Research article

Laplace transform ordering of bivariate inactivity times

  • In this paper we consider the Laplace transform of the bivariate inactivity time. We show that a weak bivariate reversed hazard rate order is characterized by the Laplace transform of the bivariate inactivity times in two different frames. The results are used to characterize the weak bivariate reversed hazard rate order using the weak bivariate mean inactivity time order. The results are also used to characterize the decreasing bivariate reversed hazard rate property using the Laplace transform of the bivariate inactivity time.

    Citation: Mansour Shrahili, Mohamed Kayid. Laplace transform ordering of bivariate inactivity times[J]. AIMS Mathematics, 2022, 7(7): 13208-13224. doi: 10.3934/math.2022728

    Related Papers:

    [1] Madeeha Tahir, Ayesha Naz, Muhammad Imran, Hasan Waqas, Ali Akgül, Hussein Shanak, Rabab Jarrar, Jihad Asad . Activation energy impact on unsteady Bio-convection nanomaterial flow over porous surface. AIMS Mathematics, 2022, 7(11): 19822-19845. doi: 10.3934/math.20221086
    [2] Khalil Ur Rehman, Wasfi Shatanawi, Zeeshan Asghar, Haitham M. S. Bahaidarah . Neural networking analysis for MHD mixed convection Casson flow past a multiple surfaces: A numerical solution. AIMS Mathematics, 2023, 8(7): 15805-15823. doi: 10.3934/math.2023807
    [3] Taqi A. M. Shatnawi, Nadeem Abbas, Wasfi Shatanawi . Comparative study of Casson hybrid nanofluid models with induced magnetic radiative flow over a vertical permeable exponentially stretching sheet. AIMS Mathematics, 2022, 7(12): 20545-20564. doi: 10.3934/math.20221126
    [4] Nadeem Abbas, Wasfi Shatanawi, Taqi A. M. Shatnawi . Innovation of prescribe conditions for radiative Casson micropolar hybrid nanofluid flow with inclined MHD over a stretching sheet/cylinder. AIMS Mathematics, 2025, 10(2): 3561-3580. doi: 10.3934/math.2025164
    [5] Muhammad Asif Zahoor Raja, Kottakkaran Sooppy Nisar, Muhammad Shoaib, Ajed Akbar, Hakeem Ullah, Saeed Islam . A predictive neuro-computing approach for micro-polar nanofluid flow along rotating disk in the presence of magnetic field and partial slip. AIMS Mathematics, 2023, 8(5): 12062-12092. doi: 10.3934/math.2023608
    [6] M. S. Alqarni . Thermo-bioconvection flow of Walter's B nanofluid over a Riga plate involving swimming motile microorganisms. AIMS Mathematics, 2022, 7(9): 16231-16248. doi: 10.3934/math.2022886
    [7] Kiran Sajjan, N. Ameer Ahammad, C. S. K. Raju, M. Karuna Prasad, Nehad Ali Shah, Thongchai Botmart . Study of nonlinear thermal convection of ternary nanofluid within Darcy-Brinkman porous structure with time dependent heat source/sink. AIMS Mathematics, 2023, 8(2): 4237-4260. doi: 10.3934/math.2023211
    [8] J. Kayalvizhi, A. G. Vijaya Kumar, Ndolane Sene, Ali Akgül, Mustafa Inc, Hanaa Abu-Zinadah, S. Abdel-Khalek . An exact solution of heat and mass transfer analysis on hydrodynamic magneto nanofluid over an infinite inclined plate using Caputo fractional derivative model. AIMS Mathematics, 2023, 8(2): 3542-3560. doi: 10.3934/math.2023180
    [9] Ali Raza, Umair Khan, Aurang Zaib, Wajaree Weera, Ahmed M. Galal . A comparative study for fractional simulations of Casson nanofluid flow with sinusoidal and slipping boundary conditions via a fractional approach. AIMS Mathematics, 2022, 7(11): 19954-19974. doi: 10.3934/math.20221092
    [10] Latifa I. Khayyat, Abdullah A. Abdullah . The onset of Marangoni bio-thermal convection in a layer of fluid containing gyrotactic microorganisms. AIMS Mathematics, 2021, 6(12): 13552-13565. doi: 10.3934/math.2021787
  • In this paper we consider the Laplace transform of the bivariate inactivity time. We show that a weak bivariate reversed hazard rate order is characterized by the Laplace transform of the bivariate inactivity times in two different frames. The results are used to characterize the weak bivariate reversed hazard rate order using the weak bivariate mean inactivity time order. The results are also used to characterize the decreasing bivariate reversed hazard rate property using the Laplace transform of the bivariate inactivity time.



    A Poisson algebra is a triple, (L,,[,]), where (L,) is a commutative associative algebra and (L,[,]) is a Lie algebra that satisfies the following Leibniz rule:

    [x,yz]=[x,y]z+y[x,z],x,y,zL.

    Poisson algebras appear naturally in the study of Hamiltonian mechanics and play a significant role in mathematics and physics, such as in applications of Poisson manifolds, integral systems, algebraic geometry, quantum groups, and quantum field theory (see [7,11,24,25]). Poisson algebras can be viewed as the algebraic counterpart of Poisson manifolds. With the development of Poisson algebras, many other algebraic structures have been found, such as Jacobi algebras [1,9], Poisson bialgebras [20,23], Gerstenhaber algebras, Lie-Rinehart algebras [16,17,26], F-manifold algebras [12], Novikov-Poisson algebras [28], quasi-Poisson algebras [8] and Poisson n-Lie algebras [10].

    As a dual notion of a Poisson algebra, the concept of a transposed Poisson algebra was recently introduced by Bai et al. [2]. A transposed Poisson algebra (L,,[,]) is defined by exchanging the roles of the two binary operations in the Leibniz rule defining the Poisson algebra:

    2z[x,y]=[zx,y]+[x,zy],x,y,zL,

    where (L,) is a commutative associative algebra and (L,[,]) is a Lie algebra.

    It is shown that a transposed Poisson algebra possesses many important identities and properties and can be naturally obtained by taking the commutator in the Novikov-Poisson algebra [2]. There are many results on transposed Poisson algebras, such as those on transposed Hom-Poisson algebras [18], transposed BiHom-Poisson algebras [21], a bialgebra theory for transposed Poisson algebras [19], the relation between 12-derivations of Lie algebras and transposed Poisson algebras [14], the relation between 12-biderivations and transposed Poisson algebras [29], and the transposed Poisson structures with fixed Lie algebras (see [6] for more details).

    The notion of an n-Lie algebra (see Definition 2.1), as introduced by Filippov [15], has found use in many fields in mathematics and physics [4,5,22,27]. The explicit construction of n-Lie algebras has become one of the important problems in this theory. In [3], Bai et al. gave a construction of (n+1)-Lie algebras through the use of n-Lie algebras and some linear functions. In [13], Dzhumadil′daev introduced the notion of a Poisson n-Lie algebra which can be used to construct an (n+1)-Lie algebra under an additional strong condition. In [2], Bai et al. showed that this strong condition for n=2 holds automatically for a transposed Poisson algebra, and they gave a construction of 3-Lie algebras from transposed Poisson algebras with derivations. They also found that this constructed 3-Lie algebra and the commutative associative algebra satisfy the analog of the compatibility condition for transposed Poisson algebras, which is called a transposed Poisson 3-Lie algebra. This motivated them to introduce the concept of a transposed Poisson n-Lie algebra (see Definition 2.2) and propose the following conjecture:

    Conjecture 1.1. [2] Let n2 be an integer and (L,,μn) a transposed Poisson n-Lie algebra. Let D be a derivation of (L,) and (L,μn). Define an (n+1)-ary operation:

    μn+1(x1,,xn+1):=n+1i=1(1)i1D(xi)μn(x1,,ˆxi,,xn+1),x1,,xn+1L,

    where ˆxi means that the i-th entry is omitted. Then, (L,,μn+1) is a transposed Poisson (n+1)-Lie algebra.

