scholarly journals Estimation of axonal conduction speed and the inter hemispheric transfer time using connectivity informed maximum entropy on the mean

2019 ◽  
Author(s):  
Samuel Deslauriers-Gauthier ◽  
Rachid Deriche
Keyword(s):  
Maximum Entropy ◽  
Transfer Time ◽  
Axonal Conduction ◽  
The Mean

scholarly journals The Maximum Entropy on the Mean Method for Image Deblurring

2020 ◽  
Author(s):  
Gabriel Rioux ◽  
Rustum Choksi ◽  
Tim Hoheisel ◽  
Pierre Marechal ◽  
Christopher Scarvelis
Keyword(s):  
Maximum Entropy ◽  
Image Deblurring ◽  
The Mean

Abstract P188: Transfer Time From Spoke ER Arrival to CSC Arrival in a Hub-Spoke Transfer Model of Thrombolysis for Acute Ischemic Stroke

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Vivien H Lee ◽  
Paul A Segerstrom ◽  
Ciarán J Powers ◽  
Sharon Heaton ◽  
Shahid M Nimjee ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Univariate Analysis ◽  
Transfer Model ◽  
Transfer Time ◽  
Intravenous Tissue Plasminogen Activator ◽  
The Mean ◽  
Comprehensive Stroke Center ◽  
Stroke Center ◽  
Er Status

Introduction: Acute ischemic stroke (AIS) patients who present to a spoke Emergency Room (ER) and require transfer to a comprehensive stroke center (CSC) hub face potential delays Methods: We performed a retrospective review of 269 suspected AIS patients who received intravenous tissue plasminogen activator (tPA) from July 2016 to October 2017 in our academic telestroke network. During this period, nearly all tPA patients were transferred to the CSC hub. Data was collected on patient demographics, National Institutes of Health Stroke Scale (NIHSS), door to needle time (DTN), and distance to CSC. ER-to-CSC was defined as the time from patient arrival at Spoke ER to arrival at CSC. Top volume ER status was assigned to the 4 Spoke ERs with the highest volume of tPA. Results: Among 269 AIS patients who received tPA at spoke ERs, the mean age was 65.4 years (range, 21 to 95), 49% were female, and 91.8% were white. The initial median NIHSS was 6 (range, 0 to 30) and the mean DTN was 73.1 minutes (range, 14 to 234). The mean distance from Spoke ER to CSC was 55.2 miles (range 5.8 to 125) and the mean ER-to-CSC was 2.6 hours (range 0.62 to 6.3) (Figure 1). In univariate analysis, the following factors were significantly associated with ER-to-CSC: distance (p < 0.0001), DTN (p < 0.0001), NIHSS (p 0.0007), and top volume ER status (p 0.0034). Patient sex, age, race, SBP, weight, initial NIHSS, daytime shift, and weekend status were not significantly associated with ER-to-CSC. Significant variables from the univariate analysis were included in multivariate linear regression model in which DTN (P < 0.0001), distance (P < 0.0001), and NIHSS (P 0.024) association with ER-to-CSC remained significant. Conclusions: In our series of AIS tPA patients transferred to CSC, the mean time from spoke ER arrival to CSC arrival was 2.6 hours. Factors associated with CSC arrival time include markers of ER performance (DTN), severity (NIHSS), and distance. Further study is warranted to improve transfer time in AIS.

Uncertainty Quantification of Energy Harvesting Systems Using Method of Quadratures and Maximum Entropy Principle

2015 ◽  
Author(s):  
Aditya Nanda ◽  
M. Amin Karami ◽  
Puneet Singla
Keyword(s):  
Energy Harvesting ◽  
Maximum Entropy ◽  
Density Function ◽  
Root Mean Square ◽  
Maximum Entropy Principle ◽  
Mean Square ◽  
Mean Power ◽  
Harvesting Systems ◽  
Entropy Principle ◽  
The Mean

This paper uses the method of Quadratures in conjunction with the Maximum Entropy principle to investigate the effect of parametric uncertainties on the mean power output and root mean square deflection of piezoelectric vibrational energy harvesting systems. Uncertainty in parameters of harvesters could arise from insufficient manufacturing controls or change in material properties over time. We investigate bimorph based harvesters that transduce ambient vibrations to electricity via the piezoelectric effect. Three varieties of energy harvesters — Linear, Nonlinear monostable and Nonlinear bistable are considered in this research. This analysis quantitatively shows the probability density function for the mean power and root mean square deflection as a function of the probability densities of the excitation frequency, excitation amplitude, initial deflection of the bimorph and magnet gap of the energy harvester. The method of Quadratures is used for numerically integrating functions by propagating weighted points from the domain and evaluating the integral as a weighted sum of the function values. In this paper, the method of Quadratures is used for evaluating central moments of the distributions of rms deflection and mean harvested power and, then, in conjunction with the principle of Maximum Entropy (MaxEnt) an optimal density function is obtained which maximizes the entropy and satisfies the moment constraints. The The computed nonlinear density functions are validated against Monte Carlo simulations thereby demonstrating the efficiency of the approach. Further, the Maximum Entropy principle is widely applicable to uncertainty quantification of a wide range of dynamic systems.

scholarly journals Functional calibration estimation by the maximum entropy on the mean principle

Statistics ◽  
2014 ◽  
Vol 49 (5) ◽  
pp. 989-1004 ◽  
Author(s):  
S. Gallón ◽  
F. Gamboa ◽  
J.M. Loubes
Keyword(s):  
Maximum Entropy ◽  
The Mean

scholarly journals On the Long-Period Variations in the Rate of the Earth's Rotation

1979 ◽  
Vol 82 ◽  
pp. 59-60
Author(s):  
A. I. Emetz ◽  
A. A. Korsun'
Keyword(s):  
Power Spectrum ◽  
Maximum Entropy ◽  
Rotational Speed ◽  
Monthly Data ◽  
Long Period ◽  
Period Variations ◽  
Similar Series ◽  
The Mean ◽  
Using Data ◽  
Linear Trends

The maximum entropy power spectrum (Smylie, et al., 1974) of the Earth's rotational speed was calculated using data from 1900 to 1976. Two series of data were analyzed. The first was a series of δω/ω) determined from annual UT1 - ET data from 1900 to 1976. The second was a similar series derived from the mean monthly data of UT1 - TAI. Linear trends were removed from both series before analysis. Using the second series of data, significant periods of 2.8, 3.7, 7.0, and 10.5 years were found. The first series showed significant periods at 6, 10, 13, 22, and 57 years. Of these periodicities those at 22 and 57 years showed the largest amplitudes (0.454 ± 0.097 × 10−8 and 1.431 ± 0.104 × 10−8 respectively).

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