Static and dynamic distribution of spray from single nozzles and the influence on biological efficacy of band applications of herbicides

2006 ◽  
Vol 25 (11) ◽  
pp. 1201-1209 ◽  
Author(s):  
Peter Kryger Jensen ◽  
Ivar Lund
2016 ◽  
Vol 07 (2) ◽  
pp. 3-7
Author(s):  
Rashid Alakbarov ◽  
◽  
Fahrad Pashayev ◽  
Mammad Hashimov ◽  
◽  
...  

2019 ◽  
Vol 35 (1) ◽  
pp. 145-153
Author(s):  
O. Uyi, ◽  
I.G. Amolo ◽  
A.D. Adetimehin

Several studies have demonstrated the biological efficacy of leaf, stem and root powders or extracts of Chromolaena odorata (L.) King and Robinson against insect pests but those that are focused on the biological efficacy of aqueous leaf extracts against Macrotermes species are scanty. Current management of termites with synthetic insecticides is being discouraged due to human and environmental hazards. Therefore, the insecticidal effectiveness of aqueous leaf extract C. odorata against Macrotermes species was investigated. Five concentrations (0, 2.5, 5.0, 7.5 and 10.0% (w/v)) of the aqueous extract of C. odorata plant were evaluated for repellency and toxicity on the worker caste of Macrotermes species following standard procedures. The filter paper impregnation technique was used for the bioassay. Percentage repellency was monitored for 30 minutes and mortality recorded at 12, 24 and 36 hours post exposure. The leaf extract of C. odorata significantly repelled 95% of Macrotermes species at the highest concentration of 10% (w/v) after 30 minutes post treatment exposure. Mortality of Macrotermes species was independent of treatment concentration, but dependent on duration of exposure. All treatment concentrations of aqueous leaf extract of C. odorata caused significant mortality against Macrotermes species ranging between 94% and 98% compared to the control; indicating very great potential for adoption and use in the management of Macrotermes species.


2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Paulus G. M. Jochems ◽  
Willem R. Keusters ◽  
Antoine H. P. America ◽  
Pascale C. S. Rietveld ◽  
Shanna Bastiaan-Net ◽  
...  

AbstractFood security is under increased pressure due to the ever-growing world population. To tackle this, alternative protein sources need to be evaluated for nutritional value, which requires information on digesta peptide composition in comparison to established protein sources and coupling to biological parameters. Here, a combined experimental and computational approach is presented, which compared seventeen protein sources with cow’s whey protein concentrate (WPC) as the benchmark. In vitro digestion of proteins was followed by proteomics analysis and statistical model-based clustering. Information on digesta peptide composition resulted in 3 cluster groups, primarily driven by the peptide overlap with the benchmark protein WPC. Functional protein data was then incorporated in the computational model after evaluating the effects of eighteen protein digests on intestinal barrier integrity, viability, brush border enzyme activity, and immune parameters using a bioengineered intestine as microphysiological gut system. This resulted in 6 cluster groups. Biological clustering was driven by viability, brush border enzyme activity, and significant differences in immune parameters. Finally, a combination of proteomic and biological efficacy data resulted in 5 clusters groups, driven by a combination of digesta peptide composition and biological effects. The key finding of our holistic approach is that protein source (animal, plant or alternative derived) is not a driving force behind the delivery of bioactive peptides and their biological efficacy.


2021 ◽  
Vol 11 (10) ◽  
pp. 4607
Author(s):  
Xiaozhou Guo ◽  
Yi Liu ◽  
Kaijun Tan ◽  
Wenyu Mao ◽  
Min Jin ◽  
...  

In password guessing, the Markov model is still widely used due to its simple structure and fast inference speed. However, the Markov model based on random sampling to generate passwords has the problem of a high repetition rate, which leads to a low cover rate. The model based on enumeration has a lower cover rate for high-probability passwords, and it is a deterministic algorithm that always generates the same passwords in the same order, making it vulnerable to attack. We design a dynamic distribution mechanism based on the random sampling method. This mechanism enables the probability distribution of passwords to be dynamically adjusted and tend toward uniform distribution strictly during the generation process. We apply the dynamic distribution mechanism to the Markov model and propose a dynamic Markov model. Through comparative experiments on the RockYou dataset, we set the optimal adjustment degree α. Compared with the Markov model without the dynamic distribution mechanism, the dynamic Markov model reduced the repetition rate from 75.88% to 66.50% and increased the cover rate from 37.65% to 43.49%. In addition, the dynamic Markov model had the highest cover rate for high-probability passwords. Finally, the model avoided the lack of a deterministic algorithm, and when it was run five times, it reached almost the same cover rate as OMEN.


2006 ◽  
Vol 52 (12) ◽  
pp. 1756-1767 ◽  
Author(s):  
Jake C. Perrins ◽  
Jeffery R. Cordell ◽  
Nissa C. Ferm ◽  
Jaime L. Grocock ◽  
Russell P. Herwig

1984 ◽  
Vol 63 (7) ◽  
pp. 1453-1456 ◽  
Author(s):  
T. MURAMATSU ◽  
H. YOKOTA ◽  
J. OKUMURA ◽  
I. TASAKI

2017 ◽  
Vol 80 ◽  
pp. 438-448 ◽  
Author(s):  
Alice R. de Oliveira ◽  
Philippe C. Mesquita ◽  
Paula R.L. Machado ◽  
Kleber J.S. Farias ◽  
Yêda M.B. de Almeida ◽  
...  

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