Confusion noise from Galactic binaries for Taiji

Gravitational waves (GWs) from tens of millions of compact binaries in our Milky Way enter the milli-Hertz band of space-based detection. The majority of them cannot be resolved individually, resulting in a foreground confusion noise for Laser Interferometer Space Antenna (LISA). The concept of Taiji mission is similar to LISA's with slightly better sensitivity, which means that the galactic GW signals will also affect the detection with Taiji. Here we generate the GW signals from 29.8 million galactic binaries for Taiji and subtract the `resolvable' sources. The confusion noise is estimated and fitted in an analytic form with 6-month, 1-year, 2-year and 4-year observation time. We find that the full sensitivity curve is slightly lower for Taiji than for LISA at frequencies of $\leq 0.8$ mHz and around 2~mHz. For a 4-year lifetime, more than 29 thousand sources are resolvable with Taiji. Compared to LISA, Taiji can subtract $\sim 20 \%$ more sources and the distribution of them in our Milky Way is consistent with that of the resolvable sources with LISA. At frequencies around 2~mHz or with the chirp masses ranging from $0.2 M_\odot$ to $0.4 M_\odot$, more sources become resolvable with Taiji.Comment: 7 pages, 5 figures. Version accepted by PR

High Temperature Corrosion Behaviors of the Superheater Materials

AbstractThe high temperature corrosion tests are performed on 20#steel, TP347H and superalloy C22. The high temperature corrosion behaviors of these superheater materials in the synthetic salt containing 80wt-%KCl+20wt-%K2SO4 have been investigated under the oxidizing atmosphere at a temperature of 650°C for 218hours. For comparison, the column diagram has been obtained by mass loss. The scanning electron microscopy (SEM) with energy dispersive spectrometer (EDS) is used to characterize the surface morphology and compositions of the corrosion products. The results have shown that the superalloy C22 exhibits the high corrosion resistance

Effect of organic acids on the growth and lipid accumulation of oleaginous yeast Trichosporon fermentans

<p>Abstract</p> <p>Background</p> <p>Microbial lipids have drawn increasing attention in recent years as promising raw materials for biodiesel production, and the use of lignocellulosic hydrolysates as carbon sources seems to be a feasible strategy for cost-effective lipid fermentation with oleaginous microorganisms on a large scale. During the hydrolysis of lignocellulosic materials with dilute acid, however, various kinds of inhibitors, especially large amounts of organic acids, will be produced, which substantially decrease the fermentability of lignocellulosic hydrolysates. To overcome the inhibitory effects of organic acids, it is critical to understand their impact on the growth and lipid accumulation of oleaginous microorganisms.</p> <p>Results</p> <p>In our present work, we investigated for the first time the effect of ten representative organic acids in lignocellulosic hydrolysates on the growth and lipid accumulation of oleaginous yeast <it>Trichosporon fermentans </it>cells. In contrast to previous reports, we found that the toxicity of the organic acids to the cells was not directly related to their hydrophobicity. It is worth noting that most organic acids tested were less toxic than aldehydes to the cells, and some could even stimulate the growth and lipid accumulation at a low concentration. Unlike aldehydes, most binary combinations of organic acids exerted no synergistic inhibitory effects on lipid production. The presence of organic acids decelerated the consumption of glucose, whereas it influenced the utilization of xylose in a different and complicated way. In addition, all the organic acids tested, except furoic acid, inhibited the malic activity of <it>T. fermentans</it>. Furthermore, the inhibition of organic acids on cell growth was dependent more on inoculum size, temperature and initial pH than on lipid content.</p> <p>Conclusions</p> <p>This work provides some meaningful information about the effect of organic acid in lignocellulosic hydrolysates on the lipid production of oleaginous yeast, which is helpful for optimization of biomass hydrolysis processes, detoxified pretreatment of hydrolysates and lipid production using lignocellulosic materials.</p

Parameter inference for coalescing massive black hole binaries using deep learning

In the 2030s, a new era of gravitational-wave (GW) observations will dawn as multiple space-based GW detectors, such as the Laser Interferometer Space Antenna, Taiji and TianQin, open the millihertz window for GW astronomy. These detectors are poised to detect a multitude of GW signals emitted by different sources. It is a challenging task for GW data analysis to recover the parameters of these sources at a low computational cost. Generally, the matched filtering approach entails exploring an extensive parameter space for all resolvable sources, incurring a substantial cost owing to the generation of GW waveform templates. To alleviate the challenge, we make an attempt to perform parameter inference for coalescing massive black hole binaries (MBHBs) using deep learning. The model trained in this work has the capability to produce 50,000 posterior samples for redshifted total mass, mass ratio, coalescence time and luminosity distance of a MBHB in about twenty seconds. Our model can serve as a potent data pre-processing tool, reducing the volume of parameter space by more than four orders of magnitude for MBHB signals with a signal-to-noise ratio larger than 100. Moreover, the model exhibits robustness when handling input data that contains multiple MBHB signals.Comment: 8 pages, 4 figure

A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification