Hassan, A. S., M. Abd-Allah, and H. F. Nagy,
"Bayesian Analysis of Record Statistics Based on Generalized Inverted Exponential Model",
International Journal on Advanced Science, Engineering and Information Technology, vol. 8, issue 2, pp. 323-335, 2018.
Hassan, A. S., M. Elgarhy, C. H. R. I. S. T. O. P. H. E. CHESNEAU, and H. F. Nagy,
"Bayesian analysis of multi-component stress-strength reliability using improved record values",
Journal of Autonomous Intelligence, vol. 7, issue 4, pp. 1-20, 2024.
Mohamed, B. A., X. Bi, L. Y. Li, L. Leng, E. - S. Salama, and H. Zhou,
Bauxite residue as a catalyst for microwave-assisted pyrolysis of switchgrass to high quality bio-oil and biochar,
, vol. 426, pp. 131294, 2021.
AbstractBauxite residue (BR) is a highly alkaline type of solid waste generated by the aluminum industry that poses a significant environmental risk upon disposal. However, BR is abundant in metals, especially iron, that offer the desired catalytic activity for microwave pyrolysis. Thus, this study aimed to use BR as a low-cost microwave absorber and catalyst for biomass microwave pyrolysis to obtain higher quality bio-oil and biochar. The addition of BR to switchgrass, the representative biomass, did not facilitate microwave absorption because most of the iron in the BR was in the form of goethite and hematite. However, the addition of an efficient microwave-absorbing catalyst (e.g., K3PO4 or clinoptilolite) to the BR triggered synergistic effects, increasing the microwave heating rate by ~ 346% compared to K3PO4 or clinoptilolite alone, which was attributed to the reduction of hematite and goethite to maghemite and/or magnetite. The addition of 10% BR to a mixture of 10% K3PO4 and 10% bentonite further triggered synergistic effects that resulted in the highest microwave heating rate of 439 °C/min, which was a 211% increase compared to using 10% K3PO4 and 10% bentonite without BR, and doubled the Brunauer-Emmett-Teller (BET) surface area of the biochar, reduced the bio-oil acidity by up to 71% compared to that obtained using a single catalyst, and increased the alkylated phenols contents in the bio-oil by 339% compared to that produced without a catalyst. These results demonstrated that the synergistic effects of BR can only be triggered when mixed with another efficient microwave-absorbing catalyst.
Sharawi, M., E. Emary, I. A. Saroit, and H. El-Mahdy,
"Bat Swarm Algorithm for Wireless Sensor Networks Lifetime Optimization",
International Journal of Science and Research (IJSR), vol. 3, issue 5, pp. 2319-7064, 2014.
AbstractChallenges of wireless sensor networks under-optimization in field of research have been globally concerned. Generally, lifetime extension is still considered to be the most dominant challenge for WSNs. Clustering and routing protocols have been proposed as optimization solutions to extend WSNs lifetime. In this paper, we introduce a newly meta-heuristic population based soft computing algorithm as an optimization technique to extend the WSNs lifetime. The proposed technique applies the population-based metaheuristic bat swarm optimization algorithm. It optimizes the network as a nonlinear problem to select the optimum cluster head nodes across number of generations. The objective; fitness function, employed to minimize the intra-cluster compactness with minimum distance between nodes in same cluster. The proposed technique is simulated and applied into four different wireless sensor networks deployments and compared with the LEACH hierarchal clustering and routing protocol. Results show that this proposed technique outperforms the classical LEACH. It efficiently optimizes the selection of cluster head nodes that ensure optimum coverage and connectivity based on intra-cluster distances. This reduces the energy consumption on each node level and hence increases the lifetime for each node, causing a significant extension in the wireless sensor network lifetime. The comparison between the hard or crisp LEACH routing and the soft or elastic proposed routing technique boasts the performance even more. The paper introduces a performance numerical analysis with the metrics of number of packets sent to sink, number of dead nodes, sum of WSN energy and the
network lifetime.
Sharawi, M., E. Emary, I. A. Saroit, and H. El-Mahdy,
"Bat Swarm Algorithm for Wireless Sensor Networks Lifetime Optimization",
International Journal of Science and Research (IJSR), vol. 3, issue 5, pp. 654-664, 2014.
Elmahdy, H. N., M. Sharawy, Eid Emary, and I. A. Saroit,
"BAT SWARM Algorithm for Wireless Sensor Networks Life Optimization",
International Journal of Science and Research, vol. 3, issue 5, pp. 654-664, 2014.
Z., H., M. M. El-Sherbieny, N. R. Saied, and H. S.,
"Bat Algorithm for Job Shop Scheduling Problem",
Journal of Multidisciplinary Engineering Science and Technology , vol. 4, issue 2458-9403, pp. 6758-6763, 2017.