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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.

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model's robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features. [ABSTRACT FROM AUTHOR]

DnaJ proteins, also known as HSP40s, play a key role in plant growth and development, and response to environmental stress. However, little comprehensive research has been conducted on the DnaJ gene family in maize. Here, we identify 91 ZmDnaJ genes from maize, which are likely distributed in the chloroplast, nucleus, and cytoplasm. Our analysis revealed that ZmDnaJs were classified into three types, with conserved protein motifs and gene structures within the same type, particularly among members of the same subfamily. Gene duplication events have likely contributed to the expansion of the ZmDnaJ family in maize. Analysis of cis-regulatory elements in ZmDnaJ promoters suggested involvement in stress responses, growth and development, and phytohormone sensitivity in maize. Specifically, four cis-acting regulatory elements associated with stress responses and phytohormone regulation indicated a role in adaptation. RNA-seq analysis showed constitutive expression of most ZmDnaJ genes, some specifically in pollen and endosperm. More importantly, certain genes also responded to salt, heat, and cold stresses, indicating potential interaction between stress regulatory networks. Furthermore, early responses to heat stress varied among five inbred lines, with upregulation of almost tested ZmDnaJ genes in B73 and B104 after 6 h, and fewer genes upregulated in QB1314, MD108, and Zheng58. After 72 h, most ZmDnaJ genes in the heat-sensitive inbred lines (B73 and B104) returned to normal levels, while many genes, including ZmDnaJ55, 79, 88, 90, and 91, remained upregulated in the heat-tolerant inbred lines (QB1314, MD108, and Zheng58) suggesting a synergistic function for prolonged protection against heat stress. In conclusion, our study provides a comprehensive analysis of the ZmDnaJ family in maize and demonstrates a correlation between heat stress tolerance and the regulation of gene expression within this family. These offer a theoretical basis for future functional validation of these genes. [ABSTRACT FROM AUTHOR]