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Faced with the ever-increasing urban environmental pollution, the electric vehicles (EVs) have received increasing attention in the automotive industry. Lithium-ion batteries, serving as electrochemical power storage, have been extensively used in EVs because of the lightweight, no local pollution and high power density. The increasing awareness on the safe operation and reliability of the battery requires an efficient battery management system (BMS), among the parameters monitored by which, state-of-charge (SOC) is critical in preventing overcharge, deep discharge, and irreversible damage. This article investigates the neural network (NN)-based modeling, learning, and estimation of SOC by comparing two different methodologies, that is, direct structure with SOC as network output and indirect structure with voltage as output. Firstly, the nonlinear autoregressive exogenous neural network (NARX-NN) is introduced, in which SOC is directly deemed as an NN output for learning and estimation. Secondly, a radial basis function (RBF)-based NN with unscented Kalman filter (RBFNN-UKF) is proposed, in which the terminal voltage is used as output. Instead, SOC is deemed as an internal state which would be estimated indirectly based on the feedback error of voltage. Experimental results demonstrate that both estimators can achieve accurate SOC estimation for regular cases, in spite of the inaccurate initial conditions. However, the direct NN structure is revealed as not capable of dealing with the cases with sensor bias, which, however, can be well accommodated in the indirect structure by extending the sensor bias as an augmented state. Benefiting from the uncertainty augmentation and feedback compensation, the indirect RBFNN-UKF shows superiority over the direct estimation in the practical experiments, depicting a promising prospect in the future onboard EV-BMS application. K E Y W O R D S lithium-ion battery, NARX neural network, sensor bias, state-of-charge (SOC), unscented kalman filter (UKF)

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Motivation Polypharmacy is the combined use of drugs for the treatment of diseases. However, it often shows a high risk of side effects. Due to unnecessary interactions of combined drugs, the side effects of polypharmacy increase the risk of disease and even lead to death. Thus, obtaining abundant and comprehensive information on the side effects of polypharmacy is a vital task in the healthcare industry. Early traditional methods employed machine learning techniques to predict side effects. However, they often make costly efforts to extract features of drugs for prediction. Later, several methods based on knowledge graphs are proposed. They are reported to outperform traditional methods. However, they still show limited performance by failing to model complex relations of side effects among drugs. Results To resolve the above problems, we propose a novel model by further incorporating complex relations of side effects into knowledge graph embeddings. Our model can translate and transmit multidirectional semantics with fewer parameters, leading to better scalability in large-scale knowledge graphs. Experimental evaluation shows that our model outperforms state-of-the-art models in terms of the average area under the ROC and precision-recall curves. Availability Code and data are available at: https://github.com/galaxysunwen/MSTE-master

CIEF was developed for the rapid analysis of charge heterogeneity of trastuzumab biosimilar using commercially available fluorocarbon-coated capillary. The CIEF master mix was composed of 0.30% w/v methyl cellulose, 2.3 M urea, 56.8 mM l-arginine, 1.52 mM iminodiacetic acid, 4.5% v/v carrier ampholytes (broad-range pI 3-10 and narrow-range pI 8-10.5 with ratio of 3:1), and 0.45% v/v 10.0, 9.5, 7.0, 5.5, 4.1 pI markers. To get a robust method to analyze charge heterogeneity, some separation parameters, including focusing time and separation temperature, were investigated and optimized. The optimized method gave good precision in estimated pI values of charge variants with RSDs of not more than 0.16% intraday analysis (n = 6) and < 0.18% interday analysis (n = 9). In addition, the applications of this method including purity, stability, lot consistency, peptide N-glycosidase F digest, and C-terminal lysine variants characterization were also investigated.

Inefficient data transmission has been a development bottleneck of Vehicular Ad-hoc NETworks (VANETs), especially in urban areas. It has been proved that many complex IP-based solutions are difficult to be applied in the highly dynamic and link-interrupted vehicular environment. In recent years, Named Data Networking (NDN) has become the most popular realization of Information-Centric Networking (ICN) for future networks. Its characteristics of multi-source, multi-path and in-network caching are helpful for improving the data transmission in VANETs. However, the bottom layer of vehicles cannot provide multiple interfaces to different domains like the routers in wired networks. Thus interface-based forwarding degenerates into directionless broadcasting with low performance and high overhead. Against this problem, we propose COMPASS, a novel named data transmission protocol for VANETs. Firstly, a dynamic directional interface model is built as the cornerstone of our COMPASS. Secondly, the forwarding strategies are improved for rapid interest dissemination and named data retrieving. Besides, an interface remapping method and the update strategies of Forwarding Information Base (FIB) and Pending Interest Table (PIT) are designed to enhance the robustness in high-mobility environment. Finally, the performance of COMPASS is verified on ndnSIM. Compared with the other three state-of-the-art protocols, COMPASS obtains the highest interest satisfaction ratio and the shortest transmission delay in urban traffic scenarios with restricted communication and storage overhead. INDEX TERMS Vehicular named data networking (VNDN), named data transmission, forwarding strategy, dynamic directional interface.

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Objective:Hidden blood loss (HBL), commonly seen after total knee or hip arthroplasty, causes postoperative anemia even after reinfusion or blood transfusion based on the visible blood loss volume. Recent studies demonstrated that oxidative stress might be involved in HBL. However, whether the antioxidants proanthocyanidin (PA) or hydrogen water (HW) can ameliorate HBL remains poorly understood. The aim of this study was to evaluate the effects of PA and HW on HBL.Materials and Methods:A rat HBL model was established through administration of linoleic acid with or without treatment with PA or HW. The levels of hemoglobin (Hb), red blood cell (RBC) count, superoxide dismutase (SOD) activity, glutathione peroxidase (GSH-PX) activity, malondialdehyde (MDA), and ferryl Hb were measured.Results:RBC and Hb values as well as the activity of SOD and GSHPX were reduced after administration of linoleic acid, which was ameliorated by treatment with PA or HW. In addition, the quantity of MDA was significantly decreased with the administration of PA or HW.Conclusion:PA and HW could ameliorate HBL in a rat model by reducing oxidative stress, suggesting that they might be used as a novel therapeutic approach in the prophylaxis or treatment of HBL in clinics.