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20 pages, 1275 KiB  
Article
Early-Time Recession Solution from a Steady-State Initial Condition for the Horizontal Unconfined Aquifer
by Elias Gravanis, Evangelos Akylas and Ernestos N. Sarris
Water 2025, 17(5), 771; https://doi.org/10.3390/w17050771 (registering DOI) - 6 Mar 2025
Abstract
In this work, we present the semi-analytical solution for the early-time recession phase of the horizontal unconfined aquifer of finite length for steady-state initial conditions. This is a case where self-similarity arguments are not applicable. The solution is built as linear perturbations from [...] Read more.
In this work, we present the semi-analytical solution for the early-time recession phase of the horizontal unconfined aquifer of finite length for steady-state initial conditions. This is a case where self-similarity arguments are not applicable. The solution is built as linear perturbations from the initial steady state. The solution is determined via a Sturm–Liouville eigenvalue problem, which should be solved numerically. On the other hand, the immediate response of the aquifer to the sudden switching off the recharge, i.e., in the earliest times, is obtained by deducing analytically the large eigenvalue asymptotic solutions of the problem. We find analytically that in this time regime, the outflow Q is given by Q = Q0 − 1.4Q05/3L−4/3n−2/3t2/3, where Q0 is the initial outflow rate, L is the length of the aquifer, n is the porosity of the formation and t is the time from the start of the recession. The stated result is very accurate for times t up to ~0.01 nL3/2k−1/2Q0−1/2, where k is the hydraulic conductivity of the formation. The analytical and quantitative relation of the presented solution with the classical recession phase asymptotic solutions derived in the past by Polubarinova (early-time solution) and Boussinesq (separable, late-time solution) is discussed in detail. The presented results can be used as a benchmark solution for modeling or numerical validation purposes. Full article
Show Figures

Figure 1

Figure 1
<p>Dotted line (primary axes): slope of κ(i). Dashed–dotted line (secondary axes): (Minus the) intercept of κ(i).</p>
Full article ">Figure 2
<p>The logarithmic derivative of the product eκdκ as function of the number of nodes.</p>
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<p>Comparison between the early-time asymptotic (24) and the power law asymptotic (34) for the flow rate Q.</p>
Full article ">Figure 4
<p>The ratio of storage squared over outflow rate for the asymptotic solutions and numerical solution of the Boussinesq equation.</p>
Full article ">Figure 5
<p>Outflow rate (<b>left</b>) and storage (<b>right</b>) time variation for the asymptotic solutions and numerical solution of the Boussinesq equation.</p>
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<p>Comparison of the water table time variation between the early-time solution (<b>left</b>) and the separable solution (<b>right</b>) with the numerical solution of the Boussinesq equation.</p>
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17 pages, 591 KiB  
Article
Enhancing Uplink Communication in Wireless Powered Communication Networks Through Rate-Splitting Multiple Access and Joint Resource Optimization
by Iqra Hameed, Mario R. Camana, Mohammad Abrar Shakil Sejan and Hyoung Kyu Song
Mathematics 2025, 13(5), 888; https://doi.org/10.3390/math13050888 - 6 Mar 2025
Abstract
Wireless powered communication networks (WPCNs) provide a sustainable solution for energy-constrained IoT devices by enabling wireless energy transfer (WET) in the downlink and wireless information transmission (WIT) in the uplink. However, their performance is often limited by interference in uplink communication and inefficient [...] Read more.
Wireless powered communication networks (WPCNs) provide a sustainable solution for energy-constrained IoT devices by enabling wireless energy transfer (WET) in the downlink and wireless information transmission (WIT) in the uplink. However, their performance is often limited by interference in uplink communication and inefficient resource allocation. To address these challenges, we propose an RSMA-aided WPCN framework, which optimizes rate-splitting factors, power allocation, and time division to enhance spectral efficiency and user fairness. To solve this non-convex joint optimization problem, we employ the simultaneous perturbation stochastic approximation (SPSA) algorithm, a gradient-free method that efficiently estimates optimal parameters with minimal function evaluations. Compared to conventional optimization techniques, SPSA provides a scalable and computationally efficient approach for real-time resource allocation in RSMA-aided WPCNs. Our simulation results demonstrate that the proposed RSMA-aided framework improves sum throughput by 12.5% and enhances fairness by 15–20% compared to conventional multiple-access schemes. These findings establish RSMA as a key enabler for next-generation WPCNs, offering a scalable, interference-resilient, and energy-efficient solution for future wireless networks. Full article
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Figure 1

Figure 1
<p>The RSMA-aided wireless powered communication network (WPCN) framework. The hybrid access point (H-AP) first transmits energy to users <math display="inline"><semantics> <msub> <mi>U</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> via wireless energy transfer (WET), followed by wireless information transmission (WIT) in the uplink. Each user employs RSMA, where messages are split into two parts, optimally allocated for power and transmitted to the H-AP.</p>
Full article ">Figure 2
<p>Convergence of proposed SPSA-based algorithm for different transmit power levels.</p>
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<p>Sum throughput versus transmit power at H-AP for different schemes.</p>
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<p>Throughput of <math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> versus transmit power at H-AP for different schemes.</p>
Full article ">Figure 5
<p>Sum throughput versus distance between <math display="inline"><semantics> <msub> <mi>U</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> for different schemes.</p>
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<p>Throughput of <math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> versus distance between <math display="inline"><semantics> <msub> <mi>U</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> for different schemes.</p>
Full article ">Figure 7
<p>Sum throughput versus transmit power at H-AP.</p>
Full article ">
14 pages, 653 KiB  
Article
Bifurcation and Dynamics Analysis of a Piecewise-Linear van der Pol Equation
by Wenke Li, Nanbin Cao and Xia Liu
Axioms 2025, 14(3), 197; https://doi.org/10.3390/axioms14030197 - 6 Mar 2025
Abstract
In this study, we examine the bifurcations and dynamics of a piecewise linear van der Pol equation—a model that captures self-sustained oscillations and is applied in various scientific disciplines, including electronics, neuroscience, biology, and economics. The van der Pol equation is transformed into [...] Read more.
In this study, we examine the bifurcations and dynamics of a piecewise linear van der Pol equation—a model that