SummaryBackgroundEdaravone is widely used for treating ischemic stroke, but it is not still confirmed in intracerebral hemorrhage (ICH) as an ideal medication targeting the brain parenchyma. We aimed to investigate the neuroprotective effects of stereotactic administration of edaravone (SI) into the brain parenchyma.MethodsIntracerebral hemorrhage rat models were established by infusion of collagenase into the caudate nucleus. Neural functional recovery was assessed using modified neurological severity scores (mNSS). A comparative study of therapeutic effects between SI and intraperitoneal injection of edaravone (IP) involved in cerebral edema, blood–brain barrier (BBB) permeability, hematoma absorption, inflammatory response and neuronal apoptosis.ResultsCompared with IP, the mNSS was significantly (P < 0.05) improved by SI; cerebral edema and BBB permeability were dramatically ameliorated (P < 0.05); IL‐4 and IL‐10 levels increased, but IL‐1β and TNF‐α levels significantly decreased; neuron apoptosis decreased markedly (P < 0.05); and caspase‐3 and Bax expression significantly dropped, but Bcl‐2 increased in SI group (P < 0.05).Conclusion
SI markedly improved neurological deficits in ICH rat models via antiinflammatory and antiapoptosis mechanisms and promoted M2‐type microglia differentiation. SI was effective in rats with collagenase‐induced ICH.
A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algorithm. Rolling bearing early fault diagnosis is a time-sensitive task, which is significant to ensure the reliability and safety of mechanical fault system. At the same time, the early fault features are masked by strong background noise, which also brings difficulties to fault diagnosis. So, we firstly utilize the composite ensemble intrinsic time-scale decomposition with adaptive noise method (CEITDAN) to decompose the signal at different scales, and then the refined composite multi-scale approximate entropy of the first signal component is calculated to analyze the complexity of describing the vibration signal. Afterwards, in order to obtain higher recognition accuracy, the improved coyote optimization algorithm based probabilistic neural network classifiers is employed for pattern recognition. Finally, the feasibility and effectiveness of this method are verified by rolling bearing early fault diagnosis experiment.
In this study, the carrier effect of zeolite sands in reducing the autogenous shrinkage and optimizing the microstructure of ultra-high-performance concrete (UHPC) is studied. Pre-wetted calcined zeolite sand (CZ), calcined at 500 °C for 30 min, and natural zeolite sand (NZ), with 15 wt.% and 30 wt.% in UHPC, are used to partially replace standard sands. On that basis, a series of experiments are executed on the developed UHPC, including compressive strength, autogenous shrinkage, X-ray diffraction (XRD), and isothermal calorimetry experiments. With the increase of the zeolite sand content, the autogenous shrinkage of UHPC decreases gradually. Moreover, when the added CZ content is 30 wt.% (CZ30 specimen), it is effective in reducing autogenous shrinkage. Meanwhile, at the age of 28 days, the compressive strength of CZ30 is 97% of the control group. In summary, it is possible to effectively reduce the autogenous shrinkage of UHPC containing 30 wt.% CZ, without sacrificing its mechanical properties.
To overcome the difficulty of extracting the feature frequency of early bearing faults, this paper proposes an adaptive feature extraction scheme. First, the improved intrinsic time-scale decomposition, proposed in this paper, is used as a noise reduction method. Then, we use the adaptive composite quantum morphology analysis method, also proposed in this paper, to perform an adaptive demodulation analysis on the signal, and finally, extract the fault characteristics in the envelope spectrum. The experimental results show that the scheme performs well in the early fault feature extraction of rolling bearings.
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