    In this paper, based on the identities for transposed Poisson n-Lie algebras given in Section 2, we prove that Conjecture 1.1 holds under a certain strong condition described in Section 3 (see Definition 2.3 and Theorem 3.2).

    Throughout the paper, all vector spaces are taken over a field of characteristic zero. To simplify notations, the commutative associative multiplication () will be omitted unless the emphasis is needed.

    In this section, we first recall some definitions, and then we exhibit a class of identities for transposed Poisson n-Lie algebras.

    Definition 2.1. [15] Let n2 be an integer. An n-Lie algebra is a vector space L, together with a skew-symmetric linear map [,,]:nLL, such that, for any xi,yjL,1in1,1jn, the following identity holds:

    [[y1,,yn],x1,,xn1]=ni=1(1)i1[[yi,x1,,xn1],y1,,ˆyi,,yn]. (2.1)

    Definition 2.2. [2] Let n2 be an integer and L a vector space. The triple (L,,[,,]) is called a transposed Poisson n-Lie algebra if (L,) is a commutative associative algebra and (L,[,,]) is an n-Lie algebra such that, for any h,xiL,1in, the following identity holds:

    nh[x1,,xn]=ni=1[x1,,hxi,,xn]. (2.2)

    Some identities for transposed Poisson algebras in [2] can be extended to the following theorem for transposed Poisson n-Lie algebras.

    Theorem 2.1. Let (L,,[,,]) be a transposed Poisson n-Lie algebra. Then, the following identities hold:

    (1) For any xiL,1in+1, we have

    n+1i=1(1)i1xi[x1,,ˆxi,,xn+1]=0; (2.3)

    (2) For any h,xi,yjL,1in1,1jn, we have

    ni=1(1)i1[h[yi,x1,,xn1],y1,,ˆyi,,yn]=[h[y1,,yn],x1,,xn1]; (2.4)

    (3) For any xi,yjL,1in1,1jn+1, we have

    n+1i=1(1)i1[yi,x1,,xn1][y1,,ˆyi,,yn+1]=0; (2.5)

    (4) For any x1,x2,yiL,1in, we have

    ni=1nj=1,ji[y1,,yix1,,yjx2,,yn]=n(n1)x1x2[y1,y2,,yn]. (2.6)

    Proof. (1) By Eq (2.2), for any 1in+1, we have

    nxi[x1,,xi1,xi+1,,xn+1]=ji[x1,,xi1,xi+1,,xixj,,xn+1].

    Thus, we obtain

    n+1i=1(1)i1nxi[x1,,ˆxi,,xn+1]=n+1i=1n+1j=1,ji(1)i1[x1,,ˆxi,,xixj,,xn+1].

    Note that, for any i>j, we have

    (1)i1[x1,,xj1,xixj,xj+1,,ˆxi,,xn]+(1)j1[x1,,ˆxj,,xi1,xjxi,xi+1,,xn]=(1)i1+(ij1)[x1,,xj1,xj+1,,xi1,xixj,xi+1,,xn]+(1)j1[x1,,ˆxj,,xi1,xjxi,xi+1,,xn]=((1)j2+(1)j1)[x1,,xj1,xj+1,,xi1,xixj,xi+1,,xn]=0,

    which gives n+1i=1n+1j=1,ji(1)i1[x1,,ˆxi,,xixj,,xn+1]=0.

    Hence, we get

    n+1i=1(1)i1nxi[x1,,ˆxi,,xn+1]=0.

    (2) By Eq (2.2), we have

    [h[y1,,yn],x1,,xn1]n1i=1[[y1,,yn],x1,,hxi,,xn1]=nh[[y1,,yn],x1,,xn1],

    and, for any 1jn,

    (1)j1([h[yj,x1,,xn1],y1,,ˆyj,,yn1]+ni=1,ij[[yj,x1,,xn1],y1,,hyi,,ˆyj,,yn1])=(1)j1nh[[yj,x1,,xn1],y1,,ˆyj,,yn1].

    By taking the sum of the above n+1 identities and applying Eq (2.1), we get

    [h[y1,,yn],x1,,xn1]n1i=1[[y1,,yn],x1,,hxi,,xn1]+nj=1(1)j1([h[yj,x1,,xn1],y1,,ˆyj,,yn1]+ni=1,ij[[yj,x1,,xn1],y1,,hyi,,ˆyj,,yn1])=nh[[y1,,yn],x1,,xn1]+nhnj=1(1)j1[[yj,x1,,xn1],y1,,ˆyj,,yn1]=0.

    We denote

    Aj:=ni=1,ij(1)i1[[yi,x1,,xn1],y1,,hyj,,ˆyi,,yn],1jn,Bi:=[[y1,,yn],x1,,hxi,,xn1],1in1.

    Then, the above equation can be rewritten as

    ni=1(1)i1[h[yi,x1,,xn1],y1,,ˆyi,,yn][h[y1,,yn],x1,,xn1]+nj=1Ajn1i=1Bi=0. (2.7)

    By applying Eq (2.1) to Aj,1jn, we have

    Aj=ni=1,ij(1)i1[[yi,x1,,xn1],y1,,hyj,,ˆyi,,yn]=[[y1,,hyj,,yn],x1,,xn1]+(1)j[[hyj,x1,,xn1],y1,,ˆyj,,yn].

    Thus, we get

    nj=1Aj=nj=1[[y1,,hyj,,yn],x1,,xn1]+nj=1(1)j[[hyj,x1,,xn1],y1,,ˆyj,,yn]=n[h[y1,,yn],x1,,xn1]+nj=1(1)j[[hyj,x1,,xn1],y1,,ˆyj,,yn].

    By applying Eq (2.1) to Bi,1in1, we have

    Bi=[[y1,,yn],x1,,hxi,,xn1]=nj=1(1)j1[[yj,x1,,hxi,,xn1],y1,ˆyj,,yn].

    Thus, we get

    n1i=1Bi=n1i=1nj=1(1)j1[[yj,x1,,hxi,,xn1],y1,ˆyj,,yn]=nj=1n1i=1(1)j1[[yj,x1,,hxi,,xn1],y1,ˆyj,,yn].

    Note that, by Eq (2.2), we have

    n1i=1(1)j1[[yj,x1,,hxi,,xn1],y1,ˆyj,,yn]=(1)j1n[h[yj,x1,,xi,,xn1],y1,ˆyj,,yn]+(1)j[[hyj,x1,,xi,,xn1],y1,ˆyj,,yn].

    Thus, we obtain

    n1i=1Bi=nj=1(1)j1n[h[yj,x1,,xi,,xn1],y1,ˆyj,,yn]+nj=1(1)j[[hyj,x1,,xi,,xn1],y1,ˆyj,,yn].

