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Owing to the high energy density, lithium–sulfur batteries (LSBs) have attracted a lot of attention as a representative of new secondary batteries. However, LSBs also face some serious issues in practical applications such as the large volume expansion of S cathode, shuttle effect of lithium polysulfide (LPSs), and dendrite formation of lithium metal anode. In this work, hollow carbon sphere (HCS) was used as a framework, and CoSe2/Se nanorods were uniformly loaded on the HCS surface to form a 3D heterostructured composite (CS/Se/HCS). When used as an active sulfur host, the HCS is conducive to the transport of electrons and Li+; meanwhile, it can suppress the large volume expansion in the electrochemical performance. CoSe2 can effectively adsorb LPSs and accelerate the transformation of LPSs. We also investigated the effect of CoSe2/Se content on the electrochemical performance of the S electrode. Among different CS/Se/HCS samples, the CS/Se/HCS-0.3/S electrode exhibited the best electrochemical performance; at 1C rate, after 500 cycles, it presents a reversible capacity of 511 mA h g−1. This work provided a new idea for the combination of transition metal selenide and carbon materials, which can be used in LSBs and other energy storage devices. [ABSTRACT FROM AUTHOR]

Due to the randomness and uncertainty in the atmospheric environment, and accompanied by a variety of unknown noise. Accurate prediction of PM2.5 concentration is very important for people to prevent injury effectively. In order to predict PM2.5 concentration more accurately in this environment, a hybrid modelling method of support vector regression and adaptive unscented Kalman filter (SVR-AUKF) is proposed to predict atmospheric PM2.5 concentration in the case of incorrect or unknown noise. Firstly, the PM2.5 concentration prediction model was established by support vector regression. Secondly, the state space framework of the model is combined with the adaptive unscented Kalman filter method to estimate the uncertain PM2.5 concentration state and noise through continuous updating when the model noise is incorrect or unknown. Finally, the proposed method is compared with SVR-UKF method, the simulation results show that the proposed method is more accurate and robust. The proposed method is compared with SVR-UKF, AR-Kalman, AR and BP methods. The simulation results show that the proposed method has higher prediction accuracy of PM2.5 concentration. [ABSTRACT FROM AUTHOR]

Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM2.5 is the main particulate matter in air pollution. Therefore, how to predict PM2.5 accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM2.5 concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM2.5 concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM2.5 concentration data, which were given by the autoregressive model (AR). In the paper, three PM2.5 time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5 prediction, and it is effective. [ABSTRACT FROM AUTHOR]

Phytoplasma-infected Chinese cherry (Cerasus pseudocerasus L.) exhibits symptoms of phyllody and stiff fruit. To reveal the molecular mechanism of stiff fruit, the current study integrated transcriptome with metabolome. Results showed that the differentially expressed genes and the differentially accumulated metabolites were related to a high proportion of two aspects: pathogen resistance and signaling or regulatory functions, and the molecular mechanism of stiff fruit that were majorly induced by plant biotic stress response via phytohormones signal transduction, especially signal pathways of salicylic acid, auxin, and abscisic acid. Notably, there was a large overlap between phytoplasma stress response and drought stress response genes. Phytohormone content displayed significant difference that abscisic acid and salicylic acid content of phytoplasma-infected fruit were higher than that of healthy fruit, whereas zeatin, jasmonic acid, and IAA showed the opposite results. In addition, the expression of key candidate genes, including IAA4, IAA9, IAA14, IAA31, ARF5, ARF9, GH3.1, GH3.17, SAUR20, SAUR32, SAUR40, PR1a, PRB1, TGA10, SnRK2.3, and AHK2, was responsible for cherry stiff fruit. In conclusion, the current study contributed a foundation for understanding the molecular mechanism of cherry phyllody disease on stiff fruit, a better understanding of fruit development, and found the potential candidate genes involved in cherry stiff fruit, which could be used for further research in associated fields. [ABSTRACT FROM AUTHOR]

The PM 2.5 concentration model is the key to predict PM 2.5 concentration. During the prediction of atmospheric PM 2.5 concentration based on prediction model, the prediction model of PM 2.5 concentration cannot be usually accurately described. For the PM 2.5 concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM 2.5 concentration may be different, and the single model cannot play the corresponding ability to predict PM 2.5 concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM 2.5 concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM 2.5 concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM 2.5 concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM 2.5 concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM 2.5 concentration prediction is improved in whole time period. [ABSTRACT FROM AUTHOR]

Objective: The work presented here was to research into a kind of automatic control system that would be automatically linked to the working status of electronic endoscope., Methods: The image video output signal and signal of negative pressure sensor were used to be a sampling signal of automatic control circuit. An electronic switch of automatic control circuit was challenged the manual control device of the negative pressure system., Results: This system simplifies the operation procedures, enhances work efficiency, reduces energy consumption and improves the service life of the machine., Conclusion: The paper concludes that this negative pressure automatic control system has many strengths like stable performance, easy installation and strong versatility. It also can be widely applied in different brand and different types of electronic endoscope in China.