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^e_\epsilon,f^e)\leqslant \hat{C}\beta(\epsilon). \end{array}$$ Proposition 25. Assume that ( H4 ) and (C) hold true and consider the family (Σ ε } ε∈[01] given in Definition 10 . Then we have that given sequences (ε n } n ∈ ℕ ⊂ (0,1] with ε n → 0+ and H n ∈ Σ ε n , for each n ∈ ℕ, there exists a convergent subsequence of (H n } n ∈ ℕ in (𝒞*, d*), with its limit belonging to Σ 0 . Proof. Since H n ∈ , item (b) of Lemma 12 implies that there exists λ n ∈ Γ ε n and h n ∈ 𝒢 εn such that Hn = B Xn h e n - A Xn , for each n e N. H n = B λ n h n e − A ˜ λ n ,  for

curves in all bands (BVRI). Table 4 and Figure 3 show that the primary and secondary minima are deeper ( A p & A s ) in short wavelength and decreased with increasing the wave length, while the depth difference in minima is larger in V-band. Table 4 Magnitude differences and minima depthes of IK Boo Filter D max. D min. A p A s max p — max s min p — min s min p — max p min s — max p B= 445 nm -0.03 0.04 0.34 0.30 V= 550 nm -0.02 0.05 0.32 0.26 R= 560 nm -0.01 0.03 0.30 0.26 I= 800 nm -0.01 0.03 0.28 0.25 Some of interesting in all light curves of IK Boo is the

102 107 10.1016/j.neucom.2019.03.055 [17] T. Duong, G. Beck, H. Azzag and M. Lebbah, (2016), Nearest neighbor estimators of density derivatives, with application to mean shift clustering, Pattern Recognition Letter, 80, 224–230, DOI: 10.1016/j.patrec.2016.06.021 . Duong T. Beck G. Azzag H. Lebbah M. 2016 Nearest neighbor estimators of density derivatives, with application to mean shift clustering Pattern Recognition Letter 80 224 230 10.1016/j.patrec.2016.06.021 [18] D. Cai and X. Chen, (2015), Large scale spectral clustering via landmark-based sparse

agent) (top), v (cytostatic agent) (middle) and their corresponding trajectories (bottom) for different weights r = (1,1,1) and q = (1,1,1) on the left and r = (8:25,8:25,8.25) and q = (0.1,0.1,0.1) on the right. The parameter values are given in Table 3 . Table 3 Numerical values for the coefficients and parameters used in computations for the optimal control problem 3 with cytostatic and cytotoxic agents, extracted from [ 41 ]. Coefficient Interpretation Numerical value a 1 Inverse transit time through G 1 /G 0 0.197 a 2 Inverse transit time through S 0

solutions of nonlinear fractional differential equations with integral boundary value conditions, J. Math. Anal. Appl. 389, 403-411. 10.1016/j.jmaa.2011.11.065 Cabada A. Wang G. 2012 Positive solutions of nonlinear fractional differential equations with integral boundary value conditions J. Math. Anal. Appl 389 403 411 doi 10.1016/j.jmaa.2011.11.065 [25] Y.Li.(2010), Solving a nonlinear fractional differential equation using Chebyshev wavelets. Commun.Nonlinear. Sci.Numer. Simul. 15, 2284-2292. 10.1016/j.cnsns.2009.09.020 Li Y. 2010 Solving a nonlinear fractional

reaction on the slip flow along with convective boundary conditions between concentric cylinders have not been studied elsewhere. In view of significance and applications, the authors are provoked to take this study. Homotopy analysis method (HAM) [ 24 , 25 , 26 , 27 , 28 , 29 ] is applied to get solution of system. Series solution of the presented with convergence analysis. The influence of flow parameters on the velocity, temperature and concentration are examined 2 Formation of the problem Let a steady, incompressible and laminar Newtonian fluid through

of data (min) 2018 06 29 2018 06 29 2018 07 01 2018 07 01 13:31:30 19:41:03.004 21:57:32.134 22:30:32.994 B * 2.5E-05 3.76E-05 1.36E-05 5.61E-05 BC = C D A/m ( m 2 /kg ) 5.4E-05 8.1E-05 6.11E-06 2.52E-05 Table 2 Norad Two - Line Element Sets For The Satellites CFESAT and MTI Satellite Orbital Elements 5-CFESAT 6-MTI Equivalent altitude (Km) 468.8831 412.5092 a (Km) 6847.02 6790.646 n (rev/min) 0.010953 0.011074 e 0.000582 0.000812 i (degree) 35.4247 97.5789 Ω (degree) 203.043 17.7612 ω (degree) 183.8662 345.6071 M (degree) 176.2019 143.5229 p ( kg=km 3 ) 0

{array}{} \displaystyle \begin{equation}\label{edor}c \mu^3h'''+b \mu h^{2 } h'+a \mu h h' + d \mu h'-{ \lambda} h'+e h+f=0.\end{equation} \end{array}$$ (25) We assume that equation ( 25 ) has a solution in the following form h ( z ) = a 0 + a 1 Y + ⋯ + a N Y N , $$\begin{array}{} \displaystyle \begin{equation}\label{hs} \begin{array}{rcl} h(z)&=&\displaystyle a_0+a_1Y+\cdots +a_N Y^N,\end{array} \end{equation} \end{array}$$ (26) where a n ( n = 0,1,..., N ) are constant to be determined and Y ( z ) is the general solution of the Riccati equation: Y ′ ( z ) + Y 2 ( z ) − α Y

], the Bäcklund transformation [ 15 ], the Darboux transformation [ 16 ], the Hirota bilinear method [ 17 ], the simplest equation method [ 18 ], the ( G ′/ G )–expansion method[ 19 , 20 ], the Jacobi elliptic function expansion method [ 21 ], the Kudryashov method [ 22 ], the Lie symmetry method [ 23 , 24 , 25 , 26 , 27 , 28 ]. The outline of the paper is as follows. In Section 2 we determine the travelling wave solutions for the system (2a) using the Lie symmetry method along with the ( G ′/ G )–expansion method. Conservation laws for (2a) are constructed in

modified Adomian decomposition method . Applied Mathematics and Computation 145 887-893. 10.1016/s0096-3003(03)00282-0 Abbasbandy S. 2003 Improving Newton–Raphson method for nonlinear equations by modified Adomian decomposition method Applied Mathematics and Computation 145 887 893 10.1016/s0096-3003(03)00282-0 [22] S.S. Ganji, D.D. Ganji, A.G. Davodi, S. Karimpour, (2009), Analytical solution to nonlinear oscillation system of the motion of a rigid rod rocking back using max–min approach , Applied Mathematical Modelling 34 2676-2684. 10.1016/j.apm.2009.12.002 Ganji S