    By substituting these equations into Eq (2.7), we have

    ni=1(1)i1[h[yi,x1,,xn1],y1,,ˆyi,,yn][h[y1,,yn],x1,,xn1]+n[h[y1,,yn],x1,,xn1]+nj=1(1)j[[hyj,x1,,xn1],y1,,ˆyj,,yn]nj=1(1)j1n[h[yj,x1,,xi,,xn1],y1,ˆyj,,yn]nj=1(1)j[[hyj,x1,,xi,,xn1],y1,ˆyj,,yn]=0,

    which implies that

    (n1)(ni=1(1)i[h[yi,x1,,xn1],y1,,ˆyi,,yn]+[h[y1,,yn],x1,,xn1])=0.

    Therefore, the proof of Eq (2.4) is completed.

    (3) By Eq (2.2), for any 1jn+1, we have

    (1)j1n[yj,x1,,xn1][y1,,ˆyj,,yn+1]=n+1i=1,ij(1)j1[y1,,yi[yj,x1,,xn1],,ˆyj,,yn+1].

    By taking the sum of the above n+1 identities, we obtain

    n+1j=1(1)j1n[yj,x1,,xn1][y1,,ˆyj,,yn+1]=n+1j=1n+1i=1,ij(1)j1[y1,,yi[yj,x1,,xn1],,ˆyj,,yn+1].

    Thus, we only need to prove the following equation:

    n+1j=1n+1i=1,ij(1)j1[y1,,yi[yj,x1,,xn1],,ˆyj,,yn+1]=0.

    Note that

    n+1j=1n+1i=1,ij(1)j1[y1,,yi[yj,x1,,xn1],,ˆyj,,yn+1]=n+1i=1n+1j=1,ji(1)j1[y1,,yi[yj,x1,,xn1],,ˆyj,,yn+1]=n+1i=1i1j=1(1)i+j1[yi[yj,x1,,xn1],y1,,ˆyj,,ˆyi,,yn+1]+n+1i=1n+1j=i+1(1)i+j[yi[yj,x1,,xn1],y1,,ˆyi,,ˆyj,,yn+1](2.4)=n+1i=1(1)i[yi[y1,,ˆyi,,yn+1],x1,,xn1](2.3)=0.

    Hence, the conclusion holds.

    (4) By applying Eq (2.2), we have

    n2x1x2[y1,y2,,yn]=nx1nj=1[y1,,yjx2,,yn]=ni=1nj=1,ji[y1,,yix1,,yjx2,,yn]+nj=1[y1,,yjx1x2,,yn]=ni=1nj=1,ji[y1,,yix1,,yjx2,,yn]+nx1x2[y1,,yn],

    which gives

    n(n1)x1x2[y1,y2,,yn]=ni=1nj=1,ji[y1,,yix1,,yjx2,,yn].

    Hence, the proof is completed.

    To prove Conjecture 1.1, we need the following extra condition.

    Definition 2.3. A transposed Poisson n-Lie algebra (L,,[,,]) is called strong if the following identity holds:

    y1[hy2,x1,,xn1]y2[hy1,x1,,xn1]+n1i=1(1)i1hxi[y1,y2,x1,,ˆxi,,xn1]=0 (2.8)

    for any y1,y2,xiL,1in1.

    Remark 2.1. When n=2, the identity is

    y1[hy2,x1]+y2[x1,hy1]+hx1[y1,y2]=0,

    which is exactly Theorem 2.5 (11) in [2]. Thus, in the case of a transposed Poisson algebra, the strong condition always holds. So far, we cannot prove that the strong condition fails to hold for n3.

    Proposition 2.1. Let (L,,[,,]) be a strong transposed Poisson n-Lie algebra. Then,

    y1[hy2,x1,,xn1]hy1[y2,x1,,xn1]=y2[hy1,x1,,xn1]hy2[y1,x1,,xn1] (2.9)

    for any y1,y2,xiL,1in1.

    Proof. By Eq (2.3), we have

    hy1[y2,x1,,xn1]+hy2[y1,x1,,xn1]=n1i=1(1)i1hxi[y1,y2,x1,,ˆxi,,xn1].

    Then, the statement follows from Eq (2.8).

    In this section, we will prove Conjecture 1.1 for strong transposed Poisson n-Lie algebras. First, we recall the notion of derivations of transposed Poisson n-Lie algebras.

    Definition 3.1. Let (L,,[,,]) be a transposed Poisson n-Lie algebra. The linear operation D:LL is called a derivation of (L,,[,,]) if the following holds for any u,v,xiL,1in:

    (1) D is a derivation of (L,), i.e., D(uv)=D(u)v+uD(v);

    (2) D is a derivation of (L,[,,]), i.e.,

    D([x1,,xn])=ni=1[x1,,xi1,D(xi),xi+1,,xn].

    Lemma 3.1. Let (L,,[,,]) be a transposed Poisson n-Lie algebra and D a derivation of (L,,[,,]). For any yiL,1in+1, we have the following:

    (1)

    n+1i=1(1)i1D(yi)D([y1,,ˆyi,,yn+1])=n+1i=1n+1j=1,ji(1)i1D(yi)[y1,,D(yj),,ˆyi,,yn+1]; (3.1)

    (2)

    n+1i=1(1)i1D(yi)D([y1,,ˆyi,,yn+1])=n+1i=1n+1j=1,jin+1k=j+1,ki(1)iyi[y1,,D(yj),,D(yk),,ˆyi,,yn+1], (3.2)

    where, for any i>j, ji denotes the empty sum, which is equal to zero.

    Proof. (1) The statement follows immediately from Definition 3.1.

    (2) By applying Eq (3.1), we need to prove the following equation:

    n+1i=1n+1j=1,ji(1)i1nD(yi)[y1,,D(yj),,ˆyi,,yn+1]=n+1i=1n+1j=1,jin+1k=j+1,ki(1)inyi[y1,,D(yj),,D(yk),,ˆyi,,yn+1].

    For any 1in+1, denote Ai:=nn+1j=1,ji(1)i1D(yi)[y1,,D(yj),,ˆyi,,yn+1]. Then, we have

    n+1i=1n+1j=1,ji(1)i1nD(yi)[y1,,D(yj),,ˆyi,,yn+1]=n+1i=1Ai.

    Note that

    Ai=(1)i1(nD(yi)[D(y1),y2,,ˆyi,,yn+1]+nD(yi)[y1,D(y2),y3,,ˆyi,,yn+1]++nD(yi)[y1,,ˆyi,,yn,D(yn+1)])=(1)i1([D(yi)D(y1),y2,,ˆyi,,yn+1]+n+1k=2,ki[D(y1),y2,,ykD(yi),,ˆyi,,yn+1]+[y1,D(yi)D(y2),y3,,ˆyi,,yn+1]+n+1k=1,k2,i[y1,D(y2),y3,,ykD(yi),,ˆyi,,yn+1]++[y1,,ˆyi,,yn,D(yi)D(yn+1)]+nk=1,ki[y1,,ykD(yi),,ˆyi,,yn,D(yn+1)])=(1)i1n+1j=1,ji[y1,,D(yi)D(yj),,ˆyi,,yn+1]+(1)i1n+1j=1,jin+1k=1,kj,i[y1,,D(yj),,ykD(yi),,ˆyi,,yn+1].

    Thus, we have

    n+1i=1Ai=n+1j=1n+1i=1,ij(1)j1[y1,,D(yj)D(yi),,ˆyj,,yn+1]+n+1i=1n+1j=1,jin+1k=1,ki,j(1)i1[y1,,D(yj),,ykD(yi),,ˆyi,,yn+1]=T1+T2,

    where

    T1:=n+1j=1n+1i=1,ij(1)j1[y1,,D(yj)D(yi),,ˆyj,,yn+1],T2:=n+1i=1n+1j=1,jin+1k=1,ki,j(1)i1[y1,,D(yj),,ykD(yi),,ˆyi,,yn+1].

