Loading [MathJax]/jax/element/mml/optable/MathOperators.js
Research article

The use of cellulolytic Aspergillus sp. inoculum to improve the quality of Pineapple compost

  • Received: 21 August 2022 Revised: 20 January 2023 Accepted: 30 January 2023 Published: 07 February 2023
  • Pineapple litter has a complex polymer of cellulose, hemicellulose, and lignin, which makes them difficult to decompose. However, pineapple litter has great potential to be a good organic material source for the soil when completely decomposed. The addition of inoculants can facilitate the composting process. This study investigated whether the addition of cellulolytic fungi inoculants to pineapple litters improves the efficiency of the composting processes. The treatments were KP1 = pineapple leaf litter: cow manure (2:1), KP2 = pineapple stem litter: cow manure (2:1), KP3 = pineapple leaf litter: pineapple stem litter: cow manure P1 (leaf litter and 1% inoculum), P2 (stem litter and 1% inoculum), and P3 (leaf + stem litters and 1% inoculum). The result showed that the number of Aspergillus sp. spores on corn media was 5.64 x 107 spores/mL, with viability of 98.58%. Aspergillus sp. inoculum improved the quality of pineapple litter compost, based on the enhanced contents of C, N, P, K, and the C/N ratio, during the seven weeks of composting. Moreover, the best treatment observed in this study was P1. The C/N ratios of compost at P1, P2, and P3 were within the recommended range of organic fertilizer which was 15–25%, with a Carbon/Nitrogen proportion of 11.3%, 11.8%, and 12.4% (P1, P2, and P3), respectively.

    Citation: Bambang Irawan, Aandi Saputra, Salman Farisi, Yulianty Yulianty, Sri Wahyuningsih, Noviany Noviany, Yandri Yandri, Sutopo Hadi. The use of cellulolytic Aspergillus sp. inoculum to improve the quality of Pineapple compost[J]. AIMS Microbiology, 2023, 9(1): 41-54. doi: 10.3934/microbiol.2023003

    Related Papers:

    [1] Youngjin Hwang, Jyoti, Soobin Kwak, Hyundong Kim, Junseok Kim . An explicit numerical method for the conservative Allen–Cahn equation on a cubic surface. AIMS Mathematics, 2024, 9(12): 34447-34465. doi: 10.3934/math.20241641
    [2] Martin Stoll, Hamdullah Yücel . Symmetric interior penalty Galerkin method for fractional-in-space phase-field equations. AIMS Mathematics, 2018, 3(1): 66-95. doi: 10.3934/Math.2018.1.66
    [3] Tomoyuki Suzuki, Keisuke Takasao, Noriaki Yamazaki . New approximate method for the Allen–Cahn equation with double-obstacle constraint and stability criteria for numerical simulations. AIMS Mathematics, 2016, 1(3): 288-317. doi: 10.3934/Math.2016.3.288
    [4] Yangfang Deng, Zhifeng Weng . Barycentric interpolation collocation method based on Crank-Nicolson scheme for the Allen-Cahn equation. AIMS Mathematics, 2021, 6(4): 3857-3873. doi: 10.3934/math.2021229
    [5] Jaeyong Choi, Seokjun Ham, Soobin Kwak, Youngjin Hwang, Junseok Kim . Stability analysis of an explicit numerical scheme for the Allen-Cahn equation with high-order polynomial potentials. AIMS Mathematics, 2024, 9(7): 19332-19344. doi: 10.3934/math.2024941
    [6] Hyun Geun Lee . A mass conservative and energy stable scheme for the conservative Allen–Cahn type Ohta–Kawasaki model for diblock copolymers. AIMS Mathematics, 2025, 10(3): 6719-6731. doi: 10.3934/math.2025307
    [7] Narcisse Batangouna . A robust family of exponential attractors for a time semi-discretization of the Ginzburg-Landau equation. AIMS Mathematics, 2022, 7(1): 1399-1415. doi: 10.3934/math.2022082
    [8] Muhammad Asim Khan, Norma Alias, Umair Ali . A new fourth-order grouping iterative method for the time fractional sub-diffusion equation having a weak singularity at initial time. AIMS Mathematics, 2023, 8(6): 13725-13746. doi: 10.3934/math.2023697
    [9] Emadidin Gahalla Mohmed Elmahdi, Jianfei Huang . Two linearized finite difference schemes for time fractional nonlinear diffusion-wave equations with fourth order derivative. AIMS Mathematics, 2021, 6(6): 6356-6376. doi: 10.3934/math.2021373
    [10] Hyun Geun Lee, Youngjin Hwang, Yunjae Nam, Sangkwon Kim, Junseok Kim . Benchmark problems for physics-informed neural networks: The Allen–Cahn equation. AIMS Mathematics, 2025, 10(3): 7319-7338. doi: 10.3934/math.2025335
  • Pineapple litter has a complex polymer of cellulose, hemicellulose, and lignin, which makes them difficult to decompose. However, pineapple litter has great potential to be a good organic material source for the soil when completely decomposed. The addition of inoculants can facilitate the composting process. This study investigated whether the addition of cellulolytic fungi inoculants to pineapple litters improves the efficiency of the composting processes. The treatments were KP1 = pineapple leaf litter: cow manure (2:1), KP2 = pineapple stem litter: cow manure (2:1), KP3 = pineapple leaf litter: pineapple stem litter: cow manure P1 (leaf litter and 1% inoculum), P2 (stem litter and 1% inoculum), and P3 (leaf + stem litters and 1% inoculum). The result showed that the number of Aspergillus sp. spores on corn media was 5.64 x 107 spores/mL, with viability of 98.58%. Aspergillus sp. inoculum improved the quality of pineapple litter compost, based on the enhanced contents of C, N, P, K, and the C/N ratio, during the seven weeks of composting. Moreover, the best treatment observed in this study was P1. The C/N ratios of compost at P1, P2, and P3 were within the recommended range of organic fertilizer which was 15–25%, with a Carbon/Nitrogen proportion of 11.3%, 11.8%, and 12.4% (P1, P2, and P3), respectively.



    For real numbers a,b,c with cN{0}, the Gaussian hypergeometric function is defined by

    F(a,b;c;x)=n=0(a,n)(b,n)(c,n)xnn!,|x|<1,

    where (a,0)=1 for a0 and (a,n) is the shifted factorial function given by

    (a,n)=a(a+1)(a+2)(a+n1)

    for nN. It is well known that the Gaussian hypergeometric function, F(a,b;c;x), has a broad range of applications, including in geometric function theory, the theory of mean values, and numerous other areas within mathematics and related disciplines. Many elementary and special functions in mathematical physics are either particular cases or limiting cases. Specifically, F(a,b;c;x) is said to be zero-balanced if c=a+b. For the case of c=a+b, as x1, Ramanujan's asymptotic formula satisfies

    F(a,b;a+b,x)=R(a,b)ln(1x)B(a,b)+O((1x)ln(1x)), (1.1)

    where

    B(a,b)=Γ(a)Γ(b)Γ(a+b)

    is the classical beta function [1] and

    R(a,b)=2γΨ(a)Ψ(b),

    here Ψ(z)=Γ(z)/Γ(z), Re(x)>0 is the psi function, and γ is the Euler–Mascheroni constant [1].

    Throughout this paper, let a[12,1), and we denote r=1r2 for r(0,1). The generalized elliptic integrals of the first and second kind are defined on (0,1) as follows [2]:

    Ka=Ka(r)=π2F(a,1a;1,r2),Ka(0)=π2,Ka(1)=, (1.2)

    and

    Ea=Ea(r)=π2F(a1,1a;1,r2),Ea(0)=π2,Ea(1)=sin(πa)2(1a). (1.3)

    Set Ka(r)=Ka(r),Ea(r)=Ea(r). Note that when a=12, Ka(r) and Ea(r) reduce to the classical complete elliptic integrals K(r) and E(r) of the first and second kind, respectively

    K(r)=π2F(12,12;1;r2),E(r)=π2F(12,12;1;r2).

    It is well known that complete elliptic integrals play a crucial role in various areas of mathematics and physics. In particular, these integrals provide a foundation for investigating numerous special functions within conformal and quasiconformal mappings, including the Grötzsch ring function, Hübner's upper bound function, and the Hersch–Pfluger distortion function[3,4]. In 2000, Anderson, et al. [5] reintroduced the generalized elliptic integrals in geometric function theory. They discovered that the generalized elliptic integral of the first kind, denoted as Ka, originates from the Schwarz–Christoffel transformation [3] of the upper half–plane onto a parallelogram and established several monotonicity theorems for Ka and Ea. The generalized Grötzsch ring function in generalized modular equations and the generalized Hübner upper bound function can also be expressed in terms of generalized elliptic integrals[6]. Recently, generalized elliptic integrals have garnered significant attention from mathematicians. A wealth of properties and inequalities for these integrals can be found in the literature. Specifically, various properties of elliptic integrals and hypergeometric functions, including monotonicity, approximation, and discrete approximation, have been investigated in [7,8,9], with sharp inequalities derived for elliptic integrals. Additionally, studies in [10,11] primarily focus on inequalities between different means, such as the Toader mean, and Hölder mean, as well as their applications in elliptic integrals.

    For r(0,1), r=1r2, it is known that the arc-length of an ellipse with semiaxes 1 and r, denoted as L(1,r), is given by L(1,r)=4E(r). Muir indicated that L(1,r) can be approximated by 2π{(1+r322)23. Later, Vuorinen conjectured the following inequality for r(0,1):

    π2(1+r322)23<E(r),

    which was subsequently proven by Barnard et al.[12].

    The Hölder mean of positive numbers x,y>0 with order sR, is defined as

    Hs(x,y)={(xs+ys2)1s,s0,xy,s=0.

    It is easy to see Hs(x,y) is strictly increasing with respect to s. Alzer and Qiu [13] established the following inequalities:

    π2Hs1(1,r)<E(r)<π2Hs2(1,r) (1.4)

    with the best constants s1=3/2 and s2=log2/log(π/2)=1.5349, see [13,14] for details.

    The generalized weighted Hölder mean of positive numbers x,y, with weight ω and order s, is defined as [14]:

    Hs(x,y;ω)={[ωxs+(1ω)ys]1s,s0,xωy1ω,s=0. (1.5)

    Wang et al. [15] proved that for r(0,1), the following inequality holds:

    π2Hs1(1,r;α)<E(r)<π2Hs2(1,r;β), (1.6)

    and the best parameters α=α(s), β=β(s) satisfy

    α(s)={12,s(,32],1η,s(32,2),(2π)s,s[2,),β(s)={1,s(,0],(2π)s,s(0,s0),12,s[s0,),

    where s0=log2log(2/π)=1.5349, η=Fs(r0)>12, Fs=1[2E(r)/π]s1rs, and r0=r0(s)(0,1) is the value such that Fs(r) is strictly increasing on (0,r0) and strictly decreasing on (r0,1)) for s(32,2).

    The extension of the inequality (1.6) to the second kind of generalized elliptic integral Ea, where a[12,1), is a natural inquiry. This paper aims to address this question. One might wonder why the parameter a is restricted to the interval [12,1) rather than (0,1). For a(0,12), our analysis has revealed a lack of the expected monotonicity in the function Fa,p(x), as defined in Theorem 3.1. This monotonicity is crucial for establishing the desired inequalities.

    To achieve our purpose, we require some more properties of generalized elliptic integrals of the first and second kind. Therefore, Section 2 will introduce several lemmas that establish these properties. Section 3 will present our main results along with their corresponding proofs. In Section 4, we establish several functional inequalities involving Ea as applications. Finally, we give the conclusion of this article.

    In this section, several key formulas and lemmas are presented to support the proof of the main results. The derivative formulas of the generalized elliptic integrals are given.

    Lemma 2.1 ([5]). For a(0,1) and r(0,1), we have

    dKadr=2(1a)(Ear2Ka)rr2,dEadr=2(1a)(KaEa)r,ddr(KaEa)=2(1a)rEar2,ddr(Ear2Ka)=2arKa.

    The following lemma provides the monotonicity of some generalized elliptic integrals with respect to r, which can be found in [16].

    Lemma 2.2. Let a(0,1). Then the following function:

    (1) rEar2Kar2 is increasing from (0,1) to (πa2,sin(πa)2(1a));

    (2) rEar2Kar2Ka is decreasing from (0,1) to (0,a);

    (3) rr2(KaEa)r2Ea is decreasing from (0,1) to (0,1a);

    (4) rKaEa)r2Ka is increasing from (0,1) to (1a,1);

    (5) rrc(KaEa)r2 is decreasing on (0,1) if and only if ca(2a).

    Lemmas 2.3 and 2.4 are important tools for proving the monotonicity of the related functions.

    Lemma 2.3 ([17]). Let α(x)=n=0anxn and β(x)=n=obnxn be real power series that converge on (r,r)(r>0), and bn>0 for all n. If the sequence {anbn}n0 is increasing (or decreasing) on (0,r), then so is α(x)β(x).

    Lemma 2.4 ([3]). For a,b(,) and a<b, let f,g:[a,b] be continuous on [a,b] and be differentiable on (a,b), and g(x)0 for all x(a,b). If f(x)g(x) is increasing (or decreasing) on (a,b), then so are

    f(x)f(a)g(x)g(a)andf(x)f(b)g(x)g(b).

    In particular, if f(a)=g(a)=0(orf(b)=g(b)=0), then the monotonicity of f(x)g(x) is the same as f(x)g(x).

    However, f(x)g(x) is not always monotonic; it is sometimes piecewise monotonic. An auxiliary function Hf,g [8] is defined as

    Hf,g:=fggf, (2.1)

    where f and g are differentiable on (a,b) and g0 on (a,b) for <a<b<. If f and g are twice differentiable on (a,b), the function Hf,g satisfies the following identities:

    (fg)=fgfgg2=gg2(fggf)=gg2Hf,g, (2.2)
    Hf,g=(fg)g. (2.3)

    Here, Hf,g establishes a connection between fg and fg.

    Lemma 2.5. Define the function f1(x) on [12,1) by

    f1(x)=2(1x)logxlog(sin(πx)/(π(1x))).

    Then 2x<f1(x)<2.

    Proof. To establish the right-hand side of the inequality, it suffices to prove that

    (1x)logxlogsin(πx)π(1x)>0.

    Denote

    g1(x)=(1x)logxlogsin(πx)π(1x).

    By differentiation, we obtain

    g1(x)=logx+1xxπcos(πx)sin(πx)11x,g1(x)=1x1x2+π2sin2(πx)1(1x)2.

    Observe that g1(12)=π210=0.130...<0, and limx1g1(x)=+. This implies that there exists x0[12,1) such that g1(x) is decreasing on [12,x0) and increasing on (x0,1). Since g1(12)=log21=0.306, and g1(1)=0, it is clear that

    g1(x)max{g1(12),g1(1)}=0,

    which implies that g1(x) is decreasing on [12,1). Consequently,

    g1(x)>g1(1)=0.

    In order to establish the left-hand side of the inequality, we define

    g2(x)=2(1x)logx(2x)logsin(πx)π(1x).

    Note

    g2(12)=log1232log2π=0.015...,g2(1)=0. (2.4)

    Differentiating g2(x) yields

    g2(x)=2logx+2(1x)x(2x)πcos(πx)sin(πx)2x1x+logsin(πx)π(1x).

    Observe that

    g2(12)=log8π1=0.065...<0,g2(34)=log3229π+5π4133=4.166...>0.

    Based on these observations and the intermediate value theorem, there exists x2[12,1) such that g2(x2)=0 and g2(x) is decreasing on [12,x2) and increasing on (x2,1). Therefore, together with (2.4), we conclude that

    g2(x)<0.

    This completes the proof.

    Lemma 2.6. For each a[12,1), the function

    f2(r)=r2a(2a)[a(KaEa)(1a)(Ear2Ka)]Ear2Kaar2Ea

    is decreasing from (0,1) to (0,a2a).

    Proof. Following from (1.2) and (1.3), we deduce that

    a(KaEa)(1a)(Ear2Ka)=π4a2(1a)r4F(a+1,2a;3;r2),Ear2Kaar2Ea=a(1a)(2a)π4r4F(a,2a;3;r2).

    To establish the desired monotonicity of f2(r), it suffices to prove that the function f3(x), defined on (0, 1) by

    f3(x)=(1x)p(a)F(a+1,2a;3;x)F(a,2a;3;x),

    is decreasing on (0,1), where p(a)=2a(2a)2. Using the power series expansion, the function can be expressed as

    xn=1Unxnn=1Vnxn,

    where the coefficients Un and Vn satisfy the recursive relations, as detailed in [18]:

    U0=1,V0=1,Un+1=anUnbnUn1,Vn=(a)n(2a)n(3)nn!, (2.5)

    with

    an=4n2+2(3a2+2a)n+(5a2+8a2)2(n+1)(n+3),bn=(2n+4a2a2)(2na2)4(n+1)(n+3).

    By Lemma 2.3, we aim to prove that the sequence {UnVn}n0 is decreasing. Note that

    Un>0,Vn>0,

    and

    U0V0=1,U1V1=5a2+8a+22a(2a),U2V2=8a3+10a2+a2a(3a)(1+a).

    Observe that

    U0V0>U1V1>U2V2,

    which implies

    U1V1V0U0<0,U2V2V1U1<0.

    Assuming that UkVkVk1Uk1<0 for all 1kn, we prove by induction that Un+1Vn+1VnUn<0. According to (2.5), we have

    Un+1Vn+1VnUn=(anUnbnUn1)Vn+1VnUn=(anVn+1Vn)Un+(anVn+1Vn)VnVn1Un1(anVn+1Vn)VnVn1Un1bnUn1=(anVn+1Vn)(UnVnVn1Un1)+[(anVn+1Vn)VnVn1bn]Un1.

    Since a[12,1), it is easy to know that

    6+4a2a2=2(1a)2+8152,5a2+8a+2=5(a4/5)2+26/5194,

    and

    anVn+1Vn=2(n1)2+(6+4a2a2)(n1)+(5a2+8a+2)2(n+1)(n+3)

    is positive for a[12,1) when n1. For a[12,1) and n1, we have that

    (anVn+1Vn)VnVn1bn=δ(n)4n(n+1)(n+2)(n+3)<0,

    where

    δ(n)=a2(a2)2n2+2(a44a3+6a22)n+2(1a)2(3a24a+2).

    In fact, δ(n) is a quadratic function of n and is decreasing on (1,), it follows that

    2(a44a3+6a22)2(a2(a2)2)=1+2a22a2(a2)2<1,δ(n)δ(2)=2(a1)(3a37a2+10a+2)<0forn2, (2.6)

    which implies that

    (anVn+1VnUn)VnVn1bn<0.

    By induction, we conclude that Un+1Vn+1VnUn<0 for all n1. Therefore, the sequence {UnVn}n0 is decreasing. Consequently, the function f2(r) is decreasing on (0,1). Moreover,

    limr0+f2(r)=a2a,limr1f2(r)=0.

    This completes the proof.

    Lemma 2.7. For each a[12,1), we define the function h(r) on (0,1) by

    h(r)=2Ea(KaEa)2(1a)r2E2a2(1a)r2(KaEa)2(KaEa)(Ear2Ka).

    Then, 2a<h(r)<2.

    Proof. First of all, we prove the right-hand side inequality. To establish the desired result, we need to show the following inequality:

    2Ea(KaEa)2(1a)r2E2a2(1a)r2(KaEa)2<2(KaEa)(Ear2Ka),

    which is equivalent to

    2(1a)Ea(Ear2Ka)+2ar2Ka(KaEa)<0.

    Denote that

    h1(r)=2(1a)Ea(Ear2Ka)+2ar2Ka(KaEa).

    By differentiation, we obtain

    h1(r)=2(1a)[2(1a)(EaKa)r(Ear2Ka)+2arEaKa]+2a[2rKa(KaEa)+2(1a)(Ear2Ka)r(KaEa)+2(1a)rEaKa]=KaEar[4(1a)(EaKa)+(48a)r2Ka]<0.

    Therefore, h1(r) is decreasing on (0,1) and

    h1(r)<limr0+h(r)=0,

    which implies h(r)<2.

    Next, we prove h(r)>2a. This is equivalent to the following inequality.

    Ea[a(KaEa)(1a)(Ear2Ka)][(1a)(Ear2Ka)ar2Ka(KaEa)]>0.

    Denote

    F(r)=Ea[a(KaEa)(1a)(Ear2Ka)][(1a)(Ear2Ka)ar2Ka(KaEa)].

    The derivative of F(r) yields

    F(r)=2(1a)KaEar[a(KaEa)(1a)(Ear2Ka)]+Ea[2a(1a)r(Ear2Ka)r2]2r(KaEa)[a(KaEa)(1a)(Ear2Ka)r2+aEar2Kar2]=r(Ear2Kaar2Ea)r2[2aEar2Kar2(2a)r2(KaEa)r2a(KaEa)(1a)(Ear2Ka)Ear2Kaar2Ea].

    Note that (Ear2Kaar2Ea)/r2 is increasing from (0, 1) to (0,). In fact, by differentiation, we know

    (Ear2Kaar2Ear2)=2a(2a)r(KaEa)r4>0.

    According to Lemma 2.2(1)(5) and Lemma 2.6, we have that F(r) is increasing on (0, 1) and F(r)>limr0+F(r)=0, which implies that F(r) is increasing on (0,1). Moreover,

    F(r)>limr0+F(r)=0.

    Thus, h(r)>2a. The proof is completed.

    For a[12,1), it is also found that h(r) is strictly increasing on (0,1).

    Lemma 2.8. For each a[12,1), r(0,1), we define the function f4(r) by

    f4(r)=r2a(KaEa)22Ea2ar2Ea2r2Ka.

    Then f4(r) is strictly decreasing from (0,1) to (0,(1a)π2a(2a)).

    Proof. Let

    f41(r)=r2a(KaEa)2,f42(r)=2Ea2ar2Ea2r2Ka.

    With Lemma 2.4 and f41(0+)=f42(0+)=0, we only prove the monotonicity of f41(r)/f42(r). Then we have

    f41(r)=rr22a(KaEa)[(42a)Ea2aKa],
    f42(r)=4a(2a)r(KaEa),
    4a(2a)f41(r)f42(r)=(42a)EaKar22af43(r).

    By differentiation, we see

    f43(r)=2(1a)rKar42a[(44a)Ear2Kar2Ka2a].

    With Lemma 2.2(2), we obtain

    (44a)Ear2Kar2Ka2a<a(44a)2a=2a(12a)0.

    Thus, f43(r) is strictly decreasing on (0,1), which shows f4(r) is strictly decreasing. And

    limr0+f4(r)=limr0+f41(r)f42(r)=(1a)π2a(2a),limr1f4(r)=0.

    The proof is completed.

    Lemma 2.9. For each a[12,1), r(0,1), we define the function f5(r) by

    f5(r)=Ea(Ear2Ka)+r2Ka(KaEa)r2r22aKa.

    Then f5(r) is strictly increasing from (0,1) to (π2,+).

    Proof. Let

    f51(r)=Ea(Ear2Ka)+r2Ka(KaEa),f52(r)=r2r22aKa.

    Taking the derivative, we have

    f51(r)=2rKa(2EaKa),f52(r)=rr2a[2r2Ka2(1a)(KaEa)],
    f5(r)=f51(r)f52(r)f51(r)f52(r)f252(r)=f53(r)r3r42aK2a,

    where

    f53(r)=(KaEa)[2a(E2ar2K2a)(4a2)Ea(Ear2Ka)].

    In fact, we see

    (2a(E2ar2K2a)(4a2)Ea(Ear2Ka))=KaEar[4a(KaEa)+2(4a2)(Ear2Ka)]>0,

    which demonstrates f5(r)>0 for r(0,1) and f5(r) is increasing on (0,1). Moreover,

    limr0+f5(r)=Ea(Ear2Ka)/r2+r2Ka(KaEa)/r2r22aKa=π2,limr1f5(r)=+.

    The proof is completed.

    Lemma 2.10. For each, a[12,1), r(0,1), h(r) is given as in Lemma 2.7. Then, h(r) is strictly increasing from (0,1) to (2a,2).

    Proof. Let

    h2(r)=2Ea(KaEa)2(1a)r2E2a2(1a)r2(KaEa)2KaEa,h3(r)=Ear2Ka.

    Clearly, h(r)=h2(r)h3(r) and h2(0+)=h3(0+)=0. By differentiations,

    h2(r)=2(1a)2r2(KaEa)2(Ear2Ka)+r2Ea[2(1a)Ea2+(4a2)r2EaKa2ar2K2a]rr2(KaEa)2,h3(r)=2arKa.

    Then,

    h2(r)h3(r)=2(1a)2a2r2(KaEa)2(Ear2Ka)+r2Ea[2(1a)Ea2+(4a2)r2EaKa2ar2K2a]r2r2Ka(KaEa)2=1aa[2Ea2ar2Ea2r2Kar2a(KaEa)2][Ea(Ear2Ka)+r2Ka(KaEa)r2r22aKa]=1aaf5(r)f4(r).

    With Lemmas 2.8 and 2.9, we obtain that h(r) is strictly increasing on (0,1). Furthermore,

    limr0+h(r)=2a,limr1h(r)=2.

    This completes the proof.

    In this section, we present some of the main results of Ea(r).

    Theorem 3.1. Let a[12,1), pR{0}, and for r(0,1), define

    Fa,p(r)=1[2Ea(r)/π]p2(1a)1rp.

    The monotonicity of Fa,p(r) is as follows:

    (1) Fa,p(r) is strictly increasing from (0,1) to (1a,1b) if and only if p2, where

    b=(sin(πa)(1a)π)p2(1a).

    (2) Fa,p(r) is strictly decreasing on (0,1) if and only if p2a. Moreover, if p(0,2a], the range of Fa,p(r) is (1b,1a), and the range is (0,1a) if p(,0).

    (3) If p(2a,2), Fa,p(r) is piecewise monotonic. To be precise, there exsists r0=r0(a,p)(0,1) such that Fa,p(r) is strictly increasing on (0,r0) and strictly decreasing on (r0,1). Furthermore, for r(0,1), if p(2a,p0), the range of Fa,p(r) satisifies

    1b<Fa,p(r)σ0, (3.1)

    while

    1a<Fa,p(r)σ0, (3.2)

    if p[p0,2), where

    p0=2(1a)logalog(sin(πa)/(1a)π)(2a,2),σ0=Fa,p(r0)>1a.

    Proof. For r(0,1),

    Fa,p(r)=1[2Ea(r)/π]p2(1a)1rp=:φ1(r)φ2(r).

    Clearly, we have φ1(0)=φ2(0)=0. By differentiation,

    φ1(r)=p2(1a)(2π)p2(1a)Ep2(1a)1a2(1a)(KaEa)r,φ2(r)=prrp2,

    and

    φ1(r)φ2(r)=(2π)p2(1a)Ep2(1a)1a(KaEa)r2rp2=:φ3(r).

    By differentiating logφ3(r), we obtain

    φ3(r)φ3(r)=p2(1a)2(a1)(KaEa)rEa+prr22r+2(1a)rEar2(KaEa)+2(1a)(KaEa)rEa2rr2=pEar2Karr2Ea+2(1a)r2E2a2Ea(KaEa)+2(1a)r2(KaEa)2rr2Ea(KaEa)=Ear2Karr2Ea[p2Ea(KaEa)2(1a)r2E2a2(1a)r2(KaEa)2(KaEa)(Ear2Ka)]=Ear2Karr2Ea(ph(r)), (3.3)

    where h(r) is defined as in Lemma 2.7. By Lemmas 2.2(2), 2.7, and 2.10, there are three cases to consider.

    (i) If p2. It follows from (3.3) that φ3(r) is strictly increasing on (0,1), and so is Fa,p(r). Furthermore, in this case,

    Fa,p(0+)=limr0+φ1(r)φ2(r)=1a,Fa,p(1)=1(sin(πa)(1a)π)p2(1a).

    (ii) If p2a, as in the proof of case (i), we know that φ3(r) is strictly decreasing on (0,1), and so is Fa,p(r). Also, Fa,p(0+)=1a, and

    Fa,p(1)={0,forp<0,1(sin(πa)(1a)π)p2(1a),for0<p2a.

    (iii) If 2a<p<2. According to Ramanujan's approximation (1.1), it shows that rcKa0 (r1) if c0. With Lemma 2.2(4) and the equation

    Hφ1,φ2(r)=φ1φ2φ2φ1=φ2φ3φ1,

    we obtain

    limr0+Hφ1,φ2(r)=0,limr1Hφ1,φ2(r)=(sin(πa)(1a)π)p2(1a)1<0. (3.4)

    Together with (3.3), (3.4), Lemmas 2.7 and 2.10, and the formulas

    Fa,p(r)=(φ1φ2)=φ2φ22Hφ1,φ2(r),Hφ1,φ2(r)=(φ1φ2)φ2=φ3(r)φ2(r),

    which follows from (2.2) and (2.3), it shows that there exists r0(0,1) such that Hφ1,φ2(r)>0 for r(0,r0) and Hφ1,φ2(r)<0 for r(r0,1). Thus, Fa,p(r) is strictly increasing on (0,r0) and strictly decreasing on (r0,1). Therefore, for all r(0,1), we get

    Fa,p(r)Fa,p(r0)=σ0.

    In fact, Fa,p(r0)Fa,p(r)>max{Fa,p(0+),Fa,p(1)}. It follows from Lemma 2.5 that

    p0=2(1a)logalog(sin(πa)/(1a)π)(2a,2),

    which makes p0 the unique root of

    1(sin(πa)(1a)π)p2(1a)=1a

    on (2a,2) and p1(sin(πa)(1a)π)p2(1a) is strictly increasing on (,). Hence we have Fa,p(0+)Fa,p(1) if p(2a,p0] and Fa,p(0+)<Fa,p(1) if p(p0,2). Consequently, the range of Fa,p(r) in case 3 is valid. The proof is completed.

    Figure 1 shows the monotonicity of Fa,p with a=0.7 as an example.

    Figure 1.  Monotonicity of Fa,p with a=0.7 as an example.

    Applying the property of Fa,p(r) from Theorem 3.1, we obtain our main result.

    Theorem 3.2. For a[12,1), let μ,ν[0,1] and p0,σ0 be given as in Theorem 3.1. Then for any fixed pR, the double inequality

    π2H2(1a)p(1,r;μ)Eaπ2H2(1a)p(1,r;ν) (3.5)

    holds for all r(0,1) with the equality only for certain values of r if and only if μμ(a,p) and νν(a,p), where μ(a,p) and ν(a,p) satisfy

    μ(a,p)={a,p(,0)(0,2a],1σ0,p(2a,2),b,p[2,+),ν(a,p)={1,p(,0),b,p(0,p0),a,p[p0,+), (3.6)

    where

    b=(sin(πa)(1a)π)p2(1a).

    Particularly, for p=0, (3.5) holds if and only if μ12(1a)2 and ν1.

    Proof. First we consider the case of p0, by (1.5), the inequality (3.5) is equivalent to

    μ<1Fa,p(r)<ν, (3.7)

    where Fa,p(r) is defined as in Theorem 3.1. It follows from Theorem 3.1 that we immediately conclude the best possible constants μ=μ(a,p) and ν=ν(a,p) in (3.6).

    For p=0, we define the function T(r) on (0, 1) by

    T(r)=log(2Ea/π)logr=:T1(r)T2(r).

    Obviously, we see that T1(0+)=T2(0+)=0. By differentiation, we have

    T1(r)T2(r)=2(1a)r2(KaEa)r2Ea.

    Together with Lemma 2.2(3), this implies T1(r)T2(r) is strictly decreasing on (0, 1), and by Lemma 2.4, T(r) shares the same monotonicity. Clearly, T(1)=0 and

    T(0+)=limr0+T1(r)T2(r)=2(1a)2,

    which indicates 12(1a)2<1T(r)<1 for r(0,1). As a result, Eq (1.5) demonstrates that the inequality

    π2H2(1a)p(1,r;μ)<Ea(r)<π2H2(1a)p(1,r;ν)

    holds for all r(0,1) if and only if μ12(1a)2 and ν1.

    This completes the proof.

    Figure 2 shows the sharpness of the bound with a=0.7 as an example.

    Figure 2.  Sharp bound for Ea with a=0.7 as an example.

    Remark 3.1. For a=12, we see that (3.5) holds if the parameters satisfy the conditions given in Theorem 3.2. This conclusion has been proved in [15].

    In this section, by applying Theorem 3.2, we present several sharp bounds of weighted Hölder mean for Ea.

    Note that for the case of μ(a,p)=ν(a,p)=a, the best bounds of Ea are attained at p=2a and p=p0, which will be proved in the following corollary.

    Corollary 4.1. Let a[12,1) and p1,p2R. Then the inequality

    π2H2(1a)p1(1,r;a)<Ea(r)<π2H2(1a)p2(1,r;a) (4.1)

    holds for all r(0,1) with the best possible constants p1=2a and p2=p0, where p0 is given as in Theorem 3.1.

    Proof. For a[12,1), we consider (μ,p)=(a,2a) and (ν,p)=(a,p0) satisfying (3.6), which yields (4.1) upon substitution into (3.5).

    To establish that a and p0 are the best possible constants, we observe that the Hölder mean is monotonically increasing with respect to p. Consequently, it suffices to analyze the case of 2a<p<p0.

    According to Theorem 3.2, the inequality

    π2H2(1a)p(1,r;1σ0)Eaπ2H2(1a)p(1,r;b) (4.2)

    holds for all r(0,1), where 1σ0 and b are sharp, with b given as in Theorem 3.2. From Theorem 3.1, together with the monotonicity of ωHp(1,r;ω), we have 1σ0<a<b for p(2a,p0), implying

    π2H2(1a)p(1,r;1σ0)π2H2(1a)p(1,r;a)π2H2(1a)p(1,r;b).

    Therefore, considering the sharpness of 1σ0 and b in inequality (4.2), we conclude that there exist two numbers r1,r2(0,1) such that

    π2H2(1a)p(1,r1;a)>Ea(r1),π2H2(1a)p(1,r2;a)<Ea(r2).

    Thus, the proof is completed.

    Figure 3 demonstrates that the sharp bounds of Ea are attained at p1=2a and p2=p0 with a=0.7 as an example.

    Figure 3.  Best constants for (4.1) with a=0.7 as an example.

    Furthermore, it is observed that computing the lower bound in (3.6) for the case μ(a,p)=1σ0 is challenging, while the case ν(a,p)=1 is trivial. Thus, we propose using μ(a,p)=b for p[2,) and ν(a,p)=b for p(0,p0) to establish new bounds. The specific inequality is as follows.

    Corollary 4.2. Inequality

    π2{(sin(πa)(1a)π)11a+[1(sin(πa)(1a)π)11a]r2}1a<Ea<π2{(sin(πa)(1a)π)p02(1a)+[1(sin(πa)(1a)π)p02(1a)]rp0}2(1a)p0 (4.3)

    holds for r(0,1).

    Lemma 4.3. Let a[12,1),

    Δ(p,r)=H2(1a)p(1,r;b)={(sin(πa)(1a)π)p2(1a)+[1(sin(πa)(1a)π)p2(1a)]rp}2(1a)p.

    Then, the function Δ(p,r) with respect to p is strictly decreasing on (0,) for r(0,1).

    Proof. By differentiating logΔ(p,r):

    1Δ(p,r)Δ(p,r)p=˜Δ(p,rp)p2ψ(p,rp), (4.4)

    where

    ψ(p,x)=(sin(πa)(1a)π)p2(1a)+[1(sin(πa)(1a)π)p2(1a)]x,

    and

    ˜Δ(p,x)=2(1a)ψ(p,x)log(ψ(p,x))p(1x)(sin(πa)(1a)π)p2(1a)log(sin(πa)(1a)π)2(1a)[1(sin(πa)(1a)π)p2(1a)]xlogx.

    Differentiating ˜Δ(p,x) with respect to x yields

    ˜Δ(p,x)x=2(1a)[1(sin(πa)(1a)π)p2(1a)]logψ(p,x)x+p(sin(πa)(1a)π)p2(1a)log(sin(πa)(1a)π)Δ0(p,x).

    Give the observation that Δ0(p,x) is strictly decreasing for x(0,1). In fact,

    Δ0(p,x)x=2(1a)[1(sin(πa)(1a)π)p2(1a)](sin(πa)(1a)π)p2(1a)xψ(p,x)<0.

    And

    Δ0(p,0+)=,Δ0(p,1)=p(sin(πa)(1a)π)p2(1a)log(sin(πa)(1a)π)<0

    indicate that ˜Δ(p,x) first strictly increases on (0,x0) and then strictly decreases on (x0,1) for some x0(0,1). Note that for p>0, it is observed that

    ˜Δ(p,0+)=˜Δ(p,1)=0. (4.5)

    Hence, ˜Δ(p,x)>0 for x(0,1).

    Consequently, monotonicity of Δ(p,r) with respect to p follows from (4.4).

    Remark 4.1. Following Lemma 4.3 and inequality (3.5), we observe that

    {Ea>π2H2(1a)2(1,r;b11a1)π2H2(1a)p(1,r;bp2(1a)1),if p[2,),Ea<π2H2(1a)p0(1,r;bp02(1a)1)π2H2(1a)p(1,r;bp2(1a)1),if p(0,p0], (4.6)

    where

    b1=sin(πa)(1a)π.

    According to the proof of (3.2), if p(p0,2), it follows that

    1σ0<b<a.

    Therefore, it results in

    π2H2(1a)p(1,r;1σ0)<π2H2(1a)p(1,r;b)<π2H2(1a)p(1,r;a)

    by the monotonicity of H2(1a)p(1,r;ζ) with respect to ζ.Theorem 3.2 presents that, for p(p0,2), 1σ0 is sharp weight of H2(1a)p(1,r;ζ) as the lower bound of Ea, while a is sharp weight as the upper bound of Ea.

    Hence, as a bound of Ea, H2(1a)p(1,r;b) can attain the best upper bound at p=p0 and the best lower bound at p=2 by (4.6).

    In this article, we have proved the monotonicity of Fa,p(r), where Fa,p(r) is given as in Theorem 3.1. Moreover, we find the sharp weighted Hölder mean approximating Ea:

    π2H2(1a)p(1,r;μ)Eaπ2H2(1a)p(1,r;ν)

    holds for all r(0,1) if and only if μμ(a,p) and νν(a,p), where μ(a,p) and ν(a,p) are given as in (3.6). Besides, we derive several bounds of Ea in terms of weights and power, which are given by Corollary 4.1, Corollary 4.2, and Remark 4.1. These conclusions provide an extension of the work of [15].

    Zixuan Wang: Investigation, Writing – original draft. Chuanlong Sun: Validation. Tiren Huang: Writing – review & editing. All authors have read and approved the final version of the manuscript for publication.

    The authors declare that they have not used artificial intelligence (AI) tools in the creation of this article.

    The authors declare no conflicts of interest regarding the publication for the paper.


    Acknowledgments



    We would like to thank to Directorate of Research and Community Services, The Ministry of Research and Technology/Research and the National Innovation Agency, Indonesia that funded this work through Applied Research Grant Schemes with contract numbers of 860/UN26.21/PN/2019; 4376/UN26.21/PN/2020; 205/E4.1/AK.04.PT/2021 and Professorship Research Grant, Universitas Lampung with contract number 1675/UN26.21/PN/2021. We also thank to the Research Division of PT. Great Giant Pineapple, Lampung, Indonesia for providing pineapple substrates and chemical analyses.

    Conflict of interest



    The authors declare no conflict of interest in this article.

