilized as power sources is a promising approach to save energy and operational cost in communication networks. However, high battery price and low utilization of ESS intended for uninterruptible power supply (UPS) necessitates active utilization of ESS. This paper proposes a multi-functional framework of ESS using dynamic programming (DP) for realizing a sustainable BS. We develop an optimal charging and discharging scheduling algorithm considering a detailed battery wear-out model to minimize operational cost as well as to prolong battery lifetime. Our approach significantly reduces total cost compared to the conventional method that does not consider battery wear-out. Extensive experiments for several scenarios exhibit that total cost is reduced by up to 70.6% while battery wear-out is also reduced by 53.6%. The virtue of the proposed framework is its wide applicability beyond sustainable BS and thus can be also used for other types of load in principle.

Abstract. Triptolide, the main active component of the traditional Chinese herbal medicine Tripterygium wilfordii Hook F, has been shown to have potent immunosuppressive and anti-inflammatory properties. Here, we investigated the pro-apoptotic effect of triptolide in human cervical cancer cells and its underlying mechanisms. Exposure of cervical cancer cells to triptolide induced apoptosis, which was accompanied by loss of mitochondrial membrane potential, caspase processing (caspase-8, -9 and -3), and cleavage of the caspase substrate, poly(ADP-ribose) polymerase. The cytotoxic effects of triptolide were significantly inhibited by the caspase inhibitor, z-VAD-fmk. Triptolide-induced apoptosis was associated with a marked reduction in Akt phosphorylation and was exacerbated by LY294002 (phosphatidylinositol-3'-kinase inhibitor). Conversely, it was attenuated by Akt overexpression. Triptolide-induced apoptosis was also associated with downregulation of Mcl-1 and was significantly inhibited by Mcl-1 overexpression. These findings show that triptolide induces caspase-dependent, mitochondria-mediated apoptosis in cervical cancer cells, in part, by negatively regulating Akt and Mcl-1.

Cancer cells can evade immune surveillance in the body. However, immune checkpoint inhibitors can interrupt this evasion and enhance the antitumor activity of T cells. Other mechanisms for promoting antitumor T-cell function are the targeting of costimulatory molecules expressed on the surface of T cells, such as 4-1BB, OX40, inducible T-cell costimulator and glucocorticoid-induced tumor necrosis factor receptor. In addition, CD40 targets the modulation of the activation of antigen-presenting cells, which ultimately leads to T-cell activation. Agonists of these costimulatory molecules have demonstrated promising results in preclinical and early-phase trials and are now being tested in ongoing clinical trials. In addition, researchers are conducting trials of combinations of such immune modulators with checkpoint blockade, radiotherapy and cytotoxic chemotherapeutic drugs in patients with advanced tumors. This review gives a comprehensive picture of the current knowledge of T-cell agonists based on their use in recent and ongoing clinical trials.