Enhanced Gas-Flow-Induced Voltage in Graphene

We show by systemically experimental investigation that gas-flow-induced voltage in monolayer graphene is more than twenty times of that in bulk graphite. Examination over samples with sheet resistances ranging from 307 to 1600 {\Omega}/sq shows that the induced voltage increase with the resistance and can be further improved by controlling the quality and doping level of graphene. The induced voltage is nearly independent of the substrate materials and can be well explained by the interplay of Bernoulli's principle and the carrier density dependent Seebeck coefficient. The results demonstrate that graphene has great potential for flow sensors and energy conversion devices

A deep learning method using SDA combined with dropout for bearing fault diagnosis

The fault diagnosis of a rolling bearing is at present very important to ensure the steadiness of rotating machinery. According to the non-stationary and non-liner characteristics of bearing vibration signals, a large number of approaches for feature extraction and fault classification have been developed. An effective unsupervised self-learning method is proposed to achieve the complicated fault diagnosis of rolling bearing in this paper, which uses stacked denoising autoencoder (SDA) to learn useful feature representations and improve fault pattern classification robustness by corrupting the input data, meanwhile employs “dropout” to prevent the overfitting of hidden units. Finally the high-level feature representations extracted are set as the inputs of softmax classifier to achieve fault classification. Experiments indicate that the deep learning method of SDA combined with dropout has an advantage in fault diagnosis of bearing, and can be applied widely in future

Rolling bearing fault diagnosis using improved LCD-TEO and softmax classifier

A novel rolling bearing fault diagnosis method based on improved local characteristic-scale decomposition (LCD), Teager energy operator (TEO) and softmax classifier is proposed in this paper. First, vibration signals are decomposed into several intrinsic scale components (ISCs) by using improved LCD; second, TEO and fast Fourier transform (FFT) are respectively used to extract instantaneous amplitude (IA) and frequency spectra of ISC1s, and then FFT is again employed to obtain spectra of IA; third, energy ratio of the resonant frequency band against the total, frequency entropy (FE) in the spectra of ISC1s and several amplitude ratios in the frequency spectra of demodulated ISC1s are extracted as fault feature vectors, and principal components analysis (PCA) is applied for dimensionality reduction; finally, these feature vectors are taken as inputs to train and test softmax classifier. As a new non-stationary signal analysis tool, LCD can decompose adaptively a signal into series of ISCs in different scales and give good results in situations where other methods failed. However, there are two main issues in this method, end effect and mode mixing, possibly leading to unexpected results. In this paper, a slope-based method and noise assisted analysis are applied to restrain the problems respectively. Experimental results show the proposed method performs effectively for bearing fault diagnosis

Effects of temperature on photosynthetic performance and nitrate reductase activity in vivo assay in Gracilariopsis lemaneiformis (Rhodophyta)

Gracilariopsis lemaneiformis is an economically-valued species and widely cultured in China at present. After being acclimated to different growth temperatures (15, 20, 25, and 30 degrees C) for 7 days, the relative growth rate (RGR), nitrate reductase activity, soluble protein content and chlorophyll a fluorescence of G. lemaneiformis were examined. Results show that RGR was markedly affected by temperature especially at 20 degrees C at which G. lemaneiformis exhibited the highest effective quantum yield of PSII [Y(II)] and light-saturated electron transport rate (ETRmax), but the lowest non-photochemical quenching. Irrespective of growth temperature, the nitrate reductase activity increased with the incubation temperature from 15 to 30 degrees C. In addition, the greatest nitrate reductase activity was found in the thalli grown at 20 degrees C. The value of temperature coefficient Q10 of alga cultured in 15 degrees C was the greatest among those of other temperatures tested. Results indicate that the optimum temperature for nitrate reductase synthesis was relatively lower than that for nitrate reductase activity, and the relationship among growth, photosynthesis, and nitrate reductase activity showed that the optimum temperature for activity of nitrate reductase in vivo assay should be the same to the optimal growth temperature

Antisaccadic eye movements in middle-aged individuals with a family history of Alzheimer's disease

BackgroundAntisaccade is closely associated with cognitive ability in Alzheimer's disease (AD). However, studies regarding antisaccade in the early stages of AD are scarce. Considering that first-degree family history is a well-established risk factor for AD, we explored the influence of family history on the performance of antisaccade tasks in individuals with normal cognition.MethodsIn total, 44 participants (aged 50–66 years) with a family history of AD (FH+) and 44 age-, gender-, and educational level-matched controls (FH-) were enrolled in our study. After cognitive assessment using the Montreal Cognitive Assessment and Mini-mental State Examination, participants underwent antisaccade trials, and all parameters were recorded using an eye tracker.ResultsWhile the average velocity was relatively lower in FH+ individuals than in FH− individuals (107.9 ± 14.3°/s vs. 132.9 ± 23.7°/s, p < 0.001), FH+ individuals surprisingly showed relatively fewer uninhibited reflexive saccades (44.7 ± 26.0% vs. 56.2 ± 24.7%, p = 0.037) than the control group. They also required a relatively shorter time to detect and correct false saccades (121.6 ± 40.7 ms vs. 143.9 ± 37.0 ms, p = 0.023).ConclusionsThis study showed that family history is associated with alterations in antisaccadic parameters, suggesting that eye tracking can be used to assess oculomotor control and executive function in individuals at risk of developing dementia

A Cocktail Nanovaccine Targeting Key Entry Glycoproteins Elicits High Neutralizing Antibody Levels Against EBV Infection

Epstein-Barr virus (EBV) infects more than 95% of adults worldwide and is closely associated with various malignancies. Considering the complex life cycle of EBV, developing vaccines targeting key entry glycoproteins to elicit robust and durable adaptive immune responses may provide better protection. EBV gHgL-, gB- and gp42-specific antibodies in healthy EBV carriers contributed to sera neutralizing abilities in vitro, indicating that they are potential antigen candidates. To enhance the immunogenicity of these antigens, we formulate three nanovaccines by co-delivering molecular adjuvants (CpG and MPLA) and antigens (gHgL, gB or gp42). These nanovaccines induce robust humoral and cellular responses through efficient activation of dendritic cells and germinal center response. Importantly, these nanovaccines generate high levels of neutralizing antibodies recognizing vulnerable sites of all three antigens. IgGs induced by a cocktail vaccine containing three nanovaccines confer superior protection from lethal EBV challenge in female humanized mice compared to IgG elicited by individual NP-gHgL, NP-gB and NP-gp42. Importantly, serum antibodies elicited by cocktail nanovaccine immunization confer durable protection against EBV-associated lymphoma. Overall, the cocktail nanovaccine shows robust immunogenicity and is a promising candidate for further clinical trials