and W. J. Plieth, G. Pfuhl, F. B. A. W. A., G. Pfuhl, A. Felske, and W. A. Badawy,
""Photoetching of 111/ V Semiconductor ". ",
Electrochim. Acta, issue 34, pp. 1133, 1989.
W. M. Amer, N. F. Elshayeb, A. K. Hegazy, M. S. Abbas, A.S.Soliman, and A. M. M. Wahab,
"Species diversity and climate an intimate relationship over the last decades in the Mediterranean region: the case study of Sallum Sector, Egypt.",
Flora Mediterranea, vol. 30, pp. 65-79, 2020.
W. Soliman, A., and et al,
Molecular detection of the most common bacterial pathogens affecting economically important Egyptian Red Sea fishes,
, vol. 25, issue 4, pp. 669 - 688, 2021.
AbstractThe current study aimed to investigate the most common pathogenic bacteria that are naturally infecting wild marine fishes collected at different localities along the coastline zone of Hurghada City, Egypt. A total of 300 samples of marbled spine foot Siganus rivulatus and the Haffara Seabream Rhabdosargus haffara were subjected to clinical and bacteriological examinations. The examined fishes showed the characteristic clinical signs and postmortem lesions of vibriosis and photobacteriosis. Based on the morpho-chemical characterization, bacterial isolates retrieved from the naturally infected fishes were identified as Vibrio spp. and Photobacterium spp. Through sequencing 16S rRNA genes, the identities of bacterial isolates were confirmed as V. alginolyticus, V. vulnificus, P. damselae subsp. damselae and P. damselae subsp < em> piscicida. Vibrio alginolyticus was the most frequent isolated bacterial pathogen and represented 54.4% and 46.7% of the total isolates recovered from S. rivulatus and R. haffara, respectively. Thus, the current study confirmed that Vibrio and Photobacterium species remain the most prevalent bacterial pathogens infecting Egyptian Red Sea fishes. From food safety perspective, these types of infections could pose potential public health hazards.
W.A., A. - W., S. A. - M. wahab, and O. T. Kamel,
"Using Safe Alternatives for Controlling Postharvest Decay,Maintaining Quality of Crimson Seedless Grape.",
World Applied Sciences Journal, vol. 31, issue 7, pp. 1345-1357, 2014.
Abstractn/a
and W.A. Badawy, A. G. Gad-Alla, A. E. - R. A. - R. H. A. M. M., A. G. Gad-Alla, A. E. - H. A. Rahman, and M. M. Abou-Romia,
""Kinetics of the Passivation of Molybdenum in Acids and Alkali Solutions as Inferred from Impedance and Potential Measurements"",
Surface and Coatings Technology, vol. 27, pp. 187, 1986.
W.A.Badawy,
""Schottky- Barrier Photovoltaic and Photoelectrochemical Cells of the System n-Si/Oxide".",
International Symposium on Electrochemical Science and Technology (ISEST), Aug.24-26 , 1995, The University of Hong Kong , Page L13,1-8., Hong Kong, 24 August, 1995.
W.A.Badawy,
"Improved Solar Cells for Environmentally Safe Energy Conversion"",
47 th ISE meeting ,1-6 Sept.(1996) , Veszrem-Balatonfured , Hungary, p5b.28., Veszrem-Balatonfured , Hungary,, 1-6 September, 1996.
W.Ali, N., S. S. Abbas, H. E. - S. Zaazaa, M. MohamedAbdelrahmana, and M. A. Kawy,
"Validated stabilityindicatingmethodsfordetermination of nitazoxanideinpresenceofitsdegradationproducts",
Journal ofPharmaceuticalAnalysis, vol. 2, issue 2, pp. 104-115, 2012.
W.Fouad, A.Badr, and I.Farag,
"AIFSA: A New Approach for Feature Selection and Weighting",
International Conference on Informatics Engineering & Information Science , vol. 252, pp. 596-609, 2011.
AbstractFeature selection is a typical search problem where each state in the search space represents a subset of features candidate for selection. Out of n features, 2n subsets can be constructed, hence, an exhaustive search of all subsets becomes infeasible when n is relatively large. Therefore, Feature selection is done by employing a heuristic search algorithm that tries to reach the optimal feature subset. Here, we propose a new wrapper feature selection and weighting algorithm called Artificial Immune Feature Selection Algorithm (AIFSA); the algorithm is based on the metaphors of the Clonal Selection Algorithm (CSA). AIFSA, by itself, is not a classification algorithm, rather it utilizes well-known classifiers to evaluate and promote candidate feature subset. Experiments were performed on textual datasets like WebKB and Syskill&Webert web page ratings. Experimental results showed AIFSA competitive performance over traditional well-known filter feature selection approaches as well as some wrapper approaches existing in literature.