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Mathematics, Volume 9, Issue 17 (September-1 2021) – 161 articles

Cover Story (view full-size image): The boundary value problem for the steady Navier–Stokes system is considered in a 2D multiply-connected bounded domain, with the boundary having a power cusp singularity at the point O. The case of a boundary value with nonzero flow rates over connected components of the boundary is studied. It is also supposed that there is a source/sink in O. In this case, the solution necessarily has an infinite Dirichlet integral. The existence of a solution to this problem is proved with the assumption that the flow rates are “sufficiently small”. This condition does not require the norm of the boundary data to be small. The solution is constructed as the sum of a function with the finite Dirichlet integral and a singular part coinciding with the asymptotic decomposition near the cusp point. View this paper.
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11 pages, 252 KiB  
Article
Uniform Dichotomy Concepts for Discrete-Time Skew Evolution Cocycles in Banach Spaces
by Ariana Găină, Mihail Megan and Carmen Florinela Popa
Mathematics 2021, 9(17), 2177; https://doi.org/10.3390/math9172177 - 6 Sep 2021
Cited by 5 | Viewed by 1798
Abstract
In the present paper, we consider the problem of dichotomic behaviors of dynamical systems described by discrete-time skew evolution cocycles in Banach spaces. We study two concepts of uniform dichotomy: uniform exponential dichotomy and uniform polynomial dichotomy. Some characterizations of these notions and [...] Read more.
In the present paper, we consider the problem of dichotomic behaviors of dynamical systems described by discrete-time skew evolution cocycles in Banach spaces. We study two concepts of uniform dichotomy: uniform exponential dichotomy and uniform polynomial dichotomy. Some characterizations of these notions and connections between these concepts are given. Full article
17 pages, 11882 KiB  
Article
Adaptive Levenberg–Marquardt Algorithm: A New Optimization Strategy for Levenberg–Marquardt Neural Networks
by Zhiqi Yan, Shisheng Zhong, Lin Lin and Zhiquan Cui
Mathematics 2021, 9(17), 2176; https://doi.org/10.3390/math9172176 - 6 Sep 2021
Cited by 23 | Viewed by 3975
Abstract
Engineering data are often highly nonlinear and contain high-frequency noise, so the Levenberg–Marquardt (LM) algorithm may not converge when a neural network optimized by the algorithm is trained with engineering data. In this work, we analyzed the reasons for the LM neural network’s [...] Read more.
Engineering data are often highly nonlinear and contain high-frequency noise, so the Levenberg–Marquardt (LM) algorithm may not converge when a neural network optimized by the algorithm is trained with engineering data. In this work, we analyzed the reasons for the LM neural network’s poor convergence commonly associated with the LM algorithm. Specifically, the effects of different activation functions such as Sigmoid, Tanh, Rectified Linear Unit (RELU) and Parametric Rectified Linear Unit (PRLU) were evaluated on the general performance of LM neural networks, and special values of LM neural network parameters were found that could make the LM algorithm converge poorly. We proposed an adaptive LM (AdaLM) algorithm to solve the problem of the LM algorithm. The algorithm coordinates the descent direction and the descent step by the iteration number, which can prevent falling into the local minimum value and avoid the influence of the parameter state of LM neural networks. We compared the AdaLM algorithm with the traditional LM algorithm and its variants in terms of accuracy and speed in the context of testing common datasets and aero-engine data, and the results verified the effectiveness of the AdaLM algorithm. Full article
(This article belongs to the Special Issue Applied Mathematics to Mechanisms and Machines)
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Figure 1

Figure 1
<p>Regardless of the value of <span class="html-italic">w</span>′, the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) curve monotonically decreases and intersects the <span class="html-italic">x</span>-axis at the point (0.5, 0). This means that when the conditions (<span class="html-italic">σ</span> = sigmoid, <span class="html-italic">h</span>′ ≤ 0.5, and <span class="html-italic">y</span><sub>label</sub> &gt; 0) are met, the algorithm has the possibility of divergence.</p>
Full article ">Figure 2
<p>When <span class="html-italic">σ</span> = tanh, the curve <span class="html-italic">g</span>(<span class="html-italic">w′</span>,<span class="html-italic">h′</span>) monotonically decreases: (<b>a</b>) when <span class="html-italic">h′</span>&lt; 0.3 and <span class="html-italic">w</span> &lt; 0, <math display="inline"><semantics> <mrow> <munder> <mrow> <mi>lim</mi> </mrow> <mrow> <mi>w</mi> <mo>→</mo> <mo>∞</mo> </mrow> </munder> <mi>g</mi> <mfenced> <mrow> <msup> <mi>w</mi> <mo>′</mo> </msup> <mo>,</mo> <msup> <mi>h</mi> <mo>′</mo> </msup> </mrow> </mfenced> </mrow> </semantics></math> = −3; (<b>b</b>) when <span class="html-italic">h′</span> &lt; −0.3 and <span class="html-italic">w</span> &gt; 0.5,<math display="inline"><semantics> <mrow> <mo> </mo> <munder> <mrow> <mi>lim</mi> </mrow> <mrow> <mi>w</mi> <mo>→</mo> <mo>∞</mo> </mrow> </munder> <mi>g</mi> <mfenced> <mrow> <msup> <mi>w</mi> <mo>′</mo> </msup> <mo>,</mo> <msup> <mi>h</mi> <mo>′</mo> </msup> </mrow> </mfenced> </mrow> </semantics></math> = −1. When the conditions (<span class="html-italic">σ</span> = tanh, <span class="html-italic">h′</span>&lt; 0.3, <span class="html-italic">w′</span> &lt; 0, and <span class="html-italic">y</span><sub>label</sub> &gt; 3) are met, the algorithm has the possibility of divergence.</p>
Full article ">Figure 3
<p>The curve <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) at <span class="html-italic">σ</span> = ReLU: (<b>a</b>) <span class="html-italic">w</span>′ &gt; 0; (<b>b</b>) <span class="html-italic">w</span>′ &lt; 0. When the conditions (<span class="html-italic">σ</span> = ReLU, <span class="html-italic">h</span>′ &lt; 0.3, <span class="html-italic">w</span>′ &lt; 0, and <span class="html-italic">y</span><sub>label</sub> &gt; 0) are met, the algorithm has the possibility of divergence.</p>
Full article ">Figure 4
<p>The curve <span class="html-italic">g</span>(<span class="html-italic">w′</span>,<span class="html-italic">h′</span>) at <span class="html-italic">σ =</span> PReLU. (<b>a</b>) <span class="html-italic">w’ &gt; 0</span>; (<b>b</b>) <span class="html-italic">w′</span> <span class="html-italic">&lt; 0.</span> When the conditions <span class="html-italic">(σ =</span> PReLU<span class="html-italic">, h’ &lt; 0</span>, <span class="html-italic">w’ &lt; 0,</span> and <span class="html-italic">y<sub>label</sub> &gt; 0)</span> are met, the algorithm has the possibility of divergence.</p>
Full article ">Figure 5
<p>The gray part is the coverage area of <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′): (<b>a</b>) The grey area covered by the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) function when <span class="html-italic">σ</span> = sigmoid and <span class="html-italic">y</span><sub>label</sub> &gt; 1. If <span class="html-italic">h′</span> ≤ 0.5, <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) is always positive; (<b>b</b>) The grey area covered by the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) function when σ = tanh, <span class="html-italic">y</span><sub>label</sub> &gt; 3, and <span class="html-italic">w</span> ≤ 0. If <span class="html-italic">h</span>′ ≤ 0.3, <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) is always positive; (<b>c</b>) The grey area covered by the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) function when σ = tanh, <span class="html-italic">y</span><sub>label</sub> &gt; 1, and <span class="html-italic">w</span> &gt; 0. If <span class="html-italic">h</span>′ ≤ 0, <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) is always positive; (<b>d</b>) The grey area covered by the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) function when σ = ReLU, <span class="html-italic">y</span><sub>label</sub> &gt; 0, and <span class="html-italic">w</span> &lt; 0. If <span class="html-italic">h</span>′ ≤ 0, <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) is always positive; (<b>e</b>) The grey area covered by the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) function when <span class="html-italic">σ</span> = PReLU, <span class="html-italic">y</span><sub>label</sub> &gt; 0, and <span class="html-italic">w</span> &lt; 0. <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) is always positive.</p>
Full article ">Figure 5 Cont.
<p>The gray part is the coverage area of <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′): (<b>a</b>) The grey area covered by the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) function when <span class="html-italic">σ</span> = sigmoid and <span class="html-italic">y</span><sub>label</sub> &gt; 1. If <span class="html-italic">h′</span> ≤ 0.5, <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) is always positive; (<b>b</b>) The grey area covered by the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) function when σ = tanh, <span class="html-italic">y</span><sub>label</sub> &gt; 3, and <span class="html-italic">w</span> ≤ 0. If <span class="html-italic">h</span>′ ≤ 0.3, <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) is always positive; (<b>c</b>) The grey area covered by the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) function when σ = tanh, <span class="html-italic">y</span><sub>label</sub> &gt; 1, and <span class="html-italic">w</span> &gt; 0. If <span class="html-italic">h</span>′ ≤ 0, <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) is always positive; (<b>d</b>) The grey area covered by the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) function when σ = ReLU, <span class="html-italic">y</span><sub>label</sub> &gt; 0, and <span class="html-italic">w</span> &lt; 0. If <span class="html-italic">h</span>′ ≤ 0, <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) is always positive; (<b>e</b>) The grey area covered by the <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) function when <span class="html-italic">σ</span> = PReLU, <span class="html-italic">y</span><sub>label</sub> &gt; 0, and <span class="html-italic">w</span> &lt; 0. <span class="html-italic">g</span>(<span class="html-italic">w</span>′,<span class="html-italic">h</span>′) is always positive.</p>
Full article ">Figure 6
<p>Neural network model.</p>
Full article ">Figure 7
<p>Aeroengine fuel flow data.</p>
Full article ">Figure 8
<p>MAE of models with different neural network nodes.</p>
Full article ">Figure 9
<p>The prediction effect of each algorithm: (<b>a</b>) LM; (<b>b</b>) HLM; (<b>c</b>) TSLM; (<b>d</b>) AdaLM.</p>
Full article ">
35 pages, 1427 KiB  
Article
On the Reachability of a Feedback Controlled Leontief-Type Singular Model Involving Scheduled Production, Recycling and Non-Renewable Resources
by Manuel De la Sen, Asier Ibeas and Santiago Alonso-Quesada
Mathematics 2021, 9(17), 2175; https://doi.org/10.3390/math9172175 - 6 Sep 2021
Cited by 3 | Viewed by 1709
Abstract
This paper proposes and studies the reachability of a singular regular dynamic discrete Leontief-type economic model which includes production industries, recycling industries, and non-renewable products in an integrated way. The designed prefixed final state to be reached, under discussed reachability conditions, is subject [...] Read more.
This paper proposes and studies the reachability of a singular regular dynamic discrete Leontief-type economic model which includes production industries, recycling industries, and non-renewable products in an integrated way. The designed prefixed final state to be reached, under discussed reachability conditions, is subject to necessary additional positivity-type constraints which depend on the initial conditions and the final time for the solution to match such a final prescribed state. It is assumed that the model may be driven by both the demand and an additional correcting control in order to achieve the final targeted state in finite time. Formal sufficiency-type conditions are established for the proposed singular Leontief model to be reachable under positive feedback, correcting controls designed for appropriate demand/supply regulation. Basically, the proposed regulation scheme allows fixing a prescribed final state of economic goods stock in finite time if the model is reachable. Full article
(This article belongs to the Special Issue Analysis and Mathematical Modeling of Economic - Related Data)
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Figure 1

Figure 1
<p>Evolution of stock vector <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>t</mi> </msub> </mrow> </semantics></math> under the control law (24) in Example 1.</p>
Full article ">Figure 2
<p>Demands vector components in Example 1.</p>
Full article ">Figure 2 Cont.
<p>Demands vector components in Example 1.</p>
Full article ">Figure 3
<p>Evolution of stock vector <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>t</mi> </msub> </mrow> </semantics></math> in Example 2.</p>
Full article ">Figure 4
<p>Demands vector in Example 2.</p>
Full article ">Figure 5
<p>Evolution of stocks vector <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>t</mi> </msub> </mrow> </semantics></math> in Example 3.</p>
Full article ">Figure 6
<p>Demands vector in Example 3.</p>
Full article ">Figure 7
<p>Evolution of the stock vector <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>t</mi> </msub> </mrow> </semantics></math> in Example 4.</p>
Full article ">Figure 8
<p>Feedback control signal in Example 4.</p>
Full article ">Figure 9
<p>Evolution of the stock vector <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>t</mi> </msub> </mrow> </semantics></math> for the uncontrolled case in Example 4.</p>
Full article ">Figure 9 Cont.
<p>Evolution of the stock vector <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>t</mi> </msub> </mrow> </semantics></math> for the uncontrolled case in Example 4.</p>
Full article ">Figure 10
<p>Evolution of the stock vector <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>t</mi> </msub> </mrow> </semantics></math> in Example 5 under a dyadic <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>x</mi> </msub> <mo>=</mo> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi>b</mi> <msubsup> <mover accent="true"> <mi>k</mi> <mo>^</mo> </mover> <mi>x</mi> <mi>T</mi> </msubsup> </mrow> </semantics></math> control matrix.</p>
Full article ">Figure 11
<p>Values of the control signal <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mi>t</mi> </msub> </mrow> </semantics></math> in Example 5.</p>
Full article ">Figure 12
<p>Relation between the 2-norm of the uncontrolled matrix <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mn>0</mn> </msub> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and error matrix <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>G</mi> <mo>˜</mo> </mover> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> in Example 6.</p>
Full article ">Figure 13
<p>Relation between the 2-norm of the controlled matrix <math display="inline"><semantics> <mrow> <mi>G</mi> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and error matrix <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>G</mi> <mo>˜</mo> </mover> <mo stretchy="false">(</mo> <mi>k</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> in Example 6.</p>
Full article ">Figure 14
<p>Output of the stable singular system <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>E</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>A</mi> <mn>0</mn> </msub> </mrow> </mfenced> </mrow> </semantics></math> in Example 7.</p>
Full article ">Figure 15
<p>Value of <math display="inline"><semantics> <mrow> <munder> <mrow> <mi>s</mi> <mi>u</mi> <mi>p</mi> </mrow> <mrow> <mi>θ</mi> <mo>∈</mo> <mfenced close=")" open="["> <mrow> <mn>0</mn> <mo>,</mo> <mn>2</mn> <mi>π</mi> </mrow> </mfenced> </mrow> </munder> <mfenced close="&#x2016;" open="&#x2016;"> <mrow> <msup> <mrow> <mfenced> <mrow> <msup> <mi>e</mi> <mrow> <mi>i</mi> <mi>θ</mi> </mrow> </msup> <msub> <mi>E</mi> <mn>0</mn> </msub> <mo>−</mo> <msub> <mi>A</mi> <mn>0</mn> </msub> </mrow> </mfenced> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mfenced> <mrow> <msup> <mi>e</mi> <mrow> <mi>i</mi> <mi>θ</mi> </mrow> </msup> <mover accent="true"> <mi>E</mi> <mo>˜</mo> </mover> <mo>−</mo> <mover accent="true"> <mi>A</mi> <mo>˜</mo> </mover> </mrow> </mfenced> </mrow> </mfenced> </mrow> </semantics></math> with <math display="inline"><semantics> <mi>θ</mi> </semantics></math> in Example 7.</p>
Full article ">Figure 16
<p>Output of the perturbed <math display="inline"><semantics> <mrow> <mfenced> <mrow> <mi>E</mi> <mo>,</mo> <mi>A</mi> </mrow> </mfenced> </mrow> </semantics></math> and unperturbed <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>E</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>A</mi> <mn>0</mn> </msub> </mrow> </mfenced> </mrow> </semantics></math> systems in Example 7.</p>
Full article ">
12 pages, 4863 KiB  
Article
A Framework for Economically Optimal Operation of Explosive Waste Incineration Process to Reduce NOx Emission Concentration
by Sunghyun Cho, Dongwoo Kang, Joseph Sang-Il Kwon, Minsu Kim, Hyungtae Cho, Il Moon and Junghwan Kim
Mathematics 2021, 9(17), 2174; https://doi.org/10.3390/math9172174 - 6 Sep 2021
Cited by 4 | Viewed by 2979
Abstract
Explosives, especially those used for military weapons, have a short lifespan and their performance noticeably deteriorates over time. These old explosives need to be disposed of safely. Fluidized bed incinerators (FBIs) are safe for disposal of explosive waste (such as TNT) and produce [...] Read more.
Explosives, especially those used for military weapons, have a short lifespan and their performance noticeably deteriorates over time. These old explosives need to be disposed of safely. Fluidized bed incinerators (FBIs) are safe for disposal of explosive waste (such as TNT) and produce fewer gas emissions compared to conventional methods, such as the rotary kiln. However, previous studies on this FBI process have only focused on minimizing the amount of NOx emissions without considering the operating and unitality costs (i.e., total cost) associated with the process. It is important to note that, in general, a number of different operating conditions are available to achieve a target NOx emission concentration and, thus, it requires a significant computational requirement to compare the total costs among those candidate operating conditions using a computational fluid dynamics simulation. To this end, a novel framework is proposed to quickly determine the most economically viable FBI process operating condition for a target NOx concentration. First, a surrogate model was developed to replace the high-fidelity model of an FBI process, and utilized to determine a set of possible operating conditions that may lead to a target NOx emission concentration. Second, the candidate operating conditions were fed to the Aspen Plus™ process simulation program to determine the most economically competitive option with respect to its total cost. The developed framework can provide operational guidelines for a clean and economical incineration process of explosive waste. Full article
(This article belongs to the Special Issue Mathematics and Engineering II)
Show Figures

Figure 1

Figure 1
<p>3D modeling of a FBI.</p>
Full article ">Figure 2
<p>Simple design of the explosive waste incineration process.</p>
Full article ">Figure 3
<p>Combined structure of FBI surrogate model and cost assessment model for obtaining total cost for each target NOx.</p>
Full article ">Figure 4
<p>Cost change with target NOx emission concentration.</p>
Full article ">
17 pages, 305 KiB  
Article
Positive Solutions for a Singular Elliptic Equation Arising in a Theory of Thermal Explosion
by Song-Yue Yu and Baoqiang Yan
Mathematics 2021, 9(17), 2173; https://doi.org/10.3390/math9172173 - 6 Sep 2021
Cited by 2 | Viewed by 1435
Abstract
In this paper, the thermal explosion model described by a nonlinear boundary value problem is studied. Firstly, we prove the comparison principle under nonlinear boundary conditions. Secondly, using the sub-super solution theorem, we prove the existence of a positive solution for the case [...] Read more.
In this paper, the thermal explosion model described by a nonlinear boundary value problem is studied. Firstly, we prove the comparison principle under nonlinear boundary conditions. Secondly, using the sub-super solution theorem, we prove the existence of a positive solution for the case K(x)>0, as well as the monotonicity of the maximal solution on parameter λ. Thirdly, the uniqueness of the solution for K(x)<0 is proved, as well as the monotonicity of the solutions on parameter λ. Finally, we obtain some new results for the existence of solutions, and the dependence on the λ for the case K(x) is sign-changing. Full article
(This article belongs to the Special Issue Nonlinear Boundary Value Problems and Their Applications)
12 pages, 415 KiB  
Article
A Novel Method for Solving Second Kind Volterra Integral Equations with Discontinuous Kernel
by Samad Noeiaghdam and Sanda Micula
Mathematics 2021, 9(17), 2172; https://doi.org/10.3390/math9172172 - 5 Sep 2021
Cited by 9 | Viewed by 2168
Abstract
Load leveling problems and energy storage systems can be modeled in the form of Volterra integral equations (VIE) with a discontinuous kernel. The Lagrange–collocation method is applied for solving the problem. Proving a theorem, we discuss the precision of the method. To control [...] Read more.
Load leveling problems and energy storage systems can be modeled in the form of Volterra integral equations (VIE) with a discontinuous kernel. The Lagrange–collocation method is applied for solving the problem. Proving a theorem, we discuss the precision of the method. To control the accuracy, we apply the CESTAC (Controle et Estimation Stochastique des Arrondis de Calculs) method and the CADNA (Control of Accuracy and Debugging for Numerical Applications) library. For this aim, we apply discrete stochastic mathematics (DSA). Using this method, we can control the number of iterations, errors and accuracy. Additionally, some numerical instabilities can be identified. With the aid of this theorem, a novel condition is used instead of the traditional conditions. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Comparison between the solutions—(<b>b</b>) error of Example 1 for <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 2
<p>(<b>a</b>) Comparison between the solutions—(<b>b</b>) error function for <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>(<b>a</b>) Comparison between the solutions—(<b>b</b>) error function for <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p>
Full article ">
13 pages, 1790 KiB  
Article
Mathematical Modeling, Analysis and Evaluation of the Complexity of Flight Paths of Groups of Unmanned Aerial Vehicles in Aviation and Transport Systems
by Andrey Kositzyn, Denis Serdechnyy, Sergey Korchagin, Ekaterina Pleshakova, Petr Nikitin and Natalia Kurileva
Mathematics 2021, 9(17), 2171; https://doi.org/10.3390/math9172171 - 5 Sep 2021
Cited by 8 | Viewed by 3263
Abstract
Recently, we have seen the rapidly growing popularity of unmanned aerial vehicles. This is due to some advantages, namely portability, the ability to fly over hard-to-reach areas without human intervention. They are also widely used for commercial purposes, agriculture, delivery, automation in warehouses. [...] Read more.
Recently, we have seen the rapidly growing popularity of unmanned aerial vehicles. This is due to some advantages, namely portability, the ability to fly over hard-to-reach areas without human intervention. They are also widely used for commercial purposes, agriculture, delivery, automation in warehouses. The potential of unmanned aerial vehicles is vast and demonstrates promising opportunities. However, when using these devices, the issue of safety is acute. This article presents a developed software application that is used to improve the efficiency of flight research of groups of unmanned aerial vehicles, based on a new method for assessing flight safety by comparing the complexity of specified air routes. A practical approach to modeling and evaluating the search for a safe way is proposed. A suitable method of research is computer and simulation modeling. It is suggested to use the spectrum of dynamic characteristics of the sequence as a formal attribute for analyzing routes. The method is illustrated by an example of comparing air trajectories according to the flight safety criterion. The software application is intended for use in the educational process when training specialists in transport security, robotics, and system analysis. Full article
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<p>General planning system based on evolutionary calculation algorithms.</p>
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<p>Overview of the proposed algorithm for constructing the optimal path.</p>
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<p>Schematic representation of an unmanned aerial vehicle.</p>
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<p>Flight path.</p>
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<p>A fragment of the map of the flight area of unmanned aerial vehicles.</p>
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<p>Functionality of the software (sections “Analysis”).</p>
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19 pages, 577 KiB  
Article
Analysis of the Tax Compliance in the EU: VECM and SEM
by Marius-Răzvan Surugiu, Cristina-Raluca Mazilescu and Camelia Surugiu
Mathematics 2021, 9(17), 2170; https://doi.org/10.3390/math9172170 - 5 Sep 2021
Cited by 3 | Viewed by 3121
Abstract
Tax compliance is an important indicator for the proper functioning of the tax authority, influencing the budget revenue level. In this study, a Vector Error Correction Model (VECM) analysis was developed to identify the long-term relationships between the compliance in individual income taxation [...] Read more.
Tax compliance is an important indicator for the proper functioning of the tax authority, influencing the budget revenue level. In this study, a Vector Error Correction Model (VECM) analysis was developed to identify the long-term relationships between the compliance in individual income taxation (taxpayer’s behavior), public trust in politicians (trust in authorities), and rule of law (power of the authorities), using unbalanced panel data for the European Union (EU28) during the 2007–2017 period. The results underline the causality of the long-run relationships between the variables. The results of the VECM analysis underline the need for various support measures for voluntary tax compliance, with the trust variable having an important impact on tax compliance. In addition, a Structural Equation Modeling (SEM) analysis was employed using an improved data set with variables such as the compliance in corporation taxation (taxpayer’s behavior), wastefulness of government spending, and quality of the education system. The results of the SEM analysis underline the positive and significant influences of the variables on tax compliance. Full article
(This article belongs to the Special Issue Analysis and Mathematical Modeling of Economic - Related Data)
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<p>Impulse response functions of the taxci (tax compliance). (<b>a</b>) response of taxci to trust (public trust in politicians); (<b>b</b>) accumulated response of taxci to trust; (<b>c</b>) response of taxci to power (rule of law); (<b>d</b>) accumulated response of taxci to power. Source: developed by the authors.</p>
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15 pages, 309 KiB  
Article
Dynamics of Stage-Structured Predator–Prey Model with Beddington–DeAngelis Functional Response and Harvesting
by Haiyin Li and Xuhua Cheng
Mathematics 2021, 9(17), 2169; https://doi.org/10.3390/math9172169 - 5 Sep 2021
Cited by 3 | Viewed by 2009
Abstract
In this paper, we investigate the stability of equilibrium in the stage-structured and density-dependent predator–prey system with Beddington–DeAngelis functional response. First, by checking the sign of the real part for eigenvalue, local stability of origin equilibrium and boundary equilibrium are studied. Second, we [...] Read more.
In this paper, we investigate the stability of equilibrium in the stage-structured and density-dependent predator–prey system with Beddington–DeAngelis functional response. First, by checking the sign of the real part for eigenvalue, local stability of origin equilibrium and boundary equilibrium are studied. Second, we explore the local stability of the positive equilibrium for τ=0 and τ0 (time delay τ is the time taken from immaturity to maturity predator), which shows that local stability of the positive equilibrium is dependent on parameter τ. Third, we qualitatively analyze global asymptotical stability of the positive equilibrium. Based on stability theory of periodic solutions, global asymptotical stability of the positive equilibrium is obtained when τ=0; by constructing Lyapunov functions, we conclude that the positive equilibrium is also globally asymptotically stable when τ0. Finally, examples with numerical simulations are given to illustrate the obtained results. Full article
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<p>Four trajectories for system (<a href="#FD14-mathematics-09-02169" class="html-disp-formula">14</a>) when <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>Evolutions <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> of system (<a href="#FD32-mathematics-09-02169" class="html-disp-formula">32</a>).</p>
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<p>Evolutions <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> of system (<a href="#FD32-mathematics-09-02169" class="html-disp-formula">32</a>).</p>
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23 pages, 13298 KiB  
Article
Application of a Fuzzy Inference System for Optimization of an Amplifier Design
by M. Isabel Dieste-Velasco
Mathematics 2021, 9(17), 2168; https://doi.org/10.3390/math9172168 - 5 Sep 2021
Cited by 4 | Viewed by 2060
Abstract
Simulation programs are widely used in the design of analog electronic circuits to analyze their behavior and to predict the response of a circuit to variations in the circuit components. A fuzzy inference system (FIS) in combination with these simulation tools can be [...] Read more.
Simulation programs are widely used in the design of analog electronic circuits to analyze their behavior and to predict the response of a circuit to variations in the circuit components. A fuzzy inference system (FIS) in combination with these simulation tools can be applied to identify both the main and interaction effects of circuit parameters on the response variables, which can help to optimize them. This paper describes an application of fuzzy inference systems to modeling the behavior of analog electronic circuits for further optimization. First, a Monte Carlo analysis, generated from the tolerances of the circuit components, is performed. Once the Monte Carlo results are obtained for each of the response variables, the fuzzy inference systems are generated and then optimized using a particle swarm optimization (PSO) algorithm. These fuzzy inference systems are used to determine the influence of the circuit components on the response variables and to select them to optimize the amplifier design. The methodology proposed in this study can be used as the basis for optimizing the design of similar analog electronic circuits. Full article
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<p>Electrical diagram of the single stage of a small signal BJT amplifier.</p>
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<p>(<b>a</b>) Input and output voltages; (<b>b</b>) FFT of the output signal.</p>
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<p>Histograms of the results with the initial design of the amplifier: (<b>a</b>) voltage gain (Av); (<b>b</b>) total harmonic distortion (THD).</p>
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<p>Response of the amplifier (Monte Carlo analysis).</p>
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<p>Zero-order Sugeno FIS.</p>
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<p>Histograms showing the values employed in the Monte Carlo Analysis (grouped in 25 bins).</p>
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<p>Membership functions employed for Av and for THD before tuning the Fuzzy Inference Systems.</p>
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<p>Membership functions obtained after tuning the FIS for Av (2nd step).</p>
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<p>Membership functions obtained after tuning the FIS for THD (2nd step).</p>
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<p>Scheme of the proposed method.</p>
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<p>Results with the tuned FIS in the first step for Av: (<b>a</b>) training data; (<b>b</b>) validation data.</p>
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<p>Results obtained with the tuned FIS in the second step for Av: (<b>a</b>) training data, (<b>b</b>) validation data.</p>
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<p>Response surface for Av vs. R<sub>3</sub> and {R<sub>1</sub>, R<sub>2</sub>, R<sub>4</sub>, R<sub>5</sub>, C<sub>1</sub>, C<sub>2</sub>, C<sub>3</sub>}.</p>
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<p>Results of the tuned FIS in the first step for THD: (<b>a</b>) training data; (<b>b</b>) validation data.</p>
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<p>Results of the tuned FIS in the second step for THD: (<b>a</b>) training data; (<b>b</b>) validation data.</p>
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<p>Response surface for THD vs. R<sub>1</sub> and {R<sub>2</sub>, R<sub>3</sub>, R<sub>4</sub>, R<sub>5</sub>, C<sub>1</sub>, C<sub>2</sub>, C<sub>3</sub>}.</p>
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<p>Main effects plot for Av using the FIS.</p>
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<p>Main effects plot for THD using the FIS.</p>
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<p>Comparison between the initial design and the optimized design after the first iteration.</p>
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<p>Histogram of (<b>a</b>) Av and (<b>b</b>) THD, when R<sub>3</sub> = 7.5 kΩ and R<sub>4</sub> = 0.075 kΩ.</p>
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<p>Response of the amplifier with R<sub>3</sub> = 7.5 kΩ and R<sub>4</sub> = 0.075 kΩ (Monte Carlo analysis).</p>
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<p>Comparison between the initial design and the optimized design with the second iteration.</p>
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<p>Histogram of (<b>a</b>) Av and (<b>b</b>) THD, when R<sub>3</sub> = 9.1 kΩ and R<sub>4</sub> = 0.056 kΩ.</p>
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<p>Response of the amplifier with R<sub>3</sub> = 9.1 kΩ and R<sub>4</sub> = 0.056 kΩ (Monte Carlo analysis).</p>
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19 pages, 6145 KiB  
Article
Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm
by Mohammad Owais Qidwai, Irfan Anjum Badruddin, Noor Zaman Khan, Mohammad Anas Khan and Saad Alshahrani
Mathematics 2021, 9(17), 2167; https://doi.org/10.3390/math9172167 - 5 Sep 2021
Cited by 7 | Viewed by 2677
Abstract
This study aimed to present the design methodology of microjet heat sinks with unequal jet spacing, using a machine learning technique which alleviates hot spots in heat sinks with non-uniform heat flux conditions. Latin hypercube sampling was used to obtain 30 design sample [...] Read more.
This study aimed to present the design methodology of microjet heat sinks with unequal jet spacing, using a machine learning technique which alleviates hot spots in heat sinks with non-uniform heat flux conditions. Latin hypercube sampling was used to obtain 30 design sample points on which three-dimensional Computational Fluid Dynamics (CFD) solutions were calculated, which were used to train the machine learning model. Radial Basis Neural Network (RBNN) was used as a surrogate model coupled with Particle Swarm Optimization (PSO) to obtain the optimized location of jets. The RBNN provides continuous space for searching the optimum values. At the predicted optimum values from the coupled model, the CFD solution was calculated for comparison. The percentage error for the target function was 0.56%, whereas for the accompanied function it was 1.3%. The coupled algorithm has variable inputs at user discretion, including gaussian spread, number of search particles, and number of iterations. The sensitivity of each variable was obtained. Analysis of Variance (ANOVA) was performed to investigate the effect of the input variable on thermal resistance. ANOVA results revealed that gaussian spread is the dominant variable affecting the thermal resistance. Full article
(This article belongs to the Special Issue Mathematical Problems in Mechanical Engineering)
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<p>(<b>a</b>) Physical model of microjet heat sink. (<b>b</b>) A non-uniform power map adopted from Sharma et al. [<a href="#B17-mathematics-09-02167" class="html-bibr">17</a>].</p>
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<p>The results of grid independence test based on thermal resistance and mean substrate temperature.</p>
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<p>Comparison of the current numerical simulation result with the experimental results of Wang et al. [<a href="#B51-mathematics-09-02167" class="html-bibr">51</a>] for a flow rate of 8 mL/min.</p>
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<p>Schematic diagram of inlet holes on the top plate with position constraints in sections A, B, A’, and B’.</p>
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<p>Radial Basis Neural Network mechanism.</p>
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<p>Optimization procedure.</p>
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<p>Global range of substrate temperature contour of the optimal solution obtained from the CFD solution.</p>
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<p>Best cost of Objective functions with γ = 0.9, ñ = 100, ɨ = 150, P = 120.</p>
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15 pages, 4102 KiB  
Article
High-Capacity Reversible Data Hiding in Encrypted Images Based on Adaptive Predictor and Compression of Prediction Errors
by Bin Huang, Chun Wan and Kaimeng Chen
Mathematics 2021, 9(17), 2166; https://doi.org/10.3390/math9172166 - 5 Sep 2021
Cited by 1 | Viewed by 2058
Abstract
Reversible data hiding in encrypted images (RDHEI) is a technology which embeds secret data into encrypted images in a reversible way. In this paper, we proposed a novel high-capacity RDHEI method which is based on the compression of prediction errors. Before image encryption, [...] Read more.
Reversible data hiding in encrypted images (RDHEI) is a technology which embeds secret data into encrypted images in a reversible way. In this paper, we proposed a novel high-capacity RDHEI method which is based on the compression of prediction errors. Before image encryption, an adaptive linear regression predictor is trained from the original image. Then, the predictor is used to obtain the prediction errors of the pixels in the original image, and the prediction errors are compressed by Huffman coding. The compressed prediction errors are used to vacate additional room with no loss. After image encryption, the vacated room is reserved for data embedding. The receiver can extract the secret data and recover the image with no errors. Compared with existing approaches, the proposed method efficiently improves the embedding capacity. Full article
(This article belongs to the Special Issue Recent Advances in Security, Privacy, and Applied Cryptography)
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<p>This is a figure. Schemes follow the same formatting.</p>
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<p>Predictable pixel and its three neighboring pixels.</p>
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<p>Reference pixels and predictable pixels.</p>
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<p>Prediction error histograms of Lena and Baboon.</p>
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<p>The eight test images.</p>
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<p>Example of the proposed method.</p>
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<p>Experimental images of <span class="html-italic">Lena</span> in the proposed method.</p>
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<p>Experimental images of <span class="html-italic">Lena</span> in the proposed method.</p>
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<p>Comparison of the marked decrypted image quality (<span class="html-italic">Airplane</span>, <span class="html-italic">Baboon</span>, <span class="html-italic">Barbara</span>, <span class="html-italic">Couple</span>).</p>
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<p>Comparison of the marked decrypted image quality (<span class="html-italic">Crowd</span>, <span class="html-italic">Lena</span>, <span class="html-italic">Man</span>, <span class="html-italic">Peppers</span>).</p>
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<p>Comparison of the marked decrypted image quality (<span class="html-italic">Crowd</span>, <span class="html-italic">Lena</span>, <span class="html-italic">Man</span>, <span class="html-italic">Peppers</span>).</p>
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30 pages, 5932 KiB  
Article
On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia
by Ľuboš Buzna and Peter Czimmermann
Mathematics 2021, 9(17), 2165; https://doi.org/10.3390/math9172165 - 5 Sep 2021
Cited by 7 | Viewed by 2971
Abstract
The efficient operation of emergency medical services is critical for any society. Typically, optimisation and simulation models support decisions on emergency ambulance stations’ locations and ambulance management strategies. Essential inputs for such models are the spatiotemporal characteristics of ambulance trips. Access to data [...] Read more.
The efficient operation of emergency medical services is critical for any society. Typically, optimisation and simulation models support decisions on emergency ambulance stations’ locations and ambulance management strategies. Essential inputs for such models are the spatiotemporal characteristics of ambulance trips. Access to data on the movements of ambulances is limited, and therefore modelling efforts often rely on assumptions (e.g., the Euclidean distance is used as a surrogate of the ambulance travel time; the closest available ambulance is dispatched to a call; or the travel time estimates, offered by application programming interfaces for ordinary vehicles, are applied to ambulances). These simplifying assumptions are often based on incomplete data or common sense without being fully supported by the evidence. Thus, data-driven research to model ambulance trips is required. We investigated a unique dataset of global positioning system-based measurements collected from seventeen emergency ambulances over three years. We enriched the data by exploring external sources and designed a rule-based procedure to extract ambulance trips for emergency cases. Trips were split into training and test sets. The training set was used to develop a series of statistical models that capture the spatiotemporal characteristics of emergency ambulance trips. The models were used to generate synthetic ambulance trips, and those were compared with the test set to decide which models are the most suitable and to evaluate degrees to which they fit the statistical properties of real-world trips. As confirmed by the low values of the Kullback–Leibler divergence (0.0040.229) and by the Kolmogorov–Smirnov test at the significance level of 0.05, we found a very good fit between the probability distributions of spatiotemporal properties of synthetic and real trips. A reasonable modelling choice is a model where the exponential dependency on the population density is used to locate emergency cases, emergency cases are allocated to hospitals following empirical probabilities, and ambulances are routed using the fastest paths. The models we developed can be used in optimisations and simulations to improve their validity. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
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<p>(<b>a</b>) The geographical area of the Žilina region with the marked positions showing ambulance stations where the crews of the observed emergency vehicles are based. Four ambulance stations, S9–S12 are located within the city of Žilina, and ambulance stations S1–S8 and S13 are distributed in the neighbouring small towns and villages. (<b>b</b>) The frequency of measurements of the car floating data.</p>
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<p>A schematic illustrating the workflow applied to GPS-based data. Symbols enclosed in brackets indicate spatial and temporal characteristics used the in rules applied by the workflow. For the explanation of symbols please see the text.</p>
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<p>An illustration of a trip that originates and terminates at the ambulance station and is formed by three co-located movements. First, GPS measurements should be combined to form movements; secondly, a trip should be constituted from a sequence of co-located movements.</p>
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<p>Number of trips extracted from the Falck dataset for each ambulance and initial ambulance station. For each ambulance, we report the start date of the time period for which the data were available for analysis.</p>
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<p>Flowchart of the workflow to model characteristics of emergency ambulance trips.</p>
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<p>Frequencies of emergency ambulance trip patterns. The characters encode the visit of an ambulance station (“s”), hospital (“h”), and patient “p”. The inset shows the frequencies of movement counts that constitute a trip. The empirical probability of observing a trip of a given pattern can be calculated by dividing the frequency by the total number of trips, 44,099.</p>
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<p>(<b>a</b>) Counts of emergency cases (only “sps” and “sphs” trips were considered) in Žilina, Kysucké Nové mesto, and Čadca districts. (<b>b</b>) Residential population in 2018. In both cases, a raster with the cell size <math display="inline"><semantics> <mrow> <mn>1500</mn> <mo>×</mo> <mn>1500</mn> </mrow> </semantics></math> m was used. (<b>c</b>) Scatter plot of the number of emergency cases versus the population. Each point in the plot corresponds to a grid cell in panels (<b>a</b>,<b>b</b>). Lines show the fit to the data with linear and power functions, respectively.</p>
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<p>The empirical probability distributions of emergency cases to be allocated to the k-th nearest ambulance station (in each row, we present the distribution for a range of the distance from an emergency case location to the nearest emergency ambulance station). In the evaluations, we used the Euclidean distances.</p>
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<p>The empirical probabilities of emergency cases to be allocated to the <span class="html-italic">k</span>-th closest hospitals. (<b>a</b>) We used the Euclidean distances. (<b>b</b>) We used the road network distances. In the analyses only “sphs” and “sps” trips were considered.</p>
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<p>The scatter plots of observed and estimated parameters of movements. (<b>a</b>) The lengths of “sp”-type movements. (<b>b</b>) The lengths of “ph”-type movements. (<b>c</b>) The lengths of “ps”-type movements. (<b>d</b>) The duration of “ps”-type movements. To facilitate the comparison, we display a diagonal line with slope <math display="inline"><semantics> <mrow> <mn>1.0</mn> </mrow> </semantics></math> in each plot, and we show the histogram for each quantity.</p>
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<p>The dependence of travel time on the travelled distance for (<b>a</b>) “sps_sp”, (<b>b</b>) “sps_ps”, (<b>c</b>) “sphs_sp”, (<b>d</b>) “sphs_ph”, and (<b>e</b>) “sphs_hs” movements. The function <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> </semantics></math> is displayed by red on interval <math display="inline"><semantics> <mrow> <mo>〈</mo> <mn>0</mn> <mo>,</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> <mo>〉</mo> </mrow> </semantics></math> and by green on interval <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, 40,000〉.</p>
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<p>The empirical distribution of provision times. (<b>a</b>) Durations of patient treatments in “sps” trips. (<b>b</b>) Durations of patient treatments in the “sphs” trips. (<b>c</b>) Durations of stops at hospitals in the “sphs” trips. The red line indicates the fit to the data given by Equation (<a href="#FD7-mathematics-09-02165" class="html-disp-formula">7</a>).</p>
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<p>Empirical probabilities of the initial time of emergency ambulance trips evaluated at three different time scales. (<b>a</b>) The time scale of hours. (<b>b</b>) The time scale of weekdays. (<b>c</b>) The time scale of months. The analysis was done on “sps” and “sphs” trips. To avoid the bias caused by different availability of data from ambulances, in panel (<b>c</b>), we considered only trips that took place in 2018, as the data from all vehicles were available for that complete year.</p>
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<p>The probability density functions of movements’ lengths for “sps” and “sphs” trips. Each panel (<b>a</b>–<b>e</b>) corresponds to a different movement type of “sps” and “sphs” trips (indicated by the x-axis label). The default settings, i.e., <math display="inline"><semantics> <mrow> <mi>L</mi> <mn>1</mn> <mo>_</mo> <mi>A</mi> <mn>1</mn> <mo>_</mo> <mi>R</mi> <mn>1</mn> </mrow> </semantics></math>, are compared with test data, a linear model (<math display="inline"><semantics> <mrow> <mi>L</mi> <mn>2</mn> </mrow> </semantics></math>), empirical probabilities (<math display="inline"><semantics> <mrow> <mi>L</mi> <mn>3</mn> </mrow> </semantics></math>), the allocation of emergency cases to the closest hospital (<math display="inline"><semantics> <mrow> <mi>A</mi> <mn>2</mn> </mrow> </semantics></math>), closest hospital-specific empirical probability distributions (<math display="inline"><semantics> <mrow> <mi>A</mi> <mn>3</mn> </mrow> </semantics></math>), and the shortest path’s routing (<math display="inline"><semantics> <mrow> <mi>R</mi> <mn>2</mn> </mrow> </semantics></math>).</p>
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<p>The probability density functions of movement duration. Each panel (<b>a</b>–<b>e</b>) corresponds to a different movement type of “sps” and “sphs” trips (indicated by the x-axis label). The default settings, i.e., <math display="inline"><semantics> <mrow> <mi>L</mi> <mn>1</mn> <mo>_</mo> <mi>A</mi> <mn>1</mn> <mo>_</mo> <mi>R</mi> <mn>1</mn> </mrow> </semantics></math>, are compared with test data, a linear model (<math display="inline"><semantics> <mrow> <mi>L</mi> <mn>2</mn> </mrow> </semantics></math>), empirical probabilities (<math display="inline"><semantics> <mrow> <mi>L</mi> <mn>3</mn> </mrow> </semantics></math>), the allocation of emergency cases to the closest hospital (<math display="inline"><semantics> <mrow> <mi>A</mi> <mn>2</mn> </mrow> </semantics></math>), closest hospital-specific empirical probability distributions (<math display="inline"><semantics> <mrow> <mi>A</mi> <mn>3</mn> </mrow> </semantics></math>), and shortest path routing (<math display="inline"><semantics> <mrow> <mi>R</mi> <mn>2</mn> </mrow> </semantics></math>).</p>
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<p>The probability density functions of trip duration. Panel (<b>a</b>) corresponds to “sps” and panel (<b>b</b>) to “sphs” trips. Five randomisations of default settings, i.e., <math display="inline"><semantics> <mrow> <mi>L</mi> <mn>1</mn> <mo>_</mo> <mi>A</mi> <mn>1</mn> <mo>_</mo> <mi>R</mi> <mn>1</mn> <mo>_</mo> <mn>1</mn> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mi>L</mi> <mn>1</mn> <mo>_</mo> <mi>A</mi> <mn>1</mn> <mo>_</mo> <mi>R</mi> <mn>1</mn> <mo>_</mo> <mn>5</mn> </mrow> </semantics></math>, are compared with linear model (<math display="inline"><semantics> <mrow> <mi>L</mi> <mn>2</mn> </mrow> </semantics></math>), empirical probabilities (<math display="inline"><semantics> <mrow> <mi>L</mi> <mn>3</mn> </mrow> </semantics></math>), the allocation of emergency cases to the closest hospital (<math display="inline"><semantics> <mrow> <mi>A</mi> <mn>2</mn> </mrow> </semantics></math>), closest hospital-specific empirical probability distributions (<math display="inline"><semantics> <mrow> <mi>A</mi> <mn>3</mn> </mrow> </semantics></math>), shortest path routing (<math display="inline"><semantics> <mrow> <mi>R</mi> <mn>2</mn> </mrow> </semantics></math>), the model presented in [<a href="#B46-mathematics-09-02165" class="html-bibr">46</a>,<a href="#B47-mathematics-09-02165" class="html-bibr">47</a>], and test data.</p>
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13 pages, 362 KiB  
Article
Evaluation of the Work-Integrated Learning Methodology: Teaching Marketing through Practitioner Experience in the Classroom
by Luis-Alberto Casado-Aranda, Juan Sánchez-Fernández, Francisco Javier Montoro-Ríos and María Isabel Arias Horcajadas
Mathematics 2021, 9(17), 2164; https://doi.org/10.3390/math9172164 - 5 Sep 2021
Cited by 7 | Viewed by 2452
Abstract
The teaching methodology in university marketing subjects has traditionally been based on “lecture classes”, which have proved to be insufficient for providing students with professional skills that can be directly applied in the workplace. This research aims to reduce this gap between the [...] Read more.
The teaching methodology in university marketing subjects has traditionally been based on “lecture classes”, which have proved to be insufficient for providing students with professional skills that can be directly applied in the workplace. This research aims to reduce this gap between the university and business by applying the active teaching methodology of work-integrated learning (WIL), which consists of providing students with knowledge and experiences directly from professionals that are invited to the classroom. We evaluated the effects of the WIL methodology on university students in a marketing degree course through self-administered questionnaires. During a semester, perceived personal, academic, and professional outcomes were assessed in two groups of students of the same module, one of which participated in the WIL program (i.e., they received lectures by professional marketing experts who were invited to the classroom and explained, through real examples, the content of the subject being taught), and the other served as a control (i.e., they learned the content only through traditional lectures from the college instructor). The results revealed that the students who took part in the WIL program experienced increased motivation, enjoyment, and interest in the subject. Furthermore, they had an increased understanding and acquisition of the concepts, as well as a greater perceived ability to manage companies and a comprehension of the economic environment. These findings constitute an advance because they identify the benefits of applying WIL in knowledge fields where the acquisition of professional skills is crucial for graduates’ entry into the labor market. Full article
(This article belongs to the Special Issue Business Games and Numeric Simulations in Economics and Management)
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<p>Graph showing the significant differences between the scores given by WIL participants and non-participants to motivation, interest, and enjoyment. (*) means that the difference was significant.</p>
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22 pages, 1017 KiB  
Article
A Novel MADM Framework under q-Rung Orthopair Fuzzy Bipolar Soft Sets
by Ghous Ali, Hanan Alolaiyan, Dragan Pamučar, Muhammad Asif and Nimra Lateef
Mathematics 2021, 9(17), 2163; https://doi.org/10.3390/math9172163 - 4 Sep 2021
Cited by 22 | Viewed by 2634
Abstract
In many real-life problems, decision-making is reckoned as a powerful tool to manipulate the data involving imprecise and vague information. To fix the mathematical problems containing more generalized datasets, an emerging model called q-rung orthopair fuzzy soft sets offers a comprehensive framework [...] Read more.
In many real-life problems, decision-making is reckoned as a powerful tool to manipulate the data involving imprecise and vague information. To fix the mathematical problems containing more generalized datasets, an emerging model called q-rung orthopair fuzzy soft sets offers a comprehensive framework for a number of multi-attribute decision-making (MADM) situations but this model is not capable to deal effectively with situations having bipolar soft data. In this research study, a novel hybrid model under the name of q-rung orthopair fuzzy bipolar soft set (q-ROFBSS, henceforth), an efficient bipolar soft generalization of q-rung orthopair fuzzy set model, is introduced and illustrated by an example. The proposed model is successfully tested for several significant operations like subset, complement, extended union and intersection, restricted union and intersection, the ‘AND’ operation and the ‘OR’ operation. The De Morgan’s laws are also verified for q-ROFBSSs regarding above-mentioned operations. Ultimately, two applications are investigated by using the proposed framework. In first real-life application, the selection of land for cropping the carrots and the lettuces is studied, while in second practical application, the selection of an eligible student for a scholarship is discussed. At last, a comparison of the initiated model with certain existing models, including Pythagorean and Fermatean fuzzy bipolar soft set models is provided. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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<p>Comparison between the IFSs [<a href="#B2-mathematics-09-02163" class="html-bibr">2</a>], PFSs [<a href="#B3-mathematics-09-02163" class="html-bibr">3</a>], FFSs [<a href="#B11-mathematics-09-02163" class="html-bibr">11</a>], and <span class="html-italic">q</span>-ROFSs [<a href="#B10-mathematics-09-02163" class="html-bibr">10</a>].</p>
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<p>Comparison between PFBSSs [<a href="#B28-mathematics-09-02163" class="html-bibr">28</a>], FFBSSs [<a href="#B30-mathematics-09-02163" class="html-bibr">30</a>], and proposed <span class="html-italic">q</span>-ROFBSSs by applying on Application 1 (Selection of an employee) in [<a href="#B28-mathematics-09-02163" class="html-bibr">28</a>].</p>
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<p>Comparison between PFBSSs [<a href="#B28-mathematics-09-02163" class="html-bibr">28</a>], FFBSSs [<a href="#B30-mathematics-09-02163" class="html-bibr">30</a>], and proposed <span class="html-italic">q</span>-ROFBSSs by applying on Application 2 (Selection of a house) in [<a href="#B28-mathematics-09-02163" class="html-bibr">28</a>].</p>
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20 pages, 343 KiB  
Article
Algorithmic Determination of a Large Integer in the Two-Term Machin-like Formula for π
by Sanjar M. Abrarov, Rajinder K. Jagpal, Rehan Siddiqui and Brendan M. Quine
Mathematics 2021, 9(17), 2162; https://doi.org/10.3390/math9172162 - 4 Sep 2021
Cited by 2 | Viewed by 2326
Abstract
In our earlier publication we have shown how to compute by iteration a rational number u2,k in the two-term Machin-like formula for π of the kind [...] Read more.
In our earlier publication we have shown how to compute by iteration a rational number u2,k in the two-term Machin-like formula for π of the kind π4=2k1arctan1u1,k+arctan1u2,k,kZ,k1, where u1,k can be chosen as an integer u1,k=ak/2ak1 with nested radicals defined as ak=2+ak1 and a0=0. In this work, we report an alternative method for determination of the integer u1,k. This approach is based on a simple iteration and does not require any irrational (surd) numbers from the set ak in computation of the integer u1,k. Mathematica programs validating these results are presented. Full article
(This article belongs to the Section Computational and Applied Mathematics)
13 pages, 299 KiB  
Article
Joint Universality of the Zeta-Functions of Cusp Forms
by Renata Macaitienė
Mathematics 2021, 9(17), 2161; https://doi.org/10.3390/math9172161 - 4 Sep 2021
Viewed by 1578
Abstract
Let F be the normalized Hecke-eigen cusp form for the full modular group and ζ(s,F) be the corresponding zeta-function. In the paper, the joint universality theorem on the approximation of a collection of analytic functions by shifts [...] Read more.
Let F be the normalized Hecke-eigen cusp form for the full modular group and ζ(s,F) be the corresponding zeta-function. In the paper, the joint universality theorem on the approximation of a collection of analytic functions by shifts (ζ(s+ih1τ,F),,ζ(s+ihrτ,F)) is proved. Here, h1,,hr are algebraic numbers linearly independent over the field of rational numbers. Full article
27 pages, 1842 KiB  
Article
Fractional Dynamics of Stuxnet Virus Propagation in Industrial Control Systems
by Zaheer Masood, Muhammad Asif Zahoor Raja, Naveed Ishtiaq Chaudhary, Khalid Mehmood Cheema and Ahmad H. Milyani
Mathematics 2021, 9(17), 2160; https://doi.org/10.3390/math9172160 - 4 Sep 2021
Cited by 30 | Viewed by 3036
Abstract
The designed fractional order Stuxnet, the virus model, is analyzed to investigate the spread of the virus in the regime of isolated industrial networks environment by bridging the air-gap between the traditional and the critical control network infrastructures. Removable storage devices are commonly [...] Read more.
The designed fractional order Stuxnet, the virus model, is analyzed to investigate the spread of the virus in the regime of isolated industrial networks environment by bridging the air-gap between the traditional and the critical control network infrastructures. Removable storage devices are commonly used to exploit the vulnerability of individual nodes, as well as the associated networks, by transferring data and viruses in the isolated industrial control system. A mathematical model of an arbitrary order system is constructed and analyzed numerically to depict the control mechanism. A local and global stability analysis of the system is performed on the equilibrium points derived for the value of α = 1. To understand the depth of fractional model behavior, numerical simulations are carried out for the distinct order of the fractional derivative system, and the results show that fractional order models provide rich dynamics by means of fast transient and super-slow evolution of the model’s steady-state behavior, which are seldom perceived in integer-order counterparts. Full article
(This article belongs to the Special Issue Control, Optimization, and Mathematical Modeling of Complex Systems)
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<p>FO-SVM model proposed graphical overview.</p>
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<p>FO-SVM model schematic flow diagram.</p>
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<p>Simulation of Stuxnet virus spread with available data of parameters <math display="inline"><semantics> <msub> <mi>A</mi> <mn>1</mn> </msub> </semantics></math> = 0.042, <math display="inline"><semantics> <msub> <mi>A</mi> <mn>2</mn> </msub> </semantics></math> = 0.042, <math display="inline"><semantics> <msub> <mi>β</mi> <mn>1</mn> </msub> </semantics></math> = 0.366, <math display="inline"><semantics> <msub> <mi>β</mi> <mn>2</mn> </msub> </semantics></math> = 0.6, <math display="inline"><semantics> <mi>ρ</mi> </semantics></math> = 0.00265, <math display="inline"><semantics> <msub> <mi>r</mi> <mn>1</mn> </msub> </semantics></math> = 0.1126, <math display="inline"><semantics> <msub> <mi>r</mi> <mn>2</mn> </msub> </semantics></math> = 0.0088, <span class="html-italic">S</span> = <math display="inline"><semantics> <mrow> <mn>2.3</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>6</mn> </msup> </mrow> </semantics></math>, <span class="html-italic">I</span> = 10,000, <span class="html-italic">M</span> = 10, <math display="inline"><semantics> <msub> <mi>U</mi> <mi>s</mi> </msub> </semantics></math> = 50,000, <math display="inline"><semantics> <msub> <mi>U</mi> <mi>I</mi> </msub> </semantics></math> = 10,000.</p>
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<p>Solution comparison of the RK method with GL solver and error analysis with susceptible <span class="html-italic">S</span> hosts: a and b for cases 2 to 4, c and d for cases 5 to 7, and e and f for cases 8 to 10. (<b>a</b>) Solution comparison of the RK method with GL solver for cases 2 to 4, (<b>b</b>) error analysis for cases 2 to 4, (<b>c</b>) Solution comparison of the RK method with GL solver for cases 5 to 7, (<b>d</b>) error analysis for cases 5 to 7, (<b>e</b>) Solution comparison of the RK method with GL solver for cases 8 to 10, (<b>f</b>) error analysis for cases 8 to 10.</p>
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<p>Solution comparison of RK method with GL solver for infected hosts <span class="html-italic">I</span> and damaged hosts <span class="html-italic">M</span>; a and b for cases 1 to 3, c and d for cases 4 to 6 while e and f for cases 7 to 9. (<b>a</b>) Comparison of RK method with GL solver for infected hosts in cases 1 to 3, (<b>b</b>) Comparison of RK method with GL solver for damaged hosts in cases 1 to 3, (<b>c</b>) Comparison of RK method with GL solver for infected hosts in cases 4 to 6, (<b>d</b>) Comparison of RK method with GL solver for damaged hosts in cases 4 to 6, (<b>e</b>) Comparison of RK method with GL solver for infected hosts in cases 7 to 9, (<b>f</b>) Comparison of RK method with GL solver for damaged hosts in cases 7 to 9.</p>
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<p>Solution comparison RK method with GL solver for susceptible and infected-removable-storage media: a and b for cases 1 to 3, c and d for cases 4 to 6, and e and f for cases 7 to 9. (<b>a</b>) Comparison of RK method with GL solver for susceptible removable storage media in cases 1 to 3, (<b>b</b>) Comparison of RK method with GL solver for infected removable storage media in cases 1 to 3, (<b>c</b>) Comparison of RK method with GL solver for susceptible removable storage media in cases 4 to 6, (<b>d</b>) Comparison of RK method with GL solver for infected removable storage media in cases 4 to 6, (<b>e</b>) Comparison of RK method with GL solver for susceptible removable storage media in cases 7 to 9, (<b>f</b>) Comparison of RK method with GL solver for infected removable storage media in cases 7 to 9.</p>
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<p>Simulation of fractional order dynamics of FO-SVM model for different values of fractional order <math display="inline"><semantics> <mi>α</mi> </semantics></math> for cases 1 (<b>a</b>–<b>c</b>), 2 (<b>b</b>–<b>f</b>) and 3 (<b>g</b>–<b>i</b>) of susceptible <span class="html-italic">S</span>, infected <span class="html-italic">I</span> and damaged hosts <span class="html-italic">M</span>.</p>
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<p>Simulation of fractional order dynamics of FO-SVM model for different values of fractional order <math display="inline"><semantics> <mi>α</mi> </semantics></math> for cases 4 (<b>a</b>–<b>c</b>), 5 (<b>b</b>–<b>f</b>) and b (<b>g</b>–<b>i</b>) of susceptible <span class="html-italic">S</span>, infected <span class="html-italic">I</span> and damaged hosts <span class="html-italic">M</span>.</p>
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<p>Simulation of fractional order dynamics of FO-SVM model for different values of fractional order <math display="inline"><semantics> <mi>α</mi> </semantics></math> for cases 7 (<b>a</b>–<b>c</b>), 8 (<b>b</b>–<b>f</b>) and 9 (<b>g</b>–<b>i</b>) of susceptible <span class="html-italic">S</span>, infected <span class="html-italic">I</span> and damaged hosts <span class="html-italic">M</span>.</p>
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<p>Simulation of fractional order dynamics of FO-SVM model for different values of fractional order <math display="inline"><semantics> <mi>α</mi> </semantics></math> for cases 1 (<b>a</b>,<b>b</b>), 2 (<b>c</b>,<b>d</b>) and 3 (<b>e</b>,<b>f</b>), 4 (<b>g</b>,<b>h</b>) and 5 (<b>i</b>) of susceptible removable-storage media <math display="inline"><semantics> <msub> <mi>U</mi> <mi>s</mi> </msub> </semantics></math> and infected-removable-storage media <math display="inline"><semantics> <msub> <mi>U</mi> <mi>I</mi> </msub> </semantics></math>.</p>
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<p>Simulation of fractional order dynamics of FO-SVM model for different values of fractional order <math display="inline"><semantics> <mi>α</mi> </semantics></math> for cases 5 (<b>a</b>), 6 (<b>b</b>,<b>c</b>) and 7 (<b>d</b>,<b>e</b>), 8 (<b>f</b>,<b>g</b>) and 9 (<b>h</b>,<b>i</b>) of susceptible removable-storage media <math display="inline"><semantics> <msub> <mi>U</mi> <mi>s</mi> </msub> </semantics></math> and infected-removable-storage media <math display="inline"><semantics> <msub> <mi>U</mi> <mi>I</mi> </msub> </semantics></math>.</p>
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12 pages, 4660 KiB  
Article
Non-Uniform Spline Quasi-Interpolation to Extract the Series Resistance in Resistive Switching Memristors for Compact Modeling Purposes
by María José Ibáñez, Domingo Barrera, David Maldonado, Rafael Yáñez and Juan Bautista Roldán
Mathematics 2021, 9(17), 2159; https://doi.org/10.3390/math9172159 - 4 Sep 2021
Cited by 10 | Viewed by 1615
Abstract
An advanced new methodology is presented to improve parameter extraction in resistive memories. The series resistance and some other parameters in resistive memories are obtained, making use of a two-stage algorithm, where the second one is based on quasi-interpolation on non-uniform partitions. The [...] Read more.
An advanced new methodology is presented to improve parameter extraction in resistive memories. The series resistance and some other parameters in resistive memories are obtained, making use of a two-stage algorithm, where the second one is based on quasi-interpolation on non-uniform partitions. The use of this latter advanced mathematical technique provides a numerically robust procedure, and in this manuscript, we focus on it. The series resistance, an essential parameter to characterize the circuit operation of resistive memories, is extracted from experimental curves measured in devices based on hafnium oxide as their dielectric layer. The experimental curves are highly non-linear, due to the underlying physics controlling the device operation, so that a stable numerical procedure is needed. The results also allow promising expectations in the massive extraction of new parameters that can help in the characterization of the electrical device behavior. Full article
(This article belongs to the Section Computational and Applied Mathematics)
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<p>(<b>a</b>) RRAM structure cross-section. (<b>b</b>) Circuit scheme to explain the different voltages employed in this work. (<b>c</b>) Current versus modified voltage for different series resistances. We choose the curve with the maximum slope (in fact, it is a vertical region, shown in symbols) as the one that allows us to extract the correct series resistances, as depicted in [<a href="#B18-mathematics-09-02159" class="html-bibr">18</a>].</p>
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<p>Current versus voltage for some resistive switching cycles. The original measured curves are shown in black, the modified curves in red, the corresponding series resistances are also shown. See the points marked by <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>T</mi> <mi>S</mi> </mrow> </msub> </semantics></math> (threshold set voltages) and <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>T</mi> <msub> <mi>S</mi> <mn>2</mn> </msub> </mrow> </msub> </semantics></math>, the points where the slope of the curve changes sign, marked with red dots in the modified curves.</p>
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<p>(<b>a</b>) Current versus modified voltage for several series resistances. The selected series resistance corresponds to the curve with the vertical slope, shown with symbols. (<b>b</b>) Modified voltage versus current for the same curves shown on the left. The plots shown are parts of the graphs in (<b>a</b>) with the X-axis and Y-axis exchanged. The curve with the vertical section in (<b>a</b>) presents in this case a horizontal slope, as it is expected. In this manner, the algorithm to extract the series resistance can be applied more easily in (<b>b</b>).</p>
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<p>Extended partition with multiple endpoints.</p>
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<p>Supports of the B-splines defined on an extended partition with multiple endpoints.</p>
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<p>Current versus voltage curves for two different cycles (<b>a</b>) cycle 2 and (<b>b</b>) cycle 106. The original measured curves are shown in black symbols and the modified curves in red symbols. See in lines, with the corresponding colors, the parametrized curves in each case. The <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>T</mi> <msub> <mi>S</mi> <mn>2</mn> </msub> </mrow> </msub> </semantics></math> points are also shown. The cycle numbers correspond to the order of these cycles in the sequence that forms the resistive switching series measured, i.e., cycle 2 consists of the processes employed to change the device resistance for the second time in the series.</p>
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16 pages, 1778 KiB  
Article
Arbitrary Coefficient Assignment by Static Output Feedback for Linear Differential Equations with Non-Commensurate Lumped and Distributed Delays
by Vasilii Zaitsev and Inna Kim
Mathematics 2021, 9(17), 2158; https://doi.org/10.3390/math9172158 - 4 Sep 2021
Cited by 5 | Viewed by 1871
Abstract
We consider a linear control system defined by a scalar stationary linear differential equation in the real or complex space with multiple non-commensurate lumped and distributed delays in the state. In the system, the input is a linear combination of multiple variables and [...] Read more.
We consider a linear control system defined by a scalar stationary linear differential equation in the real or complex space with multiple non-commensurate lumped and distributed delays in the state. In the system, the input is a linear combination of multiple variables and its derivatives, and the output is a multidimensional vector of linear combinations of the state and its derivatives. For this system, we study the problem of arbitrary coefficient assignment for the characteristic function by linear static output feedback with lumped and distributed delays. We obtain necessary and sufficient conditions for the solvability of the arbitrary coefficient assignment problem by the static output feedback controller. Corollaries on arbitrary finite spectrum assignment and on stabilization of the system are obtained. We provide an example illustrating our results. Full article
(This article belongs to the Special Issue Recent Advances in Differential Equations and Applications)
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<p>The spectrum of system (<a href="#FD42-mathematics-09-02158" class="html-disp-formula">42</a>) with <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>2</mn> </msub> <mo>=</mo> <msqrt> <mn>3</mn> </msqrt> </mrow> </semantics></math>.</p>
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<p>Solutions of system (<a href="#FD42-mathematics-09-02158" class="html-disp-formula">42</a>) with <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>2</mn> </msub> <mo>=</mo> <msqrt> <mn>3</mn> </msqrt> </mrow> </semantics></math>.</p>
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19 pages, 314 KiB  
Article
A Hybrid Genetic Algorithm for the Simple Assembly Line Balancing Problem with a Fixed Number of Workstations
by Eduardo Álvarez-Miranda, Jordi Pereira, Harold Torrez-Meruvia and Mariona Vilà
Mathematics 2021, 9(17), 2157; https://doi.org/10.3390/math9172157 - 4 Sep 2021
Cited by 10 | Viewed by 2997
Abstract
The assembly line balancing problem is a classical optimisation problem whose objective is to assign each production task to one of the stations on the assembly line so that the total efficiency of the line is maximized. This study proposes a novel hybrid [...] Read more.
The assembly line balancing problem is a classical optimisation problem whose objective is to assign each production task to one of the stations on the assembly line so that the total efficiency of the line is maximized. This study proposes a novel hybrid method to solve the simple version of the problem in which the number of stations is fixed, a problem known as SALBP-2. The hybrid differs from previous approaches by encoding individuals of a genetic algorithm as instances of a modified problem that contains only a subset of the solutions to the original formulation. These individuals are decoded to feasible solutions of the original problem during fitness evaluation in which the resolution of the modified problem is conducted using a dynamic programming based approach that uses new bounds to reduce its state space. Computational experiments show the efficiency of the method as it is able to obtain several new best-known solutions for some of the benchmark instances used in the literature for comparison purposes. Full article
(This article belongs to the Special Issue Mathematical Modeling and Optimization)
20 pages, 732 KiB  
Article
Evaluation of Clustering Algorithms on HPC Platforms
by Juan M. Cebrian, Baldomero Imbernón, Jesús Soto and José M. Cecilia
Mathematics 2021, 9(17), 2156; https://doi.org/10.3390/math9172156 - 4 Sep 2021
Cited by 3 | Viewed by 2078
Abstract
Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms [...] Read more.
Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms are very computationally expensive as they often involve the computation of expensive fitness functions that must be evaluated for all points in the dataset. This computational cost is even higher for fuzzy methods, where each data point may belong to more than one cluster. In this paper, we evaluate different parallelisation strategies on different heterogeneous platforms for fuzzy clustering algorithms typically used in the state-of-the-art such as the Fuzzy C-means (FCM), the Gustafson–Kessel FCM (GK-FCM) and the Fuzzy Minimals (FM). The experimental evaluation includes performance and energy trade-offs. Our results show that depending on the computational pattern of each algorithm, their mathematical foundation and the amount of data to be processed, each algorithm performs better on a different platform. Full article
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<p>Speed-up (normalised to scalar code running on one thread).</p>
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<p>Energy reduction factor (normalised to scalar code running on one thread).</p>
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<p>Execution time (seconds) for FCM.</p>
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<p>Execution time (seconds) for GK-FCM.</p>
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22 pages, 4870 KiB  
Article
A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents
by Julia Shemiakina, Elena Limonova, Natalya Skoryukina, Vladimir V. Arlazarov and Dmitry P. Nikolaev
Mathematics 2021, 9(17), 2155; https://doi.org/10.3390/math9172155 - 3 Sep 2021
Cited by 6 | Viewed by 3276
Abstract
In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a [...] Read more.
In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a novel, theoretically based method for estimating the projective distortion level at a restored image point. On this basis, we suggest a new method of binary quality estimation of projectively restored field images. The method analyzes the projective homography only and does not depend on the image size. The text font and height of an evaluated field are assumed to be predefined in the document template. This information is used to estimate the maximum level of distortion acceptable for recognition. The method was tested on a dataset of synthetically distorted field images. Synthetic images were created based on document template images from the publicly available dataset MIDV-2019. In the experiments, the method shows stable predictive values for different strings of one font and height. When used as a pre-recognition rejection method, it demonstrates a positive predictive value of 86.7% and a negative predictive value of 64.1% on the synthetic dataset. A comparison with other geometric quality assessment methods shows the superiority of our approach. Full article
(This article belongs to the Special Issue Mathematical Approaches to Image Processing with Applications)
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<p>An example of document projective distortion (<b>a</b>) and the resulting quality of the extracted text regions (<b>b</b>).</p>
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<p>Restoration results of (<b>a</b>) a slightly projectively distorted image and (<b>b</b>) a highly distorted image of equal area. Restoration was conducted with bilinear interpolation.</p>
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<p>The captured image of the rectangular document in a pinhole camera model.</p>
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<p>The general model of the document recognition system.</p>
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<p>The model of the quality assessment submodule.</p>
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<p>Interpolation examples [<a href="#B38-mathematics-09-02155" class="html-bibr">38</a>]. (<b>a</b>) Nearest pixel, (<b>b</b>) bilinear, (<b>c</b>) B-spline, and (<b>d</b>) bicubic.</p>
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<p>Reconstruction functions: (<b>a</b>) bilinear, (<b>b</b>) B-spline, and (<b>c</b>) bicubic.</p>
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<p>The heatmaps of the semi-minor and semi-major axis lengths: (<b>a</b>) the source quadrangle <span class="html-italic">F</span>, (<b>b</b>) semi-minor axis lengths, and (<b>c</b>) semi-majorFL axis lengths.</p>
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<p>The template of the new Austrian driving license document.</p>
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<p>Positive and negative predictive values for varied minimum scaling coefficient thresholds estimated on the set of distorted images for fields: (<b>a</b>) “31.12.1981”, (<b>b</b>) “19.01.2013”, (<b>c</b>) “18.01.2028”, and (<b>d</b>) “12345678”.</p>
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<p>Examples (<b>a</b>–<b>e</b>) of false-positive distorted and restored field images for the second experiment.</p>
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<p>Examples (<b>a</b>–<b>e</b>) of false-negative distorted and restored field images for the second experiment.</p>
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19 pages, 372 KiB  
Article
On the Convergence of the Benjamini–Hochberg Procedure
by Dean Palejev and Mladen Savov
Mathematics 2021, 9(17), 2154; https://doi.org/10.3390/math9172154 - 3 Sep 2021
Cited by 3 | Viewed by 1951
Abstract
The Benjamini–Hochberg procedure is one of the most used scientific methods up to date. It is widely used in the field of genetics and other areas where the problem of multiple comparison arises frequently. In this paper we show that under fairly general [...] Read more.
The Benjamini–Hochberg procedure is one of the most used scientific methods up to date. It is widely used in the field of genetics and other areas where the problem of multiple comparison arises frequently. In this paper we show that under fairly general assumptions for the distribution of the test statistic under the alternative hypothesis, when increasing the number of tests, the power of the Benjamini–Hochberg procedure has an exponential type of asymptotic convergence to a previously shown limit of the power. We give a theoretical lower bound for the probability that for a fixed number of tests the power is within a given interval around its limit together with a software routine that calculates these values. This result is important when planning costly experiments and estimating the achieved power after performing them. Full article
(This article belongs to the Section Probability and Statistics)
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<p>Empirical results when the <span class="html-italic">p</span>-values under the alternative hypothesis have <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>(</mo> <mn>0.5</mn> <mo>,</mo> <mn>100</mn> <mo>)</mo> </mrow> </semantics></math> distribution and the proportion of alternative tests is <math display="inline"><semantics> <mrow> <mn>2</mn> <mo>%</mo> </mrow> </semantics></math>. The <span class="html-italic">x</span>-axis shows the common logarithm of the number of tests <span class="html-italic">m</span>. The <span class="html-italic">y</span>-axis shows the empirical power. The dots depict the mean of the empirical power, the vertical lines are of length one standard deviation below and above the mean. The dotted line shows the asymptotic power.</p>
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13 pages, 291 KiB  
Article
Weaker Forms of Soft Regular and Soft T2 Soft Topological Spaces
by Samer Al Ghour
Mathematics 2021, 9(17), 2153; https://doi.org/10.3390/math9172153 - 3 Sep 2021
Cited by 19 | Viewed by 2126
Abstract
Soft ω-local indiscreetness as a weaker form of both soft local countability and soft local indiscreetness is introduced. Then soft ω-regularity as a weaker form of both soft regularity and soft ω-local indiscreetness is defined and investigated. Additionally, soft ω [...] Read more.
Soft ω-local indiscreetness as a weaker form of both soft local countability and soft local indiscreetness is introduced. Then soft ω-regularity as a weaker form of both soft regularity and soft ω-local indiscreetness is defined and investigated. Additionally, soft ω-T2 as a new soft topological property that lies strictly between soft T2 and soft T1 is defined and investigated. It is proved that soft anti-local countability is a sufficient condition for equivalence between soft ω-locally indiscreetness (resp. soft ω-regularity) and soft locally indiscreetness (resp. soft ω-regularity). Additionally, it is proved that the induced topological spaces of a soft ω-locally indiscrete (resp. soft ω-regular, soft ω-T2) soft topological space are (resp. ω-regular, ω-T2) topological spaces. Additionally, it is proved that the generated soft topological space of a family of ω-locally indiscrete (resp. ω-regular, ω-T2) topological spaces is soft ω-locally indiscrete and vice versa. In addition to these, soft product theorems regarding soft ω-regular and soft ω-T2 soft topological spaces are obtained. Moreover, it is proved that soft ω-regular and soft ω-T2 are hereditarily under soft subspaces. Full article
(This article belongs to the Special Issue Computing Mathematics with Fuzzy Sets)
9 pages, 269 KiB  
Article
On a One-Dimensional Hydrodynamic Model for Semiconductors with Field-Dependent Mobility
by Giuseppe Alì, Francesco Lamonaca, Carmelo Scuro and Isabella Torcicollo
Mathematics 2021, 9(17), 2152; https://doi.org/10.3390/math9172152 - 3 Sep 2021
Cited by 4 | Viewed by 1953
Abstract
We consider a one-dimensional, isentropic, hydrodynamical model for a unipolar semiconductor, with the mobility depending on the electric field. The mobility is related to the momentum relaxation time, and field-dependent mobility models are commonly used to describe the occurrence of saturation velocity, that [...] Read more.
We consider a one-dimensional, isentropic, hydrodynamical model for a unipolar semiconductor, with the mobility depending on the electric field. The mobility is related to the momentum relaxation time, and field-dependent mobility models are commonly used to describe the occurrence of saturation velocity, that is, a limit value for the electron mean velocity as the electric field increases. For the steady state system, we prove the existence of smooth solutions in the subsonic case, with a suitable assumption on the mobility function. Furthermore, we prove uniqueness of subsonic solutions for sufficiently small currents. Full article
(This article belongs to the Special Issue Modeling and Numerical Analysis of Energy and Environment 2021)
20 pages, 1593 KiB  
Article
Instruments and Methods for Identifying Indicators of a Digital Entrepreneurial System
by Jelena Raut, Đorđe Ćelić, Branislav Dudić, Jelena Ćulibrk and Darko Stefanović
Mathematics 2021, 9(17), 2151; https://doi.org/10.3390/math9172151 - 3 Sep 2021
Cited by 3 | Viewed by 2361
Abstract
Entrepreneurial ecosystems are the main driver of the widespread trend of digitalization, and they open opportunities for the advancement of the digital economy. The digital economy makes its progress through innovative enterprises that can ensure global progress. In order to effectively use the [...] Read more.
Entrepreneurial ecosystems are the main driver of the widespread trend of digitalization, and they open opportunities for the advancement of the digital economy. The digital economy makes its progress through innovative enterprises that can ensure global progress. In order to effectively use the opportunities that open up the process of digitalization, information is needed on how much the Republic of Serbia is able to support the process of discovering entrepreneurship, which is stimulated by digitalization, which is the subject of this paper. The aim of this paper is to analyze the digital entrepreneurial system of the Republic of Serbia, as well as to identify indicators that hinder the development of this system, using appropriate instruments and methods that will be presented in detail in the paper. The results have demonstrated that the starting point for improvement of the digital entrepreneurial system is in the field of finance, with a particular focus on companies in the startup and stand-up phases. Furthermore, a comparative analysis will showcase the digital entrepreneurial system of the Republic of Serbia and the member states of the European Union, where it will be seen that the digital entrepreneurial system of the Republic of Serbia is lagging behind the member states of the European Union in its growth and development. The results will serve as the starting point for policymakers to improve the process of digitalization and the digital entrepreneurial system as a whole. The results show the starting point for the improvement of entrepreneurship in the Republic of Serbia, that is, how small, and medium-sized enterprises can be encouraged on the path to their successful management. Full article
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<p>Values of pillars in the digital entrepreneurial system of the Republic of Serbia, in their digital dimension.</p>
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12 pages, 281 KiB  
Article
The Link between Corporate Reputation and Financial Performance and Equilibrium within the Airline Industry
by Andreas-Daniel Cocis, Larissa Batrancea and Horia Tulai
Mathematics 2021, 9(17), 2150; https://doi.org/10.3390/math9172150 - 3 Sep 2021
Cited by 11 | Viewed by 4040
Abstract
This study examines how corporate reputation is perceived by investors through the financial performance and equilibrium of several airline companies. We used a sample of 22 companies. Nineteen are listed in the World Airline Awards 2018 ranking based on client satisfaction, and three [...] Read more.
This study examines how corporate reputation is perceived by investors through the financial performance and equilibrium of several airline companies. We used a sample of 22 companies. Nineteen are listed in the World Airline Awards 2018 ranking based on client satisfaction, and three companies are included in the Fortune ranking and enjoying the best corporate reputations in the airline industry. The analyzed period was 2016–2018. The purpose of this study was to rank airline companies based on financial indicators by means of the TOPSIS method to see whether the companies included in the Fortune ranking would keep a similar hierarchy. Results indicated that companies maintained a similar order in the TOPSIS ranking after considering financial performance and equilibrium indicators. The overall conclusion was that companies with a good financial performance and equilibrium enjoyed a good corporate reputation from investors’ point of view. Full article
15 pages, 2618 KiB  
Article
Time-Delay Synchronization and Anti-Synchronization of Variable-Order Fractional Discrete-Time Chen–Rossler Chaotic Systems Using Variable-Order Fractional Discrete-Time PID Control
by Joel Perez Padron, Jose Paz Perez, José Javier Pérez Díaz and Atilano Martinez Huerta
Mathematics 2021, 9(17), 2149; https://doi.org/10.3390/math9172149 - 3 Sep 2021
Cited by 7 | Viewed by 1848
Abstract
In this research paper, we solve the problem of synchronization and anti-synchronization of chaotic systems described by discrete and time-delayed variable fractional-order differential equations. To guarantee the synchronization and anti-synchronization, we use the well-known PID (Proportional-Integral-Derivative) control theory and the Lyapunov–Krasovskii stability theory [...] Read more.
In this research paper, we solve the problem of synchronization and anti-synchronization of chaotic systems described by discrete and time-delayed variable fractional-order differential equations. To guarantee the synchronization and anti-synchronization, we use the well-known PID (Proportional-Integral-Derivative) control theory and the Lyapunov–Krasovskii stability theory for discrete systems of a variable fractional order. We illustrate the results obtained through simulation with examples, in which it can be seen that our results are satisfactory, thus achieving synchronization and anti-synchronization of chaotic systems of a variable fractional order with discrete time delay. Full article
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<p>Phase portrait of discrete-time chaotic Chen system.</p>
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<p>Phase portrait of discrete-time chaotic Rossler system.</p>
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<p>Time response (<b>a</b>–<b>c</b>) of synchronized states of master and slave.</p>
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<p>Phase space of synchronization of original master–slave system.</p>
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<p>Phase space of synchronization of master–slave system with fractional order given by c = 0.9, c1 = 0.8, c2 = 0.7.</p>
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<p>Synchronization errors with time delay between states of master and slave system of variable fractional order derivative.</p>
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<p>Time response (<b>a</b>–<b>c</b>) of synchronized states of master and slave.</p>
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<p>Phase space of anti-synchronization of an original master–slave system.</p>
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<p>Phase space of anti-synchronization of master–slave systems with fractional orders given by c = 0.9, c1 = 0.8, and c2 = 0.7.</p>
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<p>Anti-synchronization errors with time delay between states of master and slave systems of a variable fractional-order derivative.</p>
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16 pages, 18541 KiB  
Article
Planar Typical Bézier Curves with a Single Curvature Extremum
by Chuan He, Gang Zhao, Aizeng Wang, Shaolin Li and Zhanchuan Cai
Mathematics 2021, 9(17), 2148; https://doi.org/10.3390/math9172148 - 3 Sep 2021
Cited by 2 | Viewed by 2390
Abstract
This paper focuses on planar typical Bézier curves with a single curvature extremum, which is a supplement of typical curves with monotonic curvature by Y. Mineur et al. We have proven that the typical curve has at most one curvature extremum and given [...] Read more.
This paper focuses on planar typical Bézier curves with a single curvature extremum, which is a supplement of typical curves with monotonic curvature by Y. Mineur et al. We have proven that the typical curve has at most one curvature extremum and given a fast calculation formula of the parameter at the curvature extremum. This will allow designers to execute a subdivision at the curvature extremum to obtain two pieces of typical curves with monotonic curvature. In addition, we put forward a sufficient condition for typical curve solutions under arbitrary degrees for the G1 interpolation problem. Some numerical experiments are provided to demonstrate the effectiveness and efficiency of our approach. Full article
(This article belongs to the Special Issue Approximation Theory and Methods 2020)
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<p>The construction of typical Bézier curves. (<b>a</b>) Control edges are designed according to (<b>a</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi mathvariant="bold-italic">b</mi> <mi>i</mi> </msub> <mo>=</mo> <msup> <mi mathvariant="bold-italic">M</mi> <mi>i</mi> </msup> <mo>⋅</mo> <mo>Δ</mo> <msub> <mi mathvariant="bold-italic">b</mi> <mn>0</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="bold-italic">b</mi> <mi>i</mi> <mrow/> </msubsup> <msubsup> <mi mathvariant="bold-italic">b</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> <mn>1</mn> </msubsup> <mo>=</mo> <msubsup> <mi mathvariant="bold-italic">b</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> <mrow/> </msubsup> <msubsup> <mi mathvariant="bold-italic">b</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>2</mn> </mrow> <mrow/> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="bold-italic">b</mi> <mi>i</mi> <mrow/> </msubsup> <msubsup> <mi mathvariant="bold-italic">b</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>2</mn> </mrow> <mn>1</mn> </msubsup> <mo>=</mo> <msubsup> <mi mathvariant="bold-italic">b</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>2</mn> </mrow> <mrow/> </msubsup> <msubsup> <mi mathvariant="bold-italic">b</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>3</mn> </mrow> <mrow/> </msubsup> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="bold-italic">P</mi> <mo>′</mo> </msup> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="bold-italic">P</mi> <mo>″</mo> </msup> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> are generated by the de Casteljau algorithm.</p>
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<p>The domain of <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="bold-italic">b</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> <mn>1</mn> </msubsup> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>⋅</mo> <mi>cos</mi> <mi>θ</mi> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>s</mi> <mo>≥</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>∈</mo> <mo stretchy="false">[</mo> <mn>0</mn> <mo>,</mo> <mi>π</mi> <mo stretchy="false">]</mo> </mrow> </semantics></math>) in the local coordinate system.</p>
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<p>Acute triangle <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="bold-italic">b</mi> <mi>i</mi> <mrow/> </msubsup> <msubsup> <mi mathvariant="bold-italic">b</mi> <mi>h</mi> <mrow/> </msubsup> <mo>⊥</mo> <msubsup> <mi mathvariant="bold-italic">b</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>k</mi> <mo>−</mo> <mn>3</mn> </mrow> <mn>1</mn> </msubsup> <msubsup> <mi mathvariant="bold-italic">b</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>k</mi> <mo>−</mo> <mn>3</mn> </mrow> <mn>1</mn> </msubsup> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="bold-italic">b</mi> <mi>h</mi> <mrow/> </msubsup> </mrow> </semantics></math> corresponds to parameter <math display="inline"><semantics> <mrow> <msup> <mi>t</mi> <mo>∗</mo> </msup> </mrow> </semantics></math>.</p>
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<p>A typical Bézier curve with <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>⋅</mo> <mi>cos</mi> <mi>θ</mi> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>s</mi> <mo>≥</mo> <mn>1</mn> </mrow> </semantics></math>). (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>=</mo> <mn>1.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi mathvariant="bold-italic">b</mi> <mn>0</mn> <mrow/> </msubsup> <mo>=</mo> <msup> <mrow> <mfenced close="]" open="["> <mrow> <mn>1</mn> <mo>,</mo> <mn>0</mn> </mrow> </mfenced> </mrow> <mi mathvariant="bold-italic">T</mi> </msup> </mrow> </semantics></math>; (<b>b</b>) curvature plot.</p>
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<p>A typical Bézier curve with <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>&gt;</mo> <mi>cos</mi> <mi>θ</mi> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mn>0</mn> <mo>&lt;</mo> <mi>s</mi> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math>). (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi mathvariant="bold-italic">b</mi> <mn>0</mn> <mrow/> </msubsup> <mo>=</mo> <msup> <mrow> <mfenced close="]" open="["> <mrow> <mn>1</mn> <mo>,</mo> <mn>0</mn> </mrow> </mfenced> </mrow> <mi mathvariant="bold-italic">T</mi> </msup> </mrow> </semantics></math>; (<b>b</b>) curvature plot.</p>
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<p>Transformation effect of matrix <math display="inline"><semantics> <mi mathvariant="bold-italic">M</mi> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">T</mi> <mfenced> <mi>t</mi> </mfenced> </mrow> </semantics></math>.</p>
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<p>The de Casteljau algorithm of <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="bold-italic">P</mi> <mo>′</mo> </msup> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>.</p>
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<p>Regions for curvature variation of typical Bézier curves. Curvature decreases in the light yellow area and increases in the light green area. While in the white area, curvature variation is not monotonous and possesses only one extremum. Specifically, for <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>&lt;</mo> <mn>0</mn> </mrow> </semantics></math> there is a local minimum and for <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>&gt;</mo> <mn>0</mn> </mrow> </semantics></math> there is a local maximum.</p>
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<p>Symmetric typical Bézier curve with <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mo>−</mo> <mi>π</mi> <mo>/</mo> <mn>6</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi mathvariant="bold-italic">b</mi> <mn>0</mn> <mrow/> </msubsup> <mo>=</mo> <msup> <mrow> <mfenced close="]" open="["> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </mfenced> </mrow> <mi mathvariant="bold-italic">T</mi> </msup> </mrow> </semantics></math>; (<b>b</b>) symmetric curvature plot with a local minimum at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.</p>
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<p>Subdivision of a cubic typical Bézier curve at curvature extremum.</p>
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<p>Typical Bézier curve with degree <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>⋅</mo> <mi>cos</mi> <mi>θ</mi> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>s</mi> <mo>≥</mo> <mn>1</mn> </mrow> </semantics></math>). (<b>a</b>) Transformation matrix <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">M</mi> <mo>=</mo> <mn>1.1</mn> <mo>⋅</mo> <mfenced> <mrow> <mtable equalrows="true" equalcolumns="true"> <mtr> <mtd> <mrow> <mi>cos</mi> <mfenced> <mrow> <mo>−</mo> <mn>2</mn> <mi>π</mi> <mo>/</mo> <mn>7</mn> </mrow> </mfenced> </mrow> </mtd> <mtd> <mrow> <mo>−</mo> <mi>sin</mi> <mfenced> <mrow> <mo>−</mo> <mn>2</mn> <mi>π</mi> <mo>/</mo> <mn>7</mn> </mrow> </mfenced> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>sin</mi> <mfenced> <mrow> <mo>−</mo> <mn>2</mn> <mi>π</mi> <mo>/</mo> <mn>7</mn> </mrow> </mfenced> </mrow> </mtd> <mtd> <mrow> <mi>cos</mi> <mfenced> <mrow> <mo>−</mo> <mn>2</mn> <mi>π</mi> <mo>/</mo> <mn>7</mn> </mrow> </mfenced> </mrow> </mtd> </mtr> </mtable> </mrow> </mfenced> </mrow> </semantics></math>; point with curvature extremum is represented by yellow dot; (<b>b</b>) curvature plot has a minimum at <math display="inline"><semantics> <mrow> <msup> <mi>t</mi> <mo>∗</mo> </msup> <mo>=</mo> <mn>0.3747</mn> </mrow> </semantics></math>.</p>
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<p>Results under subdivision. (<b>a</b>) Two typical curve segments join at <math display="inline"><semantics> <mrow> <msup> <mi>t</mi> <mo>∗</mo> </msup> </mrow> </semantics></math>; (<b>b</b>) first segment has decreasing curvature variation; (<b>c</b>) second segment has increasing curvature variation.</p>
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<p>Typical Bézier curve with degree <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>&gt;</mo> <mi>cos</mi> <mi>θ</mi> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mn>0</mn> <mo>&lt;</mo> <mi>s</mi> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math>). (<b>a</b>) Transformation matrix <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">M</mi> <mo>=</mo> <mn>0.85</mn> <mo>⋅</mo> <mfenced> <mrow> <mtable equalrows="true" equalcolumns="true"> <mtr> <mtd> <mrow> <mi>cos</mi> <mfenced> <mrow> <mi>π</mi> <mo>/</mo> <mn>3</mn> </mrow> </mfenced> </mrow> </mtd> <mtd> <mrow> <mo>−</mo> <mi>sin</mi> <mfenced> <mrow> <mi>π</mi> <mo>/</mo> <mn>3</mn> </mrow> </mfenced> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>sin</mi> <mfenced> <mrow> <mi>π</mi> <mo>/</mo> <mn>3</mn> </mrow> </mfenced> </mrow> </mtd> <mtd> <mrow> <mi>cos</mi> <mfenced> <mrow> <mi>π</mi> <mo>/</mo> <mn>3</mn> </mrow> </mfenced> </mrow> </mtd> </mtr> </mtable> </mrow> </mfenced> </mrow> </semantics></math>; point with curvature extremum is represented by yellow dot; (<b>b</b>) curvature plot has a maximum at <math display="inline"><semantics> <mrow> <msup> <mi>t</mi> <mo>∗</mo> </msup> <mo>=</mo> <mn>0.6590</mn> </mrow> </semantics></math>.</p>
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<p>Results under subdivision. (<b>a</b>) Two typical curve segments join at <math display="inline"><semantics> <mrow> <msup> <mi>t</mi> <mo>∗</mo> </msup> </mrow> </semantics></math>; (<b>b</b>) first segment has increasing curvature variation; (<b>c</b>) second segment has decreasing curvature variation.</p>
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<p>The boundary constraints of G1 interpolation in normalized form.</p>
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<p>Cubic typical Bézier curve solution for <math display="inline"><semantics> <mrow> <mo>−</mo> <mi>π</mi> <mo>/</mo> <mn>2</mn> <mo>&lt;</mo> <mi>α</mi> <mo>&lt;</mo> <mn>0</mn> <mo>&lt;</mo> <mi>β</mi> <mo>&lt;</mo> <mi>π</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0.7482</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mfenced close="&#x2016;" open="&#x2016;"> <mrow> <mo>Δ</mo> <msubsup> <mi mathvariant="bold-italic">b</mi> <mn>0</mn> <mrow/> </msubsup> </mrow> </mfenced> </mrow> <mi>r</mi> </msub> <mo>=</mo> <mn>0.6220</mn> </mrow> </semantics></math>; (<b>b</b>) corresponding curvature plot.</p>
Full article ">Figure 17
<p>Cubic typical Bézier curve solution for <math display="inline"><semantics> <mrow> <mo>−</mo> <mi>π</mi> <mo>/</mo> <mn>2</mn> <mo>&lt;</mo> <mi>β</mi> <mo>&lt;</mo> <mn>0</mn> <mo>&lt;</mo> <mi>α</mi> <mo>&lt;</mo> <mi>π</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mfenced close="&#x2016;" open="&#x2016;"> <mrow> <mo>Δ</mo> <msubsup> <mi mathvariant="bold-italic">b</mi> <mn>0</mn> <mrow/> </msubsup> </mrow> </mfenced> </mrow> <mi>r</mi> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>b</b>) corresponding curvature plot.</p>
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