    [1] Sánchez OJ, Montoya S (2020) Assessment of polysaccharide and biomass production from three white rot fungi by solid-state fermentation using wood and agro-industrial residues: a kinetic approach. Forests 11: 1055. https://doi.org/10.3390/f11101055
    [2] Pardo MES, Casselis MER, Escobedo RM, et al. (2014) Chemical characterisation of the industrial residues of the pineapple (Ananas omosus). J Agr Chem Environ 3: 53-56. http://dx.doi.org/10.4236/jacen.2014.32B009
    [3] Ch'ng HY, Ahmed OH, Kassim S, et al. (2013) Co-composting of pineapple leaves and chicken manure slurry. Int J Recyc 2: 23. https://doi.org/10.1186/2251-7715-2-23
    [4] Gupta P, Samant K, Sahu A (2012) Isolation of cellulose-degrading bacteria and determination of their cellulolytic potential. J Microbiol 2012: 578925. https://doi.org/10.1155/2012/578925
    [5] Panda H, Hota D (2007) Bioferilizers and organic farming. New Dehli: Genetech Book 397.
    [6] Ayed LB, Hassen A, Jedidi N, et al. (2007) Microbial C and N dynamics during composting of urban solid waste. Waste Manage Res 25: 24-29. https://doi.org/10.1177%2F0734242X07073783
    [7] Lu WJ, Wang HT, Nie YE, et al. (2004) Effect of inoculating flower stalks and vegetable waste with ligno-cellulolytic microorganisms on the composting process. J Environ Sci Health B 39: 871-887. https://doi.org/10.1081/LESB-200030896
    [8] Irawan B, Septitasari AW, Zulkifli Z, et al. (2019) Effect of induced compost by cellulolitic (Aspergillus fumigatus) and ligninolitic (Geotrichum sp.) fungi inoculum application on vegetative growth of red chili (Capsicum annuum L.). J Pure Appl Microbiol 13: 815-821. https://doi.org/10.22207/JPAM.13.2.16
    [9] Nevalainen KM, Penttilä ME (2004) Molecular biology of cellulolytic fungi. Genetics and Biotechnology. The Mycota (A Comprehensive Treatise on Fungi as Experimental Systems for Basic and Applied Research) . Berlin Heidelberg: Springer 369-390. https://doi.org/10.1007/978-3-662-07426-8_18
    [10] Sarsaiya S, Jain A, Awasthi SK, et al. (2019) Microbial dynamics for lignocellulosic waste bioconversion and its importance with modern circular economy, challenges and future perspectives. Bioresources Technol 291: 121905. https://doi.org/10.1016/j.biortech.2019.121905
    [11] Sivaramanan S (2014) Isolation of cellulolytic fungi and their degradation on cellulosic agricultural wastes. J Acad Ind Res 2: 458-463. https://doi.org/10.13140/2.1.3633.4080
    [12] Diaz GV, Coniglio RO, Chungara CI, et al. (2021) Aspergillus niger LBM 134 isolated from rotten wood and its potential cellulolytic ability. Mycology 12: 160-173. https://doi.org/10.1080/21501203.2020.1823509
    [13] Irawan B, Kasiamdari RS, Sunarminto BH, et al. (2019) Effect of fungal inoculum application on changes in organic matter of leaf litter composting. Polish J Soil Sci 52: 143-152. http://dx.doi.org/10.17951/pjss.2019.52.1.143
    [14] Hafif B, Khaerati (2021) Effect of indigenous cellulolytic fungi enhancement on organic carbon and soybean production on peat soil. IOP Conf Series Earth Environ Sci 749: 012021. https://doi.org/10.1088/1755-1315/749/1/012021
    [15] Irawan B, Afandi, Hadi S (2017) Effects of saprophytic microfungi application on soil fertility based on their decomposition properties. J Appl Biol 11: 15-19.
    [16] Irawan B, Wahyuningtias I, Ayuningtyas N, et al. (2022) Potential Lignocellulolytic Microfungi from Pineapple Plantation for Composting Inoculum Additive. Int J Microbiol 2022: 1-6. https://doi.org/10.1155/2022/9252901
    [17] Teather RM, Wood PJ (1982) Use of congo red polysaccharide interaction in enumeration and characterization of cellulolytic bacteria from the bovine rumen. Appl Environ Microbiol 43: 777-780. https://doi.org/10.1128/aem.43.4.777-780.1982
    [18] Irawan B, Kasiamdari RS, Sunarminto BH, et al. (2014) Preparation of fungal inoculum for leaf litter composting from selected fungi. J Agr Biol Sci 9: 89-94.
    [19] Jayaraman K (2000) Design and analysis of experiments. A Statistical Manual for Forestry Research . Available from: http://www.fao.org/3/X6831E/X6831E07.htm
    [20] Gaind S, Nain L, Patel VB (2009) Quality evaluation of co-composted wheat straw, poultry droppings and oil seed cakes. Biodegradation 20: 307-317. https://doi.org/10.1007/s10532-008-9223-1
    [21] Ying GH, Chi LS, Ibrahim MH Changes of microbial biota during the biostabilization of cafeteria wastes by Takakura Home Method (THM) using three different fermented food products (2012).
    [22] Walkley A, Amstrong BI (1934) An examination of Degtjareff method for determining soil organic matter, and proposed modification of the chromic acid tritation method. Soil Sci 27: 29-38. https://doi.org/10.1097/00010694-193401000-00003
    [23] Jackson ML (1973) Soil Chemical Analysis. New Delhi India: Prentice Hall India Pvt. Ltd. 660.
    [24] Murphy J, Riley JP (1962) A modified single solution method for the determination of phosphorus in natural waters. Anal Chim Acta 27: 31-36. https://doi.org/10.1016/S0003-2670(00)88444-5
    [25] Gao L, Sun MH, Liu XZ, et al. (2007) Effects of carbon concentration and carbon-to-nitrogen ratio on growth and sporulation of several biocontrol fungi. Mycol Res 111: 87-92. https://doi.org/10.1016/j.mycres.2006.07.019
    [26] Mejia D (2005) Maize: postharvest operations. Post-harvest compendium FAO Rome . Available from: www.fao.org
    [27] Abubakar A, Suberu HA, Bello IM, et al. (2013) Effect of pH on mycelial growth and sporulation of Aspergillus parasiticus. J Plant 4: 64-67. https://doi.org/10.11648/j.jps.20130104.13
    [28] Russell PJ, Hertz PE, Mc Millan B (2011) Biology: The Dynamic Science. Belmont CA USA: Cengage Learning 1283.
    [29] van Kuijk SJA, Sonnenberg ASM, Baars JJP, et al. (2016) The effect of particle size and amount of inoculum on fungal treatment of wheat straw and wood chips. J Anim Sci Biotechnol 7: 39. https://doi.org/10.1186/s40104-016-0098-4
    [30] Mikata K (1999) Preservation of yeast culture by L-drying: viability after 15 years storage at 5 ºC. IFO Res Comm 19: 71-82.
    [31] Cazorla D, Morales PYA, Maria E (2007) Influence of NaCl salinity and pH on in vitro sporulation of an autochthonous isolate of Beauveria bassiana. Croizatia 2: 137-144.
    [32] García-Kirchner O, Segura-Granados M, Rodríguez-Pascual P (2005) Effect of media composition and growth conditions on production of beta-glucosidase by Aspergillus niger C-6. Appl Biochem Biotechnol 121–124: 347-359. https://doi.org/10.1385/ABAB:121:1-3:0347
    [33] Garcia C, Hernandez CT, Costa F, et al. (1992) Evaluation of the maturity of municipal waste compost using simple chemical parameters. Comm Soil Sci Plant Anal 23: 1501-1512. https://doi.org/10.1080/00103629209368683
    [34] Scheutz C, Pedicone A, Pedersen GB, et al. (2011) Evaluation of respiration in compost landfill biocovers intended for methane oxidation. Waste Manage 31: 895-902. https://doi.org/10.1016/j.wasman.2010.11.019
    [35] Suthar S, Gairola S (2014) Nutrient recovery from urban forest leaf litter waste solids using Eisenia fetida. Ecol Eng 71: 660-666. https://doi.org/10.1016/j.ecoleng.2014.08.010
    [36] Huang D, Gao L, Cheng M, et al. (2022) Carbon and N conservation during composting: A review. Sci Total Environ 840: 156355. https://doi.org/10.1016/j.scitotenv.2022.156355
    [37] Mrudula S, Murugammal R (2011) Production of cellulase by Aspergillus niger under submerged and solid state fermentation using coir waste as a substrate. Braz J Microbiol 42: 1119-1127. https://doi.org/10.1590/S1517-83822011000300033
    [38] Reischke S, Rousk J, Bååth E (2014) The effects of glucose loading rates on bacterial and fungal growth in soil. Soil Biol Biochem 70: 88-95. https://doi.org/10.1016/j.soilbio.2013.12.011
    [39] National Standardization Bureau (NSB)Compost Specifications from Domestic Organic Waste, SNI 19-7030-2004. (in Indonesian) (2004).
    [40] Azim K, Soudi B, Boukhari S, et al. (2018) Composting parameters and compost quality: a literature review. Org Agr 8: 141-158. https://doi.org/10.1007/s13165-017-0180-z
    [41] Madrid F, Murillo JM, Lopez R (2000) Use of urea to correct immature Urban composts for agricultural purposes. Comm Soil Sci Plant Anal 31: 2635-2649. https://doi.org/10.1080/00103620009370614
    [42] Luo Y, Li G, Frank S, et al. (2012) Effects of additive superphosphate on NH3, N2O and CH4 emissions during pig manure composting. Trans Chinese Soc Agr Eng 28: 235-242. http://dx.chinadoi.cn/10.3969/j.issn.1002-6819.2012.22.033
    [43] Hapsoh, Gusmawartati, Yusuf M (2015) Effect various combination of organic waste on compost quality. J Trop Soil 20: 59-65. http://dx.doi.org/10.5400/jts.2015.v20i1.59-65
    [44] Sullivan DM, Miller RO (2001) Compost quality attributes, measurements, and variability. Compost Utilization in Horticultural Cropping Systems . Boca Raton: CRC Press 95-117. https://doi.org/10.1201/9781420026221.ch4
    [45] The Decree of Ministry of Agriculture (DMA)Organic Fertilizer, Biological Fertilizer and Soil Improvement, 70, Ministry of Agriculture, Republic of Indonesia, Jakarta (2011). (in Indonesian)
  • This article has been cited by:

    1. Yunjae Nam, Jian Wang, Chaeyoung Lee, Yongho Choi, Minjoon Bang, Zhengang Li, Junseok Kim, A Finite Difference Method for a Normalized Time-Fractional Black–Scholes Equation, 2025, 11, 2349-5103, 10.1007/s40819-025-01916-8
  • 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(2237) PDF downloads(129) Cited by(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog