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Al Yacoubi, and L.
El Hmaidi, K.
El Hammoudani, ‘Hydro-chemical and bacteriological study of some Sources of groundwater in the GHIS-NEKOR and the BOKOYA Aquifers (AL HOCEIMA, Morocco)’, ACM International Conference Proceeding Series, 2020, doi: 10.1145/3399205.3399221
Nouayti et al., ‘Determination of Physicochemical Water Quality of the Ghis-Nekor Aquifer (Al Hoceima, Morocco) Using Hydrochemistry, Multiple Isotopic Tracers, and the Geographical Information System (GIS)’, Water (Switzerland), vol. 14, no. 4, 2022, doi: 10.3390/w14040606
Jaddi H, El-Hmaidi A, Acuragh MH, Saouita J, Ouamana H, Lallamen Z, Boufala M, Ragragui H, Kasse Z, El Ouali A, ‘Assessment of physicochemical and microbiological quality using the SEQ-Eau approach for groundwater in the Saïss basin (Fez-Meknes region, Morocco)’, 2024.

[15] Hou, R., Li, H., Diao, M. et al.« Fast electrochemical activation of the broadband saturable absorption of tungsten oxide nanoporous film » , Nano Res. 15, 326–332 (2022)
Zou et al., « Structural and optical properties of WO3 films deposited by pulsed laser deposition », Journal of Alloys and Compounds, vol. 583, p. 465 470, janv. 2014
El Kissani,A.
El Kissani, H.
El Aakib, A.

KIII (1050 el.)
KII (1660 el.)
KIII (1660 el.)
KI (1050 el.)
KI (1660 el.)

In 2001, Freund et al. analyzed the behavior of boost by majority and designed an robust boost algorithm that is called as BrownBoost[7].
In the experiments, we compare four margin loss based feature selection using FWL-L1 method, including exponential loss(EL), least squares loss(LSL), logistic loss(LL) and brownboost loss(BBL).
Table 2: SVM-RBF Classification Accuracy using FWL-L1 in Raw Dataset Dataset EL LSL LL BBL Hepatitis 88.2 88.2 87.5 88.2 Horse 91.6 92.1 91.3 92.9 Iono 95.0 94.3 94.6 95.2 Lung-cancer 86.7 86.7 86.7 90.0 Lymphography 84.5 84.5 84.5 86.6 Sonar 88.0 88.4 88.5 88.9 Soybean 93.6 94.4 93.6 94.5 Wdbc 97.6 97.6 98.1 97.9 Wine 98.9 99.4 98.9 98.9 Wpbc 81.4 80.9 80.9 81.4 Ave. 90.6 90.7 90.5 91.5 Table 3: Number of selected feature for SVM-RBF using FWL-L1 in Raw Dataset Dataset EL LSL LL BBL Hepatitis 13 11 14 11 Horse 5 6 12 5 Iono 28 27 31 29 Lung-cancer 56 2 56 12 Lymphography 11 13 11 15 Sonar 34 46 32 43 Soybean 27 24 27 23 Wdbc 15 14 30 23 Wine 10 7 10 6 Wpbc 33 33 33 33 Ave. 23.2 18.3 25.6 20 In order to test the robustness of different algorithms, we compare the classification accuracy on datasets with 5% and 10% noises.
In Table 4, through , we can find that EL, LSL and LL is very closer to about classification robustness, but BBL using FWL-GD show good classification robustness because it is higher at least 0.7% than the others about classification accuracy thought .
For the SVM-RBF classifier, EL, LSL and LL methods using FWL-L1 regularization show no significant differences to classification robustness and classification accuracy in raw data, but BBL based feature selection show the better classification performance than the others.

Al-Ti-B-Sr-RE master alloys were prepared firstly by the reaction of complex halide salts with molten aluminum at 800oC, and then the addition of Al-RE and Al-Sr master alloys into the melt.
Combined grain refining and modification of conventional and rheo-cast A356 Al–Si alloy could be made it by addition of Al-5Ti-B and Al-10Sr master alloy [6, 7].
Nominal The reality composition in the master alloys Al Ti B La Ce Sr Fe 5# Al-5Ti-1B-5RE Bal. 4.85 0.96 2.99 0.31 / 1.15 6# Al-1Ti-3B-5RE Bal. 0.58 1.74 1.79 0.16 / 2.50 7# Al-5Ti-1B-3Sr Bal. 4.37 0.82 / / 0.98 1.37 8# Al-1Ti-3B-3Sr Bal. 1.11 3.01 / / 0.61 0.66 9# Al-5Ti-1B-5RE-3Sr Bal. 4.71 0.92 1.69 0.17 2.06 1.36 10# Al-1Ti-3B-5RE-3Sr Bal. 0.47 1.51 1.67 0.17 1.40 3.73 Results and discussion Fig. 1 presents the XRD spectra pattern of all Al-Ti-B based master alloys.
Fig.1 XRD spectra of different Al-Ti-B master alloys a) Al-5Ti-B based alloys; b)Al-Ti-3B based alloys The high melt temperature phase TiB2 ( Tm 2980℃) is much stabler than TiAl3 phase and AlB2 phase.
El Sebaie, A.

In total, tests on 82 rectangular RC columns reported by Pessiki et al. (2001) [6], Wang and Restrepo (2001), Tan (2002), Harajli et al. (2006), Ilki et al. (2006) [7], Rocca et al. (2006) [9], Kumutha et al. (2007) [10], Wang and Hsu (2008) [11], El Maaddawy et al. (2010) [12], Toutanji et al. (2010) [13], Chen et al. (2011) [14], Hadi and Widiarsa (2012) [15], Wang et al. (2012) [8], De Luca et.al (2013) [16], Raval and Dave (2013) [17], Triantafillou et al. (2016) [18], Li et al. (2017) [19], Eid and Paultre (2017) [20], Ahmed et al. (2017) [4] and Kotp et al. (2018) [5] and conducted in the current paper were included in the dataset.
All the existing analytical models except Li et al. (2017) [19] overestimated or underestimated the experimental results ( Fig 1 and Fig 2).
· The efficiency of the proposed model was better than Li et al (2017) model with AVG=0.9775, SD=0.0785, CoV=0.0803and AAE=0.0655.
[4] Abd-El-Rahman Megahid Ahmed Omar A.Farghal Yehia A.K.
[12] El Maaddawy T, El Sayed M, Abdel-Magid B.

Problems Encountered in Superplastic Forming of Al 5083 Parts B.Gershon, I.Eldror, I.Arbel, J.Milo Israel Aircraft Industries Ltd, Ben Gurion Intl.
Materials and Experimental Procedure The object of the study were 2mm thick Al-5083-H18 SPF sheets, commercially designated by SKY Aluminum as “ALNOVI-1”, with a chemical composition of Al-4.7%Mg-0.7%Mn-0.15%Cr, per ASTM B209M.
The resulting UTS is about 25% below the minimum for O (annealed) temper, while YS and EL correspond to those of “O”.
Demonstration parts were successfully SPF prepared from Al-5083SPF and passed acceptance testing.
Smith and A.L.

Mohammed2,b 1Department of Chemical Engineering, University of Al-Nahrain, Iraq 2Department of Chemical Engineering, University of Al-Nahrain, Iraq azolfaqahtan123@gmail.com, bdr.m.alhashimii@gmail.com Keywords: De-emulsification, Crude oil, Centrifuge, Emulsion, Chemical method.
Feitosa …at el of, (2019), worked to create all-natural de-emulsifiers based on cardanol as an alternative to the more widely used petroleum-based ones.
Murtada Mohammed Abdulredha, at el, (2020), they looked into how different surfactant doses, temperatures, toluene concentrations, pressure drops, and sitting times affected the de- emulsification of water in synthetic oil.
[3] Al-Sabagh, A.
A., & El-Tabey, A.

In a recent study, El-Sharqawy et al [72] investigated the optimum design of both water heated and air heated HDH cycles showing detailed design of the humidifier and dehumidifier.
El-Mansy, S.M.
El-Bashir, M.G.
El-Bahi, D.
El-Bourawi, Z.

Phase Formation of Novel Al-Mg-Zn-Cu-Si Lightweight High Entropy Alloy Godson Nyaforkpa1, a*, Mohamed Emam2, b, Mohamed A.H.
Gepreel1, c 1Materials Science and Engineering Department, Egypt-Japan University of Science and Technology, New Borg El-Arab City 21934, Alexandria, Egypt. 2Energy Resources Engineering Department, Egypt-Japan University of Science and Technology, New Borg El-Arab City 21934, Alexandria, Egypt.
The Mg-Al-Zn phase has many diffraction peaks, this means it presents different grain orientations.
Region 1 is the ordered Mg2Si phase, the ordered Mg-Al-Zn phase represents region 2, region 3 is Al based fcc solid solution phase, and eutectic structures which represents region 4.
Lu et al., “Promising properties and future trend of eutectic high entropy alloys,” Scr.