In the present investigation, bed porosity and solid holdup in viscous three-phase inverse fluidized bed (TPIFB) are determined for aqueous solutions of carboxy methyl cellulose (CMC) system using polyethylene and polypropylene as a particles with low-density and diameter (5 mm) in a (9.2 cm) inner diameter with height (200 cm) of vertical perspex column. The effectiveness of gas velocity Ug , liquid velocity UL, liquid viscosity μL, and particle density ρs on bed porosity BP and solid holdups εg were determined. The bed porosity increases with "increasing gas velocity", "liquid velocity", and "liquid viscosity". Solid holdup decreases with increasing gas, liquid velocities and liquid viscosity. Solid holdup with "low density particles" shows a higher numerical quantity "than that in the beds" with "high density". Levenberg-Marquardt back propagation of "artificial neural network (ANNs)" was utilized to predict the bed porosity and solid holdup. The expected values are in an excellent relationship with the experimental values, where the advanced model is high-fidelity and own a large capacity to predict bed porosity and solid holdup.
The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of
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In this work a model of a source generating truly random quadrature phase shift keying (QPSK) signal constellation required for quantum key distribution (QKD) system based on BB84 protocol using phase coding is implemented by using the software package OPTISYSTEM9. The randomness of the sequence generated is achieved by building an optical setup based on a weak laser source, beam splitters and single-photon avalanche photodiodes operating in Geiger mode. The random string obtained from the optical setup is used to generate the quadrature phase shift keying signal constellation required for phase coding in quantum key distribution system based on BB84 protocol with a bit rate of 2GHz/s.
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Currently and under the COVID-19 which is considered as a kind of disaster or even any other natural or manmade disasters, this study was confirmed to be important especially when the society is proceeding to recover and reduce the risks of as possible as injuries. These disasters are leading somehow to paralyze the activities of society as what happened in the period of COVID-19, therefore, more efforts were to be focused for the management of disasters in different ways to reduce their risks such as working from distance or planning solutions digitally and send them to the source of control and hence how most countries overcame this stage of disaster (COVID-19) and collapse. Artificial intelligence should be used when there is no practica
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A simple, environmental friendly and selective sample preparation technique employing porous membrane protected micro-solid phase extraction (μ-SPE) loaded with molecularly imprinted polymer (MIP) for the determination of ochratoxin A (OTA) is described. After the extraction, the analyte was desorbed using ultrasonication and was analyzed using high performance liquid chromatography. Under the optimized conditions, the detection limits of OTA for coffee, grape juice and urine were 0.06 ng g−1, 0.02 and 0.02 ng mL−1, respectively while the quantification limits were 0.19 ng g−1, 0.06 and 0.08 ng mL−1, respectively. The recoveries of OTA from coffee spiked at 1, 25 and 50 ng g−1, grape juice and urine samples at 1, 25 and 50 ng mL
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In this work, a test room was built in Baghdad city, with (2*1.5*1.5) m3 in dimensions, while the solar chimneys (SC) were designed with aspect ratio (ar) bigger than 12. Test room was supplied by many solar collectors; vertical single side of air pass with ar equals 25, and tilted 45o double side of air passes with ar equals 50 for each pass, both collectors consist of flat thermal energy storage box collector (TESB) that covered by transparent clear acrylic sheet, third type of collector is array of evacuated tubular collectors with thermosyphon in 45o instelled in the bottom of TESB of vertical SC. The TESB was
... Show MoreThe paper proposes a methodology for predicting packet flow at the data plane in smart SDN based on the intelligent controller of spike neural networks(SNN). This methodology is applied to predict the subsequent step of the packet flow, consequently reducing the overcrowding that might happen. The centralized controller acts as a reactive controller for managing the clustering head process in the Software Defined Network data layer in the proposed model. The simulation results show the capability of Spike Neural Network controller in SDN control layer to improve the (QoS) in the whole network in terms of minimizing the packet loss ratio and increased the buffer utilization ratio.
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Discriminant between groups is one of the common procedures because of its ability to analyze many practical phenomena, and there are several methods can be used for this purpose, such as linear and quadratic discriminant functions. recently, neural networks is used as a tool to distinguish between groups.
In this paper the simulation is used to compare neural networks and classical method for classify observations to group that is belong to, in case of some variables that don’t follow the normal distribution. we use the proportion of number of misclassification observations to the all observations as a criterion of comparison.