Search

Objective To investigate the regulation and mechanism of Herkinorin preconditioning on NOD-like receptor protein 3 (NLRP3) in brain tissue of transient middle cerebral artery occlusion (tMCAO) rats. Methods Rats were randomly divided into sham operation group (sham), model group (tMCAO) and Herkinorin group (Herkinorin). Before modeling, rats in Herkinorin group were intraperitoneally given Herkinorin (10 mg/kg) once a day for a week. The rats were evaluated by neurologic score, TTC staining, TUNEL staining 24 h after reperfusion. The expression levels of IκBα, p65, p-p65, pro-IL-1β, IL-1β, pro-caspase1, caspase1 p20 and NLRP3 were detected by Western blot. The binding level of IκBα to β-arrestin2 was detected by co-immunoprecipitation (Co-IP). Results Compared with sham group, rats in tMCAO group had higher behavioral score (P＜0.01), increased cerebral infarction percentage and increased cerebral edema rate (P＜0.01). The level of cell apoptosis was increased in ischemic penumbra (P＜0.01). The expression levels of IκBα and p65 were decreased (P＜0.01) and the expression levels of p-p65, IL-1β, caspase1 p20 and NLRP3 were increased in ischemic penumbra (P＜0.01). The binding level of IκBα to β-arrestin2 was decreased in ischemic penumbra (P＜0.01). Compared with tMCAO group, rats in Herkinorin group had lower behavioral score (P＜0.01), lower cerebral infarction percentage and lower cerebral edema rate (P＜0.01). The level of cell apoptosis was decreased in ischemic penumbra(P＜0.01). The expression of IκBα and p65 was increased (P＜0.01) and the expression of p-p65, IL-1β, caspase1 p20 and NLRP3 was decreased in ischemic penumbra (P＜0.01). The binding of IκBα to β-arrestin2 was increased in ischemic penumbra (P＜0.01). Conclusions Herkinorin may negatively regulate NF-κB/NLRP3 pathway to alleviate cerebral ischemia-reperfusion injury of tMCAO rats.

Accurate prediction of photovoltaic (PV) power generation is the key to daily dispatch management and safe and stable grid operation. In order to improve the accuracy of the prediction, a finite iterative PV power prediction model with long range dependence (LRD) characteristics was developed using fractional Lévy stable motion (fLsm) and applied to a real dataset collected in the DKASC photovoltaic system in Alice Springs, Australia. The LRD prediction model considers the influence of current and past trends in the stochastic series on the future trends. Firstly, the calculation of the maximum steps prediction was introduced based on the maximum Lyapunov. The maximum prediction steps could provide the prediction steps for subsequent prediction models. Secondly, the order stochastic differential equation (FSDE) which describes the fLsm can be obtained. The parameters of the FSDE were estimated by using a novel characteristic function method. The PV power forecasting model with the LRD characteristics was obtained by discretization of FSDE. By comparing statistical performance indicators such as root max error, mean square error with Conv-LSTM, BiLSTM, and GA-LSTM models, the performance of the proposed fLsm model has been demonstrated. The proposed methods can provide better theoretical support for the stable and safe operation of PV grid connection. They have high reference value for grid dispatching department. [ABSTRACT FROM AUTHOR]

Roller bearing degradation features fractal characteristics such as self-similarity and long-range dependence (LRD). However, the existing remaining useful life (RUL) prediction models are memoryless or short-range dependent. To this end, we propose a RUL prediction model based on fractional Brownian motion (FBM). Bearing faults can happen in different places, and thus their degradation features are difficult to extract accurately. Through variational mode decomposition (VMD), the original degradation feature is decomposed into several components of different frequencies. The monotonicity, robustness and trends of the different components are calculated. The frequency component with the best metric values is selected as the training data. In this way, the performance of the prediction model is hugely improved. The unknown parameters in the degradation model are estimated by the maximum likelihood algorithm. The Monte Carlo method is applied to predict the RUL. A case study of a bearing is presented and the prediction performance is evaluated using multiple indicators. [ABSTRACT FROM AUTHOR]

To enhance the economic viability of wind energy in cold regions and ensure the safe operational management of wind farms, this paper proposes a short-term wind turbine blade icing wind power prediction method that combines principal component analysis (PCA) and fractional Lévy stable motion (fLsm). By applying supervisory control and data acquisition (SCADA) data from wind turbines experiencing icing in a mountainous area of Yunnan Province, China, the model comprehensively considers long-range dependence (LRD) and self-similar features. Adopting a combined pattern of previous-day predictions and actual measurement data, the model predicts the power under near-icing conditions, thereby enhancing the credibility and accuracy of icing forecasts. After validation and comparison with other prediction models (fBm, CNN-Attention-GRU, XGBoost), the model demonstrates a remarkable advantage in accuracy, achieving an accuracy rate and F1 score of 96.86% and 97.13%, respectively. This study proves the feasibility and wide applicability of the proposed model, providing robust data support for reducing wind turbine efficiency losses and minimizing operational risks. [ABSTRACT FROM AUTHOR]

In this paper, a fractional Weibull process is utilized in a predictive stochastic differential equation model to allow for skewness and heavy-tailed characteristics. To this aim, a fractional Weibull process with non-Gaussian characteristics and a long memory effect is proposed to drive the predictive stochastic differential equation. The difference iterative forecasting model is proposed as its stochastic difference scheme. The consistency, stability, and convergence of the model are analyzed. In the proposed model, variational mode decomposition is utilized as the data preprocessing approach to separate the stationary and non-stationary components. Actual wind speed data and stock price data are employed in two separate case studies. [ABSTRACT FROM AUTHOR]

