Search

Accurately estimating the state of health (SOH) of lithium-ion batteries is of great significance in ensuring the safe operation of the battery system. Addressing the issue where traditional SOH estimation methods fail under variable working conditions, an online SOH estimation method for lithium-ion batteries based on equivalent circuit model (ECM) and sparse Gaussian process regression (SGPR) is proposed. During the constant current charging process, the parameters of the ECM of lithium-ion battery are dynamically identified by two online filters, based on which, a condition-insensitive health indicator is constructed. In combination with the SGPR, the indirect SOH estimation is achieved. This method uses the unified signal processing method and feature mapping model under various working conditions, and features strong robustness with low redundancy. The experimental results show that the average absolute error of the method proposed under various working conditions does not exceed 0.94%, and the root mean square error stays below 1.12%. When benchmarked against existing methods, this method has significant advantages in comprehensive performance.

As a new network architecture,the software defined network(SDN)simplifies the network management logic by separating data plane and control plane,which is one of the popular research subjects of next-generation network.However,due to frequent link failures and other factors,it is difficult to guarantee the reliability of SDN,which is a problem well recognized in the industry.The existing SDN link failure recovery models often have the problems of long recovery delay,requiring too many flow entries and ignoring link congestion after recovery from failure.To solve these problems,this paper proposes a single-link failure recovery model(LFA-CA)based on segment routing(SR).The model employs the two heuristic algorithms of BPF and BPU to calculate a loop-free backup path during network initialization and update the congestion avoiding backup path during operation,respectively,so as to achieve fast recovery from single-link failure and congestion avoidance after failure.In this paper,massive simulation experiments are carried out to evaluate the performance of our model,and the results prove that compared with some of the existing SDN single-link failure recovery models,LFA-CA consumes less forwarding rules and has better load balancing ability after failure.

Aimed at the uncertainty of charging starting and ending point caused by incomplete charging and discharging in practical applications of lithium-ion battery, an estimation method of battery health based on dual charging state factors is proposed. A battery aging experiment bench is built, and eight nickel-cobalt-manganese lithium-ion batteries are subjected to aging test. Different from the traditional single state factor estimation, the average value of equal time difference current at the front end of constant voltage charging curve and the equal amplitude voltage charging time at the end of constant current charging curve are selected under different aging conditions to construct health factors. The corresponding relationship between state of charge (SOC) and open circuit voltage (OCV) of the experimental battery in different aging states is analyzed and the correctness of health factor is proved by theoretical deduction and experimental results. An improved support vector regression model with a strong generalization ability is established, and the hyperparameters of the model are optimized through the particle swarm optimization algorithm. The results show that the proposed dual-charging health factor is closely related to battery capacity aging and attenuation. The improved support vector regression model can estimate the health status in different aging states in real time, and has the ability to characterize local capacity rebound change, which can be used as an effective method for estimating the state of health of an embedded battery management system.

Background: Immune checkpoint blockade (ICB) therapy provides remarkable clinical benefits for multiple cancer types. However, the overall response rate to ICB therapy remains low in esophageal squamous cell carcinoma (ESCC). This study aimed to identify biomarkers of ICB therapy for ESCC and interrogate its potential clinical relevance. Methods: We investigated gene expression in 42 treatment-naïve ESCC tumor tissues and identified differentially expressed genes, tumor-infiltrating lymphocytes and immune-related genes signatures associated with differential immunotherapy responses. We systematically assessed the tumor microenvironment using the NanoString GeoMx digital spatial profiler, single-cell RNA-seq and multiplex immunohistochemistry in ESCC. Finally, we evaluated the associations between HLA-A-positive tertiary lymphoid structures (TLSs) and patients' responses to ICB in 60 ESCC patients. Results: Tumor infiltrating B lymphocytes and several immune-related gene signatures, such as the antigen presenting machinery (APM) signature, are significantly elevated in ICB treatment responders. Multiplex immunohistochemistry identified the presence of HLA-A+ TLSs and showed that TLS-resident cells increasingly express HLA-A as TLSs mature. Most TLS-resident HLA-A+ cells are tumor-infiltrating T (TIL-T) or tumor-infiltrating B (TIL-B) lymphocytes. Digital spatial profiling of spatially distinct TIL-T lymphocytes and single-cell RNA-seq data from 60 ESCC tumor tissues revealed that CXCL13-expressing exhausted TIL-Ts inside TLSs are reactivated with elevated expression of the APM signature as TLSs mature. Finally, we demonstrated that HLA-A+ TLSs and their major cellular components, TIL-Ts and TIL-Bs, are associated with a clinical benefit from ICB treatment for ESCC. Conclusions: HLA-A+ TLSs are present in ESCC tumor tissues. TLS-resident TIL-Ts with elevated expression of the APM signature may be reactivated. HLA-A+ TLSs and their major cellular components, TIL-Ts and TIL-Bs, may serve as biomarkers for ICB-treated ESCC patients. [ABSTRACT FROM AUTHOR]