Study of population dynamic for a recombinant bacterium during continuous cultures: Appliction of data filtering and smoothing

1992 ◽  
Vol 39 (4) ◽  
pp. 398-407 ◽  
Author(s):  
Nabil Nancib ◽  
Ridha Mosrati ◽  
Joseph Boudrant
2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.


2010 ◽  
Vol 21 (1) ◽  
pp. 107-118
Author(s):  
Ye-Qing YI ◽  
Ya-Ping LIN ◽  
Xiao-Long LI ◽  
Si-Qing YANG ◽  
Zhi-Qiang YOU

1987 ◽  
Vol 22 (3) ◽  
pp. 427-436 ◽  
Author(s):  
S.E. Hrudey ◽  
E. Knettig ◽  
P.M. Fedorak ◽  
S.A. Daignault

Abstract Rapid and preferential dechlorination of the ortho chlorine from 2,6-, 2,4- and 2,3- dichlorophenol substrates was observed in semi-continuous cultures inoculated with 50% unacclimated anaerobic sludge. The rate of further dechlorination depended on the position of the second chlorine atom. The dechlorination rates for the second chlorine ranked ortho > para > meta. Complete mineralization to methane was only observed in cultures fed 2,6-dichlorophenol. Addition of activated carbon to the anaerobic cultures showed some benefit to the degradation process.


2002 ◽  
Vol 215 (2) ◽  
pp. 253-262 ◽  
Author(s):  
YUU NAKAYAMA ◽  
HIROMI SENO ◽  
HIROYUKI MATSUDA

2020 ◽  
Vol 11 (1) ◽  
pp. 10
Author(s):  
Muchun Su ◽  
Diana Wahyu Hayati ◽  
Shaowu Tseng ◽  
Jiehhaur Chen ◽  
Hsihsien Wei

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.


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