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Shapley-Folkman-Lyapunov theorem and Asymmetric First price auctions

) ( x − a ) 2 ( b − a ) ( c − a ) $\begin{array}{} \displaystyle F(X)=\psi x+(1-\psi)\frac{(x-a)^{2}}{(b-a)(c-a)} \end{array}$ f ( x ) = F ′ ( x ) = 4 x   i f   x ∈ ( 0 , … , 0 , 5 ) 4 − 4 x i f   x ∈ ( 0.5 , … , 1 ) $\begin{array}{} \displaystyle f(x)=F^{\prime}(x)=\left\{\begin{array}{c}4x ~if~ x \in (0,\ldots,0,5)\\ 4-4x \,if~x \in (0.5,\ldots,1) \end{array}\right. \end{array}$ Uniform [0,1] F ( x ) = x − ω L ω H − ω L $\begin{array}{} \displaystyle F(x)=\frac{x-\omega_{L}}{\omega_{H}-\omega_{L}} \end{array}$ f ( x ) = 1 ω H − ω L $\begin

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Predicting the Duration of Concrete Operations Via Artificial Neural Network and by Focusing on Supply Chain Parameters

methodology for collecting, classifying, and analyzing Canadian construction court cases”, Canadian Journal of Civil Engineering 34, 177-188. [10] Chen, J. H. and Hsu, S. C. (2007), “Hybrid ANN-CBR model for disputed change orders in construction projects”, Automation in Construction 17, 56-64. [11] Cheng, M. Y., Tsai, H. C. and Liu, C. L. (2009) “Artificial intelligence approaches to achieve strategic control over project cash flows”, Automation in Construction 18, 386-393. [12] Cheng, T. M. and Yan, R. Z. (2009

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Anticipated backward doubly stochastic differential equations with non-Liphschitz coefficients

: Anticipated backward doubly stochastic differential equations , Applied Mathematics and Computation, 220 , 53-62, (2013). 10.1016/j.amc.2013.05.054 Xu X. Anticipated backward doubly stochastic differential equations Applied Mathematics and Computation 220 53 62 2013 [10] F. Zhang: Comparison theorems for anticipated BSDEs with non-lipschitz coefficients , J. Math. Appl, 416 , 768-782, (2014). Zhang F. Comparison theorems for anticipated BSDEs with non-lipschitz coefficients J. Math. Appl 416 768 782 2014

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Anticipated backward doubly stochastic differential equations with non-Liphschitz coefficients

. Ren J. Anticipated backward stochastic differential equations with non-Lipschitz coefficients Statistics and Probability Letters 82 672 – 682 2012 [9] X. Xu: Anticipated backward doubly stochastic differential equations Applied Mathematics and Computation, 220, 53-62, (2013). 10.1016/j.amc.2013.05.054 X Xu Anticipated backward doubly stochastic differential equations Applied Mathematics and Computation 220 53 – 62 2013 [10] F. Zhang: Comparison theorems for anticipated BSDEs with non-lipschitz coefficients J. Math. Appl, 416, 768

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Experimental and statistical analysis of blast-induced ground vibrations (BIGV) prediction in Senegal’s quarry

/s) Longitudinal Vertical Transversal DG 606 15 653 1.4 2.47 0.82 2.47 1 P1 (Lower exploitation level) 84 15 2022 15.94 31.82 23.5 31.82 P1 (Upper exploitation level) 143 15 139 7 8.76 4.7 8.76 Lake Ddoudj 291 15 2020 8 10.54 7.43 10.54 Macodo 529 15 1318 2.67 2.8 1.01 2.8 DG 613 30 653 1.71 4.12 1.27 4.12 2 P1 (Upper exploitation level) 136 30 139 11.05 13.84 7.62 13.84 P1 (Lower exploitation level) 92 30 2022 20

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The Consequences of Non-Uniform Founding of Concrete Tank in Weak Wet Subsoil

the changes in crack width as well as displacement along the crack ( Fig. 10 ). The location of the gauges is shown in Figure 11 . The measurement results are shown here as well. An analysis of the data from Figure 11 indicates that the crack measured with gauge No. 4 in the chamber No. 1 is immovable. The remaining cracks in this chamber show little changes in width (up to 0.2 mm). They also show very little displacement along crack (up to 0.05 mm), except the gauge No. 6 fixed next to longitudinal partition wall in the region with piles. This gauge was found to

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Intrachromosomal regulation decay in breast cancer

location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics) 54(3):507–554, 2005. 10.1111/j.1467-9876.2005.00510.x Robert A Rigby Mikis Stasinopoulos D Generalized additive models for location, scale and shape Journal of the Royal Statistical Society: Series C (Applied Statistics) 54 3 507 554 2005 [17] Georgios Roumpos, Michael Lohse, Wolfgang H Nitsche, Jonathan Keeling, Marzena Hanna Szymańska, Peter B Littlewood, Andreas Löffler, Sven Höfling, Lukas Worschech, Alfred Forchel, et al. Power-law decay of the

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Dynamics of the Modified n-Degree Lorenz System

Chaos, Solitons & Fractals 36 5 1315 1319 [9] Robinson, R.C., 2012. An introduction to dynamical systems: continuous and discrete (Vol. 19). American Mathematical Soc .. Robinson R.C. 2012 An introduction to dynamical systems: continuous and discrete 19 American Mathematical Soc [10] Curry, J.H., 1978. A generalized Lorenz system. Communications in Mathematical Physics , 60(3), pp.193-204. Curry J.H. 1978 A generalized Lorenz system Communications in Mathematical Physics 60 3 193 204 [11] Moore, D.R., Toomre, J., Knobloch, E. and Weiss, N.O., 1983. Period

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Numerical Solutions with Linearization Techniques of the Fractional Harry Dym Equation

techniques at t = 1 ,α = 0.9 for different values of Δ t , h . Lin. I Lin. II Lin. III L 2 × 10 2 L ∞ × 10 2 L 2 × 10 2 L ∞ × 10 2 L 2 × 10 2 L ∞ × 10 2 Δt = h = 0.001 0.00002 0.00002 0.00566 0.05664 0.00565 0.05659 Δt = h = 0.05 0.05254 0.08344 0.64556 0.94172 0.58516 0.84499 Δt = h = 0.04 0.02957 0.05128 0.50690 0.82106 0.47224 0.76100 Δt = h = 0.03 0.01418 0.02773 0.36800 0.68391 0.35113 0.65086 Δt = h = 0.02 0.00506 0.01186 0

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The self-similarity properties and multifractal analysis of DNA sequences

-Jensen algorithm are obtained. In section 6 , we show the multifractal spectra for the six-mers of two bacterias, two archaea, the Homosapiens chromosome 21 and a fungus, and discuss the information contained in the singularity spectra for six DNA sequences. 2 DNA Chaos Game We construct an unitary square Q with its corners labeled with a different basis of the genomic sequence V = {A,C,G,T} ; where V indicates the vertices of Q on the cartesian plane which are A = (0,0), C = (0,1), G =(1,1) and T =(1,0). It is applied the Jeffrey’s "chaos game algorithm

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