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17 pages, 293 KiB  
Article
Lie Symmetry Analysis, Closed-Form Solutions, and Conservation Laws for the Camassa–Holm Type Equation
by Jonathan Lebogang Bodibe and Chaudry Masood Khalique
Math. Comput. Appl. 2024, 29(5), 92; https://doi.org/10.3390/mca29050092 - 10 Oct 2024
Viewed by 568
Abstract
In this paper, we study the Camassa–Holm type equation, which has applications in mathematical physics and engineering. Its applications extend across disciplines, contributing to our understanding of complex systems and helping to develop innovative solutions in diverse areas of research. Our main aim [...] Read more.
In this paper, we study the Camassa–Holm type equation, which has applications in mathematical physics and engineering. Its applications extend across disciplines, contributing to our understanding of complex systems and helping to develop innovative solutions in diverse areas of research. Our main aim is to construct closed-form solutions of the equation using a powerful technique, namely the Lie group analysis method. Firstly, we derive the Lie point symmetries of the equation. Thereafter, the equation is reduced to non-linear ordinary differential equations using symmetry reductions. Furthermore, the solutions of the equation are derived using the extended Jacobi elliptic function technique, the simplest equation method, and the power series method. In conclusion, we construct conservation laws for the equation using Noether’s theorem and the multiplier approach, which plays a crucial role in understanding the behavior of non-linear equations, especially in physics and engineering, and these laws are derived from fundamental principles such as the conservation of mass, energy, momentum, and angular momentum. Full article
(This article belongs to the Special Issue Symmetry Methods for Solving Differential Equations)
27 pages, 401 KiB  
Review
A Geometric Approach to the Sundman Transformation and Its Applications to Integrability
by José F. Cariñena
Symmetry 2024, 16(5), 568; https://doi.org/10.3390/sym16050568 - 6 May 2024
Cited by 1 | Viewed by 1569
Abstract
A geometric approach to the integrability and reduction of dynamical systems, both when dealing with systems of differential equations and in classical physics, is developed from a modern perspective. The main ingredients of this analysis are infinitesimal symmetries and tensor fields that are [...] Read more.
A geometric approach to the integrability and reduction of dynamical systems, both when dealing with systems of differential equations and in classical physics, is developed from a modern perspective. The main ingredients of this analysis are infinitesimal symmetries and tensor fields that are invariant under the given dynamics. A particular emphasis is placed on the existence of alternative invariant volume forms and the associated Jacobi multiplier theory, and then the Hojman symmetry theory is developed as a complement to the Noether theorem and non-Noether constants of motion. We also recall the geometric approach to Sundman infinitesimal time-reparametrisation for autonomous systems of first-order differential equations and some of its applications to integrability, and an analysis of how to define Sundman transformations for autonomous systems of second-order differential equations is proposed, which shows the necessity of considering alternative tangent bundle structures. A short description of alternative tangent structures is provided, and an application to integrability, namely, the linearisability of scalar second-order differential equations under generalised Sundman transformations, is developed. Full article
13 pages, 295 KiB  
Article
Symmetries of Systems with the Same Jacobi Multiplier
by Gabriel González Contreras and Alexander Yakhno
Symmetry 2023, 15(7), 1416; https://doi.org/10.3390/sym15071416 - 14 Jul 2023
Cited by 1 | Viewed by 1068
Abstract
The concept of the Jacobi multiplier for ordinary differential equations up to the second order is reviewed and its connection with classical methods of canonical variables and differential invariants is established. We express, for equations of the second order, the Jacobi multiplier in [...] Read more.
The concept of the Jacobi multiplier for ordinary differential equations up to the second order is reviewed and its connection with classical methods of canonical variables and differential invariants is established. We express, for equations of the second order, the Jacobi multiplier in terms of integrating factors for reduced equations of the first order. We also investigate, from a symmetry point of view, how two different systems with the same Jacobi multiplier are interrelated. As a result, we determine the conditions when such systems admit the same two-dimensional Lie algebra of symmetries. Several illustrative examples are given. Full article
(This article belongs to the Section Mathematics)
13 pages, 304 KiB  
Article
A Nonconstant Gradient Constrained Problem for Nonlinear Monotone Operators
by Sofia Giuffrè
Axioms 2023, 12(6), 605; https://doi.org/10.3390/axioms12060605 - 18 Jun 2023
Viewed by 1106
Abstract
The purpose of the research is the study of a nonconstant gradient constrained problem for nonlinear monotone operators. In particular, we study a stationary variational inequality, defined by a strongly monotone operator, in a convex set of gradient-type constraints. We investigate the relationship [...] Read more.
The purpose of the research is the study of a nonconstant gradient constrained problem for nonlinear monotone operators. In particular, we study a stationary variational inequality, defined by a strongly monotone operator, in a convex set of gradient-type constraints. We investigate the relationship between the nonconstant gradient constrained problem and a suitable double obstacle problem, where the obstacles are the viscosity solutions to a Hamilton–Jacobi equation, and we show the equivalence between the two variational problems. To obtain the equivalence, we prove that a suitable constraint qualification condition, Assumption S, is fulfilled at the solution of the double obstacle problem. It allows us to apply a strong duality theory, holding under Assumption S. Then, we also provide the proof of existence of Lagrange multipliers. The elements in question can be not only functions in L2, but also measures. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
19 pages, 359 KiB  
Review
Some Applications of Affine in Velocities Lagrangians in Two-Dimensional Systems
by José F. Cariñena and José Fernández-Núñez
Symmetry 2022, 14(12), 2520; https://doi.org/10.3390/sym14122520 - 29 Nov 2022
Cited by 2 | Viewed by 1124
Abstract
The two-dimensional inverse problem for first-order systems is analysed and a method to construct an affine Lagrangian for such systems is developed. The determination of such Lagrangians is based on the theory of the Jacobi multiplier for the system of differential equations. We [...] Read more.
The two-dimensional inverse problem for first-order systems is analysed and a method to construct an affine Lagrangian for such systems is developed. The determination of such Lagrangians is based on the theory of the Jacobi multiplier for the system of differential equations. We illustrate our analysis with several examples of families of forces that are relevant in mechanics, on one side, and of some relevant biological systems, on the other. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry: Feature Review Papers)
13 pages, 361 KiB  
Article
Collocation Method for Optimal Control of a Fractional Distributed System
by Wen Cao and Yufeng Xu
Fractal Fract. 2022, 6(10), 594; https://doi.org/10.3390/fractalfract6100594 - 14 Oct 2022
Cited by 1 | Viewed by 1257
Abstract
In this paper, a collocation method based on the Jacobi polynomial is proposed for a class of optimal-control problems of a fractional distributed system. By using the Lagrange multiplier technique and fractional variational principle, the stated problem is reduced to a system of [...] Read more.
In this paper, a collocation method based on the Jacobi polynomial is proposed for a class of optimal-control problems of a fractional distributed system. By using the Lagrange multiplier technique and fractional variational principle, the stated problem is reduced to a system of fractional partial differential equations about control and state functions. The uniqueness of this fractional coupled system is discussed. For spatial second-order derivatives, the proposed method takes advantage of Jacobi polynomials with different parameters to approximate solutions. For a temporal fractional derivative in the Caputo sense, choosing appropriate basis functions allows the collocation method to be implemented easily and efficiently. Exponential convergence is verified numerically under continuous initial conditions. As a particular example, the relation between the state function and the order of the fractional derivative is analyzed with a discontinuous initial condition. Moreover, the numerical results show that the integration of the state function will decay as the order of the fractional derivative decreases. Full article
(This article belongs to the Special Issue Advances in Fractional Order Derivatives and Their Applications)
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Figure 1

Figure 1
<p>Numerical and analytical solution of system (<a href="#FD21-fractalfract-06-00594" class="html-disp-formula">21</a>) and (22) (see (<b>a</b>,<b>b</b>)); the relation between <span class="html-italic">N</span> and error (E) with <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>β</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> (see (<b>c</b>,<b>d</b>)).</p>
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<p>The state function at different moments with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>β</mi> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics></math> (see (<b>a</b>)) and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>β</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (see (<b>b</b>–<b>d</b>)).</p>
Full article ">Figure 2 Cont.
<p>The state function at different moments with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>β</mi> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics></math> (see (<b>a</b>)) and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>β</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (see (<b>b</b>–<b>d</b>)).</p>
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20 pages, 1264 KiB  
Article
Jaynes-Gibbs Entropic Convex Duals and Orthogonal Polynomials
by Richard Le Blanc
Entropy 2022, 24(5), 709; https://doi.org/10.3390/e24050709 - 16 May 2022
Cited by 1 | Viewed by 5887
Abstract
The univariate noncentral distributions can be derived by multiplying their central distributions with translation factors. When constructed in terms of translated uniform distributions on unit radius hyperspheres, these translation factors become generating functions for classical families of orthogonal polynomials. The ultraspherical noncentral t [...] Read more.
The univariate noncentral distributions can be derived by multiplying their central distributions with translation factors. When constructed in terms of translated uniform distributions on unit radius hyperspheres, these translation factors become generating functions for classical families of orthogonal polynomials. The ultraspherical noncentral t, normal N, F, and χ2 distributions are thus found to be associated with the Gegenbauer, Hermite, Jacobi, and Laguerre polynomial families, respectively, with the corresponding central distributions standing for the polynomial family-defining weights. Obtained through an unconstrained minimization of the Gibbs potential, Jaynes’ maximal entropy priors are formally expressed in terms of the empirical densities’ entropic convex duals. Expanding these duals on orthogonal polynomial bases allows for the expedient determination of the Jaynes–Gibbs priors. Invoking the moment problem and the duality principle, modelization can be reduced to the direct determination of the prior moments in parametric space in terms of the Bayes factor’s orthogonal polynomial expansion coefficients in random variable space. Genomics and geophysics examples are provided. Full article
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Figure 1

Figure 1
<p>Symmetrized Kullback–Leibler divergence (<b>a</b>) between the hyperspherical <math display="inline"><semantics> <mrow> <msubsup> <mi>υ</mi> <mi>t</mi> <mrow> <mo>(</mo> <msub> <mi>ν</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>θ</mi> <mo>|</mo> <mi>δ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and the classical <math display="inline"><semantics> <mrow> <msubsup> <mi>ρ</mi> <mi>t</mi> <mrow> <mo>(</mo> <msub> <mi>ν</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>θ</mi> <mo>|</mo> <mi>δ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> noncentral <span class="html-italic">t</span>—distributions in the upper-left-corner plot, and (<b>b</b>–<b>f</b>) between the hyperspherical <math display="inline"><semantics> <mrow> <msubsup> <mi>υ</mi> <mi>F</mi> <mrow> <mo>(</mo> <msub> <mi>ν</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>ν</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>θ</mi> <mo>|</mo> <mi mathvariant="sans-serif">Λ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and the classical <math display="inline"><semantics> <mrow> <msubsup> <mi>ρ</mi> <mi>F</mi> <mrow> <mo>(</mo> <msub> <mi>ν</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>ν</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>θ</mi> <mo>|</mo> <mi mathvariant="sans-serif">Λ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> noncentral <span class="html-italic">F</span> distributions for <math display="inline"><semantics> <mrow> <msub> <mi>ν</mi> <mn>1</mn> </msub> <mo>=</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>, respectively, for all the other plots. The ultraspherical and classical noncentral <span class="html-italic">t</span> and <span class="html-italic">F</span> distributions correspond to projections of translated uniform distributions on unit radius hyperspheres and translated normal distributions, respectively; are identical in their central distributions when their noncentrality parameters <math display="inline"><semantics> <mi>δ</mi> </semantics></math> or <math display="inline"><semantics> <mi mathvariant="sans-serif">Λ</mi> </semantics></math> vanish; converge in high-dimensional (large degree-of-freedom <math display="inline"><semantics> <msub> <mi>ν</mi> <mn>2</mn> </msub> </semantics></math>) spaces, but diverge in low-dimension spaces and for large noncentrality <math display="inline"><semantics> <mi>δ</mi> </semantics></math> or <math display="inline"><semantics> <mi mathvariant="sans-serif">Λ</mi> </semantics></math> parameters. The ultraspherical noncentral distributions can be used as surrogates for the classical noncentral <span class="html-italic">t</span> and <span class="html-italic">F</span> distributions in high-dimensional spaces. See the text for details.</p>
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<p>Empirical random space densities and NHST <span class="html-italic">p</span>-value densities modelization for the head and neck cancer dataset. The upper panels illustrate the Jaynes–Gibbs model <math display="inline"><semantics> <mrow> <mi>υ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>w</mi> <mi>t</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>∫</mo> <mi>T</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>|</mo> <mi>z</mi> <mo>)</mo> </mrow> <mi>π</mi> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> <mi mathvariant="normal">d</mi> <mi>z</mi> </mrow> </semantics></math>, as provided by the Gibbs prior (<a href="#FD54-entropy-24-00709" class="html-disp-formula">54</a>) and the analytical generating function (<a href="#FD11-entropy-24-00709" class="html-disp-formula">11</a>) for the Gegenbauer polynomials. (<b>a</b>) Upper left-hand panel: convergence of the Jaynes–Gibbs model with the empirical density <math display="inline"><semantics> <mrow> <mi>υ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>w</mi> <mi>t</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mspace width="4pt"/> <mi>B</mi> <mi>F</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>b</b>) Upper right-hand panel: corresponding NHST <span class="html-italic">t</span>-test <span class="html-italic">p</span>-value model densities, as modeled by the Bayes factor <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>F</mi> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> </semantics></math>. As can be observed, convergence on the empirical densities is rapidly achieved with the expansion of the entropic convex dual <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </semantics></math> on a small number <span class="html-italic">n</span> of Gegenbauer polynomials. In the lower panels, with the Hermite polynomials standing for the Gegenbauer polynomials in the large <span class="html-italic">b</span> limit (<a href="#FD21-entropy-24-00709" class="html-disp-formula">21</a>), the Bayes factor <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>F</mi> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> </semantics></math> expansion coefficients in random variable space directly provides the Gibbs prior moments <math display="inline"><semantics> <mrow> <mo>∫</mo> <mi>π</mi> <mrow> <mo>(</mo> <mi>δ</mi> <mo>)</mo> </mrow> <msup> <mi>δ</mi> <mi>n</mi> </msup> <mi mathvariant="normal">d</mi> <mi>δ</mi> </mrow> </semantics></math> in parametric space, as according to (56). (<b>c</b>) Lower left-hand panel: cumulative orthogonal polynomial expansion of the Bayes factor <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>F</mi> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> </semantics></math> in random variable space. (<b>d</b>) Lower right-hand panel: corresponding NHST central normal distribution <span class="html-italic">p</span>-value densities as modeled by the Bayes factor <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>F</mi> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> </semantics></math>. As can be observed, convergence on the empirical densities is rapidly achieved with a small number <span class="html-italic">n</span> of low-order Hermite polynomials.</p>
Full article ">Figure 3
<p>Bayes Factor <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>F</mi> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> </semantics></math> modeling a NHST <span class="html-italic">p</span>-value distribution from a genome-wide association study (GWAS) dataset. The GWAS compared 2244 critically ill patients with COVID-19 with 3 times as many ancestry-matched control individuals. The dataset comprises 4,380,209 <math display="inline"><semantics> <msubsup> <mi>χ</mi> <mrow> <msub> <mi>ν</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>2</mn> </msubsup> </semantics></math> <span class="html-italic">r</span>-statistics, accounting for all the SNPs in the set, which have been modelized by a logistic regression model and tested for statistical significance. (<b>a</b>) Left panel: Accrual of the successive Laguerre polynomial expansion terms in Equation (56) for the Bayes factor demonstrating an incrementally better fit of the <span class="html-italic">p</span>-value empirical density which strongly deviates from the NHST null hypothesis <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math> in the low <span class="html-italic">p</span>-value range. (<b>b</b>) Right panel: Local false discovery rate <math display="inline"><semantics> <mrow> <mi>fdr</mi> <mo>(</mo> <mi>p</mi> <mo>)</mo> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>B</mi> <mi>F</mi> <mo>(</mo> <mi>p</mi> <mo>)</mo> <mo>)</mo> </mrow> </semantics></math>. The Bayesian-based fdr crosses the 0.01 threshold (i.e., a fdr of 1%) when the NHST <span class="html-italic">p</span>-value reaches about <math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>7</mn> </mrow> </msup> </semantics></math> (<math display="inline"><semantics> <mrow> <mo>−</mo> <msub> <mo form="prefix">log</mo> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>7</mn> </mrow> </semantics></math>), in close concordance with the threshold of significance of <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>8</mn> </mrow> </msup> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mo>−</mo> <msub> <mo form="prefix">log</mo> <mn>10</mn> </msub> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>7.3</mn> </mrow> </semantics></math>) chosen by the authors.</p>
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<p>Earth’s emerging land/ice latitudinal density. Orthogonal Legendre <math display="inline"><semantics> <msub> <mi>P</mi> <mi>n</mi> </msub> </semantics></math> polynomial (Gegenbaeur <math display="inline"><semantics> <msubsup> <mi>C</mi> <mi>n</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> </semantics></math> polynomial) modeling of Earth’s emerging land/ice masses’ latitudinal density. (<b>a</b>) Left panel: orthogonal Legendre <math display="inline"><semantics> <msub> <mi>P</mi> <mi>n</mi> </msub> </semantics></math> polynomial modeling (<a href="#FD55-entropy-24-00709" class="html-disp-formula">55</a>) of Earth’s emerging land/ice latitudinal density as expanded on the first 30 Legendre polynomials. (<b>b</b>) Right panel: Kullback–Leibler divergence between the empirical density and the model as a function of the number of orthogonal Legendre polynomials accrued.</p>
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<p>Tide geometry. Idealized model describing the gravitational tidal pull of an ideal Moon of mass <span class="html-italic">m</span> on a thin water layer covering an ideal Earth of mass <span class="html-italic">M</span> and radius <span class="html-italic">R</span> at distance <span class="html-italic">D</span> from the Moon. The horizontal tidal force is given by the modular translation factor <math display="inline"><semantics> <mrow> <msubsup> <mi>T</mi> <mi>t</mi> <mrow> <mo>(</mo> <mi>ν</mi> <mn>2</mn> <mo>=</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>θ</mi> <mo>|</mo> <mi>δ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<a href="#FD9-entropy-24-00709" class="html-disp-formula">9</a>), defining on <math display="inline"><semantics> <msup> <mi mathvariant="script">S</mi> <mn>2</mn> </msup> </semantics></math> the noncentral spherical distribution <math display="inline"><semantics> <mrow> <msubsup> <mi>υ</mi> <mi>t</mi> <mrow> <mo>(</mo> <msub> <mi>ν</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>θ</mi> <mo>|</mo> <mi>δ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<a href="#FD8-entropy-24-00709" class="html-disp-formula">8</a>) with <math display="inline"><semantics> <mrow> <mo form="prefix">cos</mo> <msub> <mi>θ</mi> <mi>δ</mi> </msub> <mo>=</mo> <mi>R</mi> <mo>/</mo> <mi>D</mi> </mrow> </semantics></math>, minus a factor of one, accounting for the centrifugal force induced by the Moon–Earth system revolving around its center of gravity. The equatorial bulges are not symmetric in this purely geometrical model. The noncentrality parameter <math display="inline"><semantics> <mrow> <mo form="prefix">cos</mo> <msub> <mi>θ</mi> <mi>δ</mi> </msub> </mrow> </semantics></math> is set to an unrealistic value of 0.2 to enhance the visualization of the geometrical distribution of the tidal forces.</p>
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17 pages, 777 KiB  
Article
Symmetry Methods and Conservation Laws for the Nonlinear Generalized 2D Equal-Width Partial Differential Equation of Engineering
by Chaudry Masood Khalique and Karabo Plaatjie
Mathematics 2022, 10(1), 24; https://doi.org/10.3390/math10010024 - 22 Dec 2021
Cited by 5 | Viewed by 2582
Abstract
In this work, we study the generalized 2D equal-width equation which arises in various fields of science. With the aid of numerous methods which includes Lie symmetry analysis, power series expansion and Weierstrass method, we produce closed-form solutions of this model. The exact [...] Read more.
In this work, we study the generalized 2D equal-width equation which arises in various fields of science. With the aid of numerous methods which includes Lie symmetry analysis, power series expansion and Weierstrass method, we produce closed-form solutions of this model. The exact solutions obtained are the snoidal wave, cnoidal wave, Weierstrass elliptic function, Jacobi elliptic cosine function, solitary wave and exponential function solutions. Moreover, we give a graphical representation of the obtained solutions using certain parametric values. Furthermore, the conserved vectors of the underlying equation are constructed by utilizing two approaches: the multiplier method and Noether’s theorem. The multiplier method provided us with four local conservation laws, whereas Noether’s theorem yielded five nonlocal conservation laws. The conservation laws that are constructed contain the conservation of energy and momentum. Full article
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Figure 1

Figure 1
<p>The 3D and 2D wave profile of solution (<a href="#FD24-mathematics-10-00024" class="html-disp-formula">24</a>).</p>
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<p>The 3D and 2D solution profiles of (<a href="#FD27-mathematics-10-00024" class="html-disp-formula">27</a>).</p>
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<p>The 3D and 2D solution profiles of (<a href="#FD29-mathematics-10-00024" class="html-disp-formula">29</a>).</p>
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<p>The 3D and 2D solution profiles of (<a href="#FD34-mathematics-10-00024" class="html-disp-formula">34</a>).</p>
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15 pages, 331 KiB  
Article
Spectral Galerkin Approximation of Space Fractional Optimal Control Problem with Integral State Constraint
by Fangyuan Wang, Xiaodi Li and Zhaojie Zhou
Fractal Fract. 2021, 5(3), 102; https://doi.org/10.3390/fractalfract5030102 - 24 Aug 2021
Cited by 2 | Viewed by 1751
Abstract
In this paper spectral Galerkin approximation of optimal control problem governed by fractional advection diffusion reaction equation with integral state constraint is investigated. First order optimal condition of the control problem is discussed. Weighted Jacobi polynomials are used to approximate the state and [...] Read more.
In this paper spectral Galerkin approximation of optimal control problem governed by fractional advection diffusion reaction equation with integral state constraint is investigated. First order optimal condition of the control problem is discussed. Weighted Jacobi polynomials are used to approximate the state and adjoint state. A priori error estimates for control, state, adjoint state and Lagrangian multiplier are derived. Numerical experiment is carried out to illustrate the theoretical findings. Full article
(This article belongs to the Special Issue Frontiers in Fractional-Order Neural Networks)
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<p>True solutions and numerical solutions. (left) <span class="html-italic">y</span> and <math display="inline"><semantics> <msub> <mi>y</mi> <mi>N</mi> </msub> </semantics></math>, (middle) <span class="html-italic">z</span> and <math display="inline"><semantics> <msub> <mi>z</mi> <mi>N</mi> </msub> </semantics></math>, (right). Here <math display="inline"><semantics> <msub> <mi>y</mi> <mi>N</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>z</mi> <mi>N</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>u</mi> <mi>N</mi> </msub> </semantics></math> are calculated by Algorithm 1.</p>
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30 pages, 423 KiB  
Review
Jacobi Multipliers in Integrability and the Inverse Problem of Mechanics
by José F. Cariñena and José Fernández-Núñez
Symmetry 2021, 13(8), 1413; https://doi.org/10.3390/sym13081413 - 2 Aug 2021
Cited by 12 | Viewed by 2674
Abstract
We review the general theory of the Jacobi last multipliers in geometric terms and then apply the theory to different problems in integrability and the inverse problem for one-dimensional mechanical systems. Within this unified framework, we derive the explicit form of a Lagrangian [...] Read more.
We review the general theory of the Jacobi last multipliers in geometric terms and then apply the theory to different problems in integrability and the inverse problem for one-dimensional mechanical systems. Within this unified framework, we derive the explicit form of a Lagrangian obtained by several authors for a given dynamical system in terms of known constants of the motion via a Jacobi multiplier for both autonomous and nonautonomous systems, and some examples are used to illustrate the general theory. Finally, some geometric results on Jacobi multipliers and their use in the study of Hojman symmetry are given. Full article
30 pages, 4450 KiB  
Article
Closed-Form Solutions and Conserved Vectors of a Generalized (3+1)-Dimensional Breaking Soliton Equation of Engineering and Nonlinear Science
by Chaudry Masood Khalique and Oke Davies Adeyemo
Mathematics 2020, 8(10), 1692; https://doi.org/10.3390/math8101692 - 1 Oct 2020
Cited by 18 | Viewed by 2028
Abstract
In this article, we examine a (3+1)-dimensional generalized breaking soliton equation which is highly applicable in the fields of engineering and nonlinear sciences. Closed-form solutions in the form of Jacobi elliptic functions of the underlying equation are derived by the method of Lie [...] Read more.
In this article, we examine a (3+1)-dimensional generalized breaking soliton equation which is highly applicable in the fields of engineering and nonlinear sciences. Closed-form solutions in the form of Jacobi elliptic functions of the underlying equation are derived by the method of Lie symmetry reductions together with direct integration. Moreover, the (G/G)-expansion technique is engaged, which consequently guarantees closed-form solutions of the equation structured in the form of trigonometric and hyperbolic functions. In addition, we secure a power series analytical solution of the underlying equation. Finally, we construct local conserved vectors of the aforementioned equation by employing two approaches: the general multiplier method and Ibragimov’s theorem. Full article
(This article belongs to the Special Issue Applications of Partial Differential Equations in Engineering)
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<p>Evolution of periodic solution (<a href="#FD20-mathematics-08-01692" class="html-disp-formula">20</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mo>−</mo> <mn>10</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of periodic solution (<a href="#FD20-mathematics-08-01692" class="html-disp-formula">20</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mo>−</mo> <mn>11</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of periodic solution (<a href="#FD20-mathematics-08-01692" class="html-disp-formula">20</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mo>−</mo> <mn>25</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mo>−</mo> <mn>9</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of traveling wave of singular soliton solution (<a href="#FD24-mathematics-08-01692" class="html-disp-formula">24</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of traveling wave of singular soliton solution (<a href="#FD24-mathematics-08-01692" class="html-disp-formula">24</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mo>−</mo> <mn>3.5</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of traveling wave of singular soliton solution (<a href="#FD24-mathematics-08-01692" class="html-disp-formula">24</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mo>−</mo> <mn>0.2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of traveling wave of singular soliton solution (<a href="#FD25-mathematics-08-01692" class="html-disp-formula">25</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>2.1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mo>−</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of traveling wave of singular soliton solution (<a href="#FD25-mathematics-08-01692" class="html-disp-formula">25</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mo>−</mo> <mn>6</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of traveling wave of singular soliton solution (<a href="#FD25-mathematics-08-01692" class="html-disp-formula">25</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>.</p>
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<p>Profile of traveling wave solution (<a href="#FD27-mathematics-08-01692" class="html-disp-formula">27</a>) with parameters <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>.</p>
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<p>Profile of traveling wave solution (<a href="#FD27-mathematics-08-01692" class="html-disp-formula">27</a>) with parameters <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
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<p>Profile of traveling wave solution (<a href="#FD27-mathematics-08-01692" class="html-disp-formula">27</a>) with parameters <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>−</mo> <mn>4</mn> </mrow> </semantics></math>.</p>
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<p>Profile of traveling wave solution (<a href="#FD28-mathematics-08-01692" class="html-disp-formula">28</a>) when <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>.</p>
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<p>Profile of traveling wave solution (<a href="#FD28-mathematics-08-01692" class="html-disp-formula">28</a>) when <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>−</mo> <mn>4</mn> </mrow> </semantics></math>.</p>
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<p>Profile of traveling wave solution (<a href="#FD28-mathematics-08-01692" class="html-disp-formula">28</a>) when <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mo>−</mo> <mn>6</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of traveling wave of series solution (<a href="#FD34-mathematics-08-01692" class="html-disp-formula">34</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>1.1</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of traveling wave of series solution (<a href="#FD34-mathematics-08-01692" class="html-disp-formula">34</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mo>−</mo> <mn>8</mn> </mrow> </semantics></math>.</p>
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<p>Evolution of traveling wave of series solution (<a href="#FD34-mathematics-08-01692" class="html-disp-formula">34</a>) at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>.</p>
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19 pages, 3541 KiB  
Article
Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers
by José Adriano da Costa, David Alves Castelo Branco, Max Chianca Pimentel Filho, Manoel Firmino de Medeiros Júnior and Neilton Fidelis da Silva
Energies 2019, 12(9), 1728; https://doi.org/10.3390/en12091728 - 7 May 2019
Cited by 14 | Viewed by 2792
Abstract
The integration of renewable distributed generation into distribution systems has been studied comprehensively, due to the potential benefits, such as the reduction of energy losses and mitigation of the environmental impacts resulting from power generation. The problem of minimizing energy losses in distribution [...] Read more.
The integration of renewable distributed generation into distribution systems has been studied comprehensively, due to the potential benefits, such as the reduction of energy losses and mitigation of the environmental impacts resulting from power generation. The problem of minimizing energy losses in distribution systems and the methods used for optimal integration of the renewable distributed generation have been the subject of recent studies. The present study proposes an analytical method which addresses the problem of sizing the nominal power of photovoltaic generation, connected to the nodes of a radial distribution feeder. The goal of this method is to minimize the total energy losses during the daily insolation period, with an optimization constraint consisting in the energy flow in the slack bus, conditioned to the energetic independence of the feeder. The sizing is achieved from the photovoltaic generation capacity and load factors, calculated in time intervals defined in the typical production curve of a photovoltaic unit connected to the distribution system. The analytical method has its foundations on Lagrange multipliers and relies on the Gauss-Jacobi method to make the resulting equation system solution feasible. This optimization method was evaluated on the IEEE 37-bus test system, from which the scenarios of generation integration were considered. The obtained results display the optimal sizing as well as the energy losses related to additional power and the location of the photovoltaic generation in distributed generation integration scenarios. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Daily power production curve for a real 84.2 kWp PV unit connected to a local distribution feeder.</p>
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<p>System reduced to the <span class="html-italic">s</span> and <span class="html-italic">j</span> nodes.</p>
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<p>Flowchart of the proposed energy losses minimization method using Lagrange multipliers.</p>
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<p>Diagram of the 36-bus radial distribution feeder adapted from the IEEE 37-bus test system.</p>
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<p>Energy losses reduction under a condition of increasing PV generation: (<b>a</b>) Scenario I; (<b>b</b>) Scenario II; (<b>c</b>) Scenario III.</p>
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<p>Optimal PV generation sizing: (<b>a</b>) in Scenario I for ratios of 1, 1.1 and 1.4; (<b>b</b>) in Scenario II for ratios of 1, 1.1 and 1.4; (<b>c</b>) in Scenario III for a ratio of 1.</p>
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<p>Diagram of the simplified distribution feeder with 3 branches.</p>
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232 KiB  
Article
Noether Symmetries Quantization and Superintegrability of Biological Models
by Maria Clara Nucci and Giampaolo Sanchini
Symmetry 2016, 8(12), 155; https://doi.org/10.3390/sym8120155 - 20 Dec 2016
Cited by 9 | Viewed by 3890
Abstract
It is shown that quantization and superintegrability are not concepts that are inherent to classical Physics alone. Indeed, one may quantize and also detect superintegrability of biological models by means of Noether symmetries. We exemplify the method by using a mathematical model that [...] Read more.
It is shown that quantization and superintegrability are not concepts that are inherent to classical Physics alone. Indeed, one may quantize and also detect superintegrability of biological models by means of Noether symmetries. We exemplify the method by using a mathematical model that was proposed by Basener and Ross (2005), and that describes the dynamics of growth and sudden decrease in the population of Easter Island. Full article
297 KiB  
Article
Symmetries, Lagrangians and Conservation Laws of an Easter Island Population Model
by M.C. Nucci and G. Sanchini
Symmetry 2015, 7(3), 1613-1632; https://doi.org/10.3390/sym7031613 - 8 Sep 2015
Cited by 22 | Viewed by 5273
Abstract
Basener and Ross (2005) proposed a mathematical model that describes the dynamics of growth and sudden decrease in the population of Easter Island. We have applied Lie group analysis to this system and found that it can be integrated by quadrature if the [...] Read more.
Basener and Ross (2005) proposed a mathematical model that describes the dynamics of growth and sudden decrease in the population of Easter Island. We have applied Lie group analysis to this system and found that it can be integrated by quadrature if the involved parameters satisfy certain relationships. We have also discerned hidden linearity. Moreover, we have determined a Jacobi last multiplier and, consequently, a Lagrangian for the general system and have found other cases independently and dependently on symmetry considerations in order to construct a corresponding variational problem, thus enabling us to find conservation laws by means of Noether’s theorem. A comparison with the qualitative analysis given by Basener and Ross is provided. Full article
(This article belongs to the Special Issue Lie Theory and Its Applications)
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<p>The amount of resources <math display="inline"> <mrow> <mi>R</mi> <mo>≡</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> </mrow> </math> and that of the population <math display="inline"> <mrow> <mi>P</mi> <mo>≡</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> </mrow> </math> for the values of the parameters <math display="inline"> <mrow> <mi>K</mi> <mo>=</mo> <mn>20000</mn> <mo>,</mo> <mspace width="3.33333pt"/> <mi>c</mi> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>01</mn> </mrow> </math>, and for the initial conditions <math display="inline"> <mrow> <mi>P</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> </mrow> </math> 1000, <math display="inline"> <mrow> <mi>R</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> </mrow> </math> 20000.</p>
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<p>The amount of resources <math display="inline"> <mrow> <mi>R</mi> <mo>≡</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> </mrow> </math> and that of the population <math display="inline"> <mrow> <mi>P</mi> <mo>≡</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> </mrow> </math> for the values of the parameters <math display="inline"> <mrow> <mi>K</mi> <mo>=</mo> <mn>40000</mn> <mo>,</mo> <mspace width="3.33333pt"/> <mi>c</mi> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>01</mn> </mrow> </math>, and for the initial conditions <math display="inline"> <mrow> <mi>P</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> </mrow> </math> 1000, <math display="inline"> <mrow> <mi>R</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> </mrow> </math> 20000.</p>
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