Field experiment was conducted to test the effect of saline water 2 and7 dSm-1 potassium fertilizer rate 150,300 and 450 kg/donum on nitrogen fixation in Pisum sativum L. nodules. The experiment included anatomy study .Results water salinity ( 2,7 dSm-1) as a main plot and fertilizer rates as a sub plot. Results indicated that irrigation with saline water 7 dSm-¹ caused a significant decrease in N contents especially in the lower parts of the plants. The percentage of the N decreased in lower leaves to (0.01%) under 7dSm-¹ and 300 kg/donum fertilizer; however the percentage increased in the upper leaves to (2.80%) under with 2dSm-¹of irrigation water and 300 kg/ donum fertilizer rate. Fresh weight decreased to 6.26g under 7 dSm-¹ and 450 kg/donum fertilizer rate, but irrigated with 2 dSm-¹ and 300 kg/donum fertilizer rate increased it to 18.87g. The nodules number , fresh and dry weight decreased to( 114 nodule/plant ,2.18 and 1.70 g/plant ) with 7 dSm-¹ salinity water and 150 kg/donum fertilizer, respectively , the results showed that irrigation with 2 dSm-¹ saline water and 300kg/ donum fertilizer increased them to ( 167.6 nodule/plant,5.43 and 2.20 g/plant )respectively. Increasing levels of irrigation water from 2 to 7 dSm-¹ salinity, caused increased vessels thickness in both leaves and root; this is because the salt accumulated on cell wall .whereas cortex cell wall was thicker in root for same reason . Thus of nitrogen fixing symbiosis in pea was more determined with anatomy change in root tissues which caused decreasing in nodules number fresh and dry weight. Accordingly, conclusion could be made that potassium may reduce the inhibitory effect for nitrogen fixation in saline water irrigation condition.
AS Salman, SK Hameed…, Karbala Journal of Physical Education Sciences, 2020
Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor
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