Estrogenicity of agricultural runoff: A rainfall simulation study of worst-case scenarios using fresh layer and roaster litter, and farrowing swine manure

2021 ◽  
Vol 750 ◽  
pp. 141188
Author(s):  
N.W. Shappell ◽  
M.J. Shipitalo ◽  
L.O. Billey
2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Sangil Choi ◽  
Wooksik Lee ◽  
Teukseob Song ◽  
Jong-Hoon Youn

Neighbor discovery is a significant research topic in wireless sensor networks. After wireless sensor devices are deployed in specific areas, they attempt to determine neighbors within their communication range. This paper proposes a new Block design-based Asynchronous Neighbor Discovery protocol for sensor networks calledBAND. We borrow the concept of combinatorial block designs for neighbor discovery. First, we summarize a practical challenge and difficulty of using the original block designs. To address this challenge, we create a new block generation technique for neighbor discovery schedules and provide a mathematical proof of the proposed concept. A key aspect of the proposed protocol is that it combines two block designs in order to construct a new block for neighbor discovery. We analyze the worst-case neighbor discovery latency numerically between our protocol and some well-known protocols in the literature. Our protocol reveals that the worst-case latency is much lower than others. Finally, we evaluate the performance ofBANDand existing representative protocols through the simulation study. The results of our simulation study show that the average and maximum latency ofBANDis about 40% lower than that of existing protocols. Furthermore,BANDspends approximately 30% less energy than others during the neighbor discovery process.


2015 ◽  
Vol 60 (1) ◽  
pp. 235-242
Author(s):  
Qingqing Fang ◽  
Lei Zhang ◽  
Haotian Sun ◽  
Guoqiang Wang ◽  
Zongxue Xu ◽  
...  

1991 ◽  
Vol 4 (1) ◽  
pp. 79-91 ◽  
Author(s):  
D. Warrington ◽  
I. Shainberg ◽  
G.J. Levy ◽  
Y. Bar-Or

2016 ◽  
Vol 45 (3) ◽  
pp. 1062-1070 ◽  
Author(s):  
Leonard C. Kibet ◽  
Ray B. Bryant ◽  
Anthony R. Buda ◽  
Peter J. A. Kleinman ◽  
Louis S. Saporito ◽  
...  

1978 ◽  
Vol 21 (5) ◽  
pp. 0886-0892 ◽  
Author(s):  
J. L. Baker ◽  
J. M. Laflen ◽  
H. P. Johnson

2018 ◽  
Vol 47 (3) ◽  
pp. 487-495 ◽  
Author(s):  
Matthew Riddle ◽  
Lars Bergström ◽  
Frank Schmieder ◽  
Holger Kirchmann ◽  
Leo Condron ◽  
...  

2021 ◽  
Author(s):  
Suha Naser-Khdour ◽  
Rob Lanfear ◽  
Bui Quang Minh

Phylogenetic inference typically assumes that the data has evolved under Stationary, Reversible and Homogeneous (SRH) conditions. Many empirical and simulation studies have shown that assuming SRH conditions can lead to significant errors in phylogenetic inference when the data violates these assumptions. Yet, many simulation studies focused on extreme non-SRH conditions that represent worst-case scenarios and not the average empirical dataset. In this study, we simulate datasets under various degrees of non-SRH conditions using empirically derived parameters to mimic real data and examine the effects of incorrectly assuming SRH conditions on inferring phylogenies. Our results show that maximum likelihood inference is generally quite robust to a wide range of SRH model violations but is inaccurate under extreme convergent evolution.


Chemosphere ◽  
1994 ◽  
Vol 28 (4) ◽  
pp. 649-662 ◽  
Author(s):  
H. Klöppel ◽  
J. Haider ◽  
W. Kördel

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