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This analysis was also agreed by Nair et al. [11] and Yang et al. [12] in their research.
Xian et. el. and Zarandi et. el. in their research reported the gradually decreased in bandgap may attribute to the sp-d interaction.
Caglar, “Controlling of surface morphology of ZnO nanopowders via precursor material and Al doping,” Materials Science in Semiconductor Processing, vol. 99, pp 149, 2019.

A DNN learning architecture is proposed by Mohsen et al. to classify brain tumors using MRIs.
Jemimma et al. introduced a Local Directional Pattern (LDP) based deep learning classification technique that extracts features from image data segments using a probabilistic fuzzy C-means clustering (PFC) algorithm.
A brain tumor grading system is being proposed by Sajjad et al., which first do segmentation of the tumor region by using deep learning-based architecture namely Input Cascade CNN and then implemented augmentation (rotation, filliping, skewness, and shears for geometrical changes) to increase the dataset size to feed neural architecture to get satisfactory results.
El-Dahshan, E.S.M.
El-Horbaty, and A.B.M.

Psidium guajava Leaves as Corrosion Inhibitor of Al-6061 Femiana Gapsari1,a* and Andita Ganda2,b 1Department of Mechanical Engineering, Faculty of Engineering, University of Brawijaya, MT.Haryono 167, Malang, 65145, Indonesia 2Department of Mechanical Engineering, Faculty of Engineering, Universitas Negeri Surabaya, Raya Ketintang, Surabaya, 60231, Indonesia a*memi_kencrut@ub.ac.id, banditaganda@unesa.ac.id Keywords: corrosion, organic inhibitor, aluminum, adsorption Abstract.
Materials and Methods The corrosion test was done using three-electrode-cell system with platinum as the counter electrode, Ag/AgCl (3M KCl) as the reference electrode and the aluminum (Al-6061) specimen as the working electrode.
Al-Kimia, 7(1), 91-99
ISRN Corrosion, 1-13. https://doi.org/10.1155/2014/687276 [21] Maayta, A.K., & Al-Rawashdeh, N.A.F. (2004).
Materials, 12(13), 2120. https://doi.org/10.3390/ma12132120 [25] Fouda, A.S., Al-Sarawy, A.A., Ahmed, F.S., & El-Abbasy, H.M. (2009).

Material De [cm2/s] Li Reference GEOCEM-RECB 3.13x10−11 to 5.50x10−13 10.3 to 12.2 This work GEOCEM-RS 1.91x10−10 to 7.55x10−11 9.68 to 10.1 This work PC - type I 1.2x10−7 7 Jang et al. [13] PC - type IV 5.9x10−8 7.2 Guerrero et al. [37] FABC 2.2x10−7 7 Goñi et al. [38] FABC with Na–P1 zeolite 2.8x10−9 9 Goñi et al. [38] MK-GP 1.7x10−11 to 9x10−11 10.0 to 10.8 Arbel Haddad et al. [39] FA-GP 2.6x10−11 to 1.8x10−11 10.6 to 10.7 Deng et al. [40] FA-GP (22 °C curing) 5x10−8 to 1x10−10 7.3 to 10.0 Jain et al. [16] FA-GP (90 °C curing) 1x10−12 to 2.5x10−15 12.0 to 14.6 Jain et al. [16] Note: PC – Portland cement; FABC – Fly ash belite cement; MK-GP – Metakaolin-based geopolymer; FA-GP – Fly ash-based geopolymers.
El-Kamash, M.R.
El-Naggar, M.I.
El-Dessouky, Immobilization of cesium and strontium radionuclides in zeolite-cement blends, J.
Um, Effect of Si/Al molar ratio and curing temperatures on the immobilization of radioactive borate waste in metakaolin-based geopolymer waste form, J.

This analysis is based on own work [9] and that of Lentz et al. [10].
Comparison of stability maps Lentz et al. [10] applied the concepts of Prasad et al. [1,11,12] using the instability parameter defined by Eq. (9) to construct a instability map for the magnesium alloy WE54 at e=0.5.
For comparison, we first determine the Garofalo parameters according to our model RCR [13] and then we constructed stability maps by means of our stability equations, Eqs. (11) and (13), but using Lentz el al. data.
These parameters do not differ much from those obtained by Lentz et al. [10]: A=3.5 E14, Q=224 kJ/mol, n=3.2, a=0.014.
Stability maps using Lentz et al. [10] data for the WE54, a) 1st Lyapunov criterion, b) 2nd Lyapunov criterion.

TABLE.1 Tensile properties TS (MPa) EL（%） As delivered 530 10 Zone A 1474 7 Zone B 1510 6 As is seen from the data in Table 1, the tensile strength on both position A and position B exceeds 1400 MPa.
Trans Tech Publications Ltd., Palermo, Italy, [2] Gu Zheng-wei, Shan Zhong-de, Xu Hong, et al.
[3] MA Ning, HU Ping, SHEN Guo-zhe, et al.

Ahmed Mancy Mosa, Al- Mansour University, Baghdad Prof.
Salah El Haggar, The American University in Cairo, Egypt Prof.
Khansaa Al-Essa, Jerash University, Jordan Prof.

Ciulada Radioengineering Department, Electronics Faculty, Vilnius Gediminas Technical University, Naugarduko 41-437, LT-2006 Vilnius, Lithuania; rnavickas@ el.vtu.lt Keywords: self-formation processes, lateral etching, evolution of geometry masks, semiconductor devices, integrated circuits.
Pavlidis et al.: Solid-State Electronics Vol. 44 (2000), p. 2059- 2067
Mensa et al.: IEEE Electron Device Letters Vol. 20, No. 8 (1999), p. 396- 398

[2] Bognitzki M, Hou H, etc al.
[4] EL-Refaie Kenawy, John M.
Layman, etc al.

The solar pond at El Paso, Texas, was the first solar pond in the world to provide heating for the industrial purposes, and the first solar pond in the USA used in the electricity generation applications.
The automated instrumentation system at El Paso Solar Pond employed a sensor head which was driven by a computer controlled stepper motor.
[8] Fay, C., et al., Remote Real-Time Monitoring of Subsurface Landfill Gas Migration.
Reconstruction and Operation of the El Paso Solar Pond with a Geosynthetic Clay Liner System. in 1996 Solar Engineering Proceedings of ASME International Solar Energy Conference. 1996.
[14] Simic, M.N., et al.