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Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

The capacity and performance of batteries decay over time. How to deal with retired batteries is a major challenge at present. This study proposes a health state assessment method for retired batteries. The Forgetting Factor Recursive Least Square is used for parameter identification based on the operation data of new energy vehicles at different mileage periods. Ohmic internal resistance is extracted and used as a characteristic parameter to characterize the state of health. The internal resistances of the vehicles at different driving cycles are compared and their variations are derived. Parameters highly correlated with the battery ohmic internal resistance are selected as input parameters for the long and short-term memory neural network. The accurate state of health prediction model is obtained after parameter tuning. The root-mean-square error of the predicted results is less than 0.01 Ω. This shows that the proposed method can effectively assess the power battery state of health. The study can provide a basis for the stepwise utilization of retired batteries, and thus promote the development of sustainable battery utilization.

Accurate battery voltage prediction performs a critical role in electric vehicles, especially safety risks prognosis in future periods. This paper proposes a multi-forward-step battery voltage prediction method using gated recurrent units. Actual vehicle operation data and weather data are used together for model training. A two-step procedure including data cleaning and correlation analysis is conducted to improve training efficiency and prediction accuracy. Furthermore, the effects of different model parameters on the prediction results are also investigated to obtain an optimal set of model parameters. Additionally, the model is tested with the data of the second year. The results proved that the model not only has a high prediction accuracy, but also has a strong stability and robustness. On this basis, a research plan for real-time multi-forward-step battery voltage prediction is further developed, whereby identifying occurrence of battery failure and mitigate the risk of thermal runaway by voltage predicting.

Carbon dioxide emission reduction is a significant benefit of electric vehicles. State of health prediction is essential to ensure the safety and reliability of the battery system, which is a key part of electric vehicles. This paper proposes a novel state of health prediction strategy based on ohmic internal resistance and long short-term memory networks. Driving conditions are extracted based on the current and speed of real-world vehicles. Combining the equivalent circuit model and Kalman filter, the parameter identification of the power battery system is performed. Ohmic internal resistance is selected as the health state characterization parameter. According to the ohmic internal resistance results obtained from the identification, Pearson correlation analysis is used to obtain the parameters with the highest correlation to the ohmic internal resistance. These parameters are used as input samples for the long short-term memory network to get the evaluation value of health status. The model is trained and tested using data from different operating conditions and vehicles. The root mean square errors of the model outputs are below 0.02 Ω, demonstrating the method's effectiveness. The proposed method is expected to have considerable application in advanced battery management systems.

We present a simple and efficient mutual exclusion algorithm whose optimal message passing complexity isO(N), whereNis the number of processors in the network. The message complexity is measured by counting the number of communication hops in a network for a given topology. This algorithm reduces its message passing complexity by a token-chasing method, and enhances its effectiveness by dynamically adjusting state information stored in each processor. Moreover, this algorithm shortens the request delay by fully taking advantage of the network dynamic status information. The performance of the algorithm is also modeled for analytical evaluation. We have conducted a group of experiments on a network of workstations for comparisons between our algorithm and two other existing mutual exclusion algorithms. The experimental results show the effectiveness of our algorithm, especially when a large number of requests access the critical region in a distributed system. Finally, the token-chasing algorithm is further enhanced for fault tolerance under message loss and link crash conditions.