The Discrepancy of the Sequence (nα + (log n)β)

In this paper we give an upper bound for the discrepancy of the sequence (nα + (log n)β) where α = (α₁, ..., α _s ) R ^s , which satisfies that 1, α₁, ..., α _s are linearly independent over Z, is of finite type η or is of constant type.     

The attractor of the stochastic generalized Ginzburg-Landau equation

The stochastic generalized Ginzburg-Landau equation with additive noise can be solved pathwise and the unique solution generates a random system.Then we prove the random system possesses a global random attractor in H_0~1.     

The maximal (C,α,β) operator on Hp(T×T)

The two-dimensional classical Hardy space H_p(T×T) on the bidisc are introduced, and it is shown that the maximal operator of the (C,α,β) means of a distribution is bounded from the space H_p(T×T) to L_p(T²) (1/(α+1), 1/(β+1)<p≤∞), and is of weak type (H ₁ ^# (T×T), L₁(T²)), where the Hardy space H ₁ ^# (T×T) is defined by the hybrid maximal function. As a consequence we obtain that the (C, α, β) means of a function f∈H ₁ ^# (T×T)⊃LlogL(T ²) convergs a. e. to the function in question. Moreover, we prove that the (C, α, β) means are uniformly bounded on the spaces H_p(T×T) whenever 1/(α+1), 1(β+1)<p<∞. Thus, in case f∈H_p(T×T), the (C, α, β) means convergs to f in H_p(T×T) norm whenever (1/(α+1), 1/(β+1)<p<∞). The same results are proved for the conjugate (C, α, β) means, too. This research was made while the author was visiting the Humboldt University in Berlin supported by the Alexander von Humboldt Foundation.     

The spirit of capitalism,non-expected utility and asset pricing

0Intr0ducti0nAferEpsteinandZin(1989,199l)andWeil(1990),n0n-expectedutilitypreferences0ftenaPpwinassetpricingthe0ryinsteadofc0nventi0naltime-allitive,expectedutilityAnattrec-tivefeature0fthisgeneralisedspecificationisthatintertemp0ralsubStitutinnandriskaversioucanbepartiallydisentangled,incontr8ttothec0nventi0nalcaseofanadditiveandhomthgeneousVonNeumann-MorgellsternintertemPoralutilityfuncti0n,inwhichtheelasticityofsub8titutionandthec0efficielltofrelativeriskaversionarec0nstrainedtoberecipr0…     

The maximal (C,α,β) operator on Hp(T×T)

The two-dimensional classical Hardy space H_p(T×T) on the bidisc are introduced, and it is shown that the maximal operator of the (C,α,β) means of a distribution is bounded from the space H_p(T×T) to L_p(T²) (1/(α+1), 1/(β+1)<p≤∞), and is of weak type (H ₁ ^# (T×T), L₁(T²)), where the Hardy space H ₁ ^# (T×T) is defined by the hybrid maximal function. As a consequence we obtain that the (C, α, β) means of a function f∈H ₁ ^# (T×T)⊃LlogL(T ²) convergs a. e. to the function in question. Moreover, we prove that the (C, α, β) means are uniformly bounded on the spaces H_p(T×T) whenever 1/(α+1), 1(β+1)<p<∞. Thus, in case f∈H_p(T×T), the (C, α, β) means convergs to f in H_p(T×T) norm whenever (1/(α+1), 1/(β+1)<p<∞). The same results are proved for the conjugate (C, α, β) means, too.     

The stochastic fluid–fluid model: A stochastic fluid model driven by an uncountable-state process,which is a stochastic fluid model itself

We introduce the Stochastic Fluid–Fluid Model, which offers powerful modeling ability for a wide range of real-life systems of significance. We first derive the infinitesimal generator, with respect to time, of the driving stochastic fluid model. We then use this to derive the infinitesimal generator of a particular Laplace–Stieltjes transform of the model, which is the foundation of our analysis. We develop expressions for the Laplace–Stieltjes transforms of various performance measures for the transient and limiting analysis of the model. This work is the first direct analysis of a stochastic fluid model that is Markovian on a continuous state space.     

(S − 1,S) Perishable systems with stochastic leadtimes

This article discusses a one-to-one ordering perishable system, in which reorders are processed in the order of their arrival and the processing times are arbitrarily distributed, and as such, the leadtimes are not independent. The Markov renewal techniques are employed to obtain the various operating characteristics for the case of Poisson demand and exponential lifetimes. The problem of minimizing the steady state expected cost rate is also discussed, and in the special case of exponential processing times, the optimal stock level is derived explicitly.     

The Function Concept(二)

The domain of linear function f(x)=ax b(a≠0) is R, its range is R. The domain of inverse proportion function f(x)=(k/x)(k≠0) is {x|x≠0}, its range is B ={y|y≠0}. The domain of quadratic function, f(x)=ax~2 bx c(a≠0)is R, its range is B. B={y|y≥(4ac-b~2)/4a},when a>0;B={y|y≤(4ac-b~2/4a)},     

The Pythagorean Theorem(二)

By translate the triangle we can get a figure like that right.The angle measures J. and y add to 90°, so the angles of the     

The Pythagorean Theorem(一)

From the area of a square you can determine the length ofany of its sides.If the length of the side is √A.That is why√A is called the square root of A. Just as a plant rests on itsroots,a square can rest on its(positive)square root.side=√AThus if the area of a square is 400 square feet,its side has length 20cm.If the area is 13square feet,its side has length √13 feet,You can verify the latter of these with a calculator:     

The Pythagorean Theorem(三)

ProofThe original triangle withits translation and rota-tion images is below.Itslegs are a and b and thehypotenuse is c.The areaof the shaded square is     

The truncated Euler–Maruyama method for stochastic differential delay equations

The numerical solutions of stochastic differential delay equations (SDDEs) under the generalized Khasminskii-type condition were discussed by Mao (Appl. Math. Comput. 217, 5512–5524 2011), and the theory there showed that the Euler–Maruyama (EM) numerical solutions converge to the true solutions in probability. However, there is so far no result on the strong convergence (namely in L ^p ) of the numerical solutions for the SDDEs under this generalized condition. In this paper, we will use the truncated EM method developed by Mao (J. Comput. Appl. Math. 290, 370–384 2015) to study the strong convergence of the numerical solutions for the SDDEs under the generalized Khasminskii-type condition.     

The minimal non-Koszul A(Γ)

The algebras A(Γ), where Γ is a directed layered graph, were first constructed by Gelfand et al. [5 Gelfand, I., Serconek, S., Retakh, V., Wilson, R. L. (2005). On a class of algebras associated to directed graphs. Selecta Math. (N.S.) 11(2):281–295.[Crossref], [Web of Science ®] , [Google Scholar]]. These algebras are generalizations of the algebras Q_n, which are related to factorizations of non-commutative polynomials. It was originally conjectured that these algebras were Koszul. In 2008, Cassidy and Shelton found a counterexample to this claim, a non-Koszul A(Γ) corresponding to a graph Γ with 18 edges and 11 vertices. We produce an example of a directed layered graph Γ with 13 edges and 9 vertices, which produces a non-Koszul A(Γ). We also show this is the minimal example with this property.     

(α,β)-Inversion Formulas

Recently, basing on a suitable application of Milne-Bhatnagar's characterization theorem about matrix inversions, we have found that Warnaar's elliptic matrix inversion can be further extended to the following general inversion theorem.     

The accuracy of alternative stochastic models of participation in group discussion†

Four related models of participation in group discussion are presented and compared in the accuracy with which they predict proportional participation, mean run length, mean recurrence time and the variances of runs and recurrences. For some quantities, such as proportional participation and mean run length, the most restricted model does virtually as well as the least restricted model; for other quantities it does not. None of the models does a good job of predicting variances of runs or recurrences.     

Generalised soft binomial American real option pricing model (fuzzy–stochastic approach)

The stochastic discrete binomial models and continuous models are usually applied in option valuation. Valuation of the real American options is solved usually by the numerical procedures. Therefore, binomial model is suitable approach for appraising the options of American type. However, there is not in several situations especially in real option methodology application at to disposal input data of required quality. Two aspects of input data uncertainty should be distinguished; risk (stochastic) and vagueness (fuzzy). Traditionally, input data are in a form of real (crisp) numbers or crisp-stochastic distribution function. Therefore, hybrid models, combination of risk and vagueness could be useful approach in option valuation. Generalised hybrid fuzzy–stochastic binomial American real option model under fuzzy numbers (T-numbers) and Decomposition principle is proposed and described. Input data (up index, down index, growth rate, initial underlying asset price, exercise price and risk-free rate) are in a form of fuzzy numbers and result, possibility-expected option value is also determined vaguely as a fuzzy set. Illustrative example of equity valuation as an American real call option is presented.