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EL S22 Low manual effort.
EL SCF30 (3.0%) Low manual effort.
EL SPF31 (5.0%) Low manual effort.
E *E: Extrudable, EL: Extrudable Limit y NE: Non-extrudable.
Aguilar et al. [8] and Silva et al. [7] have also reported improved compressive strength in earth matrices containing chitosan solution and potato starch gel, respectively.

El Briak-BenAbdeslam, B.
(Ca+Sr)/P = 1.60, T = tri(Ca-Sr) phosphate, H = Ca-Sr- hydroxyapatite 0 100 200 300 400 500 600 700 800 10 11 12 13 14 15 16 17 18 19 20 Angle (°θθθθ) Intensity 1 2 0 100 200 300 400 500 600 700 800 900 10 11 12 13 14 15 16 17 18 19 20 Angle (°θθθθ) Intensity * * * * 1 2Yokogawa et al. [8] reported the obtaining of calcium-strontium apatite solid solutions over the entire compositional range for a Ca/(Ca+Sr) molar ratio of 0-1.0, by grinding for 24 h aqueous slurries of DCPD + CaCO3 + Sr(OH)2·8H2O in variable proportions and with a molar ratio of (Sr+Ca)/P = 1.5.
El Briak-BenAbdeslam, C.
El Briak-BenAbdeslam, M.P.

El-Tawil, and H.
El Zoheiry, “Toward a modified variational iteration method,” J.
El-Tawil, and H.
El-Tawil, and H.
El-Tawil, and H.

Osteoblast-Mediated Resorption of Porous Bioactive SCPC Granules Enhances Bone Regeneration in Human Extraction Sockets Heba E.AbdelRazik1,a, Miho Nakamura1,b, Leire Bergara-Muguruza1,c, Uruj Sarwar1,d, Mohammad Hassan2,e, Robert Horowitz3,g and Ahmed El-Ghannam2,f 1Medicity Research Laboratory, Faculty of Medicine, University of Turku, Tykistökatu 6, 20520, Turku, Finland 2Department of Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, Charlotte NC 28223 USA 3Periodontology and Implant Dentistry, The NYU College of Dentistry, New York, NY, 10010 USA aheba.abdelrazik@utu.fi, bmiho.nakamura@utu.fi, cleire.l.bergaramuguruza@utu.fi, duruj.f.sarwarhussain@utu.fi, emohamed_hassan13@h-eng.helwan.edu.eg, grah7@nyu.edu, farelgha@uncc.edu Keywords: Bone graft, bone regeneration, osteoclast, silica calcium phosphate Abstract.
Available from: https://doi.org/10.1016/ j.bioactmat.2017.05.007 [4] El-Ghannam AR.
[6] El-Ghannam A, Nakamura M, Muguruza LB, Sarwar U, Hassan M, Fotawi R Al, et al.

Fig.3 SEM BSE micrographs of the as-extruded and as-forged alloy: (a) as-extruded microstructure; (b) as-forged microstructure 0 200 400 600 800 500 550 600 650 700 750 800 850 900 δδδδ UTS YS Temper at ur e ( Temper at ur e ( Temper at ur e ( Temper at ur e ( ℃ )℃ )℃ )℃ ) tensile strength ( MPa ) tensile strength ( MPa ) tensile strength ( MPa ) tensile strength ( MPa ) 0 2 4 6 8 10 12 14 16 18 20 22 24 El ongat i on ( %) El ongat i on ( %) El ongat i on ( %) El ongat i on ( %) Fig. 4 Tensile strength, yield strength and plastic elongation to failure as a function of the temperature for as-forged Ti-45Al-9 (Nb, W, B, Y) alloy Discussion For the studied alloy, besides high Nb content, W, B and Y are alloy additions too.
Oxygen atoms come from the original oxygen content of Ti, Al and Nb master alloys [2].

The ultimate tensile strength (UTS), yield strength (TYS) and elongation (El) of the as-extruded alloy were of 415MPa, 335 MPa and 16%, respectively.
After aging at 200℃ for 32h, the tensile properties of the alloy were 480 (UTS), 410 MPa (TYS) and 13.5%(El), respectively.
This randomization of texture must contribute to an increase in the ductility of alloys. 3) Strengthening and toughening by LPSO The works by Matsuda et al indicated that the formation of the LPSO phases would increase the critical resolved shear stress (CRSS) of the basal plane and the non-basal slip would be activated by the prevention of the basal slip.
The results showed that: 1) Three kinds of phases including a-Mg, Mg3(GdYZn) and Mg12(GdY) Zn exist in the as-cast Mg-10Gd-3Y-2Zn-0.5Zr alloy; 2) After extrusion with 10:1 ratio under 420℃, the alloy was provided with fine recrystallization grain with average size 4μm; 3) The alloy was with ultimate tensile strength (UTS) of 415MPa, tensile yield strength (TYS) of 335 MPa and elongation (El) of 16% after extrusion and UTS of 480MPa, TYS of 410 and El of 13.5% after extrusion and aged at 200 for 32 hours and showed excellent performance.

