7 results on '"Ahmed M. Elmahdy"'
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2. Impact of the Adsorption of Bacteria on Enhancing the Separation Selectivity of Dolomite and Apatite
- Author
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Ahmed M. Elmahdy, Salah E. El-Mofty, Nagui A. Abdel-Khalek, and Ayman A. El-Midany
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Physical and theoretical chemistry ,QD450-801 - Abstract
The surface modification of minerals by bacteria has recently been examined in an attempt to improve their separation selectivities. In this paper, a study of the effect of Corynebacterium diphtheriae intermedius (CDI) bacteria on the dolomite/apatite separation process is reported. Bacterial interaction with both minerals was investigated employing Fourier-transform infrared (FT-IR) spectroscopy together with measurements of the adsorption isotherm and the zeta potential. FT-IR methods were used to identify the functional groups on the surface of each mineral before and after the adsorption of bacteria, while the adsorption isotherm and the zeta potential were used to illustrate the type of adsorption process involved, i.e. physical versus chemical adsorption. The application of bioflotation processes to natural ores using CDI bacteria can lower the MgO content of the ores to less than 1%.
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- 2011
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3. Efficacy of Amoxicillin and/ or Enrofloxacin Against Mixed Infection with Escherichia coli and Salmonella enteritidis In vitro and In vivo
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Mai A Fadel, Ahmed M Elmahdy, Jihan Mostafa Badr, Mohammed AM Saleh, and Mona A.A. AbdelRahman
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General Veterinary ,Animal Science and Zoology - Published
- 2022
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4. The Effect of Microwave Pre-treatment on the Magnetic Properties of Enargite and Tennantite and Their Separation from Chalcopyrite
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Ahmed M. Elmahdy, Hajime Miki, Keiko Sasaki, and Mohsen Farahat
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microwave treatment ,XPS ,Geology ,magnetic properties ,magnetic separation ,Geotechnical Engineering and Engineering Geology - Abstract
The effect of microwave pre-treatment on the magnetic properties of tennantite and enargite was investigated. Magnetic susceptibility, XRD, and XPS characterization of tennantite and enargite before and after treatment were conducted to explore the changes in their magnetic properties. Moreover, magnetic separation of chalcopyrite binary mixtures with enargite and tennantite was performed. The results showed insignificant effects on the magnetic susceptibility of the two minerals after microwave pre-treatment. Magnetic separation results showed arsenic rejection by 84.2%, and 76.3% in the case of enargite and tennantite binary mixtures with chalcopyrite; respectively.
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- 2023
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5. Non-invasive Measurement of Portal Pressure
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Ahmed M. Elmahdy and Annalisa Berzigotti
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Breath test ,medicine.medical_specialty ,Hepatology ,medicine.diagnostic_test ,business.industry ,Portal venous pressure ,Blood flow ,medicine.disease ,Chronic liver disease ,030218 nuclear medicine & medical imaging ,Endoscopy ,03 medical and health sciences ,0302 clinical medicine ,Esophageal varices ,Virology ,Internal medicine ,medicine ,Portal hypertension ,030211 gastroenterology & hepatology ,Radiology ,business - Abstract
To provide an updated overview of the existing and emerging non-invasive diagnostic methods to assess portal hypertension. Data on liver stiffness measurement confirmed that it is a mainstay for assessing the risk of clinically significant portal hypertension in patients with advanced chronic liver disease of any etiology. The Baveno criteria for identifying patients who can safely spare endoscopy have been validated in NASH and cholestatic liver disease. New expanded criteria and other simple non-invasive algorithms including MELD score or spleen stiffness have been proposed and can lead to a higher proportion of endoscopies without significantly increasing the risk of missing large esophageal varices. MR and CT improve the anatomical imaging of gastroesophageal varices and abdominal collaterals and dynamic imaging based on MR and able to quantify hepatic blood flow are in development. Contrast-enhanced ultrasound and methacetin breath test are emerging promising methods to estimate the HVPG non-invasively. Several different non-invasive methods are now available and can be used in clinical practice to achieve a successful identification of patients with clinically significant portal hypertension in chronic liver disease. However, an exact estimation of HVPG is not available yet, and changes in portal pressure cannot yet be detected by non-invasive methods.
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- 2019
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6. Modeling the combined impacts of deficit irrigation, rising temperature and compost application on wheat yield and water productivity
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Esmat F. Ali, Mahmoud F. Seleiman, Ahmed M. S. Kheir, Khaled Ragab, Zheli Ding, and Ahmed M. Elmahdy
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Irrigation ,Compost ,0208 environmental biotechnology ,Deficit irrigation ,Soil Science ,Growing season ,04 agricultural and veterinary sciences ,02 engineering and technology ,engineering.material ,020801 environmental engineering ,Water resources ,Agronomy ,Evapotranspiration ,040103 agronomy & agriculture ,engineering ,0401 agriculture, forestry, and fisheries ,Environmental science ,DSSAT ,Irrigation management ,Agronomy and Crop Science ,Earth-Surface Processes ,Water Science and Technology - Abstract
Limited water resources and climate change in arid and semi-arid regions have negative impacts on food and water security. Management of irrigation and compost may be used to tackle this issue. Crop models are the powerful tools that could predict grain yield (GY) and water productivity (WP) under a broad range of irrigation, compost and temperature interactions. In addition, modeling irrigation management requires the selection of the most suitable evapotranspiration (ET) approach to achieve robust simulations. To achieve this goal, two crop models in Decision Support System for Agrotechnology Transfer (DSSAT) (i.e. CERES-Wheat and N-Wheat), were calibrated and evaluated using a field dataset of three growing seasons in a high-temperate region in Egypt (Luxor). Then, the models were applied to explore GY and WP across a wide range of irrigation (11 options) and compost (8 rates) interactions using two ET routines such as Priestley-Taylor (PT) and FAO 56 Penman-Monteith (PM). The models were also used to predict (GY) and (WP) within the same range of irrigation and compost interactions at higher temperatures (i.e. +1,2,3 and 4) compared to the baseline outputs (1981–2010). Simulation results showed that, deficit irrigation up to 80% and 85% from soil available water achieved the highest values of GY (7.5 t ha-1) and WP (18.4 kg ha-1 mm-1) respectively, provided that using higher rate of compost (12 t ha-1). Rising temperature up to 4 °C decreased GY and WP by 17.2% and 7.4% respectively relative to the baseline without any benefits from compost. Compost technology does not help offset the negative impacts of temperature, but increased yield reduction and greenhouse gas emissions (GHG). Higher compost rates may be used to mitigate the effect of deficit irrigation on wheat yield and water productivity, but not compatible with mitigation of climate change in arid and semi-arid regions.
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- 2021
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7. Impacts of climate change on spatial wheat yield and nutritional values using hybrid machine learning
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Ahmed M S Kheir, Osama A M Ali, Ashifur Rahman Shawon, Ahmed S Elrys, Marwa G M Ali, Mohamed A Darwish, Ahmed M Elmahdy, Ayman Farid Abou-Hadid, Rogerio de S Nóia Júnior, and Til Feike
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CMIP6 ,downscaled NEX scenarios ,automatic machine learning ,stacked ensemble model ,uncertainty ,nutritional concentrations ,Environmental technology. Sanitary engineering ,TD1-1066 ,Environmental sciences ,GE1-350 ,Science ,Physics ,QC1-999 - Abstract
Wheat’s nutritional value is critical for human nutrition and food security. However, more attention is needed, particularly regarding the content and concentration of iron (Fe) and zinc (Zn), especially in the context of climate change (CC) impacts. To address this, various controlled field experiments were conducted, involving the cultivation of three wheat cultivars over three growing seasons at multiple locations with different soil and climate conditions under varying Fe and Zn treatments. The yield and yield attributes, including nutritional values such as nitrogen (N), Fe and Zn, from these experiments were integrated with national yield statistics from other locations to train and test different machine learning (ML) algorithms. Automated ML leveraging a large number of models, outperformed traditional ML models, enabling the training and testing of numerous models, and achieving robust predictions of grain yield (GY) ( R ^2 > 0.78), N ( R ^2 > 0.75), Fe ( R ^2 > 0.71) and Zn ( R ^2 > 0.71) through a stacked ensemble of all models. The ensemble model predicted GY, N, Fe, and Zn at spatial explicit in the mid-century (2020–2050) using three Global Circulation Models (GCMs): GFDL-ESM4, HadGEM3-GC31-MM, and MRI-ESM2-0 under two shared socioeconomic pathways (SSPs) specifically SSP2-45 and SSP5-85, from the downscaled NEX-GDDP-CMIP6. Averaged across different GCMs and SSPs, CC is projected to increase wheat yield by 4.5%, and protein concentration by 0.8% with high variability. However, it is expected to decrease Fe concentration by 5.5%, and Zn concentration by 4.5% in the mid-century (2020–2050) relative to the historical period (1980–2010). Positive impacts of CC on wheat yield encountered by negative impacts on nutritional concentrations, further exacerbating challenges related to food security and nutrition.
- Published
- 2024
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