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Application of natural deep eutectic solvents in bulk liquid membrane system for removal of free glycerol from crude fatty acid methyl ester
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Fatty Acid Methyl Ester (FAME) produced from biomass offers several advantages such as renewability and sustainability. The typical production process of FAME is accompanied by various impurities such as alcohol, soap, glycerol, and the spent catalyst. Therefore, the most challenging part of the FAME production is the purification process. In this work, a novel application of bulk liquid membrane (BLM) developed from conventional solvent extraction methods was investigated for the removal of glycerol from FAME. The extraction and stripping processes are combined into a single system, allowing for simultaneous solvent recovery whereby low-cost quaternary ammonium salt-glycerol-based deep eutectic solvent (DES) is used as the membrane phase. Moreover, for the first time, diethyl ether was introduced as a strip phase to strip the free glycerol in the BLM system. The effects of DES composition, DES …

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Publication Date
Sun Jul 01 2018
Journal Name
Journal Of Educational And Psychological Researches
The relationship between critical thinking, epistemological beliefs, and learning strategies with the students’ academic performance
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The present study was conducted to investigate the relationship between critical thinking, epistemological beliefs, and learning strategies with the academic performance of high school first-grade male and female students in Yazd. For this purpose, from among all first-grade students, as many as 250 students (130 females and 120 males) were selected by using multistage cluster sampling. The data needed were then collected through using California Critical Thinking Skills Test, Schommer's Epistemological Beliefs Questionnaire, Biggs’ Revised Two Factor Study Process Questionnaire. The findings indicated that there is a positive significant relationship between critical thinking and academic performance and achievement. Moreover, four fa

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Publication Date
Tue Jun 24 2025
Journal Name
Baghdad Science Journal
Accelerating Face Mask Detection Training Model Based on Multi-GPUs and Multi-core CPU
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Modern machine-learning applications require GPUs, and modern platforms can leverage numerous GPUs on one or more machines to increase performance. Contemporary deep-learning models are too huge for CPU or GPU training. Training these models with many GPUs without performance degradation is necessary to train them rapidly and maximize GPU consumption. Thus, training deep convolutional neural networks (DCNN) with multiple GPUs has become necessary for improving training. Therefore, we presented a parallel design and development of an efficient model for enhancing face mask CNN performance and improving resource efficiency. This DCNN model is a parallel training system over multiple GPUs, a multi-core CPU, and a multi-process GPU platform wit

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