Remaining useful life prediction guarantees a reliable and safe operation of turbofan engines. Long-range dependence (LRD) and heavy-tailed characteristics of degradation modeling make this method advantageous for the prediction of RUL. In this study, we propose fractional Lévy stable motion for degradation modeling. First, we define fractional Lévy stable motion simulation algorithms. Then, we demonstrate the LRD and heavy-tailed property of fLsm to provide support for the model. The proposed method is validated with the C-MAPSS dataset obtained from the turbofan engine. Principle components analysis (PCA) is conducted to extract sources of variance. Experimental data show that the predictive model based on fLsm with exponential drift exhibits superior accuracy relative to the existing methods. [ABSTRACT FROM AUTHOR]

The remaining useful life (RUL) prediction of wind turbine planetary gearboxes is crucial for the reliable operation of new energy power systems. However, the interpretability of the current RUL prediction models is not satisfactory. To this end, a multi-stage RUL prediction model is proposed in this work, with an interpretable metric-based feature selection algorithm. In the proposed model, the advantages of neural networks and long-range-dependent stochastic processes are combined. In the offline training stage, a general representation of the degradation trend is learned with the meta-long short-term memory neural network (meta-LSTM) model. The inevitable measurement error in the sensor reading is modelled by white Gaussian noise. During the online RUL prediction stage, fractional generalized Pareto motion (fGPm) with an adaptive diffusion is employed to model the stochasticity of the planetary gearbox degradation. In the case study, real planetary gearbox degradation data are used for the model validation. [ABSTRACT FROM AUTHOR]

In quantum key distribution experiments, ground motion is usually used to simulate satellite-based motion. The posture fluctuation of the platform affects the normal operation of the acquisition, tracking, and pointing (abbreviated as ATP) system seriously. To achieve the verification of the ground motion platform, the ATP parameters of the ground simulation motion system cannot be designed only according to the satellite-based ATP parameters. To solve this problem, a set of initial pointing system and inertial stabilization system is added to the simulation ATP system. This provides a technical solution for the ground simulation ATP system similar to the satellite-based motion platform. In the meanwhile, a tracking control strategy based on the identification method is proposed by establishing identification symbols. Compared with traditional proportion, integral, and differential (abbreviated as PID) control, this method overcomes the shortcoming of tentative modification of the control parameters and improves the stability and adaptability of the tracking control process. Tracking accuracy of ±0.1° is achieved under heavy-load conditions. This guarantees the success of the quantum key distribution (QKD) verification test of the ground motion platform.

Gradual degradation of the bearing vibration signal is usually studied as a nonstationary stochastic time series. Roller bearings are working at high speed in a heavy load environment so that the combination of bearing faults gradually degraded during the rotation might lead to unpredicted catastrophic accidents. The degradation process has the property of long-range dependence (LRD), so that the fractional Brownian motion (fBm) is taken into account for a prediction model. Because of the dramatic changes in the bearing degradation process, the Hurst exponent that describes the fBm will change during the degradation process. A priori Hurst value of the conventional fBm in the prediction is fixed, thus inducing a minor accuracy of the prediction. To avoid this problem, we propose an improved prediction method. Based on the following steps, at the initial data processing, a skip-over factor is selected as the characteristics parameter of the bearing degradation process. A multifractional Brownian motion (mfBm) replaces the fBm for the degradation modeling. We will show that also our mfBm has the same property of long-range dependence as the fBm. Moreover, a time-varying Hurst exponent H(t) is taken to replace the constant H in fBm. Finally, we apply the quantum-behaved partial swarm optimization (QPSO) to optimize H(t) for a finite interval. Some tests and corresponding experimental results will show that our model QPSO + mfBm have a much better performance on the prediction effect than fBm.

MXenes are promising substrates for supported noble metal electrocatalysts. Yet, it is a significant challenge to modulate the metal–support interaction (MSI) for enhancing catalytic performance. Herein, employing a facile HF etching method, the cation vacancy structures in Ti3C2Tx MXenes are controllably tuned, producing nearly vacancy‐free (Ti3C2Tx‐V0), single Ti atom vacancy (Ti3C2Tx‐VS), or Ti vacancy cluster (Ti3C2Tx‐VC) engineered MXenes. Ruthenium atomic clusters, as a model catalyst, successfully anchor on all MXene substrates. Different from the terminal O/F coordination groups on routine Ti3C2Tx MXene surfaces, the Ti vacancy clusters in Ti3C2Tx‐VC create unique lattice carbon ligand environment toward Ru species, which induces ultra‐strong MSI. As a result, compared to Ti3C2Tx‐V0 and Ti3C2Tx‐VS, the Ti3C2Tx‐VC modulated Ru clusters (Ru@Ti3C2Tx‐VC) exhibit the optimized balance of H2O adsorption/dissociation and OH/H desorption, thereby delivering superior electrocatalytic performance in the alkaline hydrogen evolution reaction (HER). Within the wide range from laboratory‐level (90 mA cm−2) to industrial‐level (1.5 A cm−2) current density, Ru@Ti3C2Tx‐VC outperforms commercial Pt/C in terms of overpotential and mass activity. Moreover, as a universal substrate for noble metal catalysts, Ti3C2Tx‐VC can also anchor Ir/Pt/Rh atomic clusters and enable excellent HER catalytic activity. This work expands the scope of the MSI between MXene and noble metal catalysts. [ABSTRACT FROM AUTHOR]