Processing math: 74%
Research article

Study of wind speed and relative humidity using stochastic technique in a semi-arid climate region

  • Received: 19 September 2019 Accepted: 21 February 2020 Published: 24 March 2020
  • This paper deals with the stochastic analysis of wind speed based on relative humidity data. We propose a stochastic regression technique to estimate the time-varying parameters of wind speed in a semi-arid climate region. The modeling of stochastic parameters of atmospheric data with consistent properties facilitates prediction with higher precision. In order to compare the estimation, we used simulated atmospheric time series and observational time series. The atmospheric time series was generated by the Weather Research and Forecasting (WRF) model, whereas the observational time series was obtained from the surface weather stations. The time-varying parameters of the model used are estimated by Maximum Likelihood process. The results obtained suggest that relative humidity exhibits a stochastic effect to predict stationary wind speed data. This type of analysis helps to characterize some key meteorological variables, which would be useful in forecasting irregular wind speed.

    Citation: Suhail Mahmud, Md Al Masum Bhuiyan, Nusrat Sarmin, Sanjida Elahee. Study of wind speed and relative humidity using stochastic technique in a semi-arid climate region[J]. AIMS Environmental Science, 2020, 7(2): 156-173. doi: 10.3934/environsci.2020010

    Related Papers:

    [1] Zhanwei Gou, Jincheng Shi . Blow-up phenomena and global existence for nonlinear parabolic problems under nonlinear boundary conditions. AIMS Mathematics, 2023, 8(5): 11822-11836. doi: 10.3934/math.2023598
    [2] Huafei Di, Yadong Shang . Blow-up phenomena for a class of metaparabolic equations with time dependent coeffcient. AIMS Mathematics, 2017, 2(4): 647-657. doi: 10.3934/Math.2017.4.647
    [3] Sen Ming, Xiaodong Wang, Xiongmei Fan, Xiao Wu . Blow-up of solutions for coupled wave equations with damping terms and derivative nonlinearities. AIMS Mathematics, 2024, 9(10): 26854-26876. doi: 10.3934/math.20241307
    [4] Mengyang Liang, Zhong Bo Fang, Su-Cheol Yi . Blow-up analysis for a reaction-diffusion equation with gradient absorption terms. AIMS Mathematics, 2021, 6(12): 13774-13796. doi: 10.3934/math.2021800
    [5] Ahmed Himadan . Well defined extinction time of solutions for a class of weak-viscoelastic parabolic equation with positive initial energy. AIMS Mathematics, 2021, 6(5): 4331-4344. doi: 10.3934/math.2021257
    [6] Hatice Taskesen . Qualitative results for a relativistic wave equation with multiplicative noise and damping terms. AIMS Mathematics, 2023, 8(7): 15232-15254. doi: 10.3934/math.2023778
    [7] Jincheng Shi, Jianye Xia, Wenjing Zhi . Blow-up of energy solutions for the semilinear generalized Tricomi equation with nonlinear memory term. AIMS Mathematics, 2021, 6(10): 10907-10919. doi: 10.3934/math.2021634
    [8] Zhiqiang Li . The finite time blow-up for Caputo-Hadamard fractional diffusion equation involving nonlinear memory. AIMS Mathematics, 2022, 7(7): 12913-12934. doi: 10.3934/math.2022715
    [9] Xiongmei Fan, Sen Ming, Wei Han, Zikun Liang . Lifespan estimate of solution to the semilinear wave equation with damping term and mass term. AIMS Mathematics, 2023, 8(8): 17860-17889. doi: 10.3934/math.2023910
    [10] Jiaqing Hu, Xian Xu, Qiangqiang Yang . Bifurcation results of positive solutions for an elliptic equation with nonlocal terms. AIMS Mathematics, 2021, 6(9): 9547-9567. doi: 10.3934/math.2021555
  • This paper deals with the stochastic analysis of wind speed based on relative humidity data. We propose a stochastic regression technique to estimate the time-varying parameters of wind speed in a semi-arid climate region. The modeling of stochastic parameters of atmospheric data with consistent properties facilitates prediction with higher precision. In order to compare the estimation, we used simulated atmospheric time series and observational time series. The atmospheric time series was generated by the Weather Research and Forecasting (WRF) model, whereas the observational time series was obtained from the surface weather stations. The time-varying parameters of the model used are estimated by Maximum Likelihood process. The results obtained suggest that relative humidity exhibits a stochastic effect to predict stationary wind speed data. This type of analysis helps to characterize some key meteorological variables, which would be useful in forecasting irregular wind speed.


    In this paper, we are concerned with the existence and multiplicity of weak solutions for the damped-like fractional differential system

    {ddt(p(t)(12 0Dξt(u(t))+12 tDξT(u(t))))+r(t)(12 0Dξt(u(t))+12 tDξT(u(t)))+q(t)u(t)=λF(t,u(t)),  a.e. t[0,T],u(0)=u(T)=0, (1.1)

    where 0Dξt and tDξT are the left and right Riemann-Liouville fractional integrals of order 0ξ<1, respectively, p,r,qC([0,T],R), L(t):=t0(r(s)/p(s))ds, 0<meL(t)p(t)M and q(t)p(t)0 for a.e. t[0,T], u(t)=(u1(t),u2(t),un(t))T, ()T denotes the transpose of a vector, n1 is a given positive integer, λ>0 is a parameter, F(t,x) is the gradient of F with respect to x=(x1,,xn)Rn, that is, F(t,x)=(Fx1,,Fxn)T, and there exists a constant δ(0,1) such that F:[0,T]ׯBδ0R (where ¯Bδ0 is a closed ball in RN with center at 0 and radius δ) satisfies the following condition

    (F0) F(t,x) is continuously differentiable in ¯Bδ0 for a.e. t[0,T], measurable in t for every x¯Bδ0, and there are aC(¯Bδ0,R+) and bL1([0,T];R+) such that

    |F(t,x)|a(|x|)b(t)

    and

    |F(t,x)|a(|x|)b(t)

    for all x¯Bδ0 and a.e. t[0,T].

    In recent years, critical point theory has been extensively applied to investigate the existence and multiplicity of fractional differential equations. An successful application to ordinary fractional differential equations with Riemann-Liouville fractional integrals was first given by [1], in which they considered the system

    {ddt((12 0Dξt(u(t))+12 tDξT(u(t))))=F(t,u(t)),  a.e. t[0,T],u(0)=u(T)=0. (1.2)

    They established the variational structure and then obtained some existence results for system (1.2). Subsequently, this topic attracted lots of attention and a series of existence and multiplicity results are established (for example, see [2,3,4,5,6,7,8,9,10,11,12] and reference therein). It is obvious that system (1.1) is more complicated than system (1.2) because of the appearance of damped-like term

    r(t)(12 0Dξt(u(t))+12 tDξT(u(t))).

    In [13], the variational functional for system (1.1) with λ=1 and N=1 has been established, and in [14], they investigated system (1.1) with λ=1, N=1 and an additional perturbation term. By mountain pass theorem and symmetric mountain pass theorem in [15] and a local minimum theorem in [16], they obtained some existence and multiplicity results when F satisfies superquadratic growth at infinity and some other reasonable conditions at origin.

    In this paper, motivated by the idea in [17,18], being different from those in [13,14], we consider the case that F has subquadratic growth only near the origin and no any growth condition at infinity. Our main tools are Ekeland's variational principle in [19], a variant of Clark's theorem in [17] and a cut-off technique in [18]. We obtain that system (1.1) has a ground state weak solution uλ if λ is in some given interval and then some estimates of uλ are given, and when F(t,x) is also even about x near the origin for a.e. t[0,T], for each given λ>0, system (1.1) has infinitely many weak solutions {uλn} with uλn0 as n. Next, we make some assumptions and state our main results.

    (f0) There exist constants M1>0 and 0<p1<2 such that

    F(t,x)M1|x|p1 (1.3)

    for all x¯Bδ0 and a.e. t[0,T].

    (f1) There exist constants M2>0 and 0<p2<p1<2 such that

    F(t,x)M2|x|p2 (1.4)

    for all x¯Bδ0 and a.e. t[0,T].

    (f0) There exist constants M1>0 and 0<p1<1 such that (1.3) holds.

    (f1) There exist constants M2>0 and 0<p2<p1<1 such that (1.4) holds.

    (f2) There exists a constant η(0,2) such that

    (F(t,x),x)ηF(t,x)

    for all x¯Bδ0 and a.e. t[0,T].

    (f3) F(t,x)=F(t,x) for all x¯Bδ0 and a.e. t[0,T].

    Theorem 1.1. Suppose that (F0), (f0), (f1) and (f2) hold. If

    0<λmin{|cos(πα)|2C,(1B)2p2(δ2)2p2|cos(πα)|2C},

    then system (1.1) has a ground state weak solution uλ satisfying

    uλ2p2min{1,(1B)2p2(δ2)2p2},uλ2p2{B2p2,(δ2)2p2}.

    where

    B=T2α12mΓ(α)(2α1)12,C=max{p,η}max{M1,M2}Tmaxt[0,T]eL(t)max{Bp1,Bp2}.

    If (f0) and (f1) are replaced by the stronger conditions (f0) and (f1), then (f2) is not necessary in Theorem 1.1. So we have the following result.

    Theorem 1.2. Suppose that (F0), (f0) and (f1) hold. If

    0<λmin{|cos(πα)|3C,(1B)2p1(δ2)2p1|cos(πα)|3C},

    then system (1.1) has a ground state weak solution uλ satisfying

    uλ2p1min{1,(1B)2p1(δ2)2p1},uλ2p1{B2p1,(δ2)2p1},

    where C=maxt[0,T]eL(t)max{(1+ρ0)a0BT0b(t)dt,M1p1TBp1,ρ0M1TBp1+1}, a0=maxs[0,δ]a(s) and ρ0=maxs[δ2,δ]|ρ(s)| and ρ(s)C1(R,[0,1]) is any given even cut-off function satisfying

    ρ(s)={1,if |s|δ/2,0,if |s|>δ. (1.5)

    Theorem 1.3. Suppose that (F0), (f0), (f1) and (f3) hold. Then for each λ>0, system (1.1) has a sequence of weak solutions {uλn} satisfying {uλn}0, as n.

    Remark 1.1. Theorem 1.1-Theorem 1.3 still hold even if r(t)0 for all t[0,T], that is, the damped-like term disappears, which are different from those in [2,3,4,5,6,7,8,9,10,11,12] because all those assumptions with respect to x in our theorems are made only near origin without any assumption near infinity.

    The paper is organized as follows. In section 2, we give some preliminary facts. In section 3, we prove Theorem 1.1–Theorem 1.3.

    In this section, we introduce some definitions and lemmas in fractional calculus theory. We refer the readers to [1,9,20,21,22]. We also recall Ekeland's variational principle in [19] and the variant of Clark's theorem in [17].

    Definition 2.1. (Left and Right Riemann-Liouville Fractional Integrals [22]) Let f be a function defined on [a,b]. The left and right Riemann-Liouville fractional integrals of order γ for function f denoted by aDγtf(t) and tDγbf(t), respectively, are defined by

    aDγtf(t)=1Γ(γ)ta(ts)γ1f(s)ds,t[a,b],γ>0,tDγbf(t)=1Γ(γ)bt(st)γ1f(s)ds,t[a,b],γ>0.

    provided the right-hand sides are pointwise defined on [a,b], where Γ>0 is the Gamma function.

    Definition 2.2. ([22]) For nN, if γ=n, Definition 2.1 coincides with nth integrals of the form

    aDntf(t)=1(n1)!ta(ts)n1f(s)ds,t[a,b],nN,tDnbf(t)=1(n1)!bt(ts)n1f(s)ds,t[a,b],nN.

    Definition 2.3. (Left and Right Riemann-Liouville Fractional Derivatives [22]) Let f be a function defined on [a,b]. The left and right Riemann-Liouville fractional derivatives of order γ for function f denoted by aDγtf(t) and tDγbf(t), respectively, are defined by

    aDγtf(t)=dndtnaDγntf(t)=1Γ(nγ)dndtn(ta(ts)nγ1f(s)ds),tDγbf(t)=(1)ndndtntDγnbf(t)=(1)nΓ(nγ)dndtn(bt(st)nγ1f(s)ds).

    where t[a,b],n1γ<n and nN. In particular, if 0γ<1, then

    aDγtf(t)=ddtaDγ1tf(t)=1Γ(1γ)ddt(ta(ts)γf(s)ds),t[a,b],tDγbf(t)=ddttDγ1bf(t)=1Γ(1γ)ddt(bt(st)γf(s)ds),t[a,b].

    Remark 2.1. ([9,13]) The left and right Caputo fractional derivatives are defined by the above-mentioned Riemann-Liuville fractional derivative. In particular, they are defined for function belonging to the space of absolutely continuous functions, which we denote by AC([a,b],RN). ACk([a,b],RN)(k=0,1,...) are the space of the function f such that fCk([a,b],RN). In particular, AC([a,b],RN)=AC1([a,b],RN).

    Definition 2.4. (Left and Right Caputo Fractional Derivatives [22]) Let γ0 and nN.

    (ⅰ) If γ(n1,n) and fACn([a,b],RN), then the left and right Caputo fractional derivatives of order γ for function f denoted by caDγtf(t) and ctDγbf(t), respectively, exist almost everywhere on [a,b]. caDγtf(t) and ctDγbf(t) are represented by

    caDγtf(t)=aDγntfn(t)=1Γ(nγ)(ta(ts)nγ1f(n)(s)ds),ctDγbf(t)=(1)n tDγnbfn(t)=(1)nΓ(nγ)(bt(st)nγ1f(n)(s)ds),

    respectively, where t[a,b]. In particular, if 0<γ<1, then

    caDγtf(t)=aDγ1tf(t)=1Γ(1γ)(ta(ts)γf(s)ds),t[a,b],ctDγbf(t)=tDγ1bf(t)=1Γ(1γ)(bt(st)γf(s)ds),t[a,b].

    (ⅱ) If γ=n1 and fACn([a,b],RN), then caDγtf(t) and ctDγbf(t) are represented by

    caDn1tf(t)=f(n1)(t),t[a,b],ctDn1bf(t)=(1)n1f(n1)(t),t[a,b].

    In particular, caD0tf(t)= ctD0bf(t)=f(t), t[a,b].

    Lemma 2.1. ([22]) The left and right Riemann-Liouville fractional integral operators have the property of a semigroup, i.e.

    aDγ1t(aDγ2tf(t))=aDγ1γ2tf(t),tDγ1b(tDγ2bf(t))=tDγ1γ2bf(t),γ1,γ2>0,

    in any point t[a,b] for continuous function f and for almost every point in [a,b] if the function fL1([a,b],RN).

    For 1r<, define

    uLr=(T0|u(t)|rdt)1r (2.1)

    and

    u=maxt[0,T]|u(t)|. (2.2)

    Definition 2.5. ([1]) Let 0<α1 and 1<p<. The fractional derivative space Eα,p0 is defined by closure of C0([0,T],RN) with respect to the norm

    uα,p=(T0|u(t)|pdt+T0|c0Dαtu(t)|pdt)1p. (2.3)

    Remark 2.2. ([9]) Eα,p0 is the space of functions uLp([0,T],RN) having an α-order Caputo fractional derivative c0Dαtu(t)Lp([0,T],RN) and u(0)=u(T)=0.

    Lemma 2.2. ([1]) Let 0<α1 and 1<p<. Eα,p0 is a reflexive and separable Banach space.

    Lemma 2.3. ([1]) Assume that 1<p< and α>1p. Then Eα,p0 compactly embedding in C([0,T],RN).

    Lemma 2.4. ([1]) Let 0<α1 and 1<p<. For all uEα,p0, we have

    uLpTαΓ(α+1)c0DαtuLp. (2.4)

    Moreover, if α>1p and 1p+1q=1, then

    uTα1pΓ(α)((α1)q+1)1qc0DαtuLp. (2.5)

    Definition 2.6. ([13]) Assume that X is a Banach space. An operator A:XX is of type (S)+ if, for any sequence {un} in X, unu and lim supn+A(un),unu0 imply unu.

    Let φ:XR. A sequence {un}X is called (PS) sequence if the sequence {un} satisfies

    φ(un) is bounded, φ(un)0.

    Furthermore, if every (PS) sequence {un} has a convergent subsequence in X, then one call that φ satisfies (PS) condition.

    Lemma 2.5. ([19]) Assume that X is a Banach space and φ:XR is Gˆateaux differentiable, lower semi-continuous and bounded from below. Then there exists a sequence {xn} such that

    φ(xn)infXφ,φ(xn)0.

    Lemma 2.6. ([17]) Let X be a Banach space, φC1(X,R). Assume φ satisfies the (PS) condition, is even and bounded below, and φ(0)=0. If for any kN, there exist a k-dimensional subspace Xk of X and ρk>0 such that supXkSpkφ<0, where Sρ={uX|u=ρ}, then at least one of the following conclusions holds.

    (ⅰ) There exist a sequence of critical points {uk} satisfying φ(uk)<0 for all k and uk0 as k.

    (ⅱ) There exists a constant r>0 such that for any 0<a<r there exists a critical point u such that u=a and φ(u)=0.

    Remark 2.3. ([17]) Lemma 2.6 implies that there exist a sequence of critical points uk0 such that φ(uk)0, φ(uk)0 and uk0 as k.

    Now, we establish the variational functional defined on the space Eα,20 with 12<α1. We follow the same argument as in [13] where the one-dimensional case N=1 and λ=1 for system (1.1) was investigated. For reader's convenience, we also present the details here. Note that L(t):=t0(r(s)/p(s))ds,0<meL(t)p(t)M and q(t)p(t)0 for a.e. t[0,T]. Then system (1.1) is equivalent to the system

    {ddt(eL(t)p(t)(12 0Dξt(u(t))+12 tDξT(u(t))))+eL(t)q(t)u(t)=λeL(t)F(t,u),  a.e. t[0,T],u(0)=u(T)=0. (2.6)

    By Lemma 2.1, for every uAC([0,T],RN), it is easy to see that system (2.6) is equivalent to the system

    {ddt[eL(t)p(t)(12 0Dξ2t(0Dξ2tu(t))+12 tDξ2T(tDξ2Tu(t)))]+eL(t)q(t)u(t)=λeL(t)F(t,u),  a.e. t[0,T],u(0)=u(T)=0, (2.7)

    where ξ[0,1).

    By Definition 2.4, we obtain that uAC([0,T],RN) is a solution of problem (2.7) if and only if u is a solution of the following system

    {ddt(eL(t)p(t)(12 0Dα1t(c0Dαtu(t))12 tDα1T(ctDαTu(t))))+eL(t)q(t)u(t)=λeL(t)F(t,u),u(0)=u(T)=0, (2.8)

    for a.e. t[0,T], where α=1ξ2(12,1]. Hence, the solutions of system (2.8) correspond to the solutions of system (1.1) if uAC([0,T],RN).

    In this paper, we investigate system (2.8) in the Hilbert space Eα,20 with the corresponding norm

    u=(T0eL(t)p(t)(|c0Dαtu(t)|2+|u(t)|2)dt)12.

    It is easy to see that u is equivalent to uα,2 and

    mT0|c0Dαtu(t)|2dtT0eL(t)p(t)|c0Dαtu(t)|2dtMT0|c0Dαtu(t)|2dt.

    So

    uL2TαmΓ(α+1)(T0eL(t)p(t)|c0Dαtu(t)|2dt)12,

    and

    uBu, (2.9)

    where

    B=T2α12mΓ(α)(2α1)12>0.

    (see [13]).

    Lemma 2.7. ([13]) If 12<α1, then for every uEα,20, we have

    |cos(πα)|u2T0eL(t)p(t)(c0Dαtu(t),ctDαTu(t))dt+T0eL(t)p(t)|u(t)|2dtmax{Mm|cos(πα)|,1}u2. (2.10)

    We follow the idea in [17] and [18]. We first modify and extend F to an appropriate ˜F defined by

    ˜F(t,x)=ρ(|x|)F(t,x)+(1ρ(|x|))M1|x|p1,  for all xRN,

    where ρ is defined by (1.5).

    Lemma 3.1. Let (F0), (f0), (f1) (or (f0), (f1)), (f2) and (f3) be satisfied. Then

    (˜F0) ˜F(t,x) is continuously differentiable in xRN for a.e. t[0,T], measurable in t for every xRN, and there exists bL1([0,T];R+) such that

    |˜F(t,x)|a0b(t)+M1|x|p1,|˜F(t,x)|(1+ρ0)a0b(t)+M1p1|x|p11+ρ0M1|x|p1

    for all xRN and a.e. t[0,T];

    (˜f0) ˜F(t,x)M1|x|p1 for all xRN and a.e. t[0,T];

    (˜f1) ˜F(t,x)max{M1,M2}(|x|p1+|x|p2) for all xRN and a.e. t[0,T];

    (˜f2) (˜F(t,x),x)θ˜F(t,x) for all xRN and a.e. t[0,T], where θ=max{p1,η};

    (˜f3) ˜F(t,x)=˜F(t,x) for all xRN and a.e. t[0,T].

    Proof. We only prove (˜f0), (˜f1) and (˜f2). (˜F0) can be proved by a similar argument by (F0). By the definition of ˜F(t,x), (f0) and (f1) (or (f0) and (f1)), we have

    M1|x|p1˜F(t,x)=F(t,x)M2|x|p2, if |x|δ/2,
    ˜F(t,x)=M1|x|p1, if |x|>δ,
    ˜F(t,x)F(t,x)+M1|x|p1M1|x|p1+M2|x|p2, if δ/2<|x|δ

    and

    ˜F(t,x)ρ(|x|)M1|x|p1+(1ρ(|x|))M1|x|p1=M1|x|p1, if δ/2<|x|δ.

    Hence, (˜f1) holds. Note that

    θ˜F(t,x)(˜F(t,x),x)=ρ(|x|)(θF(t,x)(F(t,x),x))+(θp1)(1ρ(|x|))M1|x|p1|x|ρ(|x|)(F(t,x)M1|x|p1).

    It is obvious that the conclusion holds for 0|x|δ/2 and |x|>δ. If δ/2<|x|δ, by using θp1, (f2), (˜f1) and the fact sρ(s)0 for all sR, we can get the conclusion (˜f2). Finally, since ρ(|x|) is even for all xRN, by (f3) and the definition of ˜F(t,x), it is easy to get (˜f3).

    Remark 3.1. From the proof of Lemma 3.1, it is easy to see that (F0), (f0) (or (f0)) and (f1) (or (f1)) independently imply (˜F0), (˜f0) and (˜f1), respectively.

    Consider the modified system

    {ddt(eL(t)p(t)(12 0Dα1t(c0Dαtu(t))12 tDα1T(ctDαTu(t))))+eL(t)q(t)u(t)=λeL(t)˜F(t,u),u(0)=u(T)=0, (3.1)

    for a.e. t[0,T], where α=1ξ2(12,1].

    If the equality

    T0eL(t)[12p(t)((c0Dαtu(t),ctDαTv(t))+(ctDαTu(t),c0Dαtv(t)))+p(t)(u(t),v(t))+(q(t)p(t))(u(t),v(t))λ(˜F(t,u(t)),v(t))]dt=0

    holds for every vEα,20, then we call uEα,20 is a weak solution of system (3.1).

    Define the functional ˜J:Eα,20R by

    ˜J(u)=T0eL(t)[12p(t)((c0Dαtu(t),ctDαTu(t))+|u(t)|2)+12(q(t)p(t))|u(t)|2λ˜F(t,u(t))]dt, for all uEα,20.

    Then (˜F0) and Theorem 6.1 in [9] imply that ˜JC1(Eα,20,R), and for every u,vEα,20, we have

    ˜J(u),v=T0eL(t)[12p(t)((c0Dαtu(t),ctDαTv(t))+(ctDαTu(t),c0Dαtv(t)))+p(t)(u(t),v(t))+(q(t)p(t))(u(t),v(t))λ(˜F(t,u(t)),v(t))]dt.

    Hence, a critical point of ˜J(u) corresponds to a weak solution of problem (3.1).

    Let

    Au,v:=T0eL(t)[12p(t)((c0Dαtu(t),ctDαTv(t))+(ctDαTu(t),c0Dαtv(t)))+p(t)(u(t),v(t))+(q(t)p(t))(u(t),v(t))]dt.

    Lemma 3.2. ([13])

    γ1u2Au,uγ2u2,for all uEα,20, (3.2)

    where γ1=|cos(πα)| and γ2=(max{Mm|cosπα|,1}+maxt[0,T](q(t)p(t))).

    Lemma 3.3. Assume that (F0), (f0) and (f1) (or (f0) and (f1)) hold. Then for each λ>0, ˜J is bounded from below on Eα,20 and satisfies (PS) condition.

    Proof. By (˜f1), (2.9) and (3.2), we have

    ˜J(u)=12Au,uλT0eL(t)˜F(t,u(t))dtγ12u2λmax{M1,M2}T0eL(t)(|u(t)|p1+|u(t)|p2)dtγ12u2λmax{M1,M2}Tmaxt[0,T]eL(t)(up1+up2)γ12u2λmax{M1,M2}Tmaxt[0,T]eL(t)[Bp1up1+Bp2up2].

    It follows from 0<p2<p1<2 that

    ˜J(u)+, as u.

    Hence, ˜J is coercive and then is bounded from below. Now we prove that ˜J satisfies the (PS) condition. Assume that {un} is a (PS) sequence of ˜J, that is,

    ˜J(un) is bounded, ˜J(un)0. (3.3)

    Then by the coercivity of ˜J and (3.3), there exists C0>0 such that unC0 and then by Lemma 2.3, there exists a subsequence (denoted again by {un}) such that

    unu, weakly in Eα,20, (3.4)
    unu, a.e.  in C([0,T],R). (3.5)

    Therefore, the boundness of {un} and (3.3) imply that

    |˜J(un),unu|˜J(un)(Eα,20)unu,˜J(un)(Eα,20)(un+u)0, (3.6)

    where (Eα,20) is the dual space of Eα,20, and (˜F0), (2.9) together with (3.5) imply that

    |λT0(˜F(t,un(t)),un(t)u(t))dt|λT0|˜F(t,un(t))||(un(t)u(t))|dtλunuT0[(1+ρ0)a0b(t)+M1p1|un(t)|p11+ρ0M1|un(t)|p1]dtλunu[(1+ρ0)a0T0b(t)dt+M1p1TBp11Cp110+M1Tρ0Bp1Cp10]0. (3.7)

    Note that

    \begin{eqnarray*} \label{eq3.18} \left\langle \widetilde{J}'\left(u_{n}\right), u_{n}-u\right\rangle = \left\langle A u_{n}, u_{n}-u\right\rangle-\lambda \int_{0}^{T} (\nabla\widetilde{F}\left(t, u_{n}(t)\right), u_{n}(t)-u(t))dt. \end{eqnarray*}

    Then (3.6) and (3.7) imply that \lim_{n \rightarrow \infty}\left\langle A u_{n}, u_{n}-u\right\rangle = 0 . Moreover, by (3.4), we have

    \lim _{n \rightarrow \infty}\left\langle A u, u_{n}-u\right\rangle = 0.

    Therefore

    \lim\limits_{n \rightarrow \infty}\left\langle A u_{n}-A u, u_{n}-u\right\rangle = 0.

    Since A is of type (S)_{+} (see [13]), by Definition 2.6, we obtain u_{n}\rightarrow u in E^{\alpha, 2}_{0} .

    Define a Nehari manifold by

    \mathcal{N}_\lambda = \{u\in E_0^{\alpha, 2}/\{0\}|\langle\widetilde{J}_\lambda'(u), u\rangle = 0\}.

    Lemma 3.4. Assume that (F_{0}) and (f_{0}) (or (f_{0})' ) hold. For each \lambda > 0 , \widetilde{J}_\lambda has a nontrivial least energy (ground state) weak solution u_\lambda , that is, u_\lambda\in \mathcal{N}_\lambda and \widetilde{J}_\lambda(u_\lambda) = \inf\limits_{\mathcal{N}_\lambda}\widetilde{J}_\lambda . Moreover, the least energy can be estimated as follows

    \widetilde{J}_\lambda(u_\lambda)\le G_\lambda: = \frac{{(p_{1}/\gamma_{2})}^{\frac{p_1}{2-p_1}}[\lambda M_1\min\limits_{t\in [0, T]}e^{-L(t)}\int_{0}^{T}|w_0(t)|^{p_1}dt]^{\frac{2}{2-p_1}}(p_1-2)}{2}.

    where w_0 = \frac{w}{\|w\|} , and w = \left(\frac{T}{\pi}\sin\frac{\pi t}{T}, 0, \cdots, 0\right)\in E_0^{\alpha, 2}.

