Outflow and hot dust emission in high redshift quasars

Correlations of hot dust emission with outflow properties are investigated, based on a large z~2 non-broad absorption lines quasar sample built from the Wide-field Infrared Survey and the Sloan Digital Sky Survey data releases. We use the near infrared slope and the infrared to UV luminosity ratio to indicate the hot dust emission relative to the emission from the accretion disk. In our luminous quasars, these hot dust emission indicators are almost independent of the fundamental parameters, such as luminosity, Eddington ratio and black hole mass, but moderately dependent on the blueshift and asymmetry index (BAI) and full width at half-maximum (FWHM) of CIV lines. Interestingly, the latter two correlations dramatically strengthen with increasing Eddington ratio. We suggest that, in high Eddington ratio quasars, CIV regions are dominated by outflows so the BAI and FWHM(CIV) can reliably reflect the general properties and velocity of outflows, respectively. While in low Eddington ratio quasars, CIV lines are primarily emitted by virialized gas so the BAI and FWHM(CIV) become less sensitive to outflows. Therefore, the correlations for the highest Eddington ratio quasars are more likely to represent the true dependence of hot dust emission on outflows and the correlations for the entire sample are significantly diluted by the low Eddington ratio quasars. Our results show that an outflow with a large BAI or velocity can double the hot dust emission on average. We suggest that outflows either contain hot dust in themselves or interact with the dusty interstellar medium or torus.Comment: 14 page, 4 figures, accepted for publication in ApJ

Outflow and hot dust emission in broad absorption line quasars

We have investigated a sample of 2099 broad absorption line (BAL) quasars with z=1.7-2.2 built from the Sloan Digital Sky Survey Data Release Seven and the Wide-field Infrared Survey. This sample is collected from two BAL quasar samples in the literature, and refined by our new algorithm. Correlations of outflow velocity and strength with hot dust indicator (beta_NIR) and other quasar physical parameters, such as Eddington ratio, luminosity and UV continuum slope, are explored in order to figure out which parameters drive outflows. Here beta_NIR is the near-infrared continuum slope, a good indicator of the amount of hot dust emission relative to accretion disk emission. We confirm previous findings that outflow properties moderately or weakly depends on Eddington ratio, UV slope and luminosity. For the first time, we report moderate and significant correlations of outflow strength and velocity with beta_NIR in BAL quasars. It is consistent with the behavior of blueshifted broad emission lines in non-BAL quasars. The statistical analysis and composite spectra study both reveal that outflow strength and velocity are more strongly correlated with beta_NIR than Eddington ratio, luminosity and UV slope. In particular, the composites show that the entire C IV absorption profile shifts blueward and broadens as beta_NIR increases, while Eddington ratio and UV slope only affect the high and low velocity part of outflows, respectively. We discuss several potential processes and suggest that dusty outflow scenario, i.e. dust is intrinsic to outflows and may contribute to the outflow acceleration, is most likely. The BAL quasar catalog is available from the authors upon request.Comment: 16 pages, 10 figures, 2 tables; Accepted for publication in The Astrophysical Journa

Comparative analysis of satellite-based and ground-based lightning detection data during 2013-2016 in China

464-470According to the location of sensors，there are two main ways to obtain lightning detection data：satellite-based and ground-based，each have its own advantages and disadvantages, the assessment of data quality is an important issue. In this paper, based on comprehensive analysis of the data from satellite- and ground-based detector (2013-2016), a sensitivity test is designed, and a scientific comparison method is proposed. From multiple dimensions, the differences and similarities between them, the sources of error and the method of correcting are investigated. Through the comparative analysis, we can get more information on spatial and temporal lightning distribution characteristics in China the following conclusions are drawn: 1. satellite- and ground-based lightning detection data over China show good consistency during 2013-2016, and with the development of technology and the improvement of detection stations layout, the proportion of matches go up steadily. Furthermore, limited by geographical factors, the layout of China Lightning Detection Network (CLDN) is uneven, the East is comparatively dense, and West is comparatively sparse, so the matching ratio is not exactly the same. We also noticed that Lightning Imaging Sensor (LIS) cannot continuously monitor the evolution process of lightning, and it’s a reason that the proportion of matches are not very high in general. 2. Matching proportion shows obvious time diversity. It presented a single peak in August. Matching proportion among seasons as follows: summer-autumn-spring-winter (from high to low), in particular, it had less variability during winter and spring than summer and autumn, reflecting the characters of strong convective weather in China. 3. Due to the different detection principles, the amount of lightning events that occur at “night” detected by LIS, much more than lightning events that occur in the “day” time. While the situation is completely different for CLDN, there was no significant difference in the amount of lightning that occurs between day and night, and there is still a lot of space for improvement. 4. Sample data revealed that larger radiance LIS signals did not mean a higher matching ratio, this is different from conventional wisdom. It may imply that satellite-based detection and ground-based detection are sensitive to lightning signals with different radiation levels. The work of this paper also provides the basis for the quality evaluation and fusion analysis of the satellite-based vs. ground-based multi-source lightning detection data

GFI1 downregulation promotes inflammation-linked metastasis of colorectal cancer.

Inflammation is frequently associated with initiation, progression, and metastasis of colorectal cancer (CRC). Here, we unveil a CRC-specific metastatic programme that is triggered via the transcriptional repressor, GFI1. Using data from a large cohort of clinical samples including inflammatory bowel disease and CRC, and a cellular model of CRC progression mediated by cross-talk between the cancer cell and the inflammatory microenvironment, we identified GFI1 as a gating regulator responsible for a constitutively activated signalling circuit that renders CRC cells competent for metastatic spread. Further analysis of mouse models with metastatic CRC and human clinical specimens reinforced the influence of GFI1 downregulation in promoting CRC metastatic spread. The novel role of GFI1 is uncovered for the first time in a human solid tumour such as CRC. Our results imply that GFI1 is a potential therapeutic target for interfering with inflammation-induced CRC progression and spread

Dynamic Mechanical Properties and Microstructure of Graphene Oxide Nanosheets Reinforced Cement Composites

This paper presents an experimental investigation on the effect of uniformly dispersed graphene oxide (GO) nanosheets on dynamic mechanical properties of cement based composites prepared with recycled fine aggregate (RFA). Three different amounts of GO, 0.05%, 0.10%, and 0.20% in mass of cement, were used in the experiments. The visual inspections of GO nanosheets were also carried out after ultrasonication by transmission electron microscope (TEM) atomic force microscope (AFM), and Raman to characterize the dispersion effect of graphite oxide. Dynamic mechanical analyzer test showed that the maximum increased amount of loss factor and storage modulus, energy absorption was 125%, 53%, and 200% when compared to the control sample, respectively. The flexural and compressive strengths of GO-mortar increased up to 22% to 41.3% and 16.2% to 16.4% with 0.20 wt % GO at 14 and 28 days, respectively. However the workability decreased by 7.5% to 18.8% with 0.05% and 0.2% GO addition. Microstructural analysis with environmental scanning electron microscopy (ESEM)/backscattered mode (BSEM) showed that the GO-cement composites had a much denser structure and better crystallized hydration products, meanwhile mercury intrusion porosimetry (MIP) testing and image analysis demonstrated that the incorporation of GO in the composites can help in refining capillary pore structure and reducing the air voids content

Personalized federated learning based on feature fusion

Federated learning enables distributed clients to collaborate on training while storing their data locally to protect client privacy. However, due to the heterogeneity of data, models, and devices, the final global model may need to perform better for tasks on each client. Communication bottlenecks, data heterogeneity, and model heterogeneity have been common challenges in federated learning. In this work, we considered a label distribution skew problem, a type of data heterogeneity easily overlooked. In the context of classification, we propose a personalized federated learning approach called pFedPM. In our process, we replace traditional gradient uploading with feature uploading, which helps reduce communication costs and allows for heterogeneous client models. These feature representations play a role in preserving privacy to some extent. We use a hyperparameter $a$ to mix local and global features, which enables us to control the degree of personalization. We also introduced a relation network as an additional decision layer, which provides a non-linear learnable classifier to predict labels. Experimental results show that, with an appropriate setting of $a$, our scheme outperforms several recent FL methods on MNIST, FEMNIST, and CRIFAR10 datasets and achieves fewer communications

Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios

Improving Mechanical Properties of Magnesium Phosphate Cement-Based Ultra-High Performance Concrete by Ultrafine Fly Ash Incorporation

Magnesium phosphate cement-based ultra-high-performance concrete (MPC-UHPC) develops strength rapidly but shows unsatisfactory ultimate strength (\u3c150 \u3eMPa). In this study, ultrafine fly ash (UFA) was incorporated in MPC-UHPC to improve mechanical properties. The effect of UFA content, varying from 0 % to 15 %, by weight of binder, on microstructure, fiber pullout behavior, mechanical strengths, flexural and tensile properties of MPC-UHPC reinforced with 2 vol% steel fibers was investigated. Experimental results indicate that the incorporation of 10 %–15 % UFA with mean particle size of 1.4 µm was effective in reducing porosity and microcracks of binder matrix and fiber–matrix interface, thereby resulting in significantly improved fiber–matrix bond properties. The 28-d average bond strength and pullout energy could increase by 27 % and 57 % at 15 % UFA addition, respectively. Such UFA addition level was found to yield a mix with 158.3 MPa compressive strength and substantially enhance the flexural and tensile fracture properties. The result reported herein will further promote the utilization of UFA for the development of MPC materials with outstanding mechanical properties

TRA2A Binds With LncRNA MALAT1 To Promote Esophageal Cancer Progression By Regulating EZH2/beta-catenin Pathway

The RNA binding protein TRA2A, a member of the transformer 2 homolog family, plays a crucial role in the alternative splicing of pre-mRNA. However, it remains unclear whether TRA2A is involved in non-coding RNA regulation and, if so, what are the functional consequences. By analyzing expression profiling data, we found that TRA2A is highly expressed in esophageal cancer and is associated with disease-free survival and overall survival time. Subsequent gain- and loss-of-function studies demonstrated that TRA2A promotes proliferation and migration of esophageal squamous cell carcinoma and adenocarcinoma cells. RNA immunoprecipitation and RNA pull-down assay indicated that TRA2A can directly bind specific sites on MALAT1 in cells. In addition, ectopic expression or depletion of TRA2A leads to MALAT expression changes accordingly, thus modulates EZH2/β-catenin pathway. Together, these findings elucidated that TRA2A triggers carcinogenesis via MALAT1 mediated EZH2/β-catenin axis in esophageal cancer cells