Abstract:Remaining useful life (RUL) prediction is central to the prognostics and health management (PHM) of lithium-ion batteries. This paper proposes a novel RUL prediction method for lithium-ion batteries based on the Wiener process with measurement error (WPME). First, we use the truncated normal distribution (TND) based modeling approach for the estimated degradation state and obtain an exact and closed-form RUL distribution by simultaneously considering the measurement uncertainty and the distribution of the estimated drift parameter. Then, the traditional maximum likelihood estimation (MLE) method for population based parameters estimation is remedied to improve the estimation efficiency. Additionally, we analyze the relationship between the classic MLE method and the combination of the Bayesian updating algorithm and the expectation maximization algorithm for the real time RUL prediction. Interestingly, it is found that the result of the combination algorithm is equal to the classic MLE method. Inspired by this observation, a heuristic algorithm for the real time parameters updating is presented. Finally, numerical examples and a case study of lithium-ion batteries are provided to substantiate the superiority of the proposed RUL prediction method.

Soybean cyst nematode (SCN), Heterodera glycines, is the most devastating pathogen of soybean worldwide. MicroRNAs (miRNAs) are a class of small, non-coding RNAs that are known to play important role in plant stress response. However, there are few reports profiling the miRNA expression patterns during pathogen stress. We sequenced four small RNA libraries from two soybean cultivar (Hairbin xiaoheidou, SCN race 3 resistant, Liaodou 10, SCN race 3 susceptible) that grown under un-inoculated and SCN-inoculated soil. Small RNAs were mapped to soybean genome sequence, 364 known soybean miRNA genes were identified in total. In addition, 21 potential miRNA candidates were identified. Comparative analysis of miRNA profiling indicated 101 miRNAs belong to 40 families were SCN-responsive. We also found 20 miRNAs with different express pattern even between two cultivars of the same species. These findings suggest that miRNA paly important role in soybean response to SCN and have important implications for further identification of miRNAs under pathogen stress.

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Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape, offering vital solutions for energy conservation and efficient management. Its growing importance in enhancing energy savings and understanding consumer behavior makes it a pivotal technology for addressing global energy challenges. This paper delivers an in-depth review of NILM, highlighting its critical role in smart homes and smart grids. The significant contributions of this study are threefold: Firstly, it compiles a comprehensive global dataset table, providing a valuable tool for researchers and engineers to select appropriate datasets for their NILM studies. Secondly, it categorizes NILM approaches, simplifying the understanding of various algorithms by focusing on technologies, label data requirements, feature usage, and monitoring states. Lastly, by identifying gaps in current NILM research, this work sets a clear direction for future studies, discussing potential areas of innovation.