The Cassia glauca Lam. is the tree that belongs to the Fabaceae family and is native to India has many uses in indigenous systems of medicine, folk medicine, and traditional Brazilian medicine. Has many pharmacological activities such as anti-diabetic, antibacterial, antifungal, antioxidant, anti-hemolytic, anticancer, cardio-protective, and Hepato-protection. The aim of study is to Isolation, identification, and quantification of some compounds from aerial parts of Cassia glauca since no phytochemical investigation had previously been done in Iraq for this plant. The aerial parts were defatted in n. hexane for 48 hours. The defatted materials were extracted in 85% ethanol using the hot method (soxhlet), then the extract was fractionated using different solvents (chloroform, ethyl acetate, and n-butanol). High-performance liquid chromatography (HPLC), was used for identification and quantification by using authenticated standards, and preparative layer chromatography (PLC) was used for the isolation of the identified compounds. The isolated compounds were identified after isolation by liquid chromatography Mass Spectrometry LC-MS/MS-Q-TOF method. The different chromatographic and spectroscopic methods results indicate the presence of luteolin and chlorogenic acid in the ethyl acetate fraction and n-butanol fraction respectively and estimated the quantity as (130.77µg/1g) for luteolin, and (0.0006%) for chlorogenic acid from 50g of plant material.
In this study, field results data were conducted, implemented in 64 biofilm reactors to analyses extract organic matter nutrients from wastewater through a laboratory level nutrient removal process, biofilm layer moving process using anaerobic aerobic units. The kinetic layer biofilm reactors were continuously operating in Turbo 4BIO for BOD COD with nitrogen phosphorous. The Barakia plant is designed to serve 200,000 resident works on biological treatment through merge two process (activated sludge process, moving bed bio reactio MBBR) with an average wastewater flow of 50,000 m3/day the data were collected annually from 2017-2020. The water samples were analysis in the central labor
Massive multiple-input multiple-output (massive-MIMO) is considered as the key technology to meet the huge demands of data rates in the future wireless communications networks. However, for massive-MIMO systems to realize their maximum potential gain, sufficiently accurate downlink (DL) channel state information (CSI) with low overhead to meet the short coherence time (CT) is required. Therefore, this article aims to overcome the technical challenge of DL CSI estimation in a frequency-division-duplex (FDD) massive-MIMO with short CT considering five different physical correlation models. To this end, the statistical structure of the massive-MIMO channel, which is captured by the physical correlation is exploited to find sufficiently
... Show MoreA particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.
Restoration of degraded lands by adoption of recommended conservation management practices can rehabilitate watersheds and lead to improving soil and water quality. The objective was to evaluate the effects of grass buffers (GBs), biomass crops (BCs), grass waterways (GWWs), agroforestry buffers (ABs), landscape positions, and distance from tree base for AB treatment on soil quality compared with row crop (RC) (corn [
Gundelia, a genus of flowering plants native to the Mediterranean region, particularly in Iraq, holds promise as a sustainable adsorbent for the treatment of dye-polluted water. This study explores the potential of Gundelia seeds (GS) waste as a biobased adsorbent for removing methylene blue dye from synthesized wastewater. Utilizing various analytical techniques, including scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and X-ray diffraction analysis (XRD), we assessed GS as an active adsorbent with performance comparable to fabricated and expensive composites. Key parameters such as pH (3-11), pH at the point of zero charge, temperature (298-328 K), dose (0.02-0.1 g), dye concentration (10-50 ppm), and
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