    Note that

    T1=n+1j,i=1Bji,

    where Bji=(1)j1[y1,,D(yj)D(yi),,ˆyj,,yn+1] for any 1jin+1, and Bii=0 for any 1in+1.

    For any 1i,jn+1, without loss of generality, assume that i<j; then, we have

    Bji+Bij=(1)j1[y1,,D(yj)D(yi),,ˆyj,,yn+1]+(1)i1[y1,,ˆyi,,D(yi)D(yj),,yn+1]=(1)j1[y1,,D(yj)D(yi),,ˆyj,,yn+1]+(1)i1+ji+1[y1,,D(yj)D(yi),,ˆyj,,yn+1]=0,

    which implies that T1=n+1j,i=1Bji=0.

    Thus, we get that n+1i=1Ai=T2.

    We rewrite

    n+1i=1n+1j=1,jin+1k=j+1,ki(1)inyi[y1,,D(yj),,D(yk),,ˆyi,,yn+1]=n+1i=1n+1j=1,jin+1k=j+1,kin+1t=1,tj,k,i(1)i[y1,,D(yj),,D(yk),,ytyi,,ˆyi,,yn+1]+n+1i=1n+1j=1,jin+1k=j+1,ki(1)i[y1,,yiD(yj),,D(yk),,ˆyi,,yn+1]+n+1i=1n+1j=1,jin+1k=j+1,ki(1)i[y1,,D(yj),,yiD(yk),,ˆyi,,yn+1]=M1+M2+M3,

    where

    M1:=n+1i=1n+1j=1,jin+1k=j+1,kin+1t=1,tj,k,i(1)i[y1,,D(yj),,D(yk),,ytyi,,ˆyi,,yn+1],M2:=n+1i=1n+1j=1,jin+1k=j+1,ki(1)i[y1,,yiD(yj),,D(yk),,ˆyi,,yn+1],M3:=n+1i=1n+1j=1,jin+1k=j+1,ki(1)i[y1,,D(yj),,yiD(yk),,ˆyi,,yn+1].

    Note that

    M1=n+1i=1n+1j=1,jin+1k=j+1,kin+1t=1,tj,k,i(1)i[y1,,D(yj),,D(yk),,ytyi,,ˆyi,,yn+1]=n+1i,j,k,t=1Bijkt,

    where

    Bijkt={0,if any two indices are equal or k<j;(1)i[y1,,D(yj),,D(yk),,ytyi,,ˆyi,,yn+1],otherwise.

    For any 1j,kn+1, without loss of generality, assume that t<i; then, we have

    Bijkt+Btjki=(1)i[y1,,D(yj),,D(yk),,ytyi,,ˆyi,,yn+1]+(1)t[y1,,D(yj),,D(yk),,ˆyt,,ytyi,,yn+1]=(1)i[y1,,D(yj),,D(yk),,ytyi,,ˆyi,,yn+1]+(1)t+it1[y1,,D(yj),,D(yk),,ytyi,,ˆyi,,yn+1]=0,

    which implies that M1=0.

    Therefore, we only need to prove the following equation:

    M2+M3=n+1i=1n+1j=1,jin+1k=1,ki,j(1)i1[y1,,D(yj),,ykD(yi),,ˆyi,,yn+1].

    First, we have

    n+1j=1,jin+1k=j+1,ki(1)i[y1,,yiD(yj),,D(yk),,ˆyi,,yn+1]+n+1j=1,jin+1k=j+1,ki(1)i[y1,,D(yj),,yiD(yk),,ˆyi,,yn+1]=n+1k=1,kik1j=1,ji(1)i[y1,,yiD(yj),,D(yk),,ˆyi,,yn+1]+n+1j=1,jin+1k=j+1,ki(1)i[y1,,D(yj),,yiD(yk),,ˆyi,,yn+1]=n+1j=1,jij1k=1,ki(1)i[y1,,yiD(yk),,D(yj),,ˆyi,,yn+1]+n+1j=1,jin+1k=j+1,ki(1)i[y1,,D(yj),,yiD(yk),,ˆyi,,yn+1]=n+1j=1,jin+1k=1,ki,j(1)i[y1,,D(yj),,yiD(yk),,ˆyi,,yn+1].

    Thus,

    M2+M3=n+1i=1n+1j=1,jin+1k=1,ki,j(1)i[y1,,D(yj),,yiD(yk),,ˆyi,,yn+1]=n+1j=1n+1i=1,ijn+1k=1,ki,j(1)i[y1,,D(yj),,yiD(yk),,ˆyi,,yn+1]=n+1j=1n+1i=1,iji1k=1,kj(1)i[y1,,D(yj),,yiD(yk),,ˆyi,,yn+1]+n+1j=1n+1i=1,ijn+1k=i+1,kj(1)i[y1,,D(yj),,ˆyi,,yiD(yk),,yn+1].

    Note that, for any 1jn+1, we have

    n+1i=1,iji1k=1,kj(1)i[y1,,D(yj),,yiD(yk),,ˆyi,,yn+1]=n+1i=1,iji1k=1,kj(1)i[y1,,D(yj),,yk1,yiD(yk),yk+1,,ˆyi,yn+1]=n+1i=1,iji1k=1,kj(1)k1[y1,,D(yj),,ˆyk,,yi1,yiD(yk),yi+1,,yn+1]=n+1i=1,iji1k=1,kj(1)k1[y1,,D(yj),,ˆyk,,yiD(yk),,yn+1].

    Similarly, we have

    n+1i=1,ijn+1k=i+1,kj(1)i[y1,,D(yj),,ˆyi,,yiD(yk),,yn+1]=n+1i=1,ijn+1k=i+1,kj(1)k1[y1,,D(yj),,yiD(yk),,ˆyk,,yn+1].

    Thus,

    M2+M3=n+1j=1n+1i=1,iji1k=1,kj(1)k1[y1,,D(yj),,ˆyk,,yiD(yk),,yn+1]+n+1j=1n+1i=1,ijn+1k=i+1,kj(1)k1[y1,,D(yj),,yiD(yk),,ˆyk,,yn+1]=n+1j=1n+1i=1,ijn+1k=1,ki,j(1)k1[y1,,D(yj),,yiD(yk),,ˆyk,,yn+1]=n+1k=1n+1j=1,jkn+1i=1,ij,k(1)k1[y1,,D(yj),,yiD(yk),,ˆyk,,yn+1]=n+1i=1n+1j=1,jin+1k=1,kj,i(1)i1[y1,,D(yj),,ykD(yi),,ˆyi,,yn+1].

    The proof is completed.

    Theorem 3.1. Let (L,,[,,]) be a strong transposed Poisson n-Lie algebra and D a derivation of (L,,[,,]). Define an (n+1)-ary operation:

    μn+1(x1,,xn+1):=n+1i=1(1)i1D(xi)[x1,,ˆxi,,xn+1] (3.3)

    for any xiL,1in+1. Then, (L,μn+1) is an (n+1)-Lie algebra.

    Proof. For convenience, we denote

    μn+1(x1,,xn+1):=[x1,,xn+1].

    On one hand, we have

    [[y1,,yn+1],x1,,xn](3.3)=n+1i=1(1)i1[D(yi)[y1,,ˆyi,,yn+1],x1,,xn](3.3)=n+1i=1(1)i1D(D(yi)[y1,,ˆyi,,yn+1])[x1,,xn]+n+1i=1nj=1(1)i+j1D(xj)[D(yi)[y1,,ˆyi,,yn+1],x1,,ˆxj,,xn]=n+1i=1(1)i1D2(yi)[y1,,ˆyi,,yn+1][x1,,xn]+n+1i=1(1)i1D(yi)D([y1,,ˆyi,,yn+1])[x1,,xn]+n+1i=1nj=1(1)i+j1D(xj)[D(yi)[y1,,ˆyi,,yn+1],x1,,ˆxj,,xn](3.1)=n+1i=1(1)i1D2(yi)[y1,,ˆyi,,yn+1][x1,,xn]+n+1i=1n+1k=1,ki(1)i1D(yi)[y1,,D(yk),,ˆyi,,yn+1][x1,,xn]+n+1i=1nj=1(1)i+j1D(xj)[D(yi)[y1,,ˆyi,,yn+1],x1,,ˆxj,,xn]=n+1i=1(1)i1D2(yi)[y1,,ˆyi,,yn+1][x1,,xn]+n+1k=1k1i=1(1)k+i1D(yi)[D(yk),y1,,ˆyi,,ˆyk,,yn+1][x1,,xn]+n+1k=1n+1i=k+1(1)i+kD(yi)[D(yk),y1,,ˆyk,,ˆyi,,yn+1][x1,,xn]+n+1i=1nj=1(1)i+j1D(xj)[D(yi)[y1,,ˆyi,,yn+1],x1,,ˆxj,,xn].

    On the other hand, for any 1kn, we have

    (1)k1[[yk,x1,,xn],y1,,ˆyk,,yn+1](3.3)=(1)k1[D(yk)[x1,,xn],y1,,ˆyk,,yn+1]+nj=1(1)j+k1[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyk,,yn+1](3.3)=(1)k1D(D(yk)[x1,,xn])[y1,,ˆyk,,yn+1]+k1i=1(1)i+k1D(yi)[D(yk)[x1,,xn],y1,,ˆyi,,ˆyk,,yn+1]+n+1i=k+1(1)i+kD(yi)[D(yk)[x1,,xn],y1,,ˆyk,,ˆyi,,yn+1]+nj=1(1)j+k1D(D(xj)[yk,x1,,ˆxj,,xn])[y1,,ˆyk,,yn+1]+nj=1n+1i=k+1((1)i+jD(yi)[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyk,,ˆyi,,yn+1])+nj=1k1i=1((1)i+j1D(yi)[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyi,,ˆyk,,yn+1])=(1)k1D2(yk)[x1,,xn][y1,,ˆyk,,yn+1]+k1i=1(1)i+k1D(yi)[D(yk)[x1,,xn],y1,,ˆyi,,ˆyk,,yn+1]+n+1i=k+1(1)i+kD(yi)[D(yk)[x1,,xn],y1,,ˆyk,,ˆyi,,yn+1]+nj=1(1)j+k1D2(xj)[yk,x1,,ˆxj,,xn][y1,,ˆyk,,yn+1]+nj=1n+1i=k+1((1)i+jD(yi)[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyk,,ˆyi,,yn+1])+nj=1k1i=1((1)i+j1D(yi)[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyi,,ˆyk,,yn+1])+(1)k1D(yk)D([x1,,xn])[y1,,ˆyk,,yn+1]+nj=1(1)j+k1D(xj)D([yk,x1,,ˆxj,,xn])[y1,,ˆyk,,yn+1](3.2)=(1)k1D2(yk)[x1,,xn][y1,,ˆyk,,yn+1]+k1i=1(1)i+k1D(yi)[D(yk)[x1,,xn],y1,,ˆyi,,ˆyk,,yn+1]+n+1i=k+1(1)i+kD(yi)[D(yk)[x1,,xn],y1,,ˆyk,,ˆyi,,yn+1]+nj=1(1)j+k1D2(xj)[yk,x1,,ˆxj,,xn][y1,,ˆyk,,yn+1]+nj=1n+1i=k+1((1)i+jD(yi)[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyk,,ˆyi,,yn+1])+nj=1k1i=1((1)i+j1D(yi)[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyi,,ˆyk,,yn+1])+nj=1nt=j+1(1)kyk[x1,,D(xj),,D(xt),,xn][y1,,ˆyk,,yn+1]+ni=1nj=1,ji(1)k+ixi[D(yk),x1,,D(xj),,ˆxi,,xn][y1,,ˆyk,,yn+1]+ni=1nj=1,jint=j+1,ti(1)k+ixi[yk,x1,,D(xj),D(xt),,ˆxi,,xn][y1,,ˆyk,,yn+1].

    We denote

    n+1i=1(1)i1[[yi,x1,,xn],y1,,ˆyi,,yn+1]=7i=1Ai,

    where

    A1:=n+1i=1(1)i1D2(yi)[x1,,xn][y1,,ˆyi,,yn+1],A2:=n+1k=1nj=1(1)k+j1D2(xj)[yk,x1,,ˆxj,,xn][y1,,ˆyk,,yn+1],A3:=n+1i=1nj=1nk=j+1(1)iyi[x1,,D(xj),,D(xk),,xn][y1,,ˆyi,,yn+1],A4:=n+1k=1ni=1nj=1,jint=j+1,ti((1)k+ixi[yk,x1,,D(xj),D(xt),,ˆxi,,xn][y1,,ˆyk,,yn+1]),A5:=n+1k=1k1i=1(1)k+i1D(yi)[D(yk)[x1,,xn],y1,,ˆyi,,ˆyk,,yn+1]+n+1k=1n+1i=k+1(1)i+kD(yi)[D(yk)[x1,,xn],y1,,ˆyk,,ˆyi,,yn+1],A6:=n+1k=1ni=1nj=1,ji((1)k+ixi[D(yk),x1,,D(xj),,ˆxi,,xn][y1,,ˆyk,,yn+1]),A7:=n+1k=1nj=1n+1i=k+1((1)k+i+jD(yi)[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyk,,ˆyi,,yn+1])+n+1k=1nj=1k1i=1((1)k+i+j1D(yi)[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyi,,ˆyk,,yn+1]).

    By Eq (2.5), for fixed j, we have

    n+1k=1(1)k+j1D2(xj)[yk,x1,,ˆxj,,xn][y1,,ˆyk,,yn+1]=0.

    So, we obtain that A2=0.

    By Eq (2.3), for fixed j and k, we have

    n+1i=1(1)iyi[x1,,D(xj),,D(xk),,xn][y1,,ˆyi,,yn+1]=0.

    So, we obtain that A3=0.

    By Eq (2.5), for fixed j and t, we have

    n+1k=1(1)k+ixi[yk,x1,,D(xj),D(xt),,ˆxi,,xn][y1,,ˆyk,,yn+1]=0.

    So, we obtain that A4=0.

    By Eq (2.9), for fixed i and k, we have

    (1)k+i1D(yi)[D(yk)[x1,,xn],y1,,ˆyi,,ˆyk,,yn+1]+(1)i+kD(yk)[D(yi)[x1,,xn],y1,,ˆyi,,ˆyk,,yn+1]=(1)k+i1D(yi)[D(yk),y1,,ˆyi,,ˆyk,,yn+1][x1,,xn]+(1)i+kD(yk)[D(yi),y1,,ˆyi,,ˆyk,,yn+1][x1,,xn].

    Thus, we obtain

    A5=n+1k=1k1i=1(1)k+i1D(yi)[D(yk),y1,,ˆyi,,ˆyk,,yn+1][x1,,xn]+n+1k=1n+1i=k+1(1)i+kD(yi)[D(yk),y1,,ˆyk,,ˆyi,,yn+1][x1,,xn].

    By Eq (2.3), for fixed j and k, we have

    ni=1(1)k+ixi[D(yk),x1,,D(xj),,ˆxi,,xn]=(1)k1D(yk)[x1,,D(xj),,xn]+(1)k+j1D(xj)[D(yk),x1,,xn]=(1)k+jD(yk)[D(xj),x1,,ˆxj,,xn]+(1)k+j1D(xj)[D(yk),x1,,xn].

    Thus, we get

    A6=n+1k=1nj=1(1)k+jD(yk)[D(xj),x1,,ˆxj,,xn][y1,,ˆyk,,yn+1]+n+1k=1nj=1(1)k+j1D(xj)[D(yk),x1,,xn][y1,,ˆyk,,yn+1].

    By Eq (2.4), for fixed j and i, we have

    n+1k=i+1(1)k+i+j1D(yi)[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyi,,ˆyk,,yn+1]+i1k=1(1)k+i+jD(yi)[D(xj)[yk,x1,,ˆxj,,xn],y1,,ˆyk,,ˆyi,,yn+1]=(1)j+i1D(yi)[D(xj)[y1,,ˆyi,,yn+1],x1,,ˆxj,,xn].

    So, we obtain

    A7=nj=1n+1i=1(1)j+i1D(yi)[D(xj)[y1,,ˆyi,,yn+1],x1,,ˆxj,,xn].

    By Eq (2.9), we have

    (1)i+jD(yi)[D(xj),x1,,ˆxj,,xn][y1,,ˆyi,,yn+1]+(1)i+j1D(xj)[D(yi),x1,,xn][y1,,ˆyi,,yn+1]+(1)j+i1D(yi)[D(xj)[y1,,ˆyi,,yn+1],x1,,ˆxj,,xn]=(1)j+i1D(xj)[D(yi)[y1,,ˆyi,,yn+1],x1,,ˆxj,,xn].

    So, we get

    A6+A7=n+1i=1nj=1(1)j+i1D(xj)[D(yi)[y1,,ˆyi,,yn+1],x1,,ˆxj,,xn].

    Thus, we have

    7i=1Ai=A1+A5+A6+A7=[[y1,,yn+1],x1,,xn].

    Therefore, (L,μn+1) is an (n+1)-Lie algebra.

    Now, we can prove Conjecture 1.1 for strong transposed Poisson n-Lie algebras.

    Theorem 3.2. With the notations in Theorem 3.1, (L,,μn+1) is a strong transposed Poisson (n+1)-Lie algebra.

    Proof. For convenience, we denote μn+1(x1,,xn+1):=[x1,,xn+1]. According to Theorem 3.1, we only need to prove Eqs (2.2) and (2.8).

    Proof of Eq (2.2). By Eq (3.3), we have

    n+1i=1[x1,,hxi,,xn+1]=D(hx1)[x2,,xn+1]+n+1j=2(1)j1D(xj)[hx1,x2,,ˆxj,,xn+1]D(hx2)[x1,x3,,xn+1]+n+1j=1,j2(1)j1D(xj)[x1,hx2,x3,,ˆxj,,xn+1]++(1)nD(hxn)[x1,,xn]+nj=1(1)j1D(xj)[x1,,ˆxj,,xn,hxn+1]=n+1i=1(1)i1D(hxi)[x1,,ˆxi,,xn]+n+1i=1n+1j=1,ji(1)j1D(xj)[x1,,,hxi,,ˆxj,,xn+1]=n+1i=1(1)i1D(hxi)[x1,,ˆxi,,xn+1]+n+1j=1n+1i=1,ij(1)j1D(xj)[x1,,hxi,,ˆxj,,xn+1]=n+1i=1(1)i1hD(xi)[x1,,ˆxi,,xn+1]+n+1i=1(1)i1xiD(h)[x1,,ˆxi,,xn+1]+n+1j=1n+1i=1,ij(1)j1D(xj)[x1,,hxi,,ˆxj,,xn+1](2.3)=n+1i=1(1)i1hD(xi)[x1,,ˆxi,,xn+1]+n+1j=1n+1i=1,ij(1)j1D(xj)[x1,,hxi,,ˆxj,,xn+1](3.3)=h[x1,,xn+1]+n+1j=1n+1i=1,ij(1)j1D(xj)[x1,,hxi,,ˆxj,,xn+1](2.2)=h[x1,,xn+1]+nhn+1j=1(1)j1D(xj)[x1,,ˆxj,,xn+1](3.3)=h[x1,,xn+1]+nh[x1,,xn+1]=(n+1)h[x1,,xn+1].

    Proof of Eq (2.8). By Eq (3.3), we have

    y1[hy2,x1,,xn]y2[hy1,x1,,xn]+ni=1(1)i1hxi[y1,y2,x1,,ˆxi,,xn]=y1y2D(h)[x1,,xn]+y1hD(y2)[x1,,xn]y1D(x1)[hy2,x2,,xn]+y1D(x2)[hy2,x1,x3,,xn]++(1)ny1D(xn)[hy2,x1,,xn1]y2y1D(h)[x1,,xn]y2hD(y1)[x1,,xn]+y2D(x1)[hy1,x2,,xn]y2D(x2)[hy1,x1,x3,,xn]++(1)n1y2D(xn)[hy1,x1,,xn1]+hx1D(y1)[y2,x2,,xn]hx1D(y2)[y1,x2,,xn]+hx1D(x2)[y1,y2,x3,,xn]++(1)n+1hx1D(xn)[y1,y2,x2,,xn1]hx2D(y1)[y2,x1,x3,,xn]+hx2D(y2)[y1,x1,x3,,xn]hx2D(x1)[y1,y2,x3,,xn]++(1)n+2hx2D(xn)[y1,y2,x1,x3,,xn1]++(1)n1hxnD(y1)[y2,x1,,xn1]+(1)nhxnD(y2)[y1,x1,,xn1]+(1)n+1hxnD(x1)[y1,y2,x2,,xn1]++(1)2n1hxnD(xn1)[y1,y2,x1,,xn2]=y2hD(y1)[x1,,xn]+hx1D(y1)[y2,x2,,xn]+ni=2(1)i1hxiD(y1)[y2,x1,,ˆxi,,xn]+y1hD(y2)[x1,,xn]hx1D(y2)[y1,x2,,xn]+ni=2(1)ihxiD(y2)[y1,x1,,ˆxi,,xn]y1D(x1)[hy2,x2,,xn]+y2D(x1)[hy1,x2,,xn]+ni=2(1)i1hxiD(x1)[y1,y2,x2,,ˆxi,,xn]+y1D(x2)[hy2,x1,x3,,xn]y2D(x2)[hy1,x1,x3,,xn]+hx1D(x2)[y1,y2,x3,,xn]+ni=3(1)ihxiD(x2)[y1,y2,x1,x3,,ˆxi,,xn]+(1)ny1D(xn)[hy2,x1,,xn1]+(1)n1y2D(xn)[hy1,x1,,xn1]+n1j=1(1)n+j1hxjD(xn)[y1,y2,x1,,ˆxj,,xn1]=A1+A2+ni=1Bi,

    where

    A1:=y2hD(y1)[x1,,xn]+ni=1(1)i1hxiD(y1)[y2,x1,,ˆxi,,xn],A2:=y1hD(y2)[x1,,xn]+ni=1(1)ihxiD(y2)[y1,x1,,ˆxi,,xn],

    and, for any 1in,

    Bi:=(1)iy1D(xi)[hy2,x1,,ˆxi,,xn]+(1)i1y2D(xi)[hy1,x1,,ˆxi,,xn]+i1j=1(1)i+j1hxjD(xi)[y1,y2,x1,,ˆxj,,ˆxi,,xn]+nj=i+1(1)i+jhxjD(xi)[y1,y2,x1,,ˆxi,,ˆxj,,xn].

    By Eq (2.3), we have

    A1=hD(y1)(y2[x1,,xn]+ni=1(1)i1xi[y2,x1,,ˆxi,,xn])=0.

    Similarly, we have that A2=0.

    By Eq (2.8), for any 1in, we have

    Bi=(1)iD(xi)(y1[hy2,x1,,ˆxi,,xn]y2[hy1,x1,,ˆxi,,xn]+i1j=1(1)j1hxj[y1,y2,x1,,ˆxj,,ˆxi,,xn]+nj=i+1(1)jhxj[y1,y2,x1,,ˆxi,,ˆxj,,xn])=0.

    Thus, we get

    y1[hy2,x1,,xn]y2[hy1,x1,,xn]+ni=1(1)i1hxi[y1,y2,x1,,ˆxi,,xn]=0.

    The proof is completed.

    Example 3.1. The commutative associative algebra L=k[x1,x2,x3], together with the bracket

    [x,y]:=xD1(y)yD1(x),x,yL.

    gives a transposed Poisson algebra (L,,[,]), where D1=x1 ([2, Proposition 2.2]). Note that the transposed Poisson algebra (L,,[,]) is strong according to Remark 2.5. Now, let D2=x2; one can check that D2 is a derivation of (L,,[,]). Then, there exists a strong transposed Poisson 3-Lie algebra defined by

    [x,y,z]:=D2(x)(yD1(z)zD1(y))+D2(y)(zD1(x)xD1(z))+D2(z)(xD1(y)yD1(x)), x,y,zL.

    We note that [x1,x2,x3]=x3, which is non-zero. The strong condition can be checked as follows:

    For any h,y1,y2,z1,z2L, by a direct calculation, we have

    y1[hy2,z1,z2]=y1z1hD1(z2)D2(y2)y1z2hD1(z1)D2(y2)+y1y2z1D1(z2)D2(h)y1y2z2D1(z1)D2(h)y1y2hD1(z2)D2(z1)+y1z2hD1(y2)D2(z1)+y1y2z2D1(h)D2(z1)+y1y2hD1(z1)D2(z2)y1z1hD1(y2)D2(z2)y1y2z1D1(h)D2(z2),
    y2[hy1,z1,z2]=y2z1hD1(z2)D2(y1)+y2z2hD1(z1)D2(y1)y1y2z1D1(z2)D2(h)+y1y2z2D1(z1)D2(h)+y1y2hD1(z2)D2(z1)y2z2hD1(y1)D2(z1)y1y2z2D1(h)D2(z1)y1y2hD1(z1)D2(z2)+y2z1hD1(y1)D2(z2)+y1y2z1D1(h)D2(z2),
    hz1[y1,y2,z2]=hy2z1D1(z2)D2(y1)hz1z2D1(y2)D2(y1)hy1z1D1(z2)D2(y2)+hz1z2D1(y1)D2(y2)+hy1z1D1(y2)D2(z2)hy2z1D1(y1)D2(z2),
    hz2[y1,y2,z1]=hy2z2D1(z1)D2(y1)+hz1z2D1(y2)D2(y1)+hy1z2D1(z1)D2(y2)hz1z2D1(y1)D2(y2)hy1z2D1(y2)D2(z1)+hy2z2D1(y1)D2(z1).

    Thus, we get

    y1[hy2,z1,z2]y2[hy1,z1,z2]+hz1[y1,y2,z2]hz2[y1,y2,z1]=0.

    We have studied transposed Poisson n-Lie algebras. We first established an important class of identities for transposed Poisson n-Lie algebras, which were subsequently used throughout the paper. We believe that the identities developed here will be useful in investigations of the structure of transposed Poisson n-Lie algebras in the future. Then, we introduced the notion of a strong transposed Poisson n-Lie algebra and derived an (n+1)-Lie algebra from a strong transposed Poisson n-Lie algebra with a derivation. Finally, we proved the conjecture of Bai et al. [2] for strong transposed Poisson n-Lie algebras.

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

    Ming Ding was supported by the Guangdong Basic and Applied Basic Research Foundation (2023A1515011739) and the Basic Research Joint Funding Project of University and Guangzhou City under grant number 202201020103.

    The authors declare that there is no conflict of interest.



    [1] I. A. Ahmad, M. Kayid, Characterizations of the RHR and MIT orderings and the DRHR and IMIT classes of life distributions, Prcbab. Eng. Inf. Sci., 19 (2005), 447–461. https://doi.org/10.1017/S026996480505028X doi: 10.1017/S026996480505028X
    [2] R. Ahmadi, Reliability and maintenance modeling for a load-sharing k-out-of-n system subject to hidden failures, Comput. Ind. Eng., 150 (2020), 106894. https://doi.org/10.1016/j.cie.2020.106894 doi: 10.1016/j.cie.2020.106894
    [3] H. Ahmed, M. Kayid, Preservation properties for the Laplace transform ordering of residual lives, Stat. Pap., 45 (2004), 583–590. https://doi.org/10.1007/BF02760570 doi: 10.1007/BF02760570
    [4] F. H. Al-Gashgari, A. I. Shawky, M. A. W. Mahmoud, A nonparametric test for testing exponentiality against NBUCA class of life distributions based on Laplace transform, Qual. Reliab. Eng. Int., 32 (2016), 29–36. https://doi.org/10.1002/qre.1723 doi: 10.1002/qre.1723
    [5] A. Alzaid, J. S. Kim, F. Proschan, Laplace ordering and its applications, J. Appl. Probab., 28 (1991), 116–130. https://doi.org/10.2307/3214745 doi: 10.2307/3214745
    [6] P. Andersen, O. Borgan, R. Gill, N. Keiding, Statistical models based on counting processes, Springer Series in Statistics, 1991. https://doi.org/10.1007/978-1-4612-4348-9
    [7] H. W. Block, T. H. Savits, Burn-in, Stat. Sci., 12 (1997), 1–19. https://doi.org/10.1214/ss/1029963258
    [8] F. Domma, Bivariate reversed hazard rate, notions, and measures of dependence and their relationships, Commun. Stat.-Theor. M., 40 (2011), 989–999. https://doi.org/10.1080/03610920903511777 doi: 10.1080/03610920903511777
    [9] A. Di Crescenzo, P. Di Gironimo, S. Kayal, Analysis of the past lifetime in a replacement model through stochastic comparisons and differential entropy, Mathematics, 8 (2020), 1203. https://doi.org/10.3390/math8081203 doi: 10.3390/math8081203
    [10] L. Eeckhoudt, C. Gollier, Demand for risky assets and the monotone probability ratio order, J. Risk Uncertain., 11 (1995), 113–122. https://doi.org/10.1007/BF01067680 doi: 10.1007/BF01067680
    [11] S. M. El-Arishy, L. S. Diab, E. S. El-Atfy, Characterizations on decreasing Laplace transform of time to failure class and hypotheses testing, Comput. Sci. Comput. Math., 10 (2020), 49–54. https://doi.org/10.20967/jcscm.2020.03.002 doi: 10.20967/jcscm.2020.03.002
    [12] M. Finkelstein, On the reversed hazard rate, Reliab. Eng. Syst. Saf., 78 (2002), 71–75. https://doi.org/10.1016/S0951-8320(02)00113-8
    [13] R. Gupta, A. K. Nanda, Some results on reversed hazard rate ordering, Commun. Stat.-Theor. M., 30 (2001), 2447–2457. https://doi.org10.1081/STA-100107697 doi: 10.1081/STA-100107697
    [14] Z. Guo, J. Zhang, R. Yan, On inactivity times of failed components of coherent system under double monitoring, Prcbab. Eng. Inf. Sci., 2021, 1–18. https://doi.org/10.1017/S0269964821000152
    [15] Y. Jia, J. H. Jeong, Cause-specific quantile regression on inactivity time, Stat. Med., 40 (2021), 1811–1824. https://doi.org/10.1002/sim.8871 doi: 10.1002/sim.8871
    [16] J. Jiang, Z. J. Zhou, X. X. Han, B. C. Zhang, X. D. Ling, A new BRB based method to establish hidden failure prognosis model by using life data and monitoring observation, Knowl. Based Syst., 67 (2014), 270–277. https://doi.org/10.1016/j.knosys.2014.04.045 doi: 10.1016/j.knosys.2014.04.045
    [17] S. Karlin, Total positivity, Stanford University Press, 1968.
    [18] M. Kayid, I. A. Ahmad, On the mean inactivity time ordering with reliability applications, Prcbab. Eng. Inf. Sci., 18 (2004), 395–409. https://doi.org/10.1017/S0269964804183071 doi: 10.1017/S0269964804183071
    [19] M. Kayid, S. Izadkhah, Mean inactivity time function, associated orderings, and classes of life distributions, IEEE Trans. Reliab., 63 (2014), 593–602. https://doi.org/10.1109/TR.2014.2315954 doi: 10.1109/TR.2014.2315954
    [20] M. Kayid, S. Izadkhah, S. Alshami, Laplace transform ordering of time to failure in age replacement models, J. Korean Stat. Soc., 45 (2016), 101–113. https://doi.org/10.1016/j.jkss.2015.08.001 doi: 10.1016/j.jkss.2015.08.001
    [21] N. Keiding, R. Gill, Random truncation models and Markov processes, Ann. Stat., 18 (1990), 582–602. https://doi.org/10.1214/aos/1176347617 doi: 10.1214/aos/1176347617
    [22] N. Keiding, Age-specific incidence and prevalence: A statistical perspective, J. R. Stat. Soc. A Stat., 154 (1991), 371–412. https://doi.org/10.2307/2983150 doi: 10.2307/2983150
    [23] M. Kijima, M. Ohnishi, Stochastic orders and their applications in financial optimization, Math. Methods Oper. Res., 50 (1999), 351–372. https://doi.org/10.1007/s001860050102 doi: 10.1007/s001860050102
    [24] C. Li, X. Li, On stochastic dependence in residual lifetime and inactivity time with some applications, Stat. Probab. Lett., 177 (2021), 109120. https://doi.org/10.1016/j.spl.2021.109120 doi: 10.1016/j.spl.2021.109120
    [25] J. Mulero, F. Pellerey, Bivariate aging properties under Archimedean dependence structures, Commun. Stat.-Theor. M., 39 (2010), 3108–3121. https://doi.org/10.1080/03610920903199987 doi: 10.1080/03610920903199987
    [26] A. K. Nanda, Stochastic orders in terms of Laplace transforms, Bull. Calcutta Stat. Assoc., 45 (1995), 195–202. https://doi.org/10.1177/0008068319950306 doi: 10.1177/0008068319950306
    [27] A. K. Nanda, H. Singh, N. Misra, P. Paul, Reliability properties of reversed residual lifetime, Commun. Stat.-Theor. M., 32 (2003), 2031–2042. https://doi.org/10.1081/STA-120023264 doi: 10.1081/STA-120023264
    [28] E. M. Ortega, A note on some functional relationships involving the mean inactivity time order, IEEE Trans. Reliab., 58 (2008), 172–178. https://doi.org/10.1109/TR.2008.2006576 doi: 10.1109/TR.2008.2006576
    [29] A. Patra, C. Kundu, Further results on residual life and inactivity time at random time, Commun. Stat.-Theor. M., 49 (2020), 1261–1271. https://doi.org/10.1080/03610926.2018.1563170 doi: 10.1080/03610926.2018.1563170
    [30] J. M. Ruiz, J. Navarro, Characterizations based on conditional expectations of the double truncated distribution, Ann. Inst. Stat. Math., 48 (1996), 563–572. https://doi.org/10.1007/BF00050855 doi: 10.1007/BF00050855
    [31] E. Salehi, M. Tavangar, Stochastic comparisons on conditional residual lifetime and inactivity time of coherent systems with exchangeable components, Stat. Probab. Lett., 145 (2019), 327–337. https://doi.org/10.1016/j.spl.2018.10.007 doi: 10.1016/j.spl.2018.10.007
    [32] M. Shaked, T. Wong, Stochastic orders based on ratios of Laplace transforms, J. Appl. Probab., 34 (1997), 404–419. https://doi.org/10.2307/3215380 doi: 10.2307/3215380
    [33] M. Shaked, J. G. Shanthikumar, Stochastic orders, Springer, New York, 2007. https://doi.org/10.1007/978-0-387-34675-5
    [34] T. Tang, D. Lin, D. Banjevic, A. K. Jardine, Availability of a system subject to hidden failure inspected at constant intervals with non-negligible downtime due to inspection and downtime due to repair/replacement, J. Stat. Plan. Infer., 143 (2013), 176–185. https://doi.org/10.1016/j.jspi.2012.05.011 doi: 10.1016/j.jspi.2012.05.011
    [35] C. Tepedelenlioglu, A. Rajan, Y. Zhang, Applications of stochastic ordering to wireless communications, IEEE Trans. Wirel. Commun., 10 (2011), 4249–4257. https://doi.org/10.1109/TWC.2011.093011.110187 doi: 10.1109/TWC.2011.093011.110187
    [36] Y. Wang, H. Pham, A multi-objective optimization of imperfect preventive maintenance policy for dependent competing risk systems with hidden failure, IEEE Trans. Reliab., 60 (2011), 770–781. https://doi.org/10.1109/TR.2011.2167779 doi: 10.1109/TR.2011.2167779
    [37] Y. Zhang, Z. Sun, R. Qin, H. Xiong, Idle duration prediction for manufacturing system using a gaussian mixture model integrated neural network for energy efficiency improvement, IEEE Trans. Autom. Sci. Eng., 18 (2019), 47–55. https://doi.org/10.1109/TASE.2019.2938662 doi: 10.1109/TASE.2019.2938662
  • This article has been cited by:

    1. Muhammad Sohail, Muhammad Hussain Ali, Kamaleldin Abodayeh, Syed Tehseen Abbas, Bio-convective boundary layer flow of Maxwell nanofluid via optimal homotopic procedure with radiation and Darcy-Forchheimer impacts over a stretched sheet, 2025, 46, 0143-0750, 10.1080/01430750.2025.2462583
  • Reader Comments
  • © 2022 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(1562) PDF downloads(60) Cited by(0)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog