Research article

The case for life expectancy at age 60 as a prominent health indicator. A comparative analysis

  • Received: 09 September 2022 Revised: 21 November 2022 Accepted: 27 November 2022 Published: 09 December 2022
  • JEL Codes: I10, I14, I15, I19, J10, J11, J14, J18, J19

  • The aim of this paper is to argue the case of using life expectancy at age 60 (LE60) as a significant health indicator closely related to sustainable economic development. To this purpose, we investigate the impact of GDP on LE60 in parallel with the impact of GDP on Infant Mortality Rate (IMR). The rationale for selecting IMR as a comparison indicator is twofold. First, the relationship between IMR and GDP has been widely studied. Second, the two indicators display opposite trajectories, making the comparison more striking. For our comparison, we conduct several statistical analyses on LE60, IMR and GDP using global country data grouped by income level and region. Our results endorse the effect of GDP on LE60 and IMR and suggest a differentiation of the effect based on region and ultimately on income. We observe that as countries develop, their IMR values lower and their LE60 values increase. We conclude that, once countries reach the upper stages of development, LE60 becomes a better health indicator than IMR.

    Citation: Iulia Toropoc. The case for life expectancy at age 60 as a prominent health indicator. A comparative analysis[J]. National Accounting Review, 2022, 4(4): 390-411. doi: 10.3934/NAR.2022022

    Related Papers:

    [1] Jehad Shaikhali, Gunnar Wingsle . Redox-regulated transcription in plants: Emerging concepts. AIMS Molecular Science, 2017, 4(3): 301-338. doi: 10.3934/molsci.2017.3.301
    [2] Amedea B. Seabra, Halley C. Oliveira . How nitric oxide donors can protect plants in a changing environment: what we know so far and perspectives. AIMS Molecular Science, 2016, 3(4): 692-718. doi: 10.3934/molsci.2016.4.692
    [3] Vittorio Emanuele Bianchi, Giancarlo Falcioni . Reactive oxygen species, health and longevity. AIMS Molecular Science, 2016, 3(4): 479-504. doi: 10.3934/molsci.2016.4.479
    [4] Luís J. del Valle, Lourdes Franco, Ramaz Katsarava, Jordi Puiggalí . Electrospun biodegradable polymers loaded with bactericide agents. AIMS Molecular Science, 2016, 3(1): 52-87. doi: 10.3934/molsci.2016.1.52
    [5] Isabella Martins Lourenço, Amedea Barozzi Seabra, Marcelo Lizama Vera, Nicolás Hoffmann, Olga Rubilar Araneda, Leonardo Bardehle Parra . Synthesis and application of zinc oxide nanoparticles in Pieris brassicae larvae as a possible pesticide effect. AIMS Molecular Science, 2024, 11(4): 351-366. doi: 10.3934/molsci.2024021
    [6] Vahid Pouresmaeil, Marwa Mawlood Salman Al-zand, Aida Pouresmaeil, Seyedeh Samira Saghravanian, Masoud Homayouni Tabrizi . Loading diltiazem onto surface-modified nanostructured lipid carriers to evaluate its apoptotic, cytotoxic, and inflammatory effects on human breast cancer cells. AIMS Molecular Science, 2024, 11(3): 231-250. doi: 10.3934/molsci.2024014
    [7] Giulia Ambrosi, Pamela Milani . Endoplasmic reticulum, oxidative stress and their complex crosstalk in neurodegeneration: proteostasis, signaling pathways and molecular chaperones. AIMS Molecular Science, 2017, 4(4): 424-444. doi: 10.3934/molsci.2017.4.424
    [8] Davide Lovisolo, Marianna Dionisi, Federico A. Ruffinatti, Carla Distasi . Nanoparticles and potential neurotoxicity: focus on molecular mechanisms. AIMS Molecular Science, 2018, 5(1): 1-13. doi: 10.3934/molsci.2018.1.1
    [9] Zhaoping Qin, Patrick Robichaud, Taihao Quan . Oxidative stress and CCN1 protein in human skin connective tissue aging. AIMS Molecular Science, 2016, 3(2): 269-279. doi: 10.3934/molsci.2016.2.269
    [10] Morgan Robinson, Brenda Yasie Lee, Zoya Leonenko . Drugs and drug delivery systems targeting amyloid-β in Alzheimer's disease. AIMS Molecular Science, 2015, 2(3): 332-358. doi: 10.3934/molsci.2015.3.332
  • The aim of this paper is to argue the case of using life expectancy at age 60 (LE60) as a significant health indicator closely related to sustainable economic development. To this purpose, we investigate the impact of GDP on LE60 in parallel with the impact of GDP on Infant Mortality Rate (IMR). The rationale for selecting IMR as a comparison indicator is twofold. First, the relationship between IMR and GDP has been widely studied. Second, the two indicators display opposite trajectories, making the comparison more striking. For our comparison, we conduct several statistical analyses on LE60, IMR and GDP using global country data grouped by income level and region. Our results endorse the effect of GDP on LE60 and IMR and suggest a differentiation of the effect based on region and ultimately on income. We observe that as countries develop, their IMR values lower and their LE60 values increase. We conclude that, once countries reach the upper stages of development, LE60 becomes a better health indicator than IMR.



    Nonlinear partial differential equation is a very important branch of the nonlinear science, which has been called the foreword and hot topic of current scientific development. In theoretical science and practical application, nonlinear partial differential is used to describe the problems in the fields of optics, mechanics, communication, control science and biology [1,2,3,4,5,6,7,8,9]. At present, the main problems in the study of nonlinear partial differential equations are the existence of solutions, the stability of solutions, numerical solutions and exact solutions. With the development of research, especially the study of exact solutions of nonlinear partial differential equations has important theoretical value and application value. In the last half century, many important methods for constructing exact solutions of nonlinear partial differential equations have been proposed, such as the planar dynamic system method [10], the Jacobi elliptic function method [11], the bilinear transformation method [12], the complete discriminant system method for polynomials [13], the unified Riccati equation method [14], the generalized Kudryashov method [15], and so on [16,17,18,19,20,21,22,23,24].

    There is no unified method to obtain the exact solution of nonlinear partial differential equations. Although predecessors have obtained some analytical solutions with different methods, no scholar has studied the system with complete discrimination system for polynomial method.

    The Fokas system is a very important class of nonlinear partial differential equations. In this article, we focus on the Fokas system, which is given as follows [25,26,27,28,29,30,31,32,33,34,35,36,37]

    {ipt+r1pxx+r2pq=0,r3qyr4(|p|2)x=0, (1.1)

    where p=p(x,y,t) and q=q(x,y,t) are the complex functions which stand for the nonlinear pulse propagation in monomode optical fibers. The parameters r1,r2,r3 and r4 are arbitrary non-zero constants, which are coefficients of nonlinear terms in Eq (1.1) and reflect different states of optical solitons.

    This paper is arranged as follows. In Section 2, we describe the method of the complete discrimination system for polynomial method. In Section 3, we substitute traveling wave transformation into nonlinear ordinary differential equations and obtain the different new single traveling wave solutions for the Fokas system by complete discrimination system for polynomial method. At the same time, we draw some images of solutions. In Section 4, the main results are summarized.

    First, we consider the following partial differential equations:

    {F(u,v,ux,ut,vx,vt,uxx,uxt,utt,)=0G(u,v,ux,ut,vx,vt,uxx,uxt,utt,)=0 (2.1)

    where F and G is polynomial function which is about the partial derivatives of each order of u(x,t) and v(x,t) with respect to x and t.

    Step 1: Taking the traveling wave transformation u(x,t)=u(ξ),v(x,t)=v(ξ),ξ=kx+ct into Eq (2.1), then the partial differential equation is converted to an ordinary differential equation

    {F(u,v,u,v,u,v,)=0,G(u,v,u,v,u,v,)=0. (2.2)

    Step 2: The above nonlinear ordinary differential equations (2.2) are reduced to the following ordinary differential form after a series of transformations:

    (u)2=u3+d2u2+d1u+d0. (2.3)

    The Eq (2.3) can also be written in integral form as:

    ±(ξξ0)=duu3+d2u2+d1u+d0. (2.4)

    Step 3: Let ϕ(u)=u3+d2u2+d1u+d0. According to the complete discriminant system method of third-order polynomial

    {Δ=27(2d3227+d0d1d23)24(d1d223)3,D1=d1d223, (2.5)

    the classification of the solution of the equation can be obtained, and the classification of traveling wave solution of the Fokas system will be given in the following section.

    In the current part, we obtain all exact solutions to Eq (1.1) by complete discrimination system for polynomial method. According to the wave transformation

    p(x,y,t)=φ(η)ei(λ1x+λ2y+λ3t+λ4),q(x,y,t)=ϕ(η),η=x+yvt, (3.1)

    where λ1,λ2,λ3,λ4 and v are real parameters, and v represents the wave frame speed.

    Substituting the above transformation Eq (3.1) into Eq (1.1), we get

    {(v+2r1λ1)iφλ3φ+r1φr1λ21φ+r2φϕ=0,r3ϕ2r4φφ=0. (3.2)

    Integrating the second equation in (3.2) and ignoring the integral constant, we get

    ϕ(η)=r4φ2(η)r3. (3.3)

    Substituting Eq (3.3) into the first equation in (3.2) and setting v=2r1λ1, we get the following:

    r1φ(λ3+r1λ21)φ+r2r4φ3r3=0. (3.4)

    Multiplying φ both sides of the Eq (3.4), then integrating once to get

    (φ)2=a4φ4+a2φ2+a0, (3.5)

    where a4=r2r42r1r3,a2=λ3+r1λ21r1, a0 is the arbitrary constant.

    Let  φ=±(4a4)13ω, b1=4a2(4a4)23,b0=4a0(4a4)13,η1=(4a4)13η. (3.6)

    Equation (3.5) can be expressed as the following:

    (ωη1)2=ω3+b1ω2+b0ω. (3.7)

    Then we can get the integral expression of Eq (3.7)

    ±(η1η0)=dωω(ω2+b1ω+b0), (3.8)

    where η0 is the constant of integration.

    Here, we get the F(ω)=ω2+b1ω+b0 and Δ=b214b0. In order to solve Eq (3.7), we discuss the third order polynomial discrimination system in four cases.

    Case 1:Δ=0 and ω>0.

    When b1<0, the solution of Eq (3.7) is

    ω1=b12tanh2(12b12(η1η0)). (3.9)
    ω2=b12coth2(12b12(η1η0)). (3.10)

    Thus, the classification of all solutions of Eq (3.7) is obtained by the third order polynomial discrimination system. The exact traveling wave solutions of the Eq (1.1) are obtained by combining the above solutions and the conditions (3.6) with Eq (3.1), can be expressed as below:

    p1(x,y,t)=±r3(λ3+r1λ21)r2r4tanh(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0))ei(λ1x+λ2y+λ3t+λ4). (3.11)

    In Eq (3.11), p1(x,y,t) is a dark soliton solution, it expresses the energy depression on a certain intensity background. Figure 1 depict two-dimensional graph, three-dimensional graph, contour plot and density plot of the solution.

    q1(x,y,t)=λ3+r1λ21r2tanh2(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0)) (3.12)
    p2(x,y,t)=±r3(λ3+r1λ21)r2r4coth(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0))ei(λ1x+λ2y+λ3t+λ4), (3.13)
    Figure 1.  Module length graphs of Eq (3.12) when r1=2,r2=1,r3=1,r4=1,λ1=1,λ3=3,η0=0.

    where p1(x,y,t),q1(x,y,t),p2(x,y,t),q2(x,y,t) are hyperbolic function solutions. Specially, p2(x,y,t) is a bright soliton solution.

    q2(x,y,t)=λ3+r1λ21r2coth2(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0)). (3.14)

    When b1>0, the solution of Eq (3.7) is

    ω3=b12tan2(12b12(η1η0)). (3.15)

    The exact traveling wave solutions of the Eq (1.1) can be expressed as below:

    p3(x,y,t)=±r3(λ3+r1λ21)r2r4tan(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0))ei(λ1x+λ2y+λ3t+λ4). (3.16)
    q3(x,y,t)=λ3+r1λ21r2tan2(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0)). (3.17)

    In Eq (3.16) and Eq (3.17), p3(x,y,t) and q3(x,y,t) are trigonometric function solutions. q3(x,y,t) is a periodic wave solution, and it Shows the periodicity of q3(x,y,t) in Figure 2(a), (b).

    Figure 2.  Module length graphs of Eq (3.17) when r1=2,r2=1,r3=1,r4=1,λ1=1,λ3=1,η0=0.

    When b1=0, the solution of Eq (3.7) is

    ω4=4(η1η0)2. (3.18)

    The exact traveling wave solutions of the Eq (1.1) can be expressed as below:

    p4(x,y,t)=±(2r2r4r1r3)132(2r2r4r1r3)13η+η0ei(λ1x+λ2y+λ3t+λ4), (3.19)
    q4(x,y,t)=r4r3(2r2r4r1r3)134((2r2r4r1r3)13η+η0)2, (3.20)

    where p4(x,y,t) is exponential function solution, and q4(x,y,t) is rational function solution.

    Case 2: Δ=0 and b0=0.

    When ω>b1 and b1<0, the solution of Eq (3.7) is

    ω5=b12tanh2(12b12(η1η0))b1. (3.21)
    ω6=b12coth2(12b12(η1η0))b1. (3.22)

    The exact traveling wave solutions of the Eq (1.1) can be expressed as below:

    p5(x,y,t)=±r3(λ3+r1λ21)r2r4(tanh2(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0))2)ei(λ1x+λ2y+λ3t+λ4), (3.23)
    q5(x,y,t)=λ3+r1λ21r2tanh2(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0))+2(λ3+r1λ21)r2, (3.24)
    p6(x,y,t)=±r3(λ3+r1λ21)r2r4(coth2(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0))2)ei(λ1x+λ2y+λ3t+λ4), (3.25)
    q6(x,y,t)=λ3+r1λ21r2coth2(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0))+2(λ3+r1λ21)r2, (3.26)

    where p5(x,y,t),q5(x,y,t),p6(x,y,t) and q6(x,y,t) are hyperbolic function solutions.

    When ω>b1 and b1>0, the solution of Eq (3.7) is

    ω7=b12tan2(12b12(η1η0))b1. (3.27)

    The exact traveling wave solutions of the Eq (1.1) can be expressed as below:

    p7(x,y,t)=±r3(λ3+r1λ21)r2r4(tan2(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0))+2)ei(λ1x+λ2y+λ3t+λ4), (3.28)
    q7(x,y,t)=λ3+r1λ21r2tan2(122(λ3+r1λ21)r1(2r2r4r1r3)23((2r2r4r1r3)13η+η0))+2(λ3+r1λ21)r2, (3.29)

    where p7(x,y,t) and q7(x,y,t) are trigonometric function solutions.

    Case 3: Δ>0 and b00. Let u<v<s, there u,v and s are constants satisfying one of them is zero and two others are the root of F(ω)=0.

    When u<ω<v, we can get the solution of Eq (3.7) is

    ω8=u+(vu)sn2(su2(η1η0),c), (3.30)

    where c2=vusu.

    The exact traveling wave solutions of the Eq (1.1) can be expressed as below:

    p8(x,y,t)=±(2r2r4r1r3)13[u+(vu)sn2(su2((2r2r4r1r3)13η+η0),c)]ei(λ1x+λ2y+λ3t+λ4). (3.31)
    q8(x,y,t)=r4r3(2r2r4r1r3)13[u+(vu)sn2(su2((2r2r4r1r3)13η+η0),c)]. (3.32)

    When ω>s, the solution of Eq (3.7) is

    ω9=vsn2(su(η1η0)/2,c)+scn2(su(η1η0)/2,c). (3.33)

    The exact traveling wave solutions of the Eq (1.1) can be expressed as below:

    p9(x,y,t)=±(2r2r4r1r3)13vsn2(su2((2r2r4r1r3)13η+η0),c)]+scn2(su2((2r2r4r1r3)13η+η0),c)ei(λ1x+λ2y+λ3t+λ4). (3.34)
    q9(x,y,t)=r4r3(2r2r4r1r3)13vsn2(su2((2r2r4r1r3)13η+η0),c)]+scn2(su2((2r2r4r1r3)13η+η0),c). (3.35)

    Case 4: Δ<0.

    When ω>0, similarly we get

    ω10=2b01+cn(b140(η1η0),c)b0, (3.36)

    where c2=(1b1b02)/2.

    The exact traveling wave solutions of the Eq (1.1) can be expressed as below:

    p10(x,y,t)=±2a0(2r2r4r1r3)12[21+cn((4a0(2r2r4r1r3)13)14((2r2r4r1r3)13η+η0),c)+1]ei(λ1x+λ2y+λ3t+λ4), (3.37)
    q10(x,y,t)=r4r32a0(2r2r4r1r3)12[21+cn((4a0(2r2r4r1r3)13)14((2r2r4r1r3)13η+η0),c)+1], (3.38)

    where p8(x,y,t),q8(x,y,t),p9(x,y,t),q9(x,y,t),p10(x,y,t) and q10(x,y,t) are Jacobian elliptic function solutions.

    In this paper, the complete discrimination system of polynomial method has been applied to construct the single traveling wave solutions of the Fokas system. The Jacobian elliptic function solutions, the trigonometric function solutions, the hyperbolic function solutions and the rational function solutions are obtained. The obtained solutions are very rich, which can help physicists understand the propagation of traveling wave in monomode optical fibers. Furthermore, we have also depicted two-dimensional graphs, three-dimensional graphs, contour plots and density plots of the solutions of Fokas system, which explains the state of solitons from different angles.

    This work was supported by Scientific Research Funds of Chengdu University (Grant No.2081920034).

    The authors declare no conflict of interest.



    [1] Andrasfay T, Goldman N (2021) Reductions in 2020 US life expectancy due to COVID-19 and the disproportionate impact on the Black and Latino populations. Proc Natl Acad Sci USA 118: 1–6. https://doi.org/10.1101/2020.07.12.20148387 doi: 10.1101/2020.07.12.20148387
    [2] Baird S, Friedman J, Schady N (2007) Aggregate income shocks and infant mortality rate in the developing world. Available from: https://openknowledge.worldbank.org/handle/10986/7627.
    [3] Bloom DE, Canning D (2009) Population Health and Economic Growth. Available from: https://openknowledge.worldbank.org/handle/10986/28036.
    [4] Bloom DE, Canning D, Sevilla J (2004) The Effect of Health on Economic Growth: A Production Function Approach. World Dev 32: 1–13. https://doi.org/10.1016/j.worlddev.2003.07.002 doi: 10.1016/j.worlddev.2003.07.002
    [5] Cairns AJG, Blake DP, Dowd K (2006) A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. J Risk Insur 73: 687–718. https://doi.org/10.1111/j.1539-6975.2006.00195.x doi: 10.1111/j.1539-6975.2006.00195.x
    [6] Centers for Disease and Control Prevention (2018) The 1918 Flu Pandemic: Why It Matters 100 Years Later. Available from: https://blogs.cdc.gov/publichealthmatters/2018/05/1918-flu/.
    [7] Coughlin JF (2017) The longevity economy: Unlocking the world's fastest-growing, most misunderstood market. Public Affairs.
    [8] Erdogan E, Ener M, Arica F (2013) The Strategic Role of Infant Mortality in the Process of Economic Growth: An Application for High Income OECD Countries. Procedia Soc Behav Sci 99: 19–25. https://doi.org/10.1016/j.sbspro.2013.10.467 doi: 10.1016/j.sbspro.2013.10.467
    [9] Farahani M, Subramanian SV, Canning D (2009) The effect of changes in health sector resources on infant mortality in the short-run and the long-run: A longitudinal econometric analysis. Soc Sci Med 68: 1918–1925. https://doi.org/10.1016/j.socscimed.2009.03.023 doi: 10.1016/j.socscimed.2009.03.023
    [10] Filmer D, Pritchett L (1999) The impact of public spending on health: does money matter? Soc Sci Med 49: 1309–1323. https://doi.org/10.1016/S0277-9536(99)00150-1 doi: 10.1016/S0277-9536(99)00150-1
    [11] Global Data (2021) Fragile States Index 2021. Available from: https://fragilestatesindex.org/global-data/.
    [12] World Bank (2021) Current health expenditure (% of GDP). Available from: https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS.
    [13] Harper S (2014) Economic and social implications of aging societies. Science 346: 587–591. https://doi.org/10.1126/science.1254405 doi: 10.1126/science.1254405
    [14] Hiam L, Harrison D, McKee M, et al. (2018) Why is life expectancy in England and Wales 'stalling'? J Epidemiol Community Health 72: 404–408. https://doi.org/10.1136/jech-2017-210401 doi: 10.1136/jech-2017-210401
    [15] Jaba E, Balan CB, Robu IB (2014) The Relationship between Life Expectancy at Birth and Health Expenditures Estimated by a Cross-country and Time-series Analysis. Procedia Econ Financ 15: 108–114. https://doi.org/10.1016/S2212-5671(14)00454-7 doi: 10.1016/S2212-5671(14)00454-7
    [16] Li JSH, Liu Y (2021) Recent declines in life expectancy: Implication on longevity risk hedging. Insur Math Econ 99: 376–394. https://doi.org/10.1016/j.insmatheco.2021.03.028 doi: 10.1016/j.insmatheco.2021.03.028
    [17] Novignon J, Olakojo SA, Nonvignon J (2012) The effects of public and private health care expenditure on health status in sub-Saharan Africa: new evidence from panel data analysis. Health Econ Rev 22. https://doi.org/10.1186/2191-1991-2-22 doi: 10.1186/2191-1991-2-22
    [18] O'Hare B, Makuta I, Chiwaula L, et al. (2013) Income and child mortality in developing countries: a systematic review and meta-analysis. J R Soc Med 106: 408–414. https://doi.org/10.1177/0141076813489680 doi: 10.1177/0141076813489680
    [19] Olshansky SJ, Passaro DJ, Hershow RC, et al. (2005) A Potential Decline in Life Expectancy in the United States in the 21st Century. N Engl J Med 352: 1138–1145. https://doi.org/10.1056/NEJMsr043743 doi: 10.1056/NEJMsr043743
    [20] Owusu PA, Sarkodie SA, Pedersen PA (2021) Relationship between mortality and health care expenditure: Sustainable assessment of health care system. PLOS ONE https://doi.org/10.1371/journal.pone.0247413. doi: 10.1371/journal.pone.0247413
    [21] Preston SH (1975) The Changing Relation between Mortality and Level of Economic Development. Popul Stud 29: 231–248. https://doi.org/10.2307/2173509 doi: 10.2307/2173509
    [22] Pritchett L, Summers LH (1996) Wealthier Is Healthier. J Hum Resour 31: 841–868.
    [23] Rau R, Bohk-Ewald C, Muszynska MM, et al. (2018) Visualizing mortality dynamics in the Lexis Diagram, In: Lynch SM, The Springer Series on Demographic Methods and Population Analysis, Springer Cham, 44. https://doi.org/10.1007/978-3-319-64820-0
    [24] Schell CO, Reilly M, Rosling H, et al. (2007) Socioeconomic determinants of infant mortality: A worldwide study of 152 low-, middle- and high-income countries. Scand J Public Health 35: 288–297. https://doi.org/10.1080/14034940600979171 doi: 10.1080/14034940600979171
    [25] Sommer JM (2019) Corruption and Health expenditure: A Cross-National Analysis on Infant and Child Mortality. Eur J Dev Res 32: 690–717. https://doi.org/10.1057/s41287-019-00235-1 doi: 10.1057/s41287-019-00235-1
    [26] Transparency International (2021) Corruption Perceptions Index. Available from: https://www.transparency.org/en/cpi/2020/index/.
    [27] Vaupel JW, Villavicencio F, Bergeron-Boucher MP (2021) Demographic perspectives on the rise of longevity. Proc Natl Acad Sci USA 118. https://doi.org/10.1073/pnas.2019536118 doi: 10.1073/pnas.2019536118
    [28] Vaupel JW (2010) Biodemography of human ageing. Nature 464: 536–542. https://doi.org/10.1038/nature08984 doi: 10.1038/nature08984
    [29] Ward JL, Viner RM (2020) The impact of income inequality and national wealth on child and adolescent mortality in low and middle-income countries. BMC Public Health 17: 429. https://doi.org/10.1186/s12889-017-4310-z doi: 10.1186/s12889-017-4310-z
  • This article has been cited by:

    1. Andrew Geoly, Ernest Greene, Masking the Integration of Complementary Shape Cues, 2019, 13, 1662-453X, 10.3389/fnins.2019.00178
    2. Ernest Greene, Comparing methods for scaling shape similarity, 2019, 6, 2373-7972, 54, 10.3934/Neuroscience.2019.2.54
    3. Hannah Nordberg, Michael J Hautus, Ernest Greene, Visual encoding of partial unknown shape boundaries, 2018, 5, 2373-7972, 132, 10.3934/Neuroscience.2018.2.132
    4. Ernest Greene, Hautus Michael J, Evaluating persistence of shape information using a matching protocol, 2018, 5, 2373-7972, 81, 10.3934/Neuroscience.2018.1.81
    5. Ernest Greene, Jack Morrison, Computational Scaling of Shape Similarity That has Potential for Neuromorphic Implementation, 2018, 6, 2169-3536, 38294, 10.1109/ACCESS.2018.2853656
    6. Ernest Greene, New encoding concepts for shape recognition are needed, 2018, 5, 2373-7972, 162, 10.3934/Neuroscience.2018.3.162
    7. Cheng Chen, Kang Jiao, Letao Ling, Zhenhua Wang, Yuan Liu, Jie Zheng, 2023, Chapter 47, 978-981-19-3631-9, 382, 10.1007/978-981-19-3632-6_47
    8. Bridget A. Kelly, Charles Kemp, Daniel R. Little, Duane Hamacher, Simon J. Cropper, Visual Perception Principles in Constellation Creation, 2024, 1756-8757, 10.1111/tops.12720
  • 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(1580) PDF downloads(46) Cited by(1)

Figures and Tables

Figures(7)  /  Tables(21)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog