4,942 research outputs found
Sharp Bounds for the Signless Laplacian Spectral Radius in Terms of Clique Number
In this paper, we present a sharp upper and lower bounds for the signless
Laplacian spectral radius of graphs in terms of clique number. Moreover, the
extremal graphs which attain the upper and lower bounds are characterized. In
addition, these results disprove the two conjectures on the signless Laplacian
spectral radius in [P. Hansen and C. Lucas, Bounds and conjectures for the
signless Laplacian index of graphs, Linear Algebra Appl., 432(2010) 3319-3336].Comment: 15 pages 1 figure; linear algebra and its applications 201
Risk factors for high-altitude headache upon acute high-altitude exposure at 3700 m in young Chinese men: a cohort study.
BackgroundThis prospective and observational study aimed to identify demographic, physiological and psychological risk factors associated with high-altitude headache (HAH) upon acute high-altitude exposure.MethodsEight hundred fifty subjects ascended by plane to 3700 m above Chengdu (500 m) over a period of two hours. Structured Case Report Form (CRF) questionnaires were used to record demographic information, physiological examinations, psychological scale, and symptoms including headache and insomnia a week before ascending and within 24 hours after arrival at 3700 m. Binary logistic regression models were used to analyze the risk factors for HAH.ResultsThe incidence of HAH was 73.3%. Age (p =0.011), physical labor intensity (PLI) (p =0.044), primary headache history (p <0.001), insomnia (p <0.001), arterial oxygen saturation (SaO2) (p =0.001), heart rate (HR) (p =0.002), the Self-Rating Anxiety Scale (SAS) (p <0.001), and the Epworth Sleepiness Scale (ESS) (p <0.001) were significantly different between HAH and non-HAH groups. Logistic regression models identified primary headache history, insomnia, low SaO2, high HR and SAS as independent risk factors for HAH.ConclusionsInsomnia, primary headache history, low SaO2, high HR, and high SAS score are the risk factors for HAH. Our findings will provide novel avenues for the study, prevention and treatment of HAH
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Self-sustainable protonic ceramic electrochemical cells using a triple conducting electrode for hydrogen and power production.
The protonic ceramic electrochemical cell (PCEC) is an emerging and attractive technology that converts energy between power and hydrogen using solid oxide proton conductors at intermediate temperatures. To achieve efficient electrochemical hydrogen and power production with stable operation, highly robust and durable electrodes are urgently desired to facilitate water oxidation and oxygen reduction reactions, which are the critical steps for both electrolysis and fuel cell operation, especially at reduced temperatures. In this study, a triple conducting oxide of PrNi0.5Co0.5O3-δ perovskite is developed as an oxygen electrode, presenting superior electrochemical performance at 400~600 °C. More importantly, the self-sustainable and reversible operation is successfully demonstrated by converting the generated hydrogen in electrolysis mode to electricity without any hydrogen addition. The excellent electrocatalytic activity is attributed to the considerable proton conduction, as confirmed by hydrogen permeation experiment, remarkable hydration behavior and computations
A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions
Deep learning techniques have been widely used in computed tomography (CT)
but require large data sets to train networks. Moreover, data sharing among
multiple institutions is limited due to data privacy constraints, which hinders
the development of high-performance DL-based CT imaging models from
multi-institutional collaborations. Federated learning (FL) strategy is an
alternative way to train the models without centralizing data from
multi-institutions. In this work, we propose a novel peer-to-peer federated
continual learning strategy to improve low-dose CT imaging performance from
multiple institutions. The newly proposed method is called peer-to-peer
continual FL with intermediate controllers, i.e., icP2P-FL. Specifically,
different from the conventional FL model, the proposed icP2P-FL does not
require a central server that coordinates training information for a global
model. In the proposed icP2P-FL method, the peer-to-peer federated continual
learning is introduced wherein the DL-based model is continually trained one
client after another via model transferring and inter institutional parameter
sharing due to the common characteristics of CT data among the clients.
Furthermore, an intermediate controller is developed to make the overall
training more flexible. Numerous experiments were conducted on the AAPM
low-dose CT Grand Challenge dataset and local datasets, and the experimental
results showed that the proposed icP2P-FL method outperforms the other
comparative methods both qualitatively and quantitatively, and reaches an
accuracy similar to a model trained with pooling data from all the
institutions
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