Research article Special Issues

TransUFold: Unlocking the structural complexity of short and long RNA with pseudoknots


  • The RNA secondary structure is like a blueprint that holds the key to unlocking the mysteries of RNA function and 3D structure. It serves as a crucial foundation for investigating the complex world of RNA, making it an indispensable component of research in this exciting field. However, pseudoknots cannot be accurately predicted by conventional prediction methods based on free energy minimization, which results in a performance bottleneck. To this end, we propose a deep learning-based method called TransUFold to train directly on RNA data annotated with structure information. It employs an encoder-decoder network architecture, named Vision Transformer, to extract long-range interactions in RNA sequences and utilizes convolutions with lateral connections to supplement short-range interactions. Then, a post-processing program is designed to constrain the model's output to produce realistic and effective RNA secondary structures, including pseudoknots. After training TransUFold on benchmark datasets, we outperform other methods in test data on the same family. Additionally, we achieve better results on longer sequences up to 1600 nt, demonstrating the outstanding performance of Vision Transformer in extracting long-range interactions in RNA sequences. Finally, our analysis indicates that TransUFold produces effective pseudoknot structures in long sequences. As more high-quality RNA structures become available, deep learning-based prediction methods like Vision Transformer can exhibit better performance.

    Citation: Yunxiang Wang, Hong Zhang, Zhenchao Xu, Shouhua Zhang, Rui Guo. TransUFold: Unlocking the structural complexity of short and long RNA with pseudoknots[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 19320-19340. doi: 10.3934/mbe.2023854

    Related Papers:

    [1] M. Latha Maheswari, K. S. Keerthana Shri, Mohammad Sajid . Analysis on existence of system of coupled multifractional nonlinear hybrid differential equations with coupled boundary conditions. AIMS Mathematics, 2024, 9(6): 13642-13658. doi: 10.3934/math.2024666
    [2] Ahmed Alsaedi, Fawziah M. Alotaibi, Bashir Ahmad . Analysis of nonlinear coupled Caputo fractional differential equations with boundary conditions in terms of sum and difference of the governing functions. AIMS Mathematics, 2022, 7(5): 8314-8329. doi: 10.3934/math.2022463
    [3] Zaid Laadjal, Fahd Jarad . Existence, uniqueness and stability of solutions for generalized proportional fractional hybrid integro-differential equations with Dirichlet boundary conditions. AIMS Mathematics, 2023, 8(1): 1172-1194. doi: 10.3934/math.2023059
    [4] M. Manigandan, Subramanian Muthaiah, T. Nandhagopal, R. Vadivel, B. Unyong, N. Gunasekaran . Existence results for coupled system of nonlinear differential equations and inclusions involving sequential derivatives of fractional order. AIMS Mathematics, 2022, 7(1): 723-755. doi: 10.3934/math.2022045
    [5] Subramanian Muthaiah, Dumitru Baleanu, Nandha Gopal Thangaraj . Existence and Hyers-Ulam type stability results for nonlinear coupled system of Caputo-Hadamard type fractional differential equations. AIMS Mathematics, 2021, 6(1): 168-194. doi: 10.3934/math.2021012
    [6] Manal Elzain Mohamed Abdalla, Hasanen A. Hammad . Solving functional integrodifferential equations with Liouville-Caputo fractional derivatives by fixed point techniques. AIMS Mathematics, 2025, 10(3): 6168-6194. doi: 10.3934/math.2025281
    [7] Cuiying Li, Rui Wu, Ranzhuo Ma . Existence of solutions for Caputo fractional iterative equations under several boundary value conditions. AIMS Mathematics, 2023, 8(1): 317-339. doi: 10.3934/math.2023015
    [8] Abdelkader Amara . Existence results for hybrid fractional differential equations with three-point boundary conditions. AIMS Mathematics, 2020, 5(2): 1074-1088. doi: 10.3934/math.2020075
    [9] Djamila Chergui, Taki Eddine Oussaeif, Merad Ahcene . Existence and uniqueness of solutions for nonlinear fractional differential equations depending on lower-order derivative with non-separated type integral boundary conditions. AIMS Mathematics, 2019, 4(1): 112-133. doi: 10.3934/Math.2019.1.112
    [10] Abdulwasea Alkhazzan, Wadhah Al-Sadi, Varaporn Wattanakejorn, Hasib Khan, Thanin Sitthiwirattham, Sina Etemad, Shahram Rezapour . A new study on the existence and stability to a system of coupled higher-order nonlinear BVP of hybrid FDEs under the p-Laplacian operator. AIMS Mathematics, 2022, 7(8): 14187-14207. doi: 10.3934/math.2022782
  • The RNA secondary structure is like a blueprint that holds the key to unlocking the mysteries of RNA function and 3D structure. It serves as a crucial foundation for investigating the complex world of RNA, making it an indispensable component of research in this exciting field. However, pseudoknots cannot be accurately predicted by conventional prediction methods based on free energy minimization, which results in a performance bottleneck. To this end, we propose a deep learning-based method called TransUFold to train directly on RNA data annotated with structure information. It employs an encoder-decoder network architecture, named Vision Transformer, to extract long-range interactions in RNA sequences and utilizes convolutions with lateral connections to supplement short-range interactions. Then, a post-processing program is designed to constrain the model's output to produce realistic and effective RNA secondary structures, including pseudoknots. After training TransUFold on benchmark datasets, we outperform other methods in test data on the same family. Additionally, we achieve better results on longer sequences up to 1600 nt, demonstrating the outstanding performance of Vision Transformer in extracting long-range interactions in RNA sequences. Finally, our analysis indicates that TransUFold produces effective pseudoknot structures in long sequences. As more high-quality RNA structures become available, deep learning-based prediction methods like Vision Transformer can exhibit better performance.



    Widespread applications of fractional calculus significantly contributed to the popularity of the subject. Fractional order operators are nonlocal in nature and give rise to more realistice and informative mathematical modeling of many real world phenomena, in contrast to their integer-order counterparts, for instance, see [13,21,31].

    Nonlinear fractional order boundary value problems appear in a variety of fields such as applied mathematics, physical sciences, engineering, control theory, etc. Several aspects of these problems, such as existence, uniqueness and stability, have been explored in recent studies [5,6,7,14,22,24,26,28,32].

    Coupled nonlinear fractional differential equations find their applications in various applied and technical problems such as disease models [8,10,29], ecological models [18], synchronization of chaotic systems [11,33], nonlocal thermoelasticity [30], etc. Hybrid fractional differential equations also received significant attention in the recent years, for example, see [2,3,9,15,16,17,19,20,23].

    The concept of slits-strips conditions introduced by Ahmad et al. in [1] is a new idea and has useful applications in imaging via strip-detectors [25] and acoustics [27].

    In [1], the authors investigated the following strips-slit problem:

    cDpx(t)=f1(t,x(t)), n1<pn, t[0,1]x(0)=0, x(0)=0, x(0)=0,.... x(n2)(0)=0,x(ξ)=a1η0x(s)ds+a21ξ1x(s)ds, 0<η<ξ<ξ1<1,

    where cDp denotes the Caputo fractional derivative of order p,f1 is a given continuous function and a1,a2R.

    In 2017, Ahmad et al. [4] studied a coupled system of nonlinear fractional differential equations

    cDαx(t)=f1(t,x(t),y(t)), t[0,1], 1<α2,cDβy(t)=f2(t,x(t),y(t)), t[0,1], 1<β2,

    supplemented with the coupled and uncoupled boundary conditions of the form:

    x(0)=0, x(a1)=d1η0y(s)ds+d21ξ1y(s)ds, 0<η<a1<ξ1<1,y(0)=0, y(a1)=d1η0x(s)ds+d21ξ1x(s)ds, 0<η<a1<ξ1<1,

    and

    x(0)=0, x(a1)=d1η0x(s)ds+d21ξ1x(s)ds, 0<η<a1<ξ1<1,y(0)=0, y(a1)=d1η0y(s)ds+d21ξ1y(s)ds, 0<η<a1<ξ1<1,

    where cDα and cDβ denote the Caputo fractional derivatives of orders α and β respectively, f1,f2:[0,1]×R×RR are given continuous functions and d1,d2 are real constants.

    In this article, motivated by aforementioned works, we introduce and study the following hybrid nonlinear fractional differential equations:

    cDγ[u(t)h1(t,u(t),v(t))]=θ1(t,u(t),v(t)), t[0,1], 1<γ2,cDδ[v(t)h2(t,u(t),v(t))]=θ2(t,u(t),v(t)), t[0,1], 1<δ2, (1.1)

    equipped with coupled slit-strips-type integral boundary conditions:

    u(0)=0, u(η)=ω1ξ10v(s)ds+ω21ξ2v(s)ds, 0<ξ1<η<ξ2<1,v(0)=0, v(η)=ω1ξ10u(s)ds+ω21ξ2u(s)ds, 0<ξ1<η<ξ2<1, (1.2)

    where cDγ, cDδ denote the Caputo fractional derivatives of orders γ and δ respectively, θi,hi:[0,1]×R×RR are given continuous functions with hi(0,u(0),v(0))=0,i=1,2 and ω1,ω2 are real constants.

    We arrange the rest of the paper as follows. In section 2, we present some definitions and obtain an auxiliary result, while section 3 contains the main results for the problems (1.1) and (1.2). Section 4 is devoted to the illustrative examples for the derived results.

    Let us first recall some related definitions [21].

    Definition 2.1. For a locally integrable real-valued function g1:[a,)R, we define the Riemann-Liouville fractional integral of order σ>0 as

    Iσg1(t)=1Γ(σ)t0g1(τ)(tτ)1σdτ, σ>0,

    where Γ is the Euler's gamma function.

    Definition 2.2. The Caputo derivative of order σ for an n-times continuously differentiable function g1:[0,)R is defined by

    cDσg1(t)=1Γ(nσ)t0(tτ)nσ1g(n)1(τ)dτ, n1<σ<n, n=[σ]+1

    where [σ] is the integer part of a real number.

    Lemma 2.1. For χi,ΦiC([0,1],R) with χi(0)=0,i=1,2, the following linear system of equations:

    cDγ[u(t)χ1(t)]=Φ1(t), t[0,1], 1<γ2,cDδ[v(t)χ2(t)]=Φ2(t), t[0,1], 1<δ2, (2.1)

    equipped with coupled slit-strips-type integral boundary conditions (1.2), is equivalent to the integral equations:

    u(t)=tη2Δ2[η{ω1ξ10(s0(sτ)δ1Γ(δ)Φ2(τ)dτ+χ2(s))ds+ω21ξ2(s0(sτ)δ1Γ(δ)Φ2(τ)dτ+χ2(s))dsη0(ηs)γ1Γ(γ)Φ1(s)dsχ1(η)}+Δ{ω1ξ10(s0(sτ)γ1Γ(γ)Φ1(τ)dτ+χ1(s))ds+ω21ξ2(s0(sτ)γ1Γ(γ)Φ1(τ)dτ+χ1(s))dsη0(ηs)δ1Γ(δ)Φ2(s)dsχ2(η)}]+t0(ts)γ1Γ(γ)Φ1(s)ds+χ1(t), (2.2)
    v(t)=tη2Δ2[Δ{ω1ξ10(s0(sτ)δ1Γ(δ)Φ2(τ)dτ+χ1(s))ds+ω21ξ2(s0(sτ)δ1Γ(δ)Φ2(τ)dτ+χ1(s))dsη0(ητ)γ1Γ(γ)Φ1(s)dsχ2(η)}+η{ω1ξ10(s0(sτ)γ1Γ(γ)Φ1(τ)dτ+χ2(s))ds+ω21ξ2(s0(sτ)γ1Γ(γ)Φ1(τ)dτ+χ2(s))dsη0(ηs)δ1Γ(δ)Φ2(s)dsχ2(η)}]+t0(ts)δ1Γ(δ)Φ2(s)ds+χ2(t), (2.3)

    where it is assumed that

    Δ=12(ω1ξ21+ω2(1ξ22))0. (2.4)

    Proof. Solving the fractional differential equations in (2.1), we get

    u(t)=c0+c1t+t0(ts)γ1Γ(γ)Φ1(s)ds+χ1(t) (2.5)

    and

    v(t)=c2+c3t+t0(ts)δ1Γ(δ)Φ2(s)ds+χ2(t), (2.6)

    where c0,c1,c2,c3 R are arbitrary constants.

    Using the conditions u(0)=0 and v(0)=0 in (2.5) and (2.6), we find that c0=0 and c2=0. Thus (2.5) and (2.6) become

    u(t)=c1t+t0(ts)γ1Γ(γ)Φ1(s)ds+χ1(t), (2.7)
    v(t)=c3t+t0(ts)δ1Γ(δ)Φ2(s)ds+χ2(t), (2.8)

    Making use of the coupled slit-strips-type integral boundary conditions given by (1.2) in (2.7) and (2.8) together with the notation (2.4), we obtain a system of equations:

    ω1ξ10(s0(sτ)δ1Γ(δ)Φ2(τ)dτ+χ2(s))ds+ω21ξ2(s0(sτ)δ1Γ(δ)Φ2(τ)dτ+χ2(s))dsη0(ηs)γ1Γ(γ)Φ1(s)dsχ1(η))=c1ηΔc3, (2.9)
    ω1ξ10(s0(sτ)γ1Γ(γ)Φ1(τ)dτ+χ1(s))ds+ω21ξ2(s0(sτ)γ1Γ(γ)Φ1(τ)dτ+χ1(s))dsη0(ηs)δ1Γ(δ)Φ2(s)dsχ2(η)=c3ηΔc1. (2.10)

    Solving the systems (2.9)–(2.10) for c1 and c3, we find that

    c1=tη2Δ2[η{ω1ξ10(s0(sτ)δ1Γ(δ)Φ2(τ)dτ+χ2(s))ds+ω21ξ2(s0(sτ)δ1Γ(δ)Φ2(τ)dτ+χ2(s))dsη0(ηs)γ1Γ(γ)Φ1(s)dsχ1(η)}+Δ{ω1ξ10(s0(sτ)γ1Γ(γ)Φ1(τ)dτ+χ1(s))ds+ω21ξ2(s0(sτ)γ1Γ(γ)Φ1(τ)dτ+χ1(s))dsη0(ηs)δ1Γ(δ)Φ2(s)dsχ2(η)}]

    and

    c3=tη2Δ2[Δ{ω1ξ10(s0(sτ)δ1Γ(δ)Φ2(τ)dτ+χ1(s))ds+ω21ξ2(s0(sτ)δ1Γ(δ)Φ2(τ)dτ+χ1(s))dsη0(ηs)γ1Γ(γ)Φ1(s)dsχ2(η)}+η{ω1ξ10(s0(sτ)γ1Γ(γ)Φ1(τ)dτ+χ2(s))ds+ω21ξ2(s0(sτ)γ1Γ(γ)Φ1(τ)dτ+χ2(s))dsη0(ηs)δ1Γ(δ)Φ2(s)dsχ1(η)}]

    Inserting the values of c1 and c3 in (2.7) and (2.8) leads to the integral equations (2.2) and (2.3). By direct computation, one can obtain the converse of the lemma. The proof is finished.

    Let W={˜w(t):˜w(t)C([0,1])} be a Banach space equipped with the norm ˜w=max{|˜w(t)|,t[0,1]}, Then the product space (W×W,(u,v)) endowed with the norm (u,v)=u+v, (u,v)W×W is also a Banach space.

    We need the following assumptions to derive the main results.

    (A1) Let θ1,θ2:[0,1]×R2R be continuous and bounded functions and there exists constants mi,ni such that, for all t[0,1] and xi,yiR,i=1,2,

    |θ1(t,x1,x2)θ1(t,y1,y2)|m1|x1y1|+m2|x2y2|,|θ2(t,x1,x2)θ2(t,y1,y2)|n1|x1y1|+n2|x2y2|.

    (A2) For continuous and bounded functions hi, i = 1, 2, there exist real constants μi,βi,σi>0 such that, for all xi,yiR, |hi(t,x,y)|μi for all (t,x,y)[0,1]×R×R and

    |h1(t,x1,x2)h1(t,y1,y2)|β1|x1y1|+β2|x2y2|,|h2(t,x1,x2)h2(t,y1,y2)|σ1|x1y1|+σ2|x2y2|.

    (A3) supt[0,1]θ1(t,0,0)=N1<  andsupt[0,1]θ2(t,0,0)=N2<.

    (A4) For the sake of computational convenience, we set

    M1=1Γ(γ+1)+1|η2Δ2|[ηγ+1Γ(γ+1)+|Δ||ω1|ξγ+11Γ(γ+2)+Δ|ω2|1ξγ+12Γ(γ+2)],M2=1|η2Δ2|[η|ω1|ξδ+11Γ(δ+2)+η|ω2|1ξδ+12Γ(δ+2)+|Δ|ηδΓ(δ+1)],M3=1|η2Δ2|[η|ω1|ξγ+11Γ(γ+2)+η|ω2|1ξγ+12Γ(γ+2)+|Δ|ηγΓ(γ+1)],M4=1Γ(δ+1)+1|η2Δ2|[ηδ+1Γ(δ+1)+|Δ||ω1|ξδ+11Γ(δ+2)+Δ|ω2|1ξδ+12Γ(δ+2)],N3=η|η2Δ2|[|ω1|ξ1μ2+|ω2|μ2(1ξ2)+μ1]+|Δ|[|ω1|μ1ξ1+μ1|ω2|(1ξ2)+μ2]+μ1,N4=1|η2Δ2|[|Δ||ω1|μ1ξ1+|ω2|(1ξ2)μ1+μ2]+|η|[|ω1|μ2ξ1+|ω2|(1ξ2)μ2+μ1]+μ2,N5=1|η2Δ2|[η|ω1|ξ1+|ω2|η(1ξ2)+|Δ|],N6=1|η2Δ2|[|Δ|ξ1|ω1|+|Δ||ω2|(1ξ2)+η]+1,N7=1|η2Δ2|[|Δ|+|η||ω1|ξ1+|η||ω2|(1ξ2)+|ω2|+|Δ|],

    and

    Mk=min{1(M1+M3)k1(M2+M4)λ1,1(M1+M3)k2(M2+M4)λ2},ki,λi0, i=1,2. (3.1)

    (A5) (M1+M3)(m1+m2)+(M2+M4)(n1+n2)+(N5+N6)(σ1+σ2)+(N7+N8)(β1+β2)<1.

    In view of Lemma 1, we define an operator T:W×WW×W associated with the problems (1.1) and (1.2) as follows:

    T(u,v)(t)=(T1(u,v)(t)T2(u,v)(t)), (3.2)

    where

    T1(u,v)(t)=tη2Δ2[η{ω1ξ10(s0(sτ)δ1Γ(δ)θ2(τ,u(τ),v(τ))dτ+h2(s,u(s),v(s))ds+ω21ξ2(s0(sτ)δ1Γ(δ)θ2(τ,u(τ),v(τ))dτ+h2(s,u(s),v(s))dsη0(ηs)γ1Γ(γ)θ1(s,u(s),v(s))dsh1(η,u(η),v(η))}+Δ{ω1ξ10(s0(sτ)γ1Γ(γ)θ1(τ,u(τ),v(τ))dτ+h1(s,u(s),v(s))ds+ω21ξ2(s0(sτ)γ1Γ(γ)θ1(τ,u(τ),v(τ))dτ+h1(s,u(s),v(s))dsη0(ηs)δ1Γ(δ)θ2(s,u(s),v(s))dsh2(η,u(η),v(η))}]+t0(ts)γ1Γ(γ)θ1(s,u(s),v(s))ds+h1(t,u(t),v(t))

    and

    T2(u,v)(t)=tη2Δ2[Δ{ω1ξ10(s0(sτ)δ1Γ(δ)θ2(τ,u(τ),v(τ))dτ+h1(s,u(s),v(s))ds+ω21ξ2(s0(sτ)δ1Γ(δ)θ2(τ,u(τ),v(τ))dτ+h1(s,u(s),v(s))dsη0(ητ)γ1Γ(γ)θ1(s,u(s),v(s))dsh2(η,u(η),v(η))}+η{ω1ξ10(s0(sτ)γ1Γ(γ)θ1(τ,u(τ),v(τ))dτ+h2(s,u(s),v(s))ds+ω21ξ2(s0(sτ)γ1Γ(γ)θ1(τ,u(τ),v(τ))dτ+h2(s,u(s),v(s))dsη0(ηs)δ1Γ(δ)θ2(s,u(s),v(s))dsh1(η,u(η),v(η))}]+t0(ts)γ1Γ(γ)θ2(s,u(s),v(s))ds+h2(t,u(t),v(t)).

    Theorem 3.1. Assume that conditions (A1) to (A5) are satisfied. Then there exists a unique solution for the problems (1.1) and (1.2) on [0,1].

    Proof. In the first step, we establish that TˉBrˉBr, where ˉBr={(u,v)W×W:(u,v)r} is a closed ball with

    r(M1+M3)N1+(M2+M4)N2+N3μ1[(M1+M3)(m1+m2)+(M2+M4)(n1+n2)+N3(β1+β2)],

    and the operator T:W×WW×W is defined by (3.2). For (u,v)ˉBr and t[0,1], it follows by (A1) that

    |θ1(t,u(t),v(t))||θ1(t,u(t),v(t))θ1(t,0,0)|m1||u||+m2||v||.

    Similarly one can find that |θ2(t,u(t),v(t))|n1||u||+n2||v||. Then we have

    |T1(u,v)(t)|maxt[0,1][t|η2Δ2|[η{|ω1|ξ10(s0(sτ)δ1Γ(δ)|θ2(τ,u(τ),v(τ))|dτ+|h2(s,u(s),v(s)|)ds+|ω2|1ξ2(s0(sτ)δ1Γ(δ)|θ2(τ,u(τ),v(τ))|dτ+|h2(s,u(s),v(s)|)ds+η0(ηs)γ1Γ(γ)|θ1(s,u(s),v(s))|ds+|h1(η,u(η),v(η))|}+|Δ|{|ω1|ξ10(s0(sτ)γ1Γ(γ)|θ1(τ,u(τ),v(τ))|dτ+|h1(s,u(s),v(s)|)ds+|ω2|1ξ2(s0(sτ)γ1Γ(γ)|θ1(τ,u(τ),v(τ))|dτ+|h1(s,u(s),v(s)|)ds+η0(ηs)δ1Γ(δ)|θ2(s,u(s),v(s))|ds+|h2(η,u(η),v(η))|}]+t0(ts)γ1Γ(γ)|θ1(s,u(s),v(s))|ds+|h1(t,u(t),v(t))|]1|η2Δ2|[η{|ω1|ξ10(s0(sτ)δ1Γ(δ)(n1||u||+n2||v||+N2)dτ+μ2)ds+|ω2|1ξ2(s0(sτ)δ1Γ(δ)|(n1||u||+n2||v||+N2)dτ+μ2)ds+η0(ηs)γ1Γ(γ)(m1||u||+m2||v||+N1)dτ+μ1}+|Δ|{|ω1|ξ10(s0(sτ)γ1Γ(γ)(m1||u||+m2||v||+N1)dτ+μ1)ds+|ω2|1ξ2(s0(sτ)γ1Γ(γ)(m1||u||+m2||v||+N1)dτ+μ1)ds+η0(ηs)δ1Γ(δ)(n1||u||+n2||v||+N2)ds+μ2}]+t0(ts)γ1Γ(γ)(m1||u||+m2||v||+N1)ds+μ1le1|η2Δ2|[η|ω1|ξδ+11Γ(δ+2)+η|ω2|1ξδ+12Γ(δ+2)+|Δ|ηδΓ(δ+1)](n1||u||+n2||v||+N2)+[1|η2Δ2|(ηγ+1Γ(γ+1)+|Δ||ω1|ξγ+11Γ(γ+2)+|Δ||ω2|1ξγ+12Γ(γ+2))+1Γ(γ+1)](m1||u||+m2||v||+N1)+η|η2Δ2|(|ω1|μ2ξ1+|ω2|μ2(1ξ2)+μ1)+|Δ|(|ω1|μ1ξ1+|ω2|μ1(1ξ2)+μ2)+μ1(M2n1+M1m1+M2n2+M1m2)r+M2N2+M1N1+N3.

    Analogously, one can find that

    |T2(u,v)(t)|(M4n1+M3m1+M4n2+M3m2)r+M4N2+M3N1+N4.

    From the foregoing estimates for T1 and T2, we obtain ||T(u,v)(t)||r.

    Next, for (u1,v1),(u2,v2)W×W and t[0,1], we get

    |T1(u2,v2)(t)T1(u1,v1)(t)|1|η2Δ2|[η{|ω1|(ξ10(s0(sτ)δ1Γ(δ)|θ2(τ,u2(τ),v2(τ))θ2(τ,u1(τ),v1(τ))|)dτ+|h2(s,u2(s),v2(s))h2(s,u1(s),v1(s))|ds)+|ω2|(1ξ2(s0(sτ)δ1Γ(δ)|θ2(τ,u2(τ),v2(τ))θ2(τ,u1(τ),v1(τ))|)dτ+|h2(s,u2(s),v2(s))h2(s,u1(s),v1(s))|ds)+η0(ηs)γ1Γ(γ)|θ1(τ,u2(τ),v2(τ))θ1(τ,u1(τ),v1(τ))|ds+|h1(η,u2(η),v2(η))h1(η,u1(η),v1(η))|}
    +|Δ|{|ω1|(ξ10(s0(sτ)γ1Γ(γ)|θ1(τ,u2(τ),v2(τ))θ1(τ,u1(τ),v1(τ))|)dτ+|h1(s,u2(s),v2(s))h1(s,u1(s),v1(s))|ds)+|ω2|(1ξ2(s0(sτ)γ1Γ(γ)|θ1(τ,u2(τ),v2(τ))θ1(τ,u1(τ),v1(τ))|)dτ+|h1(s,u2(s),v2(s))h1(s,u1(s),v1(s))|ds)+η0(ηs)δ1Γ(δ)|θ1(τ,u2(τ),v2(τ))θ1(τ,u2(τ),v2(τ)|ds+|h2(η,u2(η),v2(η))h2(η,u1(η),v1(η))|ds}]+t0(ts)γ1Γ(γ)(|θ1(s,u2(s),v2(s))θ1(s,u1(s),v1(s))|)ds+|h1(t,u2(t),v2(t))h1(t,u1(t),v1(t))|1|η2Δ2|[η|ω1|ξδ+11Γ(δ+2)+η|ω2|1ηδ+1Γ(δ+2)+|Δ|ηδΓ(δ+1)]×(n1||u2u1||+n2||v2v1||)+(1|η2Δ2|[ηγ+1Γ(γ+1)+|Δ||ω1|ξγ+11Γ(γ+2)+|Δ||ω2|1ξγ+12Γ(γ+2)]+1Γ(γ+1))×(m1||u2u1||+m2||v2v1||)+1|η2Δ2|[(η|ω1|ξ1+η|ω2|(1ξ2)+|Δ|)(σ1||u2u1||+σ2||v2v1||)+(η+|Δ||ω1|ξ1+|Δ||ω2|(1ξ2)+1)(β1||u2u1||+β2||v2v1||)]leM2(n1||u2u1||+n2||v2v1||)+M1(m1||u2u1||+m2||v2v1||) +N5(σ1||u2u1||+σ2||v2v1||)+N6(β1||u2u1||+β2||v2v1||)=(M2n1+M1m1+N5σ1+N6β1)||u2u1||+(M2n2+M1m2+N5σ2)||v2v1||)

    which implies that

    T1(u2,v2)(t)T1(u1,v1)(t)le(M2n1+M1m1+N5σ1+N6β1+M2n2+M1m2+N5σ2+N6β2)(||u2u1||+||v2v1||). (3.3)

    Likewise, we have

    T2(u2,v2)(t)T2(u1,v1)(t)le(M4n1+M3m1+N6σ1+N7β1+M4n2+M3m2+N6σ2+N7β2)(||u2u1||+||v2v1||). (3.4)

    From (3.3) and (3.4), we deduce that

    T(u2,v2)(t)T(u1,v1)(t)[(M1+M3)(m1+m2)+(M2+M4)(n1+n2)+(N7+N8)(β1+β2)+(N5+N6)(σ1+σ2)]×(||u2u1||+||v2v1||),

    which shows that T is a contraction by the assumption (A5) and hence it has a unique fixed point by Banach fixed point theorem. This leads to the conclusion that there exists a unique solution for the problems (1.1) and (1.2) on [0,1]. The proof is complete.

    Now, we discuss the existence of solutions for the problems (1.1) and (1.2) by means of Leray-Schauder alternative ([12], p. 4).

    Theorem 3.2. Assume that there exists real constants ˜k0>0, ˜λ0>0 and ˜ki,˜λi0, i=1,2 such that, for any uiR, i=1,2

    |θ1(t,u1,u2)|˜k0+˜k1|u1|+˜k2|u2|,|θ2(t,u1,u2)|˜λ0+˜λ1|u1|+˜λ2|u2|.

    In addition,

    (M1+M3)˜k1+(M2+M4)˜λ1<1,(M1+M3)˜k2+(M2+M4)˜λ2<1,

    where Mi, i=1,2,3,4 are given in (A4). Then the problems (1.1) and (1.2) have at least one solution on [0,1].

    Proof. The proof consists of two steps. First we show that the operator T:W×WW×W defined by (3.2) is completely continuous. Observe that continuity of the operator T follows from that of θ1 and θ2. Consider a bounded set ΩW×W so that we can find positive constants l1 and l2 such that |θ1(t,u(t),v(t))|l1 and |θ2(t,u(t),v(t))|l2 for every (u,v)Ω. Hence, for any (u,v)Ω, we find that

    |T1(u,v)(t)|1|η2Δ2|[η|ω1|ξδ+11Γ(δ+2)+η|ω2|1ξδ+12Γ(δ+2)+|Δ|ηδΓ(δ+1)]l2+{1|η2Δ2|[ηγΓ(γ+1)+|Δ||ω1|ξγ+11Γ(γ+2)+|Δ||ω2|1ξγ+12Γ(γ+2)+1Γ(γ+1)]}l1+1|η2Δ2|{η[|ω1|ξ1μ2+|ω2|μ2(1ξ2)+μ1]+ημ1+|Δ|[μ1ξ1|ω1|+μ1|ω2|(1ξ2)+μ2]}+μ1=M2l2+M1l1+N3.

    Thus we deduce that T1(u,v)M2l2+M1l1+N3. In a similar fashion, it can be found that T2(u,v)M4l2+M3l1+N4. Hence, it follows from the foregoing inequalities that T1 and T2 are uniformly bounded and hence the operator T is uniformly bounded. In order to show that T is equicontinuous, we take 0<r1<r2<1. Then, for any (u,v)Ω, we obtain

    |T1(u(r2),v(r2))T1(u(r1),v(r1))|l1Γ(γ)r10[(r2s)γ1(r1s)γ1]ds+l1Γ(γ)r2r1(r2s)γ1ds+r2r1|η2Δ2|{[η|ω1|ξδ+11Γ(δ+2)+η|ω2|1ξδ+12Γ(δ+2)+|Δ|ηδΓ(δ+1)]l2+[ηγ+1Γ(γ+1)+|Δ||ω1|ξγ+11Γ(γ+2)+|Δ||ω2|1ξγ+12Γ(γ+2)+1Γ(γ+2)]l1+N3},
    |T2(u(r2),v(r2))T2(u(r1),v(r1))|l2Γ(δ)r10[(r2s)δ1(r1s)δ1]ds+l2Γ(δ)r2r1(r2s)δ1ds  +r2r1|η2Δ2|{[η|ω1|ξδ+11Γ(δ+2)+η|ω2|1ξδ+12Γ(δ+2)+η˙.ηδ+1Γ(δ+1)]l2+[|Δ|ηγΓ(γ+1)+η|ω1|ξγ+11Γ(γ+2)+η|ω2|1ξγ+12Γ(γ+2)]l1+N4},

    which imply that the operator T(u,v) is equicontinuous. In view of the foregoing arguments, we deduce that operator T(u,v) is completely continuous.

    Next, we consider a set P={(u,v)W×W:(u,v)=λT(u,v), 0λ1} and show that it is bounded. Let us take (u,v)P and t[0,1]. Then it follows from u(t)=λT1(u,v)(t) and v(t)=λT2(u,v)(t), together with the given assumptions that

    uM1(˜k0+˜k1||u||+˜k2||v||)+M2(˜λ0+˜λ1||u||+˜λ2||v||)+N3,|vM3(˜k0+˜k1||u||+˜k2||v||)+M4(˜λ0+˜λ1||u||+˜λ2||v||)+N4,

    which lead to

    u+v[(M1+M3)˜k0+(M2+M4)˜λ0+N3+N4]+[(M1+M3)˜k1+(M2+M4)˜λ1]u+[(M1+M3)˜k2+(M2+M4)˜λ2]v.

    Thus

    (u,v)(M1+M3)˜k0+(M2+M4)˜λ0+N3+N4Mk,

    where Mk is defined by (3.1). Consequently the set P is bounded. Hence, it follows by Leray-Schauder alternative ([12], p. 4) that the operator T has at least one fixed point. Therefore, the problems (1.1) and (1.2) have at least one solution on [0,1]. This finishes the proof.

    Example 4.1. Consider a coupled boundary value problem of fractional differential equations with slit-strips-type conditions given by

    cD3/2(u(t)sint|u(t)|2(2+|u(t)|))=156u(t)+27v(t)1+v(t)+57,cD5/4(v(t)sint|v(t)|2(2+|v(t)|))=139|cosu(t)|1+|cosu(t)|+128sinv(t)+37, (4.1)
    u(0)=0, u(12)=1/50v(s)ds+14/5v(s)ds,v(0)=0, v(12)=1/50u(s)ds+14/5u(s)ds. (4.2)

    Here γ=32, δ=54, ω1=1, ω2=1, η=12, ξ1=15, ξ2=45. From the given data, we find that Δ=0.11, m1=156, m2=271, n1=139, n2=128, M11.44716, M20.51905, M30.4046, M42.51887, N52.94238, N67.3223, N75.6164, N85.2206, and (M1+M3)(m1+m2)+(M2+M4)(n1+n2)+(N5+N6)(σ1+σ2)+(N7+N8)(β1+β2)0.8030305<1.

    Clearly all the conditions of Theorem 3.1 are satisfied. In consequence, the conclusion of Theorem 3.1 applies to the problems (4.1)–(4.2).

    Example 4.2. We consider the problems (4.1)–(4.2) with

    θ1(t,u(t),v(t)) =12+239tanu(t)+241v(t),θ2(t,u(t),v(t)) =25+19sinu(t)+117v(t). (4.3)

    Observe that

    |θ1(t,u,v)|˜k0+˜k1|u|+˜k2|v|,|θ2(t,u,v)|˜λ0+˜λ1|u|+˜λ2|v|

    with ˜k0=12, ˜k1=239, ˜k2=241 ˜λ0=25, ˜λ1=19, ˜λ2=117. Furthermore,

    (M1+M3)˜k1+(M2+M4)˜λ10.432507777<1,(M1+M3)˜k2+(M2+M4)˜λ20.269030756<1.

    Thus all the conditions of Theorem 3.2 hold true and hence there exists at least one solution for the problems (4.1)–(4.2) with θ1(t,u,v) and θ2(t,u,v) given by (4.3).

    The authors thank the reviewers for their useful remarks on our paper.

    All authors declare no conflicts of interest in this paper.



    [1] J. A. Shapiro, Revisiting the central dogma in the 21st century, Ann. N. Y. Acad. Sci., 1178 (2009), 6-28. https://doi.org/10.1111/j.1749-6632.2009.04990.x doi: 10.1111/j.1749-6632.2009.04990.x
    [2] T. A. Lincoln, G. F. Joyce, Self-sustained replication of an RNA enzyme, Science, 323 (2009), 1229-1232. https://doi.org/10.1126/science.1167856 doi: 10.1126/science.1167856
    [3] P. V. Ryder, D. A. Lerit, RNA localization regulates diverse and dynamic cellular processes, Traffic, 19 (2018), 496-502. https://doi.org/10.1111/tra.12571 doi: 10.1111/tra.12571
    [4] E. Westhof, P. Auffinger, RNA tertiary structure, in Encyclopedia of Analytical Chemistry, (2000), 5222-5232. https://doi.org/10.1002/9780470027318.a1428
    [5] F. E. Reyes, C. R. Schwartz, J. A. Tainer, R. P. Rambo, Methods for using new conceptual tools and parameters to assess RNA structure by small-angle X-ray scattering, Methods Enzymol., 549 (2014), 235-263. https://doi.org/10.1016/B978-0-12-801122-5.00011-8 doi: 10.1016/B978-0-12-801122-5.00011-8
    [6] C. Helmling, S. Keyhani, F. Sochor, B. Fürtig, M. Hengesbach, H. Schwalbe, Rapid NMR screening of RNA secondary structure and binding, J. Biomol. NMR, 63 (2015), 67-76. https://doi.org/10.1007/s10858-015-9967-y doi: 10.1007/s10858-015-9967-y
    [7] R. Stark, M. Grzelak, J. Hadfield, RNA sequencing: the teenage years, Nat. Rev. Genet., 20 (2019), 631-656. https://doi.org/10.1038/s41576-019-0150-2 doi: 10.1038/s41576-019-0150-2
    [8] M. Zuker, P. Stiegler, Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information, Nucleic Acids Res., 9 (1981), 133-148. https://doi.org/10.1093/nar/9.1.133 doi: 10.1093/nar/9.1.133
    [9] D. H. Turner, D. H. Mathews, NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure, Nucleic Acids Res., 38 (2010), D280-D282. https://doi.org/10.1093/nar/gkp892 doi: 10.1093/nar/gkp892
    [10] M. Zuker, Mfold web server for nucleic acid folding and hybridization prediction, Nucleic Acids Res., 31 (2003), 3406-3415. https://doi.org/10.1093/nar/gkg595 doi: 10.1093/nar/gkg595
    [11] N. R. Markham, M. Zuker, UNAFold: software for nucleic acid folding and hybridization, in Bioinformatics, 453 (2008), 3-31. https://doi.org/10.1007/978-1-60327-429-6_1
    [12] I. L. Hofacker, W. Fontana, P. F. Stadler, L. S. Bonhoeffer, M. Tacker, P. Schuster, Fast folding and comparison of RNA secondary structures, Monatsh. Chem. Mon., 125 (1994), 167-188. https://doi.org/10.1007/BF00818163 doi: 10.1007/BF00818163
    [13] S. Bellaousov, J. S. Reuter, M. G. Seetin, D. H. Mathews, RNAstructure: web servers for RNA secondary structure prediction and analysis, Nucleic Acids Res., 41 (2013), W471-W474. https://doi.org/10.1093/nar/gkt290 doi: 10.1093/nar/gkt290
    [14] L. Huang, H. Zhang, D. Deng, K. Zhao, K. Liu, D. A. Hendrix, et al., LinearFold: linear-time approximate RNA folding by 5'-to-3'dynamic programming and beam search, Bioinformatics, 35 (2019), i295-i304. https://doi.org/10.1093/bioinformatics/btz375 doi: 10.1093/bioinformatics/btz375
    [15] X. Wang, J. Tian, Dynamic programming for NP-hard problems, Procedia Eng., 15 (2011), 3396-3400. https://doi.org/10.1016/j.proeng.2011.08.636 doi: 10.1016/j.proeng.2011.08.636
    [16] E. Rivas, S. R. Eddy, A dynamic programming algorithm for RNA structure prediction including pseudoknots, J. Mol. Biol., 285 (1999), 2053-2068. https://doi.org/10.1006/jmbi.1998.2436 doi: 10.1006/jmbi.1998.2436
    [17] R. M. Dirks, N. A. Pierce, A partition function algorithm for nucleic acid secondary structure including pseudoknots, J. Comput. Chem., 24 (2003), 1664-1677. https://doi.org/10.1002/jcc.10296 doi: 10.1002/jcc.10296
    [18] X. Xu, P. Zhao, S. J. Chen, Vfold: a web server for RNA structure and folding thermodynamics prediction, PloS One, 9 (2014), e107504. https://doi.org/10.1371/journal.pone.0107504 doi: 10.1371/journal.pone.0107504
    [19] K. Sato, M. Hamada, Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery, Briefings Bioinf., 24 (2023). https://doi.org/10.1093/bib/bbad186
    [20] T. Gong, F. Ju, D. Bu, Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials, bioRxiv, (2022). https://doi.org/10.1101/2022.09.19.508461 doi: 10.1101/2022.09.19.508461
    [21] J. Ren, B. Rastegari, A. Condon, H. H. Hoos, HotKnots: heuristic prediction of RNA secondary structures including pseudoknots, RNA, 11 (2005), 1494-1504. https://doi.org/10.1261/rna.7284905 doi: 10.1261/rna.7284905
    [22] K. Sato, Y. Kato, M. Hamada, T. Akutsu, K. Asai, IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming, Bioinformatics, 27 (2011), i85-i93. https://doi.org/10.1093/bioinformatics/btr215 doi: 10.1093/bioinformatics/btr215
    [23] C. B. Do, D. A. Woods, S. Batzoglou, CONTRAfold: RNA secondary structure prediction without physics-based models, Bioinformatics, 22 (2006), e90-e98. https://doi.org/10.1093/bioinformatics/btl246 doi: 10.1093/bioinformatics/btl246
    [24] S. Zakov, Y. Goldberg, M. Elhadad, M. Ziv-Ukelson, Rich parameterization improves RNA structure prediction, in Research in Computational Molecular Biology, 18 (2011), 1525-1542. https://doi.org/10.1007/978-3-642-20036-6_48
    [25] M. Akiyama, K. Sato, Y. Sakakibara, A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model, J. Bioinf. Comput. Biol., 16 (2018), 1840025. https://doi.org/10.1142/S0219720018400255 doi: 10.1142/S0219720018400255
    [26] K. Sato, M. Akiyama, Y. Sakakibara, RNA secondary structure prediction using deep learning with thermodynamic integration, Nat. Commun., 12 (2021), 941. https://doi.org/10.1038/s41467-021-21194-4 doi: 10.1038/s41467-021-21194-4
    [27] H. Zhang, C. Zhang, Z. Li, C. Li, X. Wei, B. Zhang, et al., A new method of RNA secondary structure prediction based on convolutional neural network and dynamic programming, Front. Genet., 10 (2019), 467. https://doi.org/10.3389/fgene.2019.00467 doi: 10.3389/fgene.2019.00467
    [28] J. Singh, J. Hanson, K. Paliwal, Y. Zhou, RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning, Nat. Commun., 10 (2019), 5407. https://doi.org/10.1038/s41467-019-13395-9 doi: 10.1038/s41467-019-13395-9
    [29] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), 770-778. https://doi.org/10.1109/CVPR.2016.90
    [30] Z. Huang, W. Xu, K. Yu, Bidirectional LSTM-CRF models for sequence tagging, preprint, arXiv: 1508.01991.
    [31] X. Chen, Y. Li, R. Umarov, X. Gao, L. Song, RNA secondary structure prediction by learning unrolled algorithms, preprint, arXiv: 2002.05810.
    [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., Attention is all you need, preprint, arXiv: 1706.03762.
    [33] L. Fu, Y. Cao, J. Wu, Q. Peng, Q. Nie, X. Xie, et al., UFold: fast and accurate RNA secondary structure prediction with deep learning, Nucleic Acids Res., 50 (2022), e14. https://doi.org/10.1093/nar/gkab1074 doi: 10.1093/nar/gkab1074
    [34] K. Darty, A. Denise, Y. Ponty, VARNA: interactive drawing and editing of the RNA secondary structure, Bioinformatics, 25 (2009), 1974. https://doi.org/10.1093/bioinformatics/btp250 doi: 10.1093/bioinformatics/btp250
    [35] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, et al., An image is worth 16x16 words: transformers for image recognition at scale, preprint, arXiv: 2010.11929.
    [36] Z. Tan, Y. Fu, G. Sharma, D. H. Mathews, TurboFold Ⅱ: RNA structural alignment and secondary structure prediction informed by multiple homologs, Nucleic Acids Res., 45 (2017), 11570-11581. https://doi.org/10.1093/nar/gkx815 doi: 10.1093/nar/gkx815
    [37] M. F. Sloma, D. H. Mathews, Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures, RNA, 22 (2016), 1808-1818. https://doi.org/10.1261/rna.053694.115 doi: 10.1261/rna.053694.115
    [38] I. Kalvari, E. P. Nawrocki, N. Ontiveros-Palacios, J. Argasinska, K. Lamkiewicz, M. Marz, et al., Rfam 14: expanded coverage of metagenomic, viral and microRNA families, Nucleic Acids Res., 49 (2021), D192-D200. https://doi.org/10.1093/nar/gkaa1047 doi: 10.1093/nar/gkaa1047
    [39] Y. Wang, Y. Liu, S. Wang, Z. Liu, Y. Gao, H. Zhang, et al., ATTfold: RNA secondary structure prediction with pseudoknots based on attention mechanism, Front. Genet., 11 (2020), 612086. https://doi.org/10.3389/fgene.2020.612086 doi: 10.3389/fgene.2020.612086
    [40] J. D. Watson, F. H. C. Crick, Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid, Nature, 171 (1953), 737-738. https://doi.org/10.1038/171737a0 doi: 10.1038/171737a0
    [41] G. Varani, W. H. McClain, The G·U wobble base pair, EMBO Rep., 1 (2000), 18-23. https://doi.org/10.1093/embo-reports/kvd001 doi: 10.1093/embo-reports/kvd001
    [42] E. J. Strobel, A. M. Yu, J. B. Lucks, High-throughput determination of RNA structures, Nat. Rev. Genet., 19 (2018), 615-634. https://doi.org/10.1038/s41576-018-0034-x doi: 10.1038/s41576-018-0034-x
    [43] S. Lusvarghi, J. Sztuba-Solinska, K. J. Purzycka, J. W. Rausch, S. F. J. Le Grice, RNA secondary structure prediction using high-throughput SHAPE, Biology, 2013 (2013), e50243. https://doi.org/10.3791/50243-v doi: 10.3791/50243-v
  • This article has been cited by:

    1. Wafa Shammakh, Hadeel Z. Alzumi, Zahra Albarqi, On multi-term proportional fractional differential equations and inclusions, 2020, 2020, 1687-1847, 10.1186/s13662-020-03104-y
    2. Karthikeyan Buvaneswari, Panjaiyan Karthikeyan, Dumitru Baleanu, On a system of fractional coupled hybrid Hadamard differential equations with terminal conditions, 2020, 2020, 1687-1847, 10.1186/s13662-020-02790-y
    3. Zhiwei Lv, Ishfaq Ahmad, Jiafa Xu, Akbar Zada, Analysis of a Hybrid Coupled System of ψ-Caputo Fractional Derivatives with Generalized Slit-Strips-Type Integral Boundary Conditions and Impulses, 2022, 6, 2504-3110, 618, 10.3390/fractalfract6100618
    4. Said Mesloub, Eman Alhazzani, Hassan Eltayeb Gadain, A Two-Dimensional Nonlocal Fractional Parabolic Initial Boundary Value Problem, 2024, 13, 2075-1680, 646, 10.3390/axioms13090646
    5. Haroon Niaz Ali Khan, Akbar Zada, Ioan-Lucian Popa, Sana Ben Moussa, The Impulsive Coupled Langevin ψ-Caputo Fractional Problem with Slit-Strip-Generalized-Type Boundary Conditions, 2023, 7, 2504-3110, 837, 10.3390/fractalfract7120837
    6. Pengyan Yu, Guoxi Ni, Chengmin Hou, Existence and uniqueness for a mixed fractional differential system with slit-strips conditions, 2024, 2024, 1687-2770, 10.1186/s13661-024-01942-3
    7. Karthikeyan Buvaneswari, Panjaiyan Karthikeyan, Kulandhivel Karthikeyan, Ozgur Ege, Existence and uniqueness results on coupled Caputo-Hadamard fractional differential equations in a bounded domain, 2024, 38, 0354-5180, 1489, 10.2298/FIL2404489B
    8. Haroon Niaz Ali Khan, Akbar Zada, Ishfaq Khan, Analysis of a Coupled System of ψ
    -Caputo Fractional Derivatives with Multipoint–Multistrip Integral Type Boundary Conditions, 2024, 23, 1575-5460, 10.1007/s12346-024-00987-0
  • Reader Comments
  • © 2023 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(2081) PDF downloads(93) Cited by(0)

Figures and Tables

Figures(12)  /  Tables(7)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog