261 research outputs found
Lianhuaqingwen Exerts Anti-Viral and Anti-Inflammatory Activity Against Novel Coronavirus (SARS-CoV-2)
No full textAuteurs : Li Runfeng, Hou Yunlong, Huang Jicheng, Pan Weiqi , Ma Qinhai, Shi Yongxia , Li Chufang, Zhao Jin, Jia Zhenhua, Jiang Haiming, Zheng Kui, Huang Shuxiang, Dai Jun, Li Xiaobo, Hou Xiaotao, Wang Lin, Zhong Nanshan, Yang Zifeng. Production : Pharmacological Research, Volume 156, June 2020, 104761 Diffusion : ScienceDirect, site web géré par l'éditeur Elsevier. Date : Reçu le 29 février 2020, révisé le 14 mars 2020, accepté le 17 mars 2020, disponible en ligne le 20 mars 2020. ..
Fabrication of fullerene-decorated graphene oxide and its influence on flame retardancy of high density polyethylene
No full textFullerene (C-60) decorated graphene oxide (GO), denoted as GO-d-C-60, was synthesized through a three step chemical process, including acylating chlorination of GO, amino-functionalization of GO and addition reaction of C-60 molecules with amino groups, with the purpose of promoting the dispersion of GO in high density polyethylene (HDPE) and further improving thermal stability and flame retardancy of HDPE/ GO composite. Infrared spectroscopy (IR), transmission electron micrographs (TEM) and X-ray photoelectron spectroscopy (XPS) proved that about 2.3 wt.% of C-60 molecules, with the size of about 40-70 nm, were bonded onto the surface of GO and mainly located on the edge of GO sheets. The chemical decoration made GO-d-C-60 to have better dispersion in HDPE than GO, favoring the formation of compact and integrated char barriers when heated or ignited. Consequently, GO-d-C-60 improved the thermal stability and flame retardancy of HDPE more effectively than pristine GO, due to the assembly of the barrier effect of GO and the radical-trapping effect of C-60. (C) 2016 Elsevier Ltd. All rights reserved
Brain Dynamic Information Flow Estimation Based on EEG and Diffusion MRI: A Proof-of-principle Study and Application in Stroke
No full textIn the hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Electroencephalography (EEG), with an excellent temporal resolution, can be used to reveal functional changes in the brain following a stroke. This study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which combines EEG, anatomical MRI and diffusion weighted imaging (DWI), to estimation brain dynamic information flow and its changes following a stroke. EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 88%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals, using matrices lateralization index and activation complexity. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.Mechanical Engineerin
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Regional Price Variations of U.S. Alfalfa Hay
No full textAlfalfa hay is one of the most important field crops in the United States, its regional price differences are driven by variations in quality, location, seasonality, and other features. This thesis investigates the impact of dairy cow inventories, lagged milk prices, corn prices, and alfalfa hay exports on alfalfa hay prices across regions and states utilizing a panel data. Furthermore, I analyze and depict a spatial economic distribution of alfalfa hay price variations with the support of SAS, ArcMap, and GeoDa. Results indicate that alfalfa hay exports are greatly contributing to higher alfalfa hay prices for the seven exporting states. Domestically, grain markets are highly linked to alfalfa hay markets and lagged milk prices as a derived demand have more influence than dairy cow inventories as a primary demand on alfalfa hay prices. Also, alfalfa hay prices are significantly and considerably different, and have positive spatial autocorrelation across states, following a consistent pattern with the lowest prices in the Midwest. Empirical evidence of this thesis may shed light on optimizing profit for dairy industries with an alternative ratio of fees crops and predicting when/where to sell/buy alfalfa hay for hay industries
A Hybrid Forecasting Model for Wind Energy based on the Complementary Ensemble Empirical Mode Decomposition and Whale Optimized Back Propagation Neural Network
No full textA Distributed Task Scheduling Method Based on Conflict Prediction for Ad Hoc UAV Swarms
No full textUAV swarms have attracted great attention, and are expected to be used in scenarios, such as search and rescue, that require many urgent jobs to be completed in a minimum time by multiple vehicles. For complex missions with tight constraints, careful assigning tasks is inseparable from the scheduling of these tasks, and multi-task distributed scheduling (MTDS) is required. The Performance Impact (PI) algorithm is an excellent solution for MTDS, but it suffers from the suboptimal solution caused by the heuristics for local task selection, and the deadlock problem that it may fall into an infinite cycle of exchanging the same task. In this paper, we improve the PI algorithm by integrating a new task-removal strategy and a conflict prediction mechanism into the task-removal phase and the task-inclusion phase, respectively. Specifically, the task-removal strategy results in better exploration of the inclusion of more tasks than the original PI by freeing up more space in the local scheduler, improving the suboptimal solution caused by the heuristics for local task selection, as done in PI. In addition, we design a conflict prediction mechanism that simulates adjacent vehicles performing inclusion operations as the criteria for local task inclusion. Therefore, it can reduce the deadlock ratio and iteration times of the MTDS algorithm. Furthermore, by combining the protocol stack with the physical transmission model, an ad-hoc network simulation platform is constructed, which is closer to the real-world network, and serves as the supporting environment for testing the MTDS algorithms. Based on the constructed ad-hoc network simulation platform, we demonstrate the advantage of the proposed algorithm over the original PI algorithm through Monte Carlo simulation of search and rescue tasks. The results show that the proposed algorithm can reduce the average time cost, increase the total allocation number under most random distributions of vehicles-tasks, and significantly reduce the deadlock ratio and the number of iteration rounds
Association between IGF-IR, m-calpain and UCP-3 gene polymorphisms and growth traits in Nanyang cattle
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