An FBG staged monitoring method for carbon fiber reinforced plastics composite fracture status based on modulus/strain wave coupling property

From the sensitivity of the FBG center wavelength changing with the macro-elastic modulus and the instantaneous fracture strain wave on the surface of carbon fiber reinforced plastics (CFRP) composite, we investigate the correlation between the macro-elastic modulus (the changing rate of the FBG center wavelength during the stretching process) and the fracture status of CFRP specimen. An FBG staged monitoring method based on modulus/strain wave coupling properties designed to monitor tensile fracture state of composite has been proposed. By monitoring the change of macro-elastic modulus during the stretching process, the damage state of composite in a macro perspective is obtained; when the internal damage reaches a critical state, the fracture distribution status of CFRP specimen is captured by monitoring the strain wave response induced by stress relaxation in different locations. Simulated analysis and experimental results in this paper show that the proposed FBG staged monitoring method can achieve the identification of the damage state and the breakage position of CFRP composite effectively, with a good prospect

THE EFFECT OF FOOT POSITION ON KINETICS OF LOWER LIMBS DURING SQUAT

The purposes of this study were to evaluate the effects of the foot position on the joint forces and moments of lower limbs during squat. Eleven male weightlifters were recruited in this study to perform squat with different foot position (forward position and toe-out 20 degrees). The VICON motion analysis system and two KISTLER force platforms were used to record the kinematical and kinetic data during squat. The results showed that the ankle joint maximal shear force, maximal adduction moment, external rotation moment and knee external rotation moment during squat with foot forward position were significantly greater than the results in toe-out position. Squat with foot forward position could be suggested to improve the ankle stability in rehabilitative training

A Highly Specific Probe for Sensing Hydrogen Sulfide in Live Cells Based on Copper-Initiated Fluorogen with Aggregation-Induced Emission Characteristics

Here we reported the first fluorescent probe with aggregation-induced emission characteristics, namely AIE-S, for the detection of hydrogen sulfide (H2S) in live cells. The detection system is selective for complicated biological application and the response is fast enough to complete within seconds. Moreover, the probe exhibits the unique advantage of being immune to aggregation-caused quenching which is a detrimental phenomenon limiting the application of most current available H2S fluorescent probes. The detection mechanism was investigated and postulated to be S2- initiated de-coordination and thereafter aggregation of the AIE-S complex

A novel intelligent fault diagnosis method of rotating machinery based on deep learning and PSO-SVM

A novel intelligent fault diagnosis method based on deep learning and particle swarm optimization support vectors machine (PSO-SVM) is proposed. The method uses deep learning neural network (DNN) to extract fault features automatically, and then uses support vector machine to classify diagnose faults based on extracted features. DNN consists of a stack of denoising autoencoders. Through pre-training and fine-tuning of DNN, features of input parameters can be extracted automatically. This paper uses particle swarm optimization algorithm to select the best parameters for SVM. The extracted features from multiple hidden layers of DNN are used as the input of PSO-SVM. Experimental data is derived from the data of rolling bearing test platform of West University. The results demonstrate that deep learning can automatically extract fault feature, which removes the need for manual feature selection, various signal processing technologies and diagnosis experience, and improves the efficiency of fault feature extraction. Under the condition of small sample size, combining the features of the multiple hidden layers as the input into the PSO-SVM can significantly increase the accuracy of fault diagnosis

Notes on four species of Russula subgenus Heterophyllidiae (Russulaceae, Russulales) from southern China

Heterophyllidiae, one of the main subgenus of Russula (Russulaceae, Russulales), is both ecologically and economically important. Although many studies have focused on subgenus Heterophyllidiae in China, the diversity, taxonomy, and molecular phylogeny still remained incompletely understood. In the present study, two new species, R. discoidea and R. niveopicta, and two known taxa, R. xanthovirens and R. subatropurpurea, were described based on morphology and molecular phylogenetic analyses of ITS and 28S DNA sequences with new collections of subgenus Heterophyllidiae from southern China. Both morphological and phylogenetic analyses consistently confirmed that R. niveopicta and R. xanthovirens belong to the subsect. Virescentinae, R. discoidea and R. subatropurpurea come under subsect. Heterophyllae, and R. prasina is synonymized with R. xanthovirens

The effects of the little Higgs models on ttˉh0t\bar{t} h^0 production via γγ\gamma \gamma collision at linear colliders

In the frameworks of the littlest Higgs($LH$) model and its extension with T-parity($LHT$), we studied the associated $t\bar th^0$ production process $e^+ e^- \to \gamma\gamma \to t \bar t h^0$ at the future $e^+e^-$ linear colliders up to QCD next-to-leading order. We present the regions of $\sqrt{s}-f$ parameter space in which the $LH$ and $LHT$ effects can and cannot be discovered with the criteria assumed in this paper. The production rates of process $\gamma\gamma \to t \bar t h^0$ in different photon polarization collision modes are also discussed. We conclude that one could observe the effects contributed by the $LH$ or $LHT$ model on the cross section for the process $e^+ e^- \to \gamma\gamma \to t \bar t h^0$ in a reasonable parameter space, or might put more stringent constraints on the $LH$/$LHT$ parameters in the future experiments at linear colliders.Comment: 22 pages, 25 figures, version to appear in Phys. Rev.

Characteristics and prognostic value of cardiac magnetic resonance strain analysis in patients with different phenotypes of heart failure

BackgroundStrain analysis of cardiac magnetic resonance imaging (CMR) is important for the prognosis of heart failure (HF). Herein, we aimed to identify the characteristics and prognostic value of strain analysis revealed by CMR in different HF phenotypes.MethodsParticipants with HF, including HF with reduced ejection fraction, HF with mildly reduced ejection fraction, and HF with preserved ejection fraction, and controls were enrolled. The baseline information and clinical parameters of participants were collected, and echocardiography and CMR examination were performed. Three-dimensional strain analysis was performed in the left ventricle, right ventricle, left atrium, and right atrium using CMR. A multifactor Cox risk proportional model was established to assess the influencing factors of cardiovascular adverse events in patients with HF.ResultsDuring a median follow-up of 999 days (range: 616–1334), 20.6% of participants (73/354) experienced adverse events (HF readmission and/or cardiovascular death). Univariable Cox regression revealed that a 1% increase in left atrial global longitudinal strain (LAGLS) was associated with a hazard ratio (HR) of 1.21 [95% confidence interval (CI):1.15–1.28; P < 0.001]. Left ventricular global circumferential strain (LVGCS) (HR, 1.18; 95% CI: 1.12–1.24; P < 0.001), and left ventricular global longitudinal strain (LVGLS) (HR, 1.27; 95% CI: 1.20–1.36; P < 0.001) were also associated with HF hospitalizations and cardiovascular deaths. Among clinical variables, hypertension (HR, 2.11; 95% CI: 1.33–13.36; P = 0.002), cardiomyopathy (HR, 2.26; 95% CI: 1.42–3.60; P < 0.001) were associated with outcomes in univariable analysis. Multivariable analyses revealed that LAGLS (95% CI: 1.08–1.29; P < 0.001), LVGLS (95% CI:1.08–1.29; P < 0.001) and LVGCS (95% CI: 1.19–1.51; P < 0.001) were significantly associated with outcomes. Among clinical variables, hypertension (95% CI: 1.09–3.73; P < 0.025) remained a risk factor.ConclusionCMR plays an obvious role in phenotyping HF. Strain analysis, particularly left atrial and left ventricular strain analysis (LAGLS, LVGLS, and LVGCS) has good value in predicting adverse outcome events