Bayesian Network Modelling: Predictor Impact Assessment In Surface Roughness Prediction Zvikomborero Hweju1,a, Khaled Abou-El-Hossein2,b* 1,2Ultra-High Precision Engineering Research Unit, Department of Mechatronics, Nelson Mandela University, Port Elizabeth, South Africa.
as219146578@mandela.ac.za, bKhaled.Abou-El-Hossein@mandela.ac.za Keywords: Surface Roughness, Bayesian Linear Regression Model, Single Point Diamond Turning, RSA 443, Computational Intelligence.
Correa et al. [1] predicted surface roughness in high-speed machining of 180 mm profile of F114 steel.
The Bayesian Network model was utilized by Nebot et al. [3] for surface roughness prediction and tool-wear diagnosis.
Regarding surface roughness prediction, Kong et. al. [5] used the Bayesian network model to extract prediction features.

Besides, EDS analyses shows in Fig. 2d indicates that Ti:(Si+Al):C atomic ratio of the crystal grain is close to 3:1:2, which is in accordance with Ti3(Si,Al)C2 formula.
References [1] El-Raghy T, Blau P, Barsoum MW.
[4] Finkel P, Barsoum MW, El-Raghy T.
[5] Barsoum MW, El-Raghy T, Rawn CJ, Porter WD, Wang H, Payzant EA, et al.
[17] Li X, Dong Z, Westwood A, Brown A, Zhang S, Brydson R, et al.

For instance, Malvoni et al. [14] used the System Advisor Model (SAM) and PVsyst to investigate the performance of 960 KWp PV plant in southern Italy under the Mediterranean climate.
Sharma et al. [15] used PVsyst to estimate the annual energy yield of a 190 KWp grid interactive solar photovoltaic power plant installed at Khatkar-Kalan, India.
Okello et al. [16] also used PVsyst to simulate a 3.2 kWp grid-connected PV system in Port Elizabeth, South Africa and compared between the simulation and the actual results of the plant, they found that the results were similar.
[10] Bouaichi A., Alami Merrouni A., El Hassani A., Naimi Z., Ikken B., Ghennioui A., Benazzouz A., El Amrani A., and Messaoudi C., Experimental evaluation of the discoloration effect on PV-modules performance drop, Egypro, 2017, vol. 119, pp. 818-827
[23] Cheikh El Banany Elhadj Sidi, Mamadou Lamine Ndiaye, Menny El Bah, Abdoulkarim Mbodji, Ababacar Ndiaye, and Papa Alioune Ndiaye., Performance analysis of the first large-scale (15 MWp) grid-connected photovoltaic plant in Mauritania, Energy Convers Manag, 2016, vol. 119, pp. 411-421

According to Stoke’s fifth-order wave theory, Bai et al.[5] compared the seismic response of bridge pier under hydrostatic force with that under water wave load, in which the wave force obtained by Morison’s formula was applied to the bridge pier as a distributed force.
(a) El Centro wave (b) Taft wave (c) Nanjing artificial wave Fig.6 The displacement time-histories of the input earthquake waves The seismic response of the bridge pier under ground motion referred to the initial boundary.
(a) El Centro wave (b) Taft wave (c) Nanjing artificial wave Fig.8 Peak shear force of the bridge pier Table.4 Peak shear force of bridge pier and FSI influence coefficient Input displacement Hydrostatic status(m) Water-flow status(m) Kf (%) El Centro wave A=25cm 1602 1725 7.68 A=50cm 2755 2932 6.4 Taft wave A=25cm 1583 1710 8.0 A=50cm 2707 2876 6.24 Nanjing artificial wave A=25cm 828 905 9.9 A=50cm 1064 1149 8.0 Bending moment response of bridge pier Fig. 9 gives the peak bending moment of bridge pier.
(a) El Centro wave (b) Taft wave (c) Nanjing artificial wave Fig.9 Peak bending moment of the bridge pier Table.5 Peak moment response of bridge pier and FSI influence coefficient Input displacement Hydrostatic action (kN·m) Water-flow status (kN·m) Km (%) El Centro wave A=25cm 1913 2203 15.16 A=50cm 3558 3877 8.97 Taft wave A=25cm 1649 1765 7.0 A=50cm 3232 3441 6.47 Nanjing artificial wave A=25cm 969 1112 14.75 A=50cm 1133 1257 10.9 Influence of water level on bridge pier Generally, the water level surrounding the bridge pier kept changing.
[2] Yamada, Y., Iemura, H., Kawano, K., et al.