    Proof. By Lemma 3.3 and \widetilde{J}\in C^1(E^{\alpha, 2}_{0}, \mathbb{R}) , for each \lambda > 0, Lemma 2.5 implies that there exists some u_{\lambda}\in E^{\alpha, 2}_{0} such that

    \begin{eqnarray} \widetilde{J}\left(u_{\lambda}\right) = \inf _{v \in E^{\alpha, 2}_{0}} \widetilde{J}(v)\quad \mbox{and } \tilde{J}'\left(u_{\lambda}\right) = 0. \end{eqnarray} (3.8)

    By (3.2) and (\widetilde{f}_{0}) , we have

    \begin{eqnarray} \widetilde{J}_\lambda(sw_0) & = & \frac{1}{2}\langle A(sw_0), sw_0\rangle-\lambda\int_{0}^{T}e^{-L(t)}\widetilde{F}(t, sw_0(t))dt\\ &\le & \frac{\gamma_2}{2}s^2\|w_0\|^2-\lambda\int_{0}^{T}e^{-L(t)}M_1|sw_0(t)|^{p_1}dt\\ &\le & \frac{\gamma_2}{2}s^2-\lambda M_1\min\limits_{t\in [0, T]}e^{-L(t)}s^{p_1}\int_{0}^{T}|w_0(t)|^{p_1}dt. \end{eqnarray} (3.9)

    for all s\in [0, \infty) . Define g:[0, +\infty)\to \mathbb{R} by

    g(s) = \frac{\gamma_2}{2}s^2-\lambda M_1\min\limits_{t\in [0, T]}e^{-L(t)}s^{p_1}\int_{0}^{T}|w_0(t)|^{p_1}dt.

    Then g(s) achieves its minimum at

    s_{0, \lambda} = \left(\frac{p_1\lambda M_1\min\limits_{t\in [0, T]}e^{-L(t)}\int_{0}^{T}|w_0(t)|^{p_1}dt}{\gamma_2}\right)^{\frac{1}{2-p_1}}

    and

    g(s_{0, \lambda}) = \frac{{(p_{1}/\gamma_{2})}^{\frac{p_1}{2-p_1}}[\lambda M_1\min\limits_{t\in [0, T]}e^{-L(t)}\int_{0}^{T}|w_0(t)|^{p_1}dt]^{\frac{2}{2-p_1}}(p_1-2)}{2}.

    Note that p_1 < 2 . So g(s_{0, \lambda}) < 0 . Hence, (3.9) implies that

    \begin{eqnarray*} \label{eq3.19} \widetilde{J}_\lambda\left(u_{\lambda}\right) = \inf _{v \in E^{\alpha, 2}_{0}} \widetilde{J}_\lambda(v)\le \widetilde{J}_\lambda(s_{0, \lambda}w_0)\le g(s_{0, \lambda}) \lt 0 = \widetilde{J}_\lambda(0) \end{eqnarray*}

    and then u_{\lambda}\not = 0 which together with (3.8) implies that u_\lambda\in \mathcal{N}_\lambda and \widetilde{J}_\lambda(u_\lambda) = \inf\limits_{\mathcal{N}_\lambda}\widetilde{J}_\lambda .

    Lemma 3.5. Assume that ({F}_{0}) , ({f}_{1}) and ({f}_{2}) hold. If 0 < \lambda\le\frac{|\cos(\pi\alpha)|}{2 C} , then the following estimates hold

    \begin{eqnarray*} \label{eq3.26} \|u_{\lambda}\|^{2-p_{2}}\leq\frac{2\lambda C }{|\cos(\pi\alpha)|}, \quad \|u_{\lambda}\|_{\infty}^{2-p_{2}}\le \frac{2\lambda C B^{2-p_{2}} }{|\cos(\pi\alpha)|}. \end{eqnarray*}

    Proof. It follows from Lemma 3.1, (2.9) and \langle\widetilde{J}'(u_{\lambda}), u_{\lambda}\rangle = 0 that

    \begin{eqnarray} &&\int_{0}^{T} e^{-L(t)}\bigg[-p(t)(_{0}^{c} D_{t}^{\alpha} u_{\lambda}(t), \;_{t}^{c} D_{T}^{\alpha} u_{\lambda}(t)) +p(t)(u_{\lambda}(t), u_{\lambda}(t))+(q(t)-p(t))(u_{\lambda}(t), u_{\lambda}(t))\bigg]dt\\ & = & \lambda\int_{0}^{T}e^{-L(t)} (\nabla \widetilde{F}(t, u_{\lambda}(t)), u_{\lambda}(t))dt\\ & \le & \lambda\theta\int_{0}^{T} e^{-L(t)} \widetilde{F}(t, u_{\lambda}(t))dt\\ & \leq & \lambda\theta \max\{M_1, M_2\}\max\limits_{t\in [0, T]}e^{-L(t)}\int_{0}^{T} (|u_{\lambda}(t)|^{p_{1}}+|u_{\lambda}(t)|^{p_{2}})dt\\ & \leq & \lambda \theta\max\{M_1, M_2\}T\max\limits_{t\in [0, T]}e^{-L(t)} (\|u_{\lambda}\|^{p_{1}}_{\infty}+\|u_{\lambda}\|^{p_{2}}_{\infty})\\ & \leq & \lambda \theta \max\{M_1, M_2\}T\max\limits_{t\in [0, T]}e^{-L(t)} \left[B^{p_{1}}\|u_{\lambda}\|^{p_{1}}+B^{p_{2}}\|u_{\lambda}\|^{p_{2}}\right]\\ & \leq & \lambda C (\|u_{\lambda}\|^{p_{1}}+\|u_{\lambda}\|^{p_{2}}). \end{eqnarray} (3.10)

    We claim that \|u_{\lambda}\|\leq 1 uniformly for all 0 < \lambda\le\frac{|\cos(\pi\alpha)|}{2 C} . Otherwise, we have a sequence of \{\lambda_{n}\le \frac{|\cos(\pi\alpha)|}{2 C}\} such that \|u_{\lambda_{n}}\| > 1 . Thus \|u_{\lambda_{n}}\|^{p_{2}} < \|u_{\lambda_{n}}\|^{p_{1}} since p_{2} < p_{1} < 2. By (2.10) and (3.10), we obtain

    \begin{eqnarray} &&\int_{0}^{T} e^{-L(t)}\bigg[-p(t)(_{0}^{c} D_{t}^{\alpha} u_{\lambda_n}(t), \; _{t}^{c} D_{T}^{\alpha} u_{\lambda_n}(t)) +p(t)(u_{\lambda_n}(t), u_{\lambda_n}(t))+(q(t)-p(t))(u_{\lambda_n}(t), u_{\lambda_n}(t))\bigg]dt\\ &&\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\geq |\cos(\pi\alpha)|\|u_{\lambda_n}\|^{2}+\int_{0}^{T} e^{-L(t)}(q(t)-p(t))|u_{\lambda_n}(t)|^{2}dt. \end{eqnarray} (3.11)

    By (3.10) and (3.11), we obtain

    \begin{eqnarray*} \label{eq3.22} |\cos(\pi\alpha)|\|u_{\lambda_{n}}\|^{2}+\int_{0}^{T} e^{-L(t)}(q(t)-p(t))|u_{\lambda_{n}}(t)|^{2}dt\leq\lambda_n C (\|u_{\lambda_{n}}\|^{p_{1}}+\|u_{\lambda_{n}}\|^{p_{2}}). \end{eqnarray*}

    Since q(t)-p(t) > 0,

    \begin{eqnarray*} \label{eq3.23} |\cos(\pi\alpha)|\|u_{\lambda_{n}}\|^{2}\leq\lambda_n C (\|u_{\lambda_{n}}\|^{p_{1}}+\|u_{\lambda_{n}}\|^{p_{2}}) \leq 2\lambda_n C \|u_{\lambda_{n}}\|^{p_{1}}. \end{eqnarray*}

    Then

    \begin{eqnarray*} \label{eq3.24} &&\|u_{\lambda_{n}}\|^{2-p_{1}}\leq\frac{2\lambda_n C}{|\cos(\pi\alpha)|}\le 1, \end{eqnarray*}

    which contradicts with the assumption \|u_{\lambda_{n}}\| > 1. Now, from (3.10) we can get

    \begin{eqnarray*} \label{eq3.25} |\cos(\pi\alpha)|\|u_{\lambda}\|^{2}&\leq&\lambda C (\|u_{\lambda}\|^{p_{1}}+\|u_{\lambda}\|^{p_{2}})\nonumber\\ &\leq& 2\lambda C \|u_{\lambda}\|^{p_{2}}. \end{eqnarray*}

    So

    \|u_{\lambda}\|^{2-p_{2}}\leq\frac{2\lambda C }{|\cos(\pi\alpha)|}.

    By (2.9), we can obtain

    \|u_{\lambda}\|_{\infty}\leq B\|u_{\lambda}\|\le B\left(\frac{2\lambda C }{|\cos(\pi\alpha)|}\right)^{\frac{1}{2-p_{2}}}.

    Observe that, in the proof of Lemma 3.5, (\widetilde{f}_2) is used only in (3.10). If we directly use (\widetilde{F}_0) to rescale (\nabla \widetilde{F}(t, u_{\lambda}(t)), u_{\lambda}(t)) in (3.10). Then the assumption ({f}_2) is not necessary but we have to pay the price that p\in (0, 1) . To be precise, we have the following lemma.

    Lemma 3.6. Assume that ({F}_{0}) and (f_{0})' hold. If 0 < \lambda\le\frac{|\cos(\pi\alpha)|}{3C^*} , then the following estimates hold

    \|u_{\lambda}\|^{2-p_{1}}\leq\frac{3\lambda C^* }{|\cos(\pi\alpha)|}, \quad \|u_{\lambda}\|_{\infty}^{2-p_{1}}\le \frac{3\lambda C^* B^{2-p_{1}} }{|\cos(\pi\alpha)|}.

    Proof. It follows from ({F}_{0}) , Lemma 3.1, Remark 3.1, (2.9) and \langle\widetilde{J}'(u_{\lambda}), u_{\lambda}\rangle = 0 that

    \begin{eqnarray} &&\int_{0}^{T} e^{-L(t)}\bigg[-p(t)(_{0}^{c} D_{t}^{\alpha} u_{\lambda}(t), \;_{t}^{c} D_{T}^{\alpha} u_{\lambda}(t)) +p(t)(u_{\lambda}(t), u_{\lambda}(t))+(q(t)-p(t))(u_{\lambda}(t), u_{\lambda}(t))\bigg]dt\\ & = &\lambda\int_{0}^{T} e^{-L(t)} (\nabla \widetilde{F}(t, u_{\lambda}(t)), u_{\lambda}(t))dt\\ & \le &\lambda\max\limits_{t\in [0, T]}e^{-L(t)}\int_{0}^{T}|\nabla \widetilde{F}(t, u_{\lambda}(t))||u_{\lambda}(t)|dt\\ & \le &\lambda\max\limits_{t\in [0, T]}e^{-L(t)}\int_{0}^{T}\left[(1+\rho_0)a_0b(t)|u_{\lambda}(t)|+M_1p_1|u_{\lambda}(t)|^{p_1}+\rho_0M_1|u_{\lambda}(t)|^{p_1+1}\right]dt\\ &\leq&\lambda \max\limits_{t\in [0, T]}e^{-L(t)} \left[(1+\rho_0)a_0\|u_{\lambda}\|_\infty \int_0^Tb(t)dt +M_1p_1\|u_{\lambda}\|_\infty^{p_1}+\rho_0M_1T\|u_{\lambda}\|_\infty^{p_1+1}\right]\\ &\leq&\lambda \max\limits_{t\in [0, T]}e^{-L(t)} \left[(1+\rho_0)a_0B\|u_{\lambda}\| \int_0^Tb(t)dt +M_1p_1TB^{p_1}\|u_{\lambda}\|^{p_1}+\rho_0M_1TB^{p_1+1}\|u_{\lambda}\|^{p_1+1}\right]\\ &\leq&\lambda C^* (\|u_{\lambda}\|+\|u_{\lambda}\|^{p_{1}}+\|u_{\lambda}\|^{p_{1}+1}). \end{eqnarray} (3.12)

    We claim that \|u_{\lambda}\|\leq 1 uniformly for all 0 < \lambda\le\frac{|\cos(\pi\alpha)|}{3 C^*} . Otherwise, we have a sequence of \{\lambda_{n}\le \frac{|\cos(\pi\alpha)|}{3 C^*}\} such that \|u_{\lambda_{n}}\| > 1 . Thus \|u_{\lambda_{n}}\|^{p_{1}} < \|u_{\lambda_{n}}\| < \|u_{\lambda_{n}}\|^{p_{1}+1} since p_{1} < 1. By (2.10) and (3.12), we obtain

    \begin{eqnarray} &&\int_{0}^{T} e^{-L(t)}\bigg[-p(t)(_{0}^{c} D_{t}^{\alpha} u_{\lambda_n}(t), \; _{t}^{c} D_{T}^{\alpha} u_{\lambda_n}(t)) +p(t)(u_{\lambda_n}(t), u_{\lambda_n}(t))+(q(t)-p(t))(u_{\lambda_n}(t), u_{\lambda_n}(t))\bigg]dt\\ &&\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\geq |\cos(\pi\alpha)|\|u_{\lambda_n}\|^{2}+\int_{0}^{T} e^{-L(t)}(q(t)-p(t))|u_{\lambda_n}(t)|^{2}dt. \end{eqnarray} (3.13)

    By (3.12) and (3.13), we obtain

    |\cos(\pi\alpha)|\|u_{\lambda_{n}}\|^{2}+\int_{0}^{T} e^{-L(t)}(q(t)-p(t))|u_{\lambda_{n}}|^{2}dt\leq\lambda_n C^* (\|u_{\lambda}\|+\|u_{\lambda}\|^{p_{1}}+\|u_{\lambda}\|^{p_{1}+1}).

    Since q(t)-p(t) > 0,

    |\cos(\pi\alpha)|\|u_{\lambda_{n}}\|^{2}\leq\lambda_n C^* (\|u_{\lambda}\|+\|u_{\lambda}\|^{p_{1}}+\|u_{\lambda}\|^{p_{1}+1}) \leq 3\lambda_n C^* \|u_{\lambda_{n}}\|^{p_{1}+1}.

    Then

    \|u_{\lambda_{n}}\|^{1-p_{1}}\leq\frac{3\lambda_n C^*}{|\cos(\pi\alpha)|}\le 1,

    which contradicts with the assumption \|u_{\lambda_{n}}\| > 1. Now, we can get from (3.12) that

    |\cos(\pi\alpha)|\|u_{\lambda}\|^{2} \leq \lambda C^* (\|u_{\lambda}\|+\|u_{\lambda}\|^{p_{1}}+\|u_{\lambda}\|^{p_{1}+1})\nonumber\\ \leq 3\lambda C^* \|u_{\lambda}\|^{p_{1}}.

    So

    \|u_{\lambda}\|^{2-p_{1}}\leq\frac{3\lambda C^* }{|\cos(\pi\alpha)|}.

    By (2.9), we can obtain

    \|u_{\lambda}\|_{\infty}\leq B\|u_{\lambda}\|\le B\left(\frac{3\lambda C^* }{|\cos(\pi\alpha)|}\right)^{\frac{1}{2-p_{1}}}.

    Proof of Theorem 1.1. Since 0 < \lambda\le\min\left\{\frac{|\cos(\pi\alpha)|}{2 C}, \left(\frac{1}{B}\right)^{2-p_{2}}\left(\frac{\delta}{2}\right)^{2-p_2}\frac{|\cos(\pi\alpha)|}{2C}\right\} , Lemma 3.5 implies that

    \begin{eqnarray*} \label{eq3.28} \|u_{\lambda}\|_{\infty}\le \frac{\delta}{2}. \end{eqnarray*}

    Therefore, for all 0 < \lambda\le\min\left\{\frac{|\cos(\pi\alpha)|}{2 C}, \left(\frac{1}{B}\right)^{2-p_{2}}\left(\frac{\delta}{2}\right)^{2-p_2}\frac{|\cos(\pi\alpha)|}{2C}\right\}, we have \widetilde{F}(t, u(t)) = F(t, u(t)) and then u_{\lambda} is a nontrivial weak solution of the original problem (1.1). Moreover, Lemma 3.5 implies that \lim_{\lambda\to 0}\|u_\lambda\| = 0 as \lambda\to 0 and

    \begin{eqnarray*} \|u_{\lambda}\|^{2-p_{2}}\leq\min\left\{1, \left(\frac{1}{B}\right)^{2-p_{2}}\left(\frac{\delta}{2}\right)^{2-p_2}\right\}, \quad \|u_{\lambda}\|_{\infty}^{2-p_{2}}\le B^{2-p_{2}}\left\{1, \left(\frac{1}{B}\right)^{2-p_{2}}\left(\frac{\delta}{2}\right)^{2-p_2}\right\}. \end{eqnarray*}

    Proof of Theorem 1.2. Note that 0 < \lambda\le\min\left\{\frac{|\cos(\pi\alpha)|}{3 C^*}, \left(\frac{1}{B}\right)^{2-p_{1}}\left(\frac{\delta}{2}\right)^{2-p_1}\frac{|\cos(\pi\alpha)|}{3C^*}\right\} . Similar to the proof of Theorem 1.1, by Lemma 3.6, it is easy to complete the proof.

    Proof of Theorem 1.3. By Lemma 3.1 and Lemma 3.3, we obtain that \widetilde{J} satisfies (PS) condition and is even and bounded from below, and \widetilde{J}(0) = 0. Next, we prove that for any k \in \mathbb {N}, there exists a subspace k -dimensional subspace X_{k}\subset E^{\alpha, 2}_{0} and \rho_{k} > 0 such that

    \begin{eqnarray*} \label{eq3.29} \sup _{u \in X^{k} \cap S_{\rho_{k}}} \widetilde{J}_{\lambda}(u) \lt 0. \end{eqnarray*}

    In fact, for any k \in \mathbb {N}, assume that X^{k} is any subspace with dimension k in E^{\alpha, 2}_{0} . Then by (2.10) and Lemma 3.1, there exist constants C_1, C_2 > 0 such that

    \begin{eqnarray*} \label{eq3.30} \widetilde{J}(u)&\leq&\max \left\{\frac{M}{m|\cos(\pi\alpha)|}, 1\right\}\|u\|^{2}+\frac{1}{2}\int_{0}^{T} e^{-L(t)}(q(t)-p(t))|u(t)|^{2}dt -\lambda\int_{0}^{T} e^{-L(t)}\widetilde{F}(t, u(t))dt\nonumber\\ &\le& \max \left\{\frac{M}{m|\cos(\pi\alpha)|}, 1\right\}\|u\|^{2}+\frac{C_1}{2}\|u\|^{2}_{\infty} -\lambda C_2\int_{0}^{T}\widetilde{F}(t, u(t))dt\nonumber\\ &\leq&\max \left\{\frac{M}{m|\cos(\pi\alpha)|}, 1\right\}\|u\|^{2}+\frac{C_1B^2}{2} \|u\|^{2}-\lambda C_2M_1\int_{0}^{T}|u(t)|^{p_{1}}dt\nonumber\\ &\leq&\left[\max\left\{\frac{M}{m|\cos(\pi\alpha)|}, 1\right\}+\frac{C_1B^2}{2} \right]\|u\|^{2}-\lambda C_2M_1\|u\|_{L^{p_1}}^{p_{1}}. \end{eqnarray*}

    Since all norms on X^{k} are equivalent and p_{1} < 2, for each fixed \lambda > 0 , we can choose \rho_{k} > 0 small enough such that

    \begin{eqnarray*} \label{eq3.31} \sup _{u \in X^{k} \cap S_{\rho_{k}}} \widetilde{J}_{\lambda}(u) \lt 0. \end{eqnarray*}

    Thus, by Lemma 2.6 and Remark 2.3. \widetilde{J}_\lambda has a sequence of nonzero critical points \{u^{\lambda}_{n}\}\subset E^{\alpha, 2}_{0} converging to 0 and \widetilde{J}_\lambda(u^{\lambda}_{n})\le 0 . Hence, for each fixed \lambda > 0 , (3.1) has a sequence of weak solutions \{u^{\lambda}_{n}\}\subset E^{\alpha, 2}_{0} with \|u^{\lambda}_{n}\|\rightarrow0 , as n\to \infty . Furthermore, there exists n_0 large enough such that \|u^{\lambda}_{n}\|\le \frac{\delta}{2B} for all n\ge n_0 and then (2.9) implies that \|u^{\lambda}_{n}\|_\infty\le \frac{\delta}{2} for all n\ge n_0 . Thus, \widetilde{F}(t, u(t)) = F(t, u(t)) and then \{u_n^{\lambda}\}_{n_0}^\infty is a sequence of weak solutions of the original problem (1.1) for each fixed \lambda > 0 .

    When the nonlinear term F(t, x) is local subquadratic only near the origin with respect to x , system (1.1) with \lambda in some given interval has a ground state weak solution u_\lambda . If the nonlinear term F(t, x) is also locally even near the origin with respect to x , system (1.1) with \lambda > 0 has infinitely many weak solutions \{u_n^\lambda\} .

    This project is supported by Yunnan Ten Thousand Talents Plan Young & Elite Talents Project and Candidate Talents Training Fund of Yunnan Province (No: 2017HB016).

    The authors declare that they have no conflicts of interest.



    [1] Kavasseri RG, Seetharaman K (2009) Day-ahead wind speed forecasting using f-ARIMA models. Renew Energ 34: 1388-1393. doi: 10.1016/j.renene.2008.09.006
    [2] Cassola F, Burlando M (2012) Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output. Appl Energ 99: 154-166. doi: 10.1016/j.apenergy.2012.03.054
    [3] Warner TT, Peterson RA, Treadon RE (1997) A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. B Am Meteorol Soc 78: 2599-2618. doi: 10.1175/1520-0477(1997)078<2599:ATOLBC>2.0.CO;2
    [4] Franzke CL, O'Kane TJ, Berner J, et al. (2015) Stochastic climate theory and modeling. Wiley Interdis Rev: Climate Change 6: 63-78. doi: 10.1002/wcc.318
    [5] Yu Media Group. El Paso, TX. Detailed climate information and monthly weather forecast. Available from https://www.weather-us.com/en/texas-usa/el-paso-climate.
    [6] Misachi J (2017) What Are The Characteristics Of A Semi-arid Climate Pattern. Available from: https://www.worldatlas.com/articles/what-are-the-characteristics-of-a-semi-arid-climate pattern.html
    [7] Novlan DJ, Hardiman M, Gill TE (2007) A synoptic climatology of blowing dust events in El Paso, Texas from 1932-2005. In Preprints, 16th Conference on Applied Climatology, American Meteorological Society J
    [8] Breshears DD, Kirchner TB, Whicker JJ, et al. (2012) Modeling aeolian transport in response to succession, disturbance and future climate: Dynamic long-term risk assessment for contaminant redistribution. Aeolian Res 3: 445-457. doi: 10.1016/j.aeolia.2011.03.012
    [9] Regional Stakeholders Committee (2009) The Paso Del Norte Region, US-Mexico: Self-Evaluation Report, OECD Reviews of Higher Education in Regional and City Development, IMHE. Available from: https://www.oecd.org/unitedstates/44210876.pdf
    [10] Baumbach JP, Foster LN, Mueller M, et al. (2008) Seroprevalence of select blood borne pathogens and associated risk behaviors among injection drug users in the Paso del Norte region of the United States-Mexico border. Harm Reduct J 5: 33. doi: 10.1186/1477-7517-5-33
    [11] Lu D, Reddy R, Fitzgerald R, et al. (2008) Sensitivity modeling study for an ozone occurrence during the 1996 Paso del Norte ozone campaign. Int J Environ Res Pub He 5: 181-203. doi: 10.3390/ijerph5040181
    [12] Pearson R, Fitzgerald R (2005) Application of a wind model for the El Paso-Juarez airshed. J Air Waste Manage Assoc 51: 669-680.
    [13] Cai C, Kelly JT, Avise Stockwell WR, et al. (2001) Photochemical modeling in California with two chemical mechanisms: model intercomparison and response to emission reductions. J Air Waste Manage Assoc 61: 559-572.
    [14] Mahmud S, Wangchuk P, Fitzgerald R, et al. (2016) Study of Photolysis Rate Coefficients to Improve Air Quality Models. B Am Phy Soc 61.
    [15] Mahmud S (2016) The use of remote sensing technologies and models to study pollutants in the Paso del Norte region. The University of Texas at El Paso. Available from: https://scholarworks.utep.edu/open_etd/685/
    [16] Ullwer C, Sprung D, Sucher E, et al. (2019) Global simulations of Cn2 using the Weather Research and Forecast Model WRF and comparison to experimental results. In Laser communication and Propagation through the Atmosphere and Oceans VIII: 111330I
    [17] Brown MJ, Muller C, Wang W (2001) Costigan, K. Meteorological simulations of boundary layer structure during the 1996 Paso del Norte Ozone Study. Sci Total Environ. 276: 111-133.
    [18] Michalakes J, Dudhia J, Gill D, et al. (2005) The weather research and forecast model: software architecture and performance. Use High Perform Comput Meteorol 2005: 156-168.
    [19] Michalakes J, Chen S, Dudhia J, et al. (2001) Development of a next-generation regional weather research and forecast model. Dev Teracomput 2001: 269-276.
    [20] Skamarock WC, Klemp J B, Dudhia J, et al. (2005) A description of the advanced research WRF version 2 (No. NCAR/TN-468+ STR). National Center for Atmospheric Research Boulder Co Mesoscale and Microscale Meteorology Div.
    [21] Islam MR, Peace A, Medina D, Oraby T (2020) Integer versus Fractional Order SEIR Deterministic and Stochastic Models of Measles. Int J Env Res Pub He 17: 2014. doi: 10.3390/ijerph17062014
    [22] Allen DT, Torres VM (2010) TCEQ Flare Study Project, Final Report. The University of Texas at Austin The Center for Energy and Environmental Resources.
    [23] Wilby RL, Charles SP, Zorita E, et al. (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change, available from the DDC of IPCC TGCIA 27.
    [24] Raysoni AU, Sarnat JA, Sarnat SE, et al. (2011) Binational school-based monitoring of traffic-related air pollutants in El Paso, Texas (USA) and Ciudad Jurez, Chihuahua (Mxico). Env Pol 159: 2476-2486. doi: 10.1016/j.envpol.2011.06.024
    [25] Said SE, Dickey D (1984) Testing for Unit Roots in Autoregressive Moving-Average Models with Unknown Order. Biometrika 71: 599-607. doi: 10.1093/biomet/71.3.599
    [26] Phillips PCB, Perron Pierre (1988) Testing for a Unit Root in Time Series Regression. Biometrika 75: 335-346. doi: 10.1093/biomet/75.2.335
    [27] Wellner Jon A (2003) Gaussian White Noise Models: Some Results for Monotone Functions. Lecture Notes-Monograph Series 2003: 87-104.
    [28] Kitagawa G (1994) State Space Modeling of Time Series. The Institute of Statistical Mathematics 43-64.
    [29] Grineski SE, Collins TW, McDonald YJ, et al. (2015) Double exposure and the climate gap: changing demographics and extreme heat in Ciudad Jurez, Mexico. Local Env 20: 180-201. doi: 10.1080/13549839.2013.839644
    [30] Wilder M, Garfin G, Ganster P, et al. (2013) Climate change and US-Mexico border communities. In Assessment of Climate Change in the Southwest United States, Island Press, Washington DC: 340-384.
  • This article has been cited by:

    1. Minggang Xia, Xingyong Zhang, Danyang Kang, Cuiling Liu, Existence and concentration of nontrivial solutions for an elastic beam equation with local nonlinearity, 2021, 7, 2473-6988, 579, 10.3934/math.2022037
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4114) PDF downloads(455) Cited by(8)

Figures and Tables

Figures(9)  /  Tables(10)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog