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Multi-carrier modulation technology is an advanced modulation method commonly used in broadband communication systems. However, multi-carrier signals will produce a very large Peak to Average Power Ratio (PAPR) in the time domain, which will lead to non-linear damage and seriously affects the performance of system. In this paper, a joint PAPR reduction technology enabled by neural network-based coding and companding is proposed. The frequency-domain coding is implemented by a fully connected layer, which greatly reduces the complexity and difficulty of network training. A small proportion of spread spectrum is introduced to provide coding gain, and the time-domain companding network uses a nonlinear convolutional neural network to reduce PAPR. The effect of the scheme is shown through simulation under various parameter conditions, and compared with various schemes. The simulation results show that the scheme can reduce PAPR by 5 dB while introducing rare distortion. Finally, the scheme is experimentally verified. The experimental results show that the Bit Error Ratio (BER) is reduced by 75% compared with the clipping scheme when the PAPR is reduced by 5 dB, which verifies the feasibility of the scheme.

The development of polymeric optical materials with a higher refractive index, transparency in the visible spectrum region and easier processability is increasingly desirable for advanced optical applications such as microlenses, image sensors, and organic light-emitting diodes. Most acrylates have a low refractive index (around 1.50) which does not meet the high performance requirements of advanced optical materials. In this research, three novel acrylates were synthesized via a facile one-step approach and used to fabricate optical transparent polymers. All of the polymers reveal good optical properties including high transparency (≥90%) in the visible spectrum region and high refractive index values (1.6363) at 550 nm. Moreover, nanostructures of these acrylate polymers with various feature sizes including nanogratings and photonic crystals were successfully fabricated using nanoimprint lithography. These results indicate that these acrylates can be used in a wide range of optical and optoelectronic devices where nanopatterned films with high refractive index and transparency are required.

Objective To study the dynamic changes of left ventricular hypertrophy with age found in spontaneously hypertensive rats (SHR). Methods Forty-five male SHR and thirty-five male Wistar Kyoto rats (WKY), which were four-week-old and specific-pathogen free, were observed continuously for 42 weeks. At 6,8,12,18,24,32 and 42 weeks of age, cardiac structure and function were examined by echocardiography, and systolic blood pressure was measured by tail cuff method; At the same time points, left ventricular mass index (LVMI) was calculated. The morphology of left ventricle was microscopied by HE staining, genes ANP and β-MHC were detected by RT-qPCR. Results The systolic blood pressure of SHR increased rapidly during the period from 6th to 12th weeks, and was significantly higher than that of WKY at 12 weeks (P＜0.05), after that the blood pressure of SHR increased slightly and slowly. With age and blood pressure changes, left ventricular hypertrophy gradually developed in SHR.The related hypertrophic indexes,interventricular septal thickness(IVST), left ventricular posterior wall thickness(LVPWT),transverse diameter of myocardial cell (TDM) and LVMI all showed characteristic changes. Correspondingly, the expression of hypertrophy-related genes ANP and β-MHC in SHR showed age- and blood pressure-dependent dynamic changes. Conclusions The left ventricular hypertrophy in SHR is characterized by the dynamic changes with aging and change of blood pressure.

© 2019. The American Astronomical Society. All rights reserved. During the past five years, 6, 7, and 26 transit observations were carried out for the HAT-P-9b, HAT-P-32b, and HAT-P-36b systems, respectively, through the Transiting Exoplanet Monitoring Project network. Combined with the published photometric data and radial-velocity measurements, our new photometry allows us to revisit the system parameters and search for additional close-in planetary companions in these hot Jupiter systems. We measure an updated R P /R ∗ = 0.1260 ±0.0011 for HAT-P-36 system in the R band, which is 4.5σ larger than the published i-band radius ratio of 0.1186 ±0.0012. We also perform a transit timing variation (TTV) analysis for each system. Because no significant TTVs were found, we place an upper mass limit on an additional planet for each system.

Macronutrients, including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S), are the most basic nutrient elements in the solution for the hydroponic system. However, the current management of hydroponic nutrient solutions usually depends on EC and pH sensors due to the lack of accurate specific macronutrient sensing equipment, which easily leads to nutritional imbalance for the cultivated plant. In this study, the UV-NIR absorption spectroscopy (200–1100 nm) was used to predict six macronutrients in hydroponic solutions; two kinds of single-task learning algorithms, including partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO), and two kinds of multi-task learning algorithms, including dirty multi-task learning (DMTL) and robust multi-task learning (RMTL), were investigated to develop prediction models and assess capabilities of UV-NIR. The results showed that N and Ca could be quantitatively predicted by UV-NIR with the ratio of performance to deviation (RPD) more than 2, K could be qualitatively predicted (1.4 < RPD < 2), and P, Mg, and S could not be successfully predicted (RPD < 1.4); the RMTL algorithm outperformed others for predicting K and Ca benefit from the underlying task relationships with N; and predicting P, Mg, and S were identified as irrelevant (outlier) tasks. Our study provides a potential approach for predicting several macronutrients in hydroponic solutions with UV-NIR, especially using RMTL to improve model prediction ability. [ABSTRACT FROM AUTHOR]