ABSTRACT

            A laboratory experiment was carried out during winter season of 2021 in the Seed Technology Laboratory- College of Agricultural Engineering Sciences/ University of Baghdad, to find out the allopathic effects of aerobic and terrestrial aqueous extracts of Artemisia vulgaris L. on the seed germination and seedling growth of linseed. A factorial experiment according to a completely randomized design (CRD)at three replicates was used; the first factor in clouded type of aqueous extract for two plant parts which were aerobic (stems and leaves) and terrestrial (root and rhizomes), while the second factor included five concentrations

A method was developed that offers a rapid, simple and accurate technique for the determination of chlorophenols at trace levels in aqueous samples with very limited volumes of organic solvents. These compounds were acetylated, then preliminarily extracted with n-hexane. The enriched chlorophenols were directly analyzed using gas chromatography with an electron-capture detector. The detection limits were in the range of 0.001–0.005 mg/L, except for 2-chlorophenol, which was always above 0.013 mg/L. Relative standard deviation for the spiked water samples ranged from 2.2 to 6.1%, while relative recoveries were in the range of 67.1 to 101.3%.

في البداية اود الاشارة الى ان فهم حقيقة الازمة هو ذو جانب فني يتعلق بالجينات الوراثية لنظام يملك في احيناته قدرة عالية على تفريخ المشتقات. هذا النظام الذي يزداد عقما وتدميرا يزداد قدرة على خلق النقود الائتمانية/المشتقات، وكلما اقتربنا اكثر من فهم هذا الجانب كلما اسقطت في ايدينا تلك التوصيفات الاكاديمية الجاهزة في نقص الرقابة والاشراف، تركيز المخاطر،....الخ التي تناولتها الكتابات الشائعة في معظم طروحات

Existing leachate models over–or underestimates leachate generation by up to three orders of magnitude. Practical experiments show that channeled flow in waste leads to rapid discharge of large leachate volumes and heterogeneous moisture distribution. In order to more accurately predict leachate generation, leachate models must be improved. To predict moisture movement through waste, the two–domain PREFLO, are tested. Experimental waste and leachate flow values are compared with model predictions. When calibrated with experimental parameters, the PREFLO provides estimates of breakthrough time. In the short term, field capacity has to be reduced to 0.12 and effective storage and hydraulic conductivity of the waste must be increased to

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