Rooting response in stem cuttings of mung bean increased considerably with inresing
seedling age, due to endogenous IAA or supplied IBA. However, after the day 7- or 8-old of
seedling age. The cotyledons sheivel and drop-off spontaneously at day-8 of seedling age. So
that cotyledons excision after cuttings were made during the period between seedling
emergence (the day 4) and cotyledons dropping off (which starts at day 8 and its completion
at day 10) causes decrease in rooting at any time during cutting treatment ,in particular, at
zero time . In addition, results of this study revealed that terminal buds do not influence
significantly adventitious root formation whether IBA supplied or not. Whereas in leafless
cuttings, excision of terminal buds at any time enhance rooting of cuttings specially at zero
time, compared with its presence. The correlative role of cotyledons and terminal buds as a
source of endogenous IAA and rooting co-factors and their influence on seedling
development and subsequently on rooting response of cuttings derived from them. In addition
to role of leaves on uptake and subsequent transport of supplied IBA have been discussed.
Key Words: Adventitious roots, Auxin, Stem cuttings, Seedling age, Cotyledons, Terminal
buds, Correlative phenomena.
Commercial, industrial, and military activity, largely in the 19th and 20th centuries, have led to environmental pollution that can threaten human health and ecosystem function, liquid gas petroleum (LPG) products are the major sources of energy for industry and daily life that cause environmental contamination during various stages of production, transportation, refining and use. Screening of bacterial isolate by using clear zone techniques and biomass and optical density. Results revealed that isolate Burkholdaria cepatia showed a high ability for hydrocarbons biodegradation and this isolate identified depending on morphological cultural, gram stain, microscopic features, biochemical tests, and VITEK2 compact. In this study,
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Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s
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