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Mathematics, Volume 8, Issue 1 (January 2020) – 145 articles

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23 pages, 365 KiB  
Editorial
Acknowledgement to Reviewers of Mathematics in 2019
by Mathematics Editorial Office
Mathematics 2020, 8(1), 145; https://doi.org/10.3390/math8010145 - 20 Jan 2020
Viewed by 3338
Abstract
The editorial team greatly appreciates the reviewers who have dedicated their considerable time and expertise to the journal’s rigorous editorial process over the past 12 months, regardless of whether the papers are finally published or not [...] Full article
43 pages, 1041 KiB  
Article
On Degenerate Truncated Special Polynomials
by Ugur Duran and Mehmet Acikgoz
Mathematics 2020, 8(1), 144; https://doi.org/10.3390/math8010144 - 20 Jan 2020
Cited by 12 | Viewed by 3359
Abstract
The main aim of this paper is to introduce the degenerate truncated forms of multifarious special polynomials and numbers and is to investigate their various properties and relationships by using the series manipulation method and diverse special proof techniques. The degenerate truncated exponential [...] Read more.
The main aim of this paper is to introduce the degenerate truncated forms of multifarious special polynomials and numbers and is to investigate their various properties and relationships by using the series manipulation method and diverse special proof techniques. The degenerate truncated exponential polynomials are first considered and their several properties are given. Then the degenerate truncated Stirling polynomials of the second kind are defined and their elementary properties and relations are proved. Also, the degenerate truncated forms of the bivariate Fubini and Bell polynomials and numbers are introduced and various relations and formulas for these polynomials and numbers, which cover several summation formulas, addition identities, recurrence relationships, derivative property and correlations with the degenerate truncated Stirling polynomials of the second kind, are acquired. Thereafter, the truncated degenerate Bernoulli and Euler polynomials are considered and multifarious correlations and formulas including summation formulas, derivation rules and correlations with the degenerate truncated Stirling numbers of the second are derived. In addition, regarding applications, by introducing the degenerate truncated forms of the classical Bernstein polynomials, we obtain diverse correlations and formulas. Some interesting surface plots of these polynomials in the special cases are provided. Full article
(This article belongs to the Special Issue Special Functions and Applications)
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<p>Surface Plot of <inline-formula><mml:math id="mm523" display="block"><mml:semantics><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn><mml:mo>;</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mfenced separators="" open="(" close=")"><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mfenced></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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<p>Surface Plot of <inline-formula><mml:math id="mm524" display="block"><mml:semantics><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>;</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:mfenced separators="" open="(" close=")"><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mfenced></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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<p>Surface plot of <inline-formula><mml:math id="mm525" display="block"><mml:semantics><mml:mrow><mml:msub><mml:mi>B</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>;</mml:mo><mml:mi>λ</mml:mi></mml:mrow></mml:msub><mml:mfenced open="(" close=")"><mml:mi>x</mml:mi></mml:mfenced></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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<p>Surface plot of <inline-formula><mml:math id="mm526" display="block"><mml:semantics><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn><mml:mo>;</mml:mo><mml:mi>λ</mml:mi></mml:mrow></mml:msub><mml:mfenced open="(" close=")"><mml:mi>x</mml:mi></mml:mfenced></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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17 pages, 2180 KiB  
Article
An Inequality Approach to Approximate Solutions of Set Optimization Problems in Real Linear Spaces
by Elisabeth Köbis, Markus A. Köbis and Xiaolong Qin
Mathematics 2020, 8(1), 143; https://doi.org/10.3390/math8010143 - 20 Jan 2020
Viewed by 2610
Abstract
This paper explores new notions of approximate minimality in set optimization using a set approach. We propose characterizations of several approximate minimal elements of families of sets in real linear spaces by means of general functionals, which can be unified in an inequality [...] Read more.
This paper explores new notions of approximate minimality in set optimization using a set approach. We propose characterizations of several approximate minimal elements of families of sets in real linear spaces by means of general functionals, which can be unified in an inequality approach. As particular cases, we investigate the use of the prominent Tammer–Weidner nonlinear scalarizing functionals, without assuming any topology, in our context. We also derive numerical methods to obtain approximate minimal elements of families of finitely many sets by means of our obtained results. Full article
(This article belongs to the Special Issue Applied Functional Analysis and Its Applications)
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<p>Illustration of the functional <math display="inline"><semantics> <mrow> <msup> <mi>z</mi> <mrow> <mi>C</mi> <mo>,</mo> <mi>k</mi> </mrow> </msup> <mrow> <mo>(</mo> <mi>y</mi> <mo>)</mo> </mrow> <mrow> <mo>:</mo> <mo>=</mo> </mrow> <mo movablelimits="true" form="prefix">inf</mo> <mrow> <mo>{</mo> <mi>t</mi> <mo>∈</mo> <mi mathvariant="double-struck">R</mi> <mo>|</mo> <mi>y</mi> <mo>∈</mo> <mi>t</mi> <mi>k</mi> <mo>−</mo> <mi>C</mi> <mo>}</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>A randomly generated family of sets. The lightly and darkly filled circles belong to the set <math display="inline"><semantics> <mi mathvariant="script">T</mi> </semantics></math> generated by Algorithm 2, while the <math display="inline"><semantics> <msup> <mi>H</mi> <mn>2</mn> </msup> </semantics></math>-approximate minimal elements of the set <math display="inline"><semantics> <mi mathvariant="script">A</mi> </semantics></math> are exactly the darkly filled circles (see Example 2).</p>
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<p>The randomly generated family of sets from Example 2 with <math display="inline"><semantics> <mrow> <mi>H</mi> <mo>=</mo> <mo>{</mo> <msup> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mo>}</mo> </mrow> </semantics></math>, i.e., we do not consider approximate minimal elements here, but look for the minimal elements of the family of sets <math display="inline"><semantics> <mi mathvariant="script">A</mi> </semantics></math>. The lightly and darkly filled circles belong to the set <math display="inline"><semantics> <mi mathvariant="script">T</mi> </semantics></math> generated by Algorithm 2, while the minimal elements of the set <math display="inline"><semantics> <mi mathvariant="script">A</mi> </semantics></math> are the darkly filled circles.</p>
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20 pages, 2415 KiB  
Article
A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis
by Qianli Zhou, Hongming Mo and Yong Deng
Mathematics 2020, 8(1), 142; https://doi.org/10.3390/math8010142 - 20 Jan 2020
Cited by 103 | Viewed by 5359
Abstract
As the extension of the fuzzy sets (FSs) theory, the intuitionistic fuzzy sets (IFSs) play an important role in handling the uncertainty under the uncertain environments. The Pythagoreanfuzzy sets (PFSs) proposed by Yager in 2013 can deal with more uncertain situations than intuitionistic [...] Read more.
As the extension of the fuzzy sets (FSs) theory, the intuitionistic fuzzy sets (IFSs) play an important role in handling the uncertainty under the uncertain environments. The Pythagoreanfuzzy sets (PFSs) proposed by Yager in 2013 can deal with more uncertain situations than intuitionistic fuzzy sets because of its larger range of describing the membership grades. How to measure the distance of Pythagorean fuzzy sets is still an open issue. Jensen–Shannon divergence is a useful distance measure in the probability distribution space. In order to efficiently deal with uncertainty in practical applications, this paper proposes a new divergence measure of Pythagorean fuzzy sets, which is based on the belief function in Dempster–Shafer evidence theory, and is called PFSDM distance. It describes the Pythagorean fuzzy sets in the form of basic probability assignments (BPAs) and calculates the divergence of BPAs to get the divergence of PFSs, which is the step in establishing a link between the PFSs and BPAs. Since the proposed method combines the characters of belief function and divergence, it has a more powerful resolution than other existing methods. Additionally, an improved algorithm using PFSDM distance is proposed in medical diagnosis, which can avoid producing counter-intuitive results especially when a data conflict exists. The proposed method and the magnified algorithm are both demonstrated to be rational and practical in applications. Full article
(This article belongs to the Special Issue Fuzzy Sets, Fuzzy Logic and Their Applications)
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<p>The results in the proof.</p>
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<p>The results in Example 2.</p>
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12 pages, 1186 KiB  
Article
A Rectifying Acceptance Sampling Plan Based on the Process Capability Index
by Ching-Ho Yen, Chun-Chia Lee, Kuo-Hung Lo, Yeou-Ren Shiue and Shu-Hua Li
Mathematics 2020, 8(1), 141; https://doi.org/10.3390/math8010141 - 20 Jan 2020
Cited by 12 | Viewed by 5105
Abstract
The acceptance sampling plan and process capability index (PCI) are critical decision tools for quality control. Recently, numerous research papers have examined the acceptance sampling plan in combination with the PCI. However, most of these papers have not considered the aspect of rectifying [...] Read more.
The acceptance sampling plan and process capability index (PCI) are critical decision tools for quality control. Recently, numerous research papers have examined the acceptance sampling plan in combination with the PCI. However, most of these papers have not considered the aspect of rectifying inspections. In this paper, we propose a quality cost model of repetitive sampling to develop a rectifying acceptance sampling plan based on the one-sided PCI. This proposed model minimizes the total quality cost (TQC) of sentencing one lot, including inspection cost, internal failure cost, and external failure cost. In addition, sensitivity analysis is conducted to investigate the behavior of relevant parameters against TQC. To demonstrate the advantages of the proposed methodology, a comparison is implemented with the existing rectifying sampling plan in terms of TQC and average outgoing quality limit. This comparison reveals that our proposed methodology exhibits superior performance. Full article
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<p>Flow chart of a repetitive sampling plan based on <span class="html-italic">C<sub>S</sub></span>.</p>
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<p>Conventional OC curve.</p>
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<p>Graphs of average outgoing quality (AOQ) versus <span class="html-italic">C<sub>s</sub></span> for the two rectifying sampling plans.</p>
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<p>Sensitivity analysis graphs for <span class="html-italic">C<sub>S</sub></span>, <span class="html-italic">C<sub>i</sub></span>, <span class="html-italic">C<sub>if</sub></span>, and <span class="html-italic">C<sub>ef.</sub></span></p>
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16 pages, 948 KiB  
Article
Venture Capital Contracting with Ambiguity Sharing and Effort Complementarity Effect
by Jiajia Chang, Zhijun Hu and Hui Yang
Mathematics 2020, 8(1), 140; https://doi.org/10.3390/math8010140 - 19 Jan 2020
Cited by 3 | Viewed by 2328
Abstract
In this paper, we established a continuous-time agency model in which an ambiguity-averse venture capitalist (VC) employs an ambiguity-neutral entrepreneur (EN) to manage an innovative project. We analyzed the connection between ambiguity sharing and incentives under double moral hazard. Applying a stochastic dynamic [...] Read more.
In this paper, we established a continuous-time agency model in which an ambiguity-averse venture capitalist (VC) employs an ambiguity-neutral entrepreneur (EN) to manage an innovative project. We analyzed the connection between ambiguity sharing and incentives under double moral hazard. Applying a stochastic dynamic programming approach, we solved the VC’s maximization problem and obtained the Hamilton–Jacobi–Bellman (HJB) equation under a special form of the value function. We showed that the optimal pay-performance sensitivity was a fixed point of a nonlinear equation. The model ambiguity on the probability measure induced a tradeoff between ambiguity sharing and the incentive compensation that improved the EN’s pay-performance sensitivity level. Besides, we simulated the model and showed that when two efforts were complementary, the VC’s effort did not monotonically decrease with respect to the pay-performance sensitivity, while the EN’s effort did not monotonically increase in the pay-performance sensitivity level. More importantly, we found that as efforts tended to be more complementary, the optimal pay-performance sensitivity tended to approach those that maximized the efforts exerted by the EN and the VC. Full article
(This article belongs to the Section Financial Mathematics)
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<p>Changes of <math display="inline"><semantics> <mrow> <msubsup> <mi>β</mi> <mi>t</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math> in ambiguity aversion: (<b>a</b>) <math display="inline"><semantics> <mi>θ</mi> </semantics></math> for <math display="inline"><semantics> <mrow> <msup> <mi>ρ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mtext> </mtext> <mn>0.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mn>1</mn> </semantics></math>. (Note: <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> ); (<b>b</b>) <math display="inline"><semantics> <mi>θ</mi> </semantics></math> for <math display="inline"><semantics> <mrow> <msup> <mi>ρ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mtext> </mtext> <mn>0.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mn>1</mn> </semantics></math>. (Note: <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>).</p>
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<p>Efforts dynamics for different complementarity levels <math display="inline"><semantics> <mi>α</mi> </semantics></math>.</p>
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<p>Optimal pay-performance sensitivity level for a different degree of complementarity <math display="inline"><semantics> <mi>α</mi> </semantics></math>.</p>
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26 pages, 479 KiB  
Article
Nash Equilibrium Investment-Reinsurance Strategies for an Insurer and a Reinsurer with Intertemporal Restrictions and Common Interests
by Yanfei Bai, Zhongbao Zhou, Rui Gao and Helu Xiao
Mathematics 2020, 8(1), 139; https://doi.org/10.3390/math8010139 - 19 Jan 2020
Cited by 5 | Viewed by 2434
Abstract
This paper investigates the generalized multi-period mean-variance investment-reinsurance optimization model in a discrete-time framework for a general insurance company that contains a reinsurer and an insurer. The intertemporal restrictions and the common interests of the reinsurer and the insurer are considered. The common [...] Read more.
This paper investigates the generalized multi-period mean-variance investment-reinsurance optimization model in a discrete-time framework for a general insurance company that contains a reinsurer and an insurer. The intertemporal restrictions and the common interests of the reinsurer and the insurer are considered. The common goal of the reinsurer and the insurer is to maximize the expectation of the weighted sum of their wealth processes and minimize the corresponding variance. Based on the game method, we obtain the Nash equilibrium investment-reinsurance strategies for the above-proposed model and find out the equilibrium strategies when unilateral interest is considered. In addition, the Nash equilibrium investment-reinsurance strategies are deduced under two special premium calculated principles (i.e., the expected value premium principle and the variance value premium principle). We theoretically study the effect of the intertemporal restrictions on Nash equilibrium investment-reinsurance strategies and find the effect depends on the value of some parameters, which differs from the previous researches that generally believed that intertemporal restrictions would make investors avoid risks. Finally, we perform corresponding numerical analyses to verify our theoretical results. Full article
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<p>Nash equilibrium investment strategies for (<b>a</b>) the insurer and (<b>b</b>) the reinsurer (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>1.2</mn> </mrow> </semantics></math>).</p>
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<p>Nash equilibrium investment strategies for (<b>a</b>) the insurer and (<b>b</b>) the reinsurer (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>1.01</mn> </mrow> </semantics></math>).</p>
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<p>Nash equilibrium investment strategies for (<b>a</b>) the insurer and (<b>b</b>) the reinsurer (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>1.2</mn> </mrow> </semantics></math>).</p>
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<p>Nash equilibrium investment strategies for (<b>a</b>) the insurer and (<b>b</b>) the reinsurer (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>1.01</mn> </mrow> </semantics></math>).</p>
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<p>Nash equilibrium reinsurance strategy under the expected value premium principle (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>).</p>
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<p>Nash equilibrium reinsurance strategy under the expected value premium principle (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>).</p>
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<p>Nash equilibrium reinsurance strategy under the variance value premium principle (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>).</p>
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<p>Nash equilibrium reinsurance strategy under the variance value premium principle (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>).</p>
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16 pages, 316 KiB  
Article
The Basic Algorithm for the Constrained Zero-One Quadratic Programming Problem with k-diagonal Matrix and Its Application in the Power System
by Shenshen Gu and Xinyi Chen
Mathematics 2020, 8(1), 138; https://doi.org/10.3390/math8010138 - 19 Jan 2020
Cited by 2 | Viewed by 2593
Abstract
Zero-one quadratic programming is a classical combinatorial optimization problem that has many real-world applications. However, it is well known that zero-one quadratic programming is non-deterministic polynomial-hard (NP-hard) in general. On one hand, the exact solution algorithms that can guarantee the global optimum are [...] Read more.
Zero-one quadratic programming is a classical combinatorial optimization problem that has many real-world applications. However, it is well known that zero-one quadratic programming is non-deterministic polynomial-hard (NP-hard) in general. On one hand, the exact solution algorithms that can guarantee the global optimum are very time consuming. And on the other hand, the heuristic algorithms that generate the solution quickly can only provide local optimum. Due to this reason, identifying polynomially solvable subclasses of zero-one quadratic programming problems and their corresponding algorithms is a promising way to not only compromise these two sides but also offer theoretical insight into the complicated nature of the problem. By combining the basic algorithm and dynamic programming method, we propose an effective algorithm in this paper to solve the general linearly constrained zero-one quadratic programming problem with a k-diagonal matrix. In our algorithm, the value of k is changeable that covers different subclasses of the problem. The theoretical analysis and experimental results reveal that our proposed algorithm is reasonably effective and efficient. In addition, the placement of the phasor measurement units problem in the power system is adopted as an example to illustrate the potential real-world applications of this algorithm. Full article
(This article belongs to the Special Issue Optimization for Decision Making II)
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<p>Flow chart of the zero-one unconstrained quadratic programming (01UQP) algorithm.</p>
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<p>Calculation time with different dimensions when the diagonal number <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>9</mn> <mo>,</mo> <mn>13</mn> <mo>,</mo> <mn>19</mn> <mo>,</mo> <mn>23</mn> </mrow> </semantics></math>.</p>
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<p>Calculation time with different diagonal numbers when the dimension <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>20</mn> <mo>,</mo> <mn>40</mn> <mo>,</mo> <mn>60</mn> <mo>,</mo> <mn>80</mn> </mrow> </semantics></math>.</p>
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<p>The variation of calculation time with different dimensions <span class="html-italic">n</span> and diagonal number <span class="html-italic">k</span>.</p>
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11 pages, 280 KiB  
Article
Geodesic Vector Fields on a Riemannian Manifold
by Sharief Deshmukh, Patrik Peska and Nasser Bin Turki
Mathematics 2020, 8(1), 137; https://doi.org/10.3390/math8010137 - 19 Jan 2020
Cited by 12 | Viewed by 5930
Abstract
A unit geodesic vector field on a Riemannian manifold is a vector field whose integral curves are geodesics, or in other worlds have zero acceleration. A geodesic vector field on a Riemannian manifold is a smooth vector field with acceleration of each of [...] Read more.
A unit geodesic vector field on a Riemannian manifold is a vector field whose integral curves are geodesics, or in other worlds have zero acceleration. A geodesic vector field on a Riemannian manifold is a smooth vector field with acceleration of each of its integral curves is proportional to velocity. In this paper, we show that the presence of a geodesic vector field on a Riemannian manifold influences its geometry. We find characterizations of n-spheres as well as Euclidean spaces using geodesic vector fields. Full article
(This article belongs to the Special Issue Differential Geometry of Spaces with Structures)
23 pages, 369 KiB  
Article
On the Fractional Order Rodrigues Formula for the Shifted Legendre-Type Matrix Polynomials
by Mohra Zayed, Mahmoud Abul-Ez, Mohamed Abdalla and Nasser Saad
Mathematics 2020, 8(1), 136; https://doi.org/10.3390/math8010136 - 18 Jan 2020
Cited by 14 | Viewed by 3142
Abstract
The generalization of Rodrigues’ formula for orthogonal matrix polynomials has attracted the attention of many researchers. This generalization provides new integral and differential representations in addition to new mathematical results that are useful in theoretical and numerical computations. Using a recently studied operational [...] Read more.
The generalization of Rodrigues’ formula for orthogonal matrix polynomials has attracted the attention of many researchers. This generalization provides new integral and differential representations in addition to new mathematical results that are useful in theoretical and numerical computations. Using a recently studied operational matrix for shifted Legendre polynomials with the variable coefficients fractional differential equations, the present work introduces the shifted Legendre-type matrix polynomials of arbitrary (fractional) orders utilizing some Rodrigues matrix formulas. Many interesting mathematical properties of these matrix polynomials are investigated and reported in this paper, including recurrence relations, differential properties, hypergeometric function representation, and integral representation. Furthermore, the orthogonality property of these polynomials is examined in some particular cases. The developed results provide a matrix framework that generalizes and enhances the corresponding scalar version and introduces some new properties with proposed applications. Some of these applications are explored in the present work. Full article
17 pages, 454 KiB  
Article
A New Three-Parameter Exponential Distribution with Variable Shapes for the Hazard Rate: Estimation and Applications
by Ahmed Z. Afify and Osama Abdo Mohamed
Mathematics 2020, 8(1), 135; https://doi.org/10.3390/math8010135 - 16 Jan 2020
Cited by 50 | Viewed by 4627
Abstract
In this paper, we study a new flexible three-parameter exponential distribution called the extended odd Weibull exponential distribution, which can have constant, decreasing, increasing, bathtub, upside-down bathtub and reversed-J shaped hazard rates, and right-skewed, left-skewed, symmetrical, and reversed-J shaped densities. Some mathematical properties [...] Read more.
In this paper, we study a new flexible three-parameter exponential distribution called the extended odd Weibull exponential distribution, which can have constant, decreasing, increasing, bathtub, upside-down bathtub and reversed-J shaped hazard rates, and right-skewed, left-skewed, symmetrical, and reversed-J shaped densities. Some mathematical properties of the proposed distribution are derived. The model parameters are estimated via eight frequentist estimation methods called, the maximum likelihood estimators, least squares and weighted least-squares estimators, maximum product of spacing estimators, Cramér-von Mises estimators, percentiles estimators, and Anderson-Darling and right-tail Anderson-Darling estimators. Extensive simulations are conducted to compare the performance of these estimation methods for small and large samples. Four practical data sets from the fields of medicine, engineering, and reliability are analyzed, proving the usefulness and flexibility of the proposed distribution. Full article
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<p>Plots of the probability density function (PDF) of the EOWEx distribution.</p>
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<p>Plots of the hazard rate function (HRF) of the EOWEx distribution.</p>
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<p>The fitted EOWEx PDF, CDF, SF, and P–P plots for cancer data (<b>left panel</b>) and for gauge lengths data (<b>right panel</b>).</p>
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<p>The fitted EOWEx PDF, CDF, SF, and P–P plots for failure times data (<b>left panel</b>) and for breaking stress of carbon fibers data (<b>right panel</b>).</p>
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<p>P–P plots of the EOWEx distribution using the four best estimation methods for cancer data (<b>left panel</b>) and for gauge lengths data (<b>right panel</b>).</p>
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<p>P-P plots of the EOWEx distribution using the four best estimation methods for failure times data (<b>left panel</b>) and for breaking stress of carbon fibers data (<b>right panel</b>).</p>
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22 pages, 1084 KiB  
Article
Parameter and State Estimation of One-Dimensional Infiltration Processes: A Simultaneous Approach
by Song Bo, Soumya R. Sahoo, Xunyuan Yin, Jinfeng Liu and Sirish L. Shah
Mathematics 2020, 8(1), 134; https://doi.org/10.3390/math8010134 - 16 Jan 2020
Cited by 15 | Viewed by 2791
Abstract
The Richards equation plays an important role in the study of agro-hydrological systems. It models the water movement in soil in the vadose zone, which is driven by capillary and gravitational forces. Its states (capillary potential) and parameters (hydraulic conductivity, saturated and residual [...] Read more.
The Richards equation plays an important role in the study of agro-hydrological systems. It models the water movement in soil in the vadose zone, which is driven by capillary and gravitational forces. Its states (capillary potential) and parameters (hydraulic conductivity, saturated and residual soil moistures and van Genuchten-Mualem parameters) are essential for the accuracy of mathematical modeling, yet difficult to obtain experimentally. In this work, an estimation approach is developed to estimate the parameters and states of Richards equation simultaneously. In the proposed approach, parameter identifiability and sensitivity analysis are used to determine the most important parameters for estimation purpose. Three common estimation schemes (extended Kalman filter, ensemble Kalman filter and moving horizon estimation) are investigated. The estimation performance is compared and analyzed based on extensive simulations. Full article
(This article belongs to the Special Issue Mathematics and Engineering)
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<p>A schematic diagram of an agro-hydrological system.</p>
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<p>A flowchart of the procedure to determine the significant parameters and number of sensors.</p>
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<p>A schematic diagram of the investigated loam soil column.</p>
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<p>Selected trajectories of the process state and estimated states using MHE, extended Kalman filter (EKF) and ensemble Kalman filter (EnKF).</p>
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<p>Trajectories of estimated parameters using MHE, EKF and EnKF, compared with their actual values.</p>
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<p>Trajectories of RMSE measuring the estimation performance of MHE, EKF, and EnKF.</p>
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<p>Trajectories of RMSE measuring the error between actual model and estimated states and parameters of MHE using 4, 8 and 12 measurements.</p>
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<p>Trajectories of RMSE measuring actual model and estimated states and parameters of MHE with window sizes of 8, 12, 16 and 20.</p>
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11 pages, 730 KiB  
Article
Approximation Algorithm for the Single Machine Scheduling Problem with Release Dates and Submodular Rejection Penalty
by Xiaofei Liu and Weidong Li
Mathematics 2020, 8(1), 133; https://doi.org/10.3390/math8010133 - 16 Jan 2020
Cited by 12 | Viewed by 2166
Abstract
In this paper, we consider the single machine scheduling problem with release dates and nonmonotone submodular rejection penalty. We are given a single machine and multiple jobs with probably different release dates and processing times. For each job, it is either accepted and [...] Read more.
In this paper, we consider the single machine scheduling problem with release dates and nonmonotone submodular rejection penalty. We are given a single machine and multiple jobs with probably different release dates and processing times. For each job, it is either accepted and processed on the machine or rejected. The objective is to minimize the sum of the makespan of the accepted jobs and the rejection penalty of the rejected jobs which is determined by a nonmonotone submodular function. We design a combinatorial algorithm based on the primal-dual framework to deal with the problem, and study its property under two cases. For the general case where the release dates can be different, the proposed algorithm have an approximation ratio of 2. When all the jobs release at the same time, the proposed algorithm becomes a polynomial-time exact algorithm. Full article
21 pages, 956 KiB  
Article
Dynamic Modelling of Interactions between Microglia and Endogenous Neural Stem Cells in the Brain during a Stroke
by Awatif Jahman Alqarni, Azmin Sham Rambely and Ishak Hashim
Mathematics 2020, 8(1), 132; https://doi.org/10.3390/math8010132 - 16 Jan 2020
Cited by 6 | Viewed by 2885
Abstract
In this paper, we study the interactions between microglia and neural stem cells and the impact of these interactions on the brain cells during a stroke. Microglia cells, neural stem cells, the damage on brain cells from the stroke and the impacts these [...] Read more.
In this paper, we study the interactions between microglia and neural stem cells and the impact of these interactions on the brain cells during a stroke. Microglia cells, neural stem cells, the damage on brain cells from the stroke and the impacts these interactions have on living brain cells are considered in the design of mathematical models. The models consist of ordinary differential equations describing the effects of microglia on brain cells and the interactions between microglia and neural stem cells in the case of a stroke. Variables considered include: resident microglia, classically activated microglia, alternatively activated microglia, neural stem cells, tissue damage on cells in the brain, and the impacts these interactions have on living brain cells. The first model describes what happens in the brain at the stroke onset during the first three days without the generation of any neural stem cells. The second model studies the dynamic effect of microglia and neural stem cells on the brain cells following the generation of neural stem cells and potential recovery after this stage. We look at the stability and the instability of the models which are both studied analytically. The results show that the immune cells can help the brain by cleaning dead cells and stimulating the generation of neural stem cells; however, excessive activation may cause damage and affect the injured region. Microglia have beneficial and harmful functions after ischemic stroke. The microglia stimulate neural stem cells to generate new cells that substitute dead cells during the recovery stage but sometimes the endogenous neural stem cells are highly sensitive to inflammatory in the brain. Full article
(This article belongs to the Special Issue Mathematical Methods in Applied Sciences)
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<p>Residual error for the step and time of the numerical method in the SMD model.</p>
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<p>Residual error for the step and time of the numerical method in the SMNR model.</p>
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<p>The behaviour of the SMD model within 72 h from stroke onset and the SMNR model within 30 days after stroke onset.</p>
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14 pages, 833 KiB  
Review
Analysis of the Cryptographic Tools for Blockchain and Bitcoin
by Víctor Gayoso Martínez, Luis Hernández-Álvarez and Luis Hernández Encinas
Mathematics 2020, 8(1), 131; https://doi.org/10.3390/math8010131 - 15 Jan 2020
Cited by 29 | Viewed by 10903
Abstract
Blockchain is one of the most interesting emerging technologies nowadays, with applications ranging from cryptocurrencies to smart contracts. This paper presents a review of the cryptographic tools necessary to understand the fundamentals of this technology and the foundations of its security. Among other [...] Read more.
Blockchain is one of the most interesting emerging technologies nowadays, with applications ranging from cryptocurrencies to smart contracts. This paper presents a review of the cryptographic tools necessary to understand the fundamentals of this technology and the foundations of its security. Among other elements, hash functions, digital signatures, elliptic curves, and Merkle trees are reviewed in the scope of their usage as building blocks of this technology. Full article
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<p>Point addition on an elliptic curve defined over the field <math display="inline"><semantics> <mi mathvariant="double-struck">R</mi> </semantics></math>.</p>
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18 pages, 1053 KiB  
Article
Impacts of Thermal Radiation and Heat Consumption/Generation on Unsteady MHD Convection Flow of an Oldroyd-B Fluid with Ramped Velocity and Temperature in a Generalized Darcy Medium
by Talha Anwar, Ilyas Khan, Poom Kumam and Wiboonsak Watthayu
Mathematics 2020, 8(1), 130; https://doi.org/10.3390/math8010130 - 15 Jan 2020
Cited by 19 | Viewed by 2903
Abstract
This article analyzes the time-dependent magnetohydrodynamic flow of Oldroyed-B fluid in the presence of heat consumption/generation and thermal radiation. The flow is restricted to a vertical infinite plate saturated in porous material along with ramp wall velocity and ramp wall temperature conditions. This [...] Read more.
This article analyzes the time-dependent magnetohydrodynamic flow of Oldroyed-B fluid in the presence of heat consumption/generation and thermal radiation. The flow is restricted to a vertical infinite plate saturated in porous material along with ramp wall velocity and ramp wall temperature conditions. This flow also incorporates the generalized Darcy’s law. In this paper, accurate equation of velocity field is presented first and then solutions of mass and energy equation are derived in Laplace domain. Real-time domain solutions are obtained by tackling the complexity of Laplace domain expressions through numerical Laplace inversion. Skin friction coefficient and Nusselt number are also calculated. A comparison for ramp wall temperature condition and isothermal temperature condition is also drawn to investigate the difference. A graphical study is conducted to analyze the influence of parameters on fluid flow and heat transfer. It is found that radiation parameter and heat generation elevate the energy profile, while flow is accelerated by increasing the retardation time and porosity parameter and an opposite behavior is noted for increasing relaxation time and magnetic parameter. Furthermore, heat transfer rate is higher for increasing Prandtl number and velocity on plate decreases with increase in relaxation time λ 1 . Full article
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<p>Geometrical presentation of flow.</p>
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<p>Velocity comparison.</p>
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<p>Effect of different values of <math display="inline"><semantics> <mi>Gr</mi> </semantics></math>.</p>
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<p>Effect of different values of <math display="inline"><semantics> <mi mathvariant="normal">M</mi> </semantics></math>.</p>
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<p>Effect of different values of <math display="inline"><semantics> <mi mathvariant="normal">K</mi> </semantics></math>.</p>
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<p>Effect of different values of <math display="inline"><semantics> <msub> <mi>λ</mi> <mn>1</mn> </msub> </semantics></math>.</p>
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<p>Effect of different values of <math display="inline"><semantics> <mi>Pr</mi> </semantics></math>.</p>
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<p>Effect of different values of <math display="inline"><semantics> <mi>Nr</mi> </semantics></math>.</p>
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<p>Effect of different values of <math display="inline"><semantics> <msub> <mi>λ</mi> <mn>2</mn> </msub> </semantics></math>.</p>
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<p>Effect of different values of <math display="inline"><semantics> <mi>τ</mi> </semantics></math>.</p>
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<p>Temperature comparison.</p>
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<p>Effect of different values of <math display="inline"><semantics> <mi>Pr</mi> </semantics></math>.</p>
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<p>Effect of different values of <math display="inline"><semantics> <mi>Nr</mi> </semantics></math>.</p>
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<p>Effect of different values of <math display="inline"><semantics> <mi mathvariant="normal">Q</mi> </semantics></math>.</p>
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<p>Effect of different values of <math display="inline"><semantics> <mi>τ</mi> </semantics></math>.</p>
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<p>Nusselt number for different values of <math display="inline"><semantics> <mi>Pr</mi> </semantics></math>.</p>
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<p>Nusselt number for different values of <math display="inline"><semantics> <mi>Nr</mi> </semantics></math>.</p>
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<p>Nusselt number for different values of <math display="inline"><semantics> <mi mathvariant="normal">Q</mi> </semantics></math>.</p>
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<p>Skin friction for different values of <math display="inline"><semantics> <msub> <mi>λ</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>λ</mi> <mn>2</mn> </msub> </semantics></math>.</p>
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30 pages, 631 KiB  
Article
Design and Analysis of a Non-Iterative Estimator for Target Location in Multistatic Sonar Systems with Sensor Position Uncertainties
by Xin Wang, Zhi Yu, Le Yang and Ji Li
Mathematics 2020, 8(1), 129; https://doi.org/10.3390/math8010129 - 15 Jan 2020
Viewed by 2127
Abstract
Target location is the basic application of a multistatic sonar system. Determining the position/velocity vector of a target from the related sonar observations is a nonlinear estimation problem. The presence of possible sensor position uncertainties turns this problem into a more challenging hybrid [...] Read more.
Target location is the basic application of a multistatic sonar system. Determining the position/velocity vector of a target from the related sonar observations is a nonlinear estimation problem. The presence of possible sensor position uncertainties turns this problem into a more challenging hybrid parameter estimation problem. Conventional gradient-based iterative estimators suffer from the problems of initialization difficulties and local convergence. Even if there is no problem with initialization and convergence, a large computational cost is required in most cases. In view of these drawbacks, we develop a computationally efficient non-iterative position/velocity estimator. The main numerical computation involved is the weighted least squares optimization, which makes the estimator computationally efficient. Parameter transformation, model linearization and two-stage processing are exploited to prevent the estimator from iterative computation. Through performance analysis and experimental verification, we find that the proposed estimator reaches the hybrid Cramér–Rao bound and has linear computational complexity. Full article
(This article belongs to the Section Engineering Mathematics)
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<p>Flowchart of the proposed estimator. Algorithm 1 is called in the flowchart.</p>
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<p>Nominal location geometry for computer simulations.</p>
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<p>RMSE and HCRB for position estimator.</p>
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<p>RMSE and HCRB for velocity estimator.</p>
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<p>Surface plot of norm of the approximate bias of <math display="inline"><semantics> <mover accent="true"> <mi mathvariant="bold">u</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>.</p>
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<p>Surface plot of norm of the approximate bias of <math display="inline"><semantics> <mover accent="true"> <mover accent="true"> <mi mathvariant="bold">u</mi> <mo>˙</mo> </mover> <mo stretchy="false">^</mo> </mover> </semantics></math>.</p>
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<p>Normalized running time for locating multiple disjoint targets.</p>
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<p>Normalized running time for locating multiple disjoint targets.</p>
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<p>Normalized running time for locating multiple disjoint targets.</p>
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15 pages, 309 KiB  
Article
Multiplicity of Radially Symmetric Small Energy Solutions for Quasilinear Elliptic Equations Involving Nonhomogeneous Operators
by Jun Ik Lee and Yun-Ho Kim
Mathematics 2020, 8(1), 128; https://doi.org/10.3390/math8010128 - 15 Jan 2020
Cited by 4 | Viewed by 1737
Abstract
We investigate the multiplicity of radially symmetric solutions for the quasilinear elliptic equation of Kirchhoff type. This paper is devoted to the study of the L -bound of solutions to the problem above by applying De Giorgi’s iteration method and the localization [...] Read more.
We investigate the multiplicity of radially symmetric solutions for the quasilinear elliptic equation of Kirchhoff type. This paper is devoted to the study of the L -bound of solutions to the problem above by applying De Giorgi’s iteration method and the localization method. Employing this, we provide the existence of multiple small energy radially symmetric solutions whose L -norms converge to zero. We utilize the modified functional method and the dual fountain theorem as the main tools. Full article
(This article belongs to the Section Difference and Differential Equations)
20 pages, 361 KiB  
Article
Banach Lattice Structures and Concavifications in Banach Spaces
by Lucia Agud, Jose Manuel Calabuig, Maria Aranzazu Juan and Enrique A. Sánchez Pérez
Mathematics 2020, 8(1), 127; https://doi.org/10.3390/math8010127 - 14 Jan 2020
Viewed by 3048
Abstract
Let ( Ω , Σ , μ ) be a finite measure space and consider a Banach function space Y ( μ ) . We say that a Banach space E is representable by Y ( μ ) if there is a continuous [...] Read more.
Let ( Ω , Σ , μ ) be a finite measure space and consider a Banach function space Y ( μ ) . We say that a Banach space E is representable by Y ( μ ) if there is a continuous bijection I : Y ( μ ) E . In this case, it is possible to define an order and, consequently, a lattice structure for E in such a way that we can identify it as a Banach function space, at least regarding some local properties. General and concrete applications are shown, including the study of the notion of the pth power of a Banach space, the characterization of spaces of operators that are isomorphic to Banach lattices of multiplication operators, and the representation of certain spaces of homogeneous polynomials on Banach spaces as operators acting in function spaces. Full article
(This article belongs to the Section Mathematics and Computer Science)
22 pages, 316 KiB  
Article
Existence of Solutions for Fractional Multi-Point Boundary Value Problems on an Infinite Interval at Resonance
by Wei Zhang and Wenbin Liu
Mathematics 2020, 8(1), 126; https://doi.org/10.3390/math8010126 - 14 Jan 2020
Cited by 7 | Viewed by 2243
Abstract
This paper aims to investigate a class of fractional multi-point boundary value problems at resonance on an infinite interval. New existence results are obtained for the given problem using Mawhin’s coincidence degree theory. Moreover, two examples are given to illustrate the main results. [...] Read more.
This paper aims to investigate a class of fractional multi-point boundary value problems at resonance on an infinite interval. New existence results are obtained for the given problem using Mawhin’s coincidence degree theory. Moreover, two examples are given to illustrate the main results. Full article
11 pages, 286 KiB  
Article
The Simultaneous Strong Resolving Graph and the Simultaneous Strong Metric Dimension of Graph Families
by Ismael González Yero
Mathematics 2020, 8(1), 125; https://doi.org/10.3390/math8010125 - 14 Jan 2020
Cited by 1 | Viewed by 1957
Abstract
We consider in this work a new approach to study the simultaneous strong metric dimension of graphs families, while introducing the simultaneous version of the strong resolving graph. In concordance, we consider here connected graphs G whose vertex sets are represented as [...] Read more.
We consider in this work a new approach to study the simultaneous strong metric dimension of graphs families, while introducing the simultaneous version of the strong resolving graph. In concordance, we consider here connected graphs G whose vertex sets are represented as V ( G ) , and the following terminology. Two vertices u , v V ( G ) are strongly resolved by a vertex w V ( G ) , if there is a shortest w v path containing u or a shortest w u containing v. A set A of vertices of the graph G is said to be a strong metric generator for G if every two vertices of G are strongly resolved by some vertex of A. The smallest possible cardinality of any strong metric generator (SSMG) for the graph G is taken as the strong metric dimension of the graph G. Given a family F of graphs defined over a common vertex set V, a set S V is an SSMG for F , if such set S is a strong metric generator for every graph G F . The simultaneous strong metric dimension of F is the minimum cardinality of any strong metric generator for F , and is denoted by Sd s ( F ) . The notion of simultaneous strong resolving graph of a graph family F is introduced in this work, and its usefulness in the study of Sd s ( F ) is described. That is, it is proved that computing Sd s ( F ) is equivalent to computing the vertex cover number of the simultaneous strong resolving graph of F . Several consequences (computational and combinatorial) of such relationship are then deduced. Among them, we remark for instance that we have proved the NP-hardness of computing the simultaneous strong metric dimension of families of paths, which is an improvement (with respect to the increasing difficulty of the problem) on the results known from the literature. Full article
(This article belongs to the Special Issue Distances and Domination in Graphs)
8 pages, 382 KiB  
Article
On the Diophantine Equation z(n) = (2 − 1/k)n Involving the Order of Appearance in the Fibonacci Sequence
by Eva Trojovská
Mathematics 2020, 8(1), 124; https://doi.org/10.3390/math8010124 - 14 Jan 2020
Cited by 8 | Viewed by 3476
Abstract
Let ( F n ) n 0 be the sequence of the Fibonacci numbers. The order (or rank) of appearance z ( n ) of a positive integer n is defined as the smallest positive integer m such that n divides [...] Read more.
Let ( F n ) n 0 be the sequence of the Fibonacci numbers. The order (or rank) of appearance z ( n ) of a positive integer n is defined as the smallest positive integer m such that n divides F m . In 1975, Sallé proved that z ( n ) 2 n , for all positive integers n. In this paper, we shall solve the Diophantine equation z ( n ) = ( 2 1 / k ) n for positive integers n and k. Full article
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<p>The graph of <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>∈</mo> </mrow> </semantics></math> [1, 20,000], with the upper bounds on the blue line <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>2</mn> <mi>n</mi> </mrow> </semantics></math> and the lower bounds on the red line <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>1</mn> <mo>+</mo> <mrow> <mo>(</mo> <mn>22</mn> <mo>−</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>/</mo> <mrow> <mo>(</mo> <msub> <mi>F</mi> <mn>22</mn> </msub> <mo>−</mo> <mn>1</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mi>n</mi> <mo>−</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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14 pages, 646 KiB  
Article
A Class of Itô Diffusions with Known Terminal Value and Specified Optimal Barrier
by Bernardo D’Auria and Alessandro Ferriero
Mathematics 2020, 8(1), 123; https://doi.org/10.3390/math8010123 - 14 Jan 2020
Cited by 1 | Viewed by 1972
Abstract
In this paper, we study the optimal stopping-time problems related to a class of Itô diffusions, modeling for example an investment gain, for which the terminal value is a priori known. This could be the case of an insider trading or of the [...] Read more.
In this paper, we study the optimal stopping-time problems related to a class of Itô diffusions, modeling for example an investment gain, for which the terminal value is a priori known. This could be the case of an insider trading or of the pinning at expiration of stock options. We give the explicit solution to these optimization problems and in particular we provide a class of processes whose optimal barrier has the same form as the one of the Brownian bridge. These processes may be a possible alternative to the Brownian bridge in practice as they could better model real applications. Moreover, we discuss the existence of a process with a prescribed curve as optimal barrier, for any given (decreasing) curve. This gives a modeling approach for the optimal liquidation time, i.e., the optimal time at which the investor should liquidate a position to maximize the gain. Full article
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<p>Plotting <math display="inline"><semantics> <mi>β</mi> </semantics></math> as a function of <math display="inline"><semantics> <mi>α</mi> </semantics></math>. The marked points are <math display="inline"><semantics> <mrow> <mi>β</mi> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> <mo>≈</mo> <mn>0.839924</mn> </mrow> </semantics></math>.</p>
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<p>Plots of the value function <math display="inline"><semantics> <mrow> <msup> <mi>V</mi> <mi>*</mi> </msup> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>, computed as in (<a href="#FD22-mathematics-08-00123" class="html-disp-formula">22</a>) associated with the process <math display="inline"><semantics> <mrow> <mo stretchy="false">{</mo> <msup> <mi>X</mi> <mi>s</mi> </msup> <mo stretchy="false">}</mo> </mrow> </semantics></math> in (<a href="#FD24-mathematics-08-00123" class="html-disp-formula">24</a>) for <span class="html-italic">d</span> = 2 and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>. The black curves represent the optimal stopping boundary <math display="inline"><semantics> <mrow> <mi>b</mi> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> <mo>=</mo> <msup> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo>−</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msup> <mi>V</mi> <mi>*</mi> </msup> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> as a funtion of <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>]</mo> </mrow> </semantics></math> for fixed values of <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>∈</mo> <mo stretchy="false">{</mo> <mn>0</mn> <mo>,</mo> <mn>0.2</mn> <mo>,</mo> <mn>0.4</mn> <mo>,</mo> <mn>0.6</mn> <mo>,</mo> <mn>0.8</mn> <mo stretchy="false">}</mo> </mrow> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <mrow> <msup> <mi>V</mi> <mi>*</mi> </msup> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> as a funtion of <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo stretchy="false">)</mo> <mo>∈</mo> <msup> <mrow> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>]</mo> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math>.</p>
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<p>Four simulations of <math display="inline"><semantics> <msub> <mi>X</mi> <mi>s</mi> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>]</mo> </mrow> </semantics></math>, as defined in (<a href="#FD26-mathematics-08-00123" class="html-disp-formula">26</a>) with <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, for six different values of <math display="inline"><semantics> <mi>α</mi> </semantics></math>. The light grey areas represent one standard deviation of <math display="inline"><semantics> <msub> <mi>X</mi> <mi>s</mi> </msub> </semantics></math> above and below its expected value (null in the simulations). The black solid curves represent the optimal stopping boundaries.</p>
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23 pages, 1437 KiB  
Article
The Trapezoidal Fuzzy Two-Dimensional Linguistic Power Generalized Hamy Mean Operator and Its Application in Multi-Attribute Decision-Making
by Yisheng Liu and Ye Li
Mathematics 2020, 8(1), 122; https://doi.org/10.3390/math8010122 - 13 Jan 2020
Cited by 6 | Viewed by 2033
Abstract
As a common information aggregation tool, the Hamy mean (HM) operator can consider the relationships among multiple input elements, but cannot adjust the effect of elements. In this paper, we integrate the idea of generalized a weighted average (GWA) operator into the HM [...] Read more.
As a common information aggregation tool, the Hamy mean (HM) operator can consider the relationships among multiple input elements, but cannot adjust the effect of elements. In this paper, we integrate the idea of generalized a weighted average (GWA) operator into the HM operator, and reduce the influence of related elements by adjusting the value of the parameter. In addition, considering that extreme input data may lead to a deviation in the results, we further combine the power average (PA) operator with HM, and propose the power generalized Hamy mean (PGHM) operator. Then, we extend the PGHM operator to the trapezoidal fuzzy two-dimensional linguistic environment, and propose two new information aggregation tools, the trapezoidal fuzzy two-dimensional linguistic power generalized Hamy mean (TF2DLPGHM) operator and the weighted TF2DLPGHM (WTF2DLPGHM) operator. Some properties and special cases of these operators are discussed. Furthermore, based on the proposed WTF2DLPGHM operator, a new multi-attribute decision-making method is proposed for lean management evaluation of industrial residential projects. Finally, an example is given to show the specific steps, effectiveness, and superiority of the method. Full article
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<p>Expected values of the alternatives with different parameter <inline-formula><mml:math id="mm456" display="block"><mml:semantics><mml:mi>τ</mml:mi></mml:semantics></mml:math></inline-formula>.</p>
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17 pages, 1621 KiB  
Article
Mathematical Model and Evaluation Function for Conflict-Free Warranted Makespan Minimization of Mixed Blocking Constraint Job-Shop Problems
by Christophe Sauvey, Wajdi Trabelsi and Nathalie Sauer
Mathematics 2020, 8(1), 121; https://doi.org/10.3390/math8010121 - 13 Jan 2020
Cited by 6 | Viewed by 3278
Abstract
In this paper, we consider a job-shop scheduling problem with mixed blocking constraints. Contrary to most previous studies, where no blocking or only one type of blocking constraint was used among successive operations, we assume that, generally, we may address several different blocking [...] Read more.
In this paper, we consider a job-shop scheduling problem with mixed blocking constraints. Contrary to most previous studies, where no blocking or only one type of blocking constraint was used among successive operations, we assume that, generally, we may address several different blocking constraints in the same scheduling problem depending on the intermediate storage among machines, the characteristics of the machines, the technical constraints, and even the jobs. Our objective was to schedule a set of jobs to minimize the makespan. Thus, we propose, for the first time, a mathematical model of the job-shop problem taking into account the general case of mixed blocking constraints, and the results were obtained using Mosel Xpress software. Then, after explaining why and how groups of jobs have to be processed, a blocking constraint conflict-free warranted evaluation function is proposed and tested with the particle swarm optimization and genetic algorithm methods. The results prove that we obtained a near-optimal solution to this problem in a very short time. Full article
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<p>Jobshop with different blocking constraints. (<b>a</b>) <span class="html-italic">Wb</span>, (<b>b</b>) <span class="html-italic">RCb</span>*, (<b>c</b>) <span class="html-italic">RSb</span>, and (<b>d</b>) <span class="html-italic">RCb</span>.</p>
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<p>JS problem with mixed blocking constraints. (<b>a</b>) blocking constraints matrix, and (<b>b</b>) corresponding Gantt chart.</p>
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<p>Blocking constraint matrices used for the (<span class="html-italic">n</span> = 5, <span class="html-italic">m</span> = 5) test.</p>
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<p>Release times of machine <span class="html-italic">M</span><sub>1</sub> in the function of blocking constraints.</p>
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<p>Mixed blocking evaluation function mechanism.</p>
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27 pages, 11681 KiB  
Article
Analyzing the Causality and Dependence between Gold Shocks and Asian Emerging Stock Markets: A Smooth Transition Copula Approach
by Woraphon Yamaka and Paravee Maneejuk
Mathematics 2020, 8(1), 120; https://doi.org/10.3390/math8010120 - 13 Jan 2020
Cited by 15 | Viewed by 3709
Abstract
This study aims to investigate the causality and dependence structure of gold shocks and Asian emerging stock markets. The positive and negative shocks of gold prices are quantified, and Granger causality-based Vector autoregressive and Copula approaches are employed to measure the causality and [...] Read more.
This study aims to investigate the causality and dependence structure of gold shocks and Asian emerging stock markets. The positive and negative shocks of gold prices are quantified, and Granger causality-based Vector autoregressive and Copula approaches are employed to measure the causality and contagion effect, respectively, between the positive and negative gold shocks and Asian emerging stock markets’ volatilities. In addition, the nonlinear link between gold and stock markets is of concern and this motivates us to propose a Smooth Transition Dynamic Copula that allows for the structural change in time-varying dependence between gold shocks and Asian stock markets’ volatilities. Several Copula families are also considered, and the best-fit Copula model is used to explain the correlation or contagion effects. The findings of the study show that there is some significant causality between gold shocks and Asian stock markets’ volatilities in some parts of the sample period. We also observe a stronger correlation during the global financial crisis when compared to the pre- and post-crisis periods. In addition, the tail dependence is found between Indian stock and negative gold shock and between Korean stock and negative gold shock, which indicated the existence of the risk contagion effects between gold and these two stock markets. Full article
(This article belongs to the Special Issue Advanced Methods in Mathematical Finance)
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<p>Rolling window bootstrapped <span class="html-italic">p</span>-values of LR test statistic testing of 10 pairs: BSESN-PGS, SET-PGS, KOS-PGS, PSE-PGS, JKS-PGS, BSESN-NGS, SET-NGS, KOS-NGS, PSE-NGS, and JKS-NGS.</p>
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<p>Rolling window bootstrapped <span class="html-italic">p</span>-values of LR test statistic testing of 10 pairs: HSI -PGS, QE-PGS, SSE-PGS, TAD-PGS, VNI-PGS, HSI -NGS, QE -NGS, SSE-NGS, TAD-NGS, and VNI-NGS.</p>
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<p>Time-varying Copula dependence measures. The graphs in the left column plot the time-varying Kendall’s tau between stock volatility and positive gold shock, while those in the right column plot the time-varying Kendall’s tau between stock volatility and negative gold shock for SSEC, BSESN, JKSE, KOSPI, and HSI markets: The black dashed line is the Static Copula, the green line is the Dynamic Copula, the red line is the Markov Switching Dynamic Copula (MSDC), and the blue line is the Smooth Transition Dynamic Copula (STDC).</p>
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<p>Time-varying Copula dependence measures. The graphs in the left column plot the time-varying Kendall’s tau between stock volatility and positive gold shock, while those in the right column plot the time-varying Kendall’s tau between stock volatility and negative gold shock for SSEC, BSESN, JKSE, KOSPI, and HSI markets: The black dashed line is the Static Copula, the green line is the Dynamic Copula, the red line is the Markov Switching Dynamic Copula (MSDC), and the blue line is the Smooth Transition Dynamic Copula (STDC).</p>
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<p>Time-varying Copula dependence measures. The graphs in the left column plot the time-varying Kendall’s tau between stock volatility and positive gold shock, while those in the right column plot the time-varying Kendall’s tau between stock volatility and negative gold shock for <b>PSI</b>, <b>TASI</b>, QE, SET, and VNI markets: The black dashed line is the Static Copula, the green line is the Dynamic Copula, the red line is the MSDC, and the blue line is the STDC.</p>
Full article ">Figure 4 Cont.
<p>Time-varying Copula dependence measures. The graphs in the left column plot the time-varying Kendall’s tau between stock volatility and positive gold shock, while those in the right column plot the time-varying Kendall’s tau between stock volatility and negative gold shock for <b>PSI</b>, <b>TASI</b>, QE, SET, and VNI markets: The black dashed line is the Static Copula, the green line is the Dynamic Copula, the red line is the MSDC, and the blue line is the STDC.</p>
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<p>The evolution of the tail dependence in the BSESN-NGS and KOSPI-NGS pairs.</p>
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20 pages, 10035 KiB  
Article
Improving Initial Guess for the Iterative Solution of Linear Equation Systems in Incompressible Flow
by Shuai Ye, Yufei Lin, Liyang Xu and Jiaming Wu
Mathematics 2020, 8(1), 119; https://doi.org/10.3390/math8010119 - 13 Jan 2020
Cited by 3 | Viewed by 4297
Abstract
The pressure equation, generated while solving the incompressible Navier–Stokes equations with the segregated iterative algorithm such as PISO, produces a series of linear equation systems as the time step advances. In this paper, we target at accelerating the iterative solution of these linear [...] Read more.
The pressure equation, generated while solving the incompressible Navier–Stokes equations with the segregated iterative algorithm such as PISO, produces a series of linear equation systems as the time step advances. In this paper, we target at accelerating the iterative solution of these linear systems by improving their initial guesses. We propose a weighted group extrapolation method to obtain a superior initial guess instead of a general one, the solution of the previous linear equation system. In this method, the previous solutions that are used to extrapolate the predicted solutions are carefully organized to address the oscillatory solution on each grid. The proposed method uses a weighted average of the predicted solutions as the new initial guess to avoid over extrapolating. Three numerical test results show that the proposed method can accelerate the iterative solution of most linear equation systems and reduce the simulation time up to 61.3%. Full article
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<p>The flowchart of PIMPLE algorithm and the difference using the proposed method.</p>
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<p>The trace of the fields in the PIMPLE algorithm from time step <span class="html-italic">n</span> to time step <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>The illustration of the oscillatory field in the incompressible flow.</p>
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<p>An example of illustration of the lanes, windows, and groups in the method (<math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>W</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>G</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>). (<b>a</b>) The solution sequence as the time step advances; the points with different shapes represent solutions from different correctors. (<b>b</b>) The partition of the linear systems, and each line represents a lane; the rectangle represents the window; the points with different colors belong to different groups.</p>
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<p>The mesh used in the test case of pitzDaily.</p>
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<p>The residual history of the specific linear equation system in pitzDaily.</p>
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<p>The distribution of the <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </semantics></math> field at 0.3s using different methods in pitzDaily.</p>
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<p>(<b>a</b>) The probe locations, and (<b>b</b>) the corresponding <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </semantics></math> field at 0.3s using different methods in pitzDaily.</p>
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<p>The iteration numbers of the linear equation systems in pitzDaily.</p>
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<p>The computational domain and near mesh of 2D NACA0012 airfoil.</p>
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<p>The distribution and contour of the <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </semantics></math> field at 0.1 s using different methods in 2D NACA0012 airfoil.</p>
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<p>(<b>a</b>) The probe locations on the surface of the airfoil, (<b>b</b>) the corresponding <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </semantics></math> field at 0.1 s, and (<b>c</b>) the continuity errors using different methods in 2D NACA0012.</p>
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<p>The iteration numbers of the linear equations in 2D NACA0012 airfoil.</p>
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<p>The computational domain and near mesh of 3D blended-wing-body airfoil.</p>
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<p>The distribution of <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </semantics></math> field at 0.1 s using different methods in 3D blended-wing-body airfoil.</p>
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<p>(<b>a</b>) The probe locations on the cross-sections of the airfoil, (<b>b</b>) the corresponding <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>R</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </semantics></math> field at 0.1 s, and (<b>c</b>) the continuity errors using different methods in 3D blended-wing-body airfoil.</p>
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<p>The iteration numbers of the linear equations in 3D blended-wing-body airfoil.</p>
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16 pages, 3338 KiB  
Article
On the Number of Shortest Weighted Paths in a Triangular Grid
by Benedek Nagy and Bashar Khassawneh
Mathematics 2020, 8(1), 118; https://doi.org/10.3390/math8010118 - 13 Jan 2020
Cited by 3 | Viewed by 3123
Abstract
Counting the number of shortest paths in various graphs is an important and interesting combinatorial problem, especially in weighted graphs with various applications. We consider a specific infinite graph here, namely the honeycomb grid. Changing to its dual, the triangular grid, paths between [...] Read more.
Counting the number of shortest paths in various graphs is an important and interesting combinatorial problem, especially in weighted graphs with various applications. We consider a specific infinite graph here, namely the honeycomb grid. Changing to its dual, the triangular grid, paths between triangle pixels (we abbreviate this term to trixels) are counted. The number of shortest weighted paths between any two trixels of the triangular grid is discussed. For each trixel, there are three different types of neighbor trixels, 1-, 2- and 3-neighbours, depending the Euclidean distance of their midpoints. When considering weighted distances, the positive values α, β and γ are assigned to the ‘steps’ to various neighbors. We gave formulae for the number of shortest weighted paths between any two trixels in various cases by the respective weight values. The results are nicely connected to various numbers well-known in combinatorics, e.g., to binomial coefficients and Fibonacci numbers. Full article
(This article belongs to the Special Issue Discrete and Computational Geometry)
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Graphical abstract

Graphical abstract
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<p>Coordinate system for the triangular grid with the Origin and the axes.</p>
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<p>Three types of neighbors of trixel 0. 1-neighbors are red, 2-neighbors are yellow and 3-neighbors are green.</p>
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<p>The number of shortest weighed paths from trixel (0,0,0) to other trixels of the grid with the condition 2<span class="html-italic">α</span> &lt; <span class="html-italic">β</span> and 3<span class="html-italic">α</span> &lt; <span class="html-italic">γ</span>.</p>
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<p>The number of shortest weighed paths between trixel (0,0,0) and some other trixels in the case 2<span class="html-italic">α</span> &gt; <span class="html-italic">β</span> and 3<span class="html-italic">α</span> &lt; <span class="html-italic">γ.</span></p>
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<p>The number of shortest weighed paths from trixel (0,0,0) to some other trixels with the condition 2<span class="html-italic">α</span> &lt; <span class="html-italic">β</span> &lt; <span class="html-italic">γ</span> &lt; 3<span class="html-italic">α</span>.</p>
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<p>The number of the shortest weighed paths from trixel the origin to some other trixels with the condition 2<span class="html-italic">α</span> = <span class="html-italic">β</span> and 3<span class="html-italic">α</span> &lt; <span class="html-italic">γ</span>.</p>
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<p>The number of shortest weighed paths from trixel (0,0,0) to all other displayed trixels in the case of 2<span class="html-italic">α</span> &lt; <span class="html-italic">β</span> and 3<span class="html-italic">α</span> = <span class="html-italic">γ.</span></p>
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20 pages, 2323 KiB  
Article
Nonlocal Reaction–Diffusion Model of Viral Evolution: Emergence of Virus Strains
by Nikolai Bessonov, Gennady Bocharov, Andreas Meyerhans, Vladimir Popov and Vitaly Volpert
Mathematics 2020, 8(1), 117; https://doi.org/10.3390/math8010117 - 12 Jan 2020
Cited by 10 | Viewed by 5053
Abstract
This work is devoted to the investigation of virus quasi-species evolution and diversification due to mutations, competition for host cells, and cross-reactive immune responses. The model consists of a nonlocal reaction–diffusion equation for the virus density depending on the genotype considered to be [...] Read more.
This work is devoted to the investigation of virus quasi-species evolution and diversification due to mutations, competition for host cells, and cross-reactive immune responses. The model consists of a nonlocal reaction–diffusion equation for the virus density depending on the genotype considered to be a continuous variable and on time. This equation contains two integral terms corresponding to the nonlocal effects of virus interaction with host cells and with immune cells. In the model, a virus strain is represented by a localized solution concentrated around some given genotype. Emergence of new strains corresponds to a periodic wave propagating in the space of genotypes. The conditions of appearance of such waves and their dynamics are described. Full article
(This article belongs to the Special Issue Mathematical Modelling in Biomedicine)
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<p>(<b>Left</b>) Graphical solution of Equation (<a href="#FD9-mathematics-08-00117" class="html-disp-formula">9</a>): functions <math display="inline"><semantics> <mrow> <mo>(</mo> <mo form="prefix">sin</mo> <mo>(</mo> <mi>ξ</mi> <mi>N</mi> <mo>)</mo> <mo>)</mo> <mo>/</mo> <mo>(</mo> <mi>ξ</mi> <mi>N</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>−</mo> <msup> <mi>f</mi> <mo>′</mo> </msup> <mrow> <mo>(</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>−</mo> <mi>D</mi> <msup> <mi>ξ</mi> <mn>2</mn> </msup> <mo>/</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mi>f</mi> <mo>′</mo> </msup> <mrow> <mo>(</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mo>−</mo> <mn>0.34</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.00033</mn> <mo>,</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.93</mn> </mrow> </semantics></math>. (<b>Right</b>) Graphical solution of Equation (<a href="#FD12-mathematics-08-00117" class="html-disp-formula">12</a>): the function <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>b</mi> <mover accent="true"> <mi>ψ</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for the values of parameters <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>3</mn> <mo>,</mo> <msub> <mi>N</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> (solid line), <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>3</mn> <mo>,</mo> <msub> <mi>N</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> (dashed line), and the function <math display="inline"><semantics> <mrow> <mo>−</mo> <mi>D</mi> <mo>/</mo> <msub> <mi>u</mi> <mo>−</mo> </msub> <msup> <mi>ξ</mi> <mn>2</mn> </msup> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>/</mo> <msub> <mi>u</mi> <mo>−</mo> </msub> <mo>=</mo> <mn>0.1508</mn> </mrow> </semantics></math> (point line).</p>
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<p>Numerical simulations of Equation (<a href="#FD14-mathematics-08-00117" class="html-disp-formula">14</a>) for the linear function <math display="inline"><semantics> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mi>u</mi> </mrow> </semantics></math>. Spatial and temporal perturbation decay if the solution <math display="inline"><semantics> <msub> <mi>u</mi> <mo>−</mo> </msub> </semantics></math> is stable (<b>left</b>). The spatial perturbation at the center of the interval leads to the emergence of a spatiotemporal pattern propagating from the center and gradually filling the whole spatial domain (<b>right</b>). The value of parameters: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>1.5</mn> <mo>,</mo> <mi>D</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>. Here and in all figures below, <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, unless another value is indicated.</p>
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<p>Snapshots of different regimes of wave propagation in numerical simulations of Equation (<a href="#FD19-mathematics-08-00117" class="html-disp-formula">19</a>) in the monostable–bistable case. The speed of the monostable wave is greater than the speed of the bistable wave, and the distance between them grows (<b>upper row, left</b>). The intermediate equilibrium between the wave becomes unstable, and the monostable wave is space periodic (<b>upper row, middle</b>). This periodic wave can be followed by complex spatiotemporal oscillations (<b>upper row, right</b>). The lower row shows the position of local maxima of the same solutions on the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>-plane. Reprinted from [<a href="#B37-mathematics-08-00117" class="html-bibr">37</a>] with permission.</p>
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<p>Numerical simulations of Equation (<a href="#FD17-mathematics-08-00117" class="html-disp-formula">17</a>) show the waves propagating from the center of the interval towards its boundaries in the monostable case. In the first monostable case (<b>left</b>) the periodic perturbation propagates slower than the <math display="inline"><semantics> <mrow> <mo>[</mo> <msub> <mi>u</mi> <mo>+</mo> </msub> <mo>,</mo> <msub> <mi>u</mi> <mo>−</mo> </msub> <mo>]</mo> </mrow> </semantics></math>-wave, and the distance between them grows. In the second monostable case (<b>right</b>), the periodic perturbation propagates faster, it merges with the wave, and they form a single periodic wave. The values of parameters: <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mo>,</mo> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> <mo>,</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>0.035</mn> </mrow> </semantics></math> (<b>left</b>), <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> (<b>right</b>); <math display="inline"><semantics> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mi>u</mi> <msup> <mi>e</mi> <mrow> <mo>−</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> <mi>u</mi> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>Numerical simulations of Equation (<a href="#FD17-mathematics-08-00117" class="html-disp-formula">17</a>) with <math display="inline"><semantics> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mi>u</mi> </mrow> </semantics></math>. If the solution <math display="inline"><semantics> <msub> <mi>u</mi> <mo>−</mo> </msub> </semantics></math> is stable, then there is a <math display="inline"><semantics> <mrow> <mo>[</mo> <msub> <mi>w</mi> <mo>+</mo> </msub> <mo>,</mo> <msub> <mi>w</mi> <mo>−</mo> </msub> <mo>]</mo> </mrow> </semantics></math>-wave propagating with a constant speed and profile with possible spatial oscillations independent of time (<b>left</b>). If this solution is unstable, then this wave is followed by spatiotemporal oscillations (<b>right</b>). The values of parameters: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mi>D</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mo>,</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.9</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>3</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> (<b>left</b>), <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>150</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>Emergence of a periodic wave in numerical simulations of Equation (<a href="#FD17-mathematics-08-00117" class="html-disp-formula">17</a>). (<b>a</b>) At the first stage, solution growth remaining localized at the center of the interval. (<b>b</b>) Then it decreases and widens, and after some time, other peaks of solution appear. (<b>c</b>) Another representation of the same solution as in (<b>b</b>). Values of parameters: <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mo>,</mo> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>(</mo> <mi>u</mi> <mo>)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, the maximum of the initial condition <math display="inline"><semantics> <mrow> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>75</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 7
<p>Numerical simulations of Equation (<a href="#FD17-mathematics-08-00117" class="html-disp-formula">17</a>) with two different initial conditions and the same values of parameters: <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mo>,</mo> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <msup> <mi>e</mi> <mrow> <mo>−</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> <mi>u</mi> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math>, the maximum of the initial condition equals <math display="inline"><semantics> <mrow> <mn>0.1</mn> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <mn>0.9</mn> </mrow> </semantics></math> (<b>right</b>), <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.48</mn> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.52</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>75</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>Numerical simulations of Equation (<a href="#FD17-mathematics-08-00117" class="html-disp-formula">17</a>). Virus evolution with time delay in the term describing the immune response represented as level lines of the solution <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> on the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>-plane. Different regimes coexist for the same values of parameters depending on the initial conditions, with high initial viral load (<b>left</b>) and low initial viral load (<b>middle</b>). Values of parameters: <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> <mo>,</mo> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mspace width="3.33333pt"/> <mo>=</mo> <mspace width="3.33333pt"/> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <msup> <mi>e</mi> <mrow> <mo>−</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> <mi>u</mi> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>80</mn> </mrow> </semantics></math> (<b>left</b> and <b>middle</b>), <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> (<b>right</b>); the maximum of the initial condition <math display="inline"><semantics> <mrow> <mn>0.9</mn> </mrow> </semantics></math> (<b>left</b>), <math display="inline"><semantics> <mrow> <mn>0.1</mn> </mrow> </semantics></math> (<b>middle</b> and <b>right</b>).</p>
Full article ">Figure 9
<p>Numerical simulations of Equation (<a href="#FD17-mathematics-08-00117" class="html-disp-formula">17</a>). Virus evolution without immune response and with the genotype-dependent mortality <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </semantics></math> represented as level lines of the solution <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> on the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>-plane. Values of parameters: <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mo>,</mo> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>0.09</mn> </mrow> </semantics></math> (<b>left</b>), <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>0.09</mn> </mrow> </semantics></math> (<b>middle</b>), <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math> (<b>right</b>).</p>
Full article ">Figure A1
<p>Numerical simulations of Equation (<a href="#FD14-mathematics-08-00117" class="html-disp-formula">14</a>) for the linear function <math display="inline"><semantics> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mi>u</mi> </mrow> </semantics></math>. Level lines of the solution <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> on the plane <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> (<b>left</b>). Two snapshots of solution (<b>right</b>). The value of parameters: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.95</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.0001</mn> </mrow> </semantics></math> (<b>left</b>), <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.00001</mn> </mrow> </semantics></math> (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>150</mn> </mrow> </semantics></math>.</p>
Full article ">Figure A2
<p>Level lines of the solution <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> of Equation (<a href="#FD17-mathematics-08-00117" class="html-disp-formula">17</a>) on the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>-plane. Values of parameters: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>(</mo> <mi>u</mi> <mo>)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (left and middle), the maximum of the initial condition <math display="inline"><semantics> <mrow> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.0001</mn> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>35</mn> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.0005</mn> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> (<b>right</b>).</p>
Full article ">Figure A3
<p>Level lines of the solution <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> of Equation (<a href="#FD17-mathematics-08-00117" class="html-disp-formula">17</a>) on the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>-plane. Values of parameters: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>(</mo> <mi>u</mi> <mo>)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (left and middle), the maximum of the initial condition <math display="inline"><semantics> <mrow> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.00001</mn> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>130</mn> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>0.0001</mn> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>75</mn> </mrow> </semantics></math> (<b>right</b>).</p>
Full article ">Figure A4
<p>Level lines of the solution <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> of Equation (<a href="#FD17-mathematics-08-00117" class="html-disp-formula">17</a>). The values of parameters: <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mi>D</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mo>,</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.9</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>4</mn> <mo>,</mo> <mi>N</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math> (<b>left</b>), <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> (<b>right</b>), <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>150</mn> </mrow> </semantics></math>.</p>
Full article ">
35 pages, 473 KiB  
Article
Cohomology of Presheaves of Monoids
by Pilar Carrasco and Antonio M. Cegarra
Mathematics 2020, 8(1), 116; https://doi.org/10.3390/math8010116 - 12 Jan 2020
Cited by 1 | Viewed by 2345
Abstract
The purpose of this work is to extend Leech cohomology for monoids (and so Eilenberg-Mac Lane cohomology of groups) to presheaves of monoids on an arbitrary small category. The main result states and proves a cohomological classification of monoidal prestacks on a category [...] Read more.
The purpose of this work is to extend Leech cohomology for monoids (and so Eilenberg-Mac Lane cohomology of groups) to presheaves of monoids on an arbitrary small category. The main result states and proves a cohomological classification of monoidal prestacks on a category with values in groupoids with abelian isotropy groups. The paper also includes a cohomological classification for extensions of presheaves of monoids, which is useful to the study of H -extensions of presheaves of regular monoids. The results apply directly in several settings such as presheaves of monoids on a topological space, simplicial monoids, presheaves of simplicial monoids on a topological space, monoids or simplicial monoids on which a fixed monoid or group acts, and so forth. Full article
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