Search

To investigate the relationship between rotating instability and fluctuating tip clearance flow, experimental tests as well as numerical studies are conducted on a 1.5 stage low-speed axial compressor rig. The rotating instability is detected through casing mounted dynamic pressure sensors. The phase-locked and root-mean-square pressure contours show that the interaction of tip clearance flow with the neighboring blade is the most likely cause of casing flow field fluctuation. From the calculated results of entire-annulus cascade, it is found that the tip clearance vortex structures oscillate periodically at low flow rate conditions. That leads to intense fluctuation of the casing flow field. Further, phase lag of pressure fluctuations exists between two neighboring passages, which induces a rotating pressure pattern of multiple frequencies. The frequency characteristics of this rotating pressure pattern caused by fluctuating tip clearance flow is coincident with the frequency of rotating instability. This suggests that the fluctuating tip clearance flow is the major contributor to the initiation of rotating instability. [ABSTRACT FROM AUTHOR]

In this paper, three different reduced-order models (auto-regressive and moving average, Volterra series and a surrogate-based recurrence framework model) are presented for the prediction of the unsteady dynamic loading of wind turbine airfoils. A wind turbine blade section can experience unsteady aerodynamic loads when subjected to an unsteady aerodynamic environment. The generations of three reduced-order models for the evaluation of unsteady aerodynamic loads are investigated, with the different models used as a representation of linear or non-linear loading. The validity of the presented reduced-order models is assessed mainly by comparing the model output with unsteady time-accurate computational fluid dynamics (CFD) simulations. The results reveal an encouraging agreement between the computational fluid dynamics simulations and the model predictions under different conditions. All three reduced-order models would therefore be useful in engineering conditioning for aeroelastic analysis and wind turbine design optimization. [ABSTRACT FROM AUTHOR]

This paper concentrates on the noise reduction effect of stator lean in rotor–stator interaction. The compressor with a serial stator-blade lean angle has been employed to acoustically test and numerically calculate. The experiment results show that stator-leaned positive has better effect on noise reduction than leaned negative; the tone noise is determinant on total sound pressure level, and the lean angle of the stator should exceed 10°. Based on the results of unsteady calculation about the compressor, the amplitude of the unsteady loading of stator decreases with the increase in the lean angle. Leaned positive stator has lower unsteady force and loading than leaned negative. The phase parameters q and pcle of wakes are almost proportionate to the stator-blade lean angle. The distribution of q and phase close-leading edge (pcle) shows that the compressor with 25° and −20° stator-blade lean angles has the maximum and minimum phase variation, respectively. [ABSTRACT FROM AUTHOR]

• Established corn yield prediction models based on the gradient boosting decision tree (GBDT) algorithm. • The performance of GBDT model is better than random forest model. • The relationship between soil organic matter, seeding rate, and yield were studied based on the GBDT model. • Innovative seeding rate decision rules were developed. • Using the yield prediction model based on machine learning to study corn seeding rate decision is a promising method. Variable-rate seeding (VRS) technology can adjust the seeding rate according to the growth environment of the crop so as to improve crop yield and resource utilization. The information collection of sensor-based VRS is carried out at the same time as the seeding operation, which improves the operation efficiency and reduces the operation cost. Adjusting the seeding rate according to the indicators that characterize soil fertility is the key to sensor-based VRS technology. However, most of the current research on seeding rate decisions is based on delineating farmland management zones, which cannot be applied to sensor-based VRS. In most studies, traditional linear regression was used to establish the relationship among soil indicators, seeding rate, and yield, which did not consider the impact of weather and management factors on yield. Given the above problems, in this study, corn yield prediction model based on gradient boosting decision tree (GBDT) algorithm was established by combining soil organic matter (SOM) data, weather data and management data, and was compared with the model built by random forest (RF) algorithm. Innovative seeding rate decision rules were developed by using the GBDT corn yield prediction model to simulate corn yield responses to a series of SOM contents and seeding rates. The result showed that the GBDT model (R2 cv = 0.799) was better than the RF model (R2 cv = 0.749). The simulation results of the GBDT model indicated that the yield showed a parabolic form with the increase of seeding rate under the same SOM, the yield increased first and then decreased with the increase of SOM under the same seeding rate, there was an agronomic optimum seeding rate (AOSR) under each SOM and the AOSR increased in a stepped shape with the increase of SOM. The following decision rules were obtained by summarizing the simulation results: when 10 g/kg ≤ SOM ≤ 12 g/kg, seeding rate = 84,900 seeds/ha; when 12 g/kg < SOM ≤ 13 g/kg, 84,900 seeds/ha < seeding rate ≤ 90,600 seeds/ha; when 13 g/kg < SOM ≤ 14 g/kg, 90,600 seeds/ha < seeding rate ≤ 92,110 seeds/ha; when 14 g/kg < SOM ≤ 20 g/kg, 92,110 seeds/ha < seeding rate ≤ 93,000 seeds/ha; when 20 g/kg < SOM ≤ 26 g/kg, 93,000 seeds/ha < seeding rate ≤ 93,880 seeds/ha. The crop yield prediction model is established through a machine-learning algorithm to create a controllable environment for exploring the change of yield with soil attributes and seeding rate, which is a promising method to study sensor-based variable-rate seeding decisions. [ABSTRACT FROM AUTHOR]

Remifentanil significantly represses cell immune responses and influences neutrophil migration through endothelial cell monolayers. The present study determines the beneficial effects of remifentanil and the mechanisms by which it attenuates lipopolysaccharide (LPS)-induced acute lung injury (ALI). Rats were intratracheally instilled with 2 mg/kg LPS to induce ALI. Results showed that remifentanil could resolve lung injury, as evidenced by remarkable decreases in lung edema (wet-to-dry weight ratio), neutrophil infiltration (myeloperoxidase activity), and pulmonary permeability [total number of cells and protein concentrations in bronchoalveolar lavage fluid (BALF)]. Remifentanil also attenuated the concentrations of proinflammatory cytokines tumor necrosis factor alpha, interleukin-1β, and interleukin-6 in BALF, as well as effectively repressed the activation of nuclear factor-kappaB (NF-κB), which has been associated with the inhibition of IκBα degradation.These results suggest that remifentanil may be a suitable treatment for LPS-induced ALI. Remifentanil exerts beneficial effects on the inhibition of proinflammatory cytokine production by downregulating the NF-κB pathway. [ABSTRACT FROM AUTHOR]

The triple-negative breast cancer (TNBC), with a particularly poor prognosis, is increasingly recognized as heterogeneous in molecular signatures. MicroRNA expression profiles have been used for the classification and prognostication of breast cancer, numerous significantly upregulated microRNAs, i.e. miR-21, have been verified oncogenic in non-TNBCs. In present study, we determined the miR-21 levels in TNBC specimens, and TNBC cell levels in vitro, and then identified the role of miR-21 on tumor cell proliferation, apoptosis, and then identified PTEN as the possible target of the microRNA. It was shown that miR-21 expression is upregulated generally, and heterogeneous in TNBC specimens, posing a correlation with poor prognosis for TNBC patients. Further results demonstrated that the upregulated miR-21 promoted the tumor proliferation and inhibited cell apoptosis in vitro. And pro-apoptotic PTEN had been shown being targeted and downregulated. Therefore, our finding emphasized the oncogenic role of miR-21 in TNBC. [ABSTRACT FROM AUTHOR]

Abstract: It is essential to extract impulse components embedded in heavy background noise in engineering applications. The methods based on wavelet have obtained huge success in removing noises, leading to state-of-the-art results. However, complying with the minimum noise principle, the shrinkage/thresholding algorithms unreasonably remove most energy of the features, and sometimes even discard some important features. Thus it is not easy to guarantee satisfactory performance in actual applications. Based on a recently proposed theory named compressed sensing, this paper presents a new scheme, Sparse Extraction of Impulse by Adaptive Dictionary (SpaEIAD), to extract impulse components. It relies on the sparse model of compressed sensing, involving the sparse dictionary learning and redundant representations over the learned dictionary. SpaEIAD learns a sparse dictionary from a whole noisy signal itself and then employs greedy algorithms to search impulse information in the learned sparse dictionary. The performance of the algorithm compares favourably with that of the mature shrinkage/thresholding methods. There are two main advantages: firstly, the learned atoms are tailored to the data being analyzed and the process of extracting impulse information is highly adaptive. Secondly, sparse level of representation coefficients is promoted largely. This algorithm is evaluated through simulations and its effectiveness of extracting impulse components is demonstrated on vibration signal of motor bearings. The advantage of SpaEIAD is further validated through detecting fault components of gearbox, which illustrates that SpaEIAD can be generalized to engineering application, such as rotating machinery signal processing. [Copyright &y& Elsevier]

Electrical stimulation of the vagus nerve may have positive effects on many inflammatory diseases. This study determined the beneficial effects of vagus nerve stimulation and the mechanisms by which it attenuates lipopolysaccharide (LPS)-induced acute lung injury (ALI). Rats were intraperitoneally injected with 10 mg/kg LPS to induce ALI. The results showed that vagus nerve stimulation could improve lung injury, as evidenced by remarkable reductions in lung edema (wet-to-dry weight ratio), neutrophil infiltration (myeloperoxidase activity), and pulmonary permeability [total number of cells and protein concentrations in bronchoalveolar lavage fluid (BALF)]. In addition, vagus nerve stimulation not only decreased the expressions of Src-suppressed C kinase substrate and E-selectin proteins in lung tissue but also effectively attenuated the concentrations of the proinflammatory cytokines tumor necrosis factor-α, interleukin-1β, and interleukin-6 in BALF. These suggest that vagus nerve stimulation is a suitable treatment for LPS-induced ALI and indicate that it helps ameliorate pulmonary microvascular endothelial cell injury by downregulating inflammatory responses. [ABSTRACT FROM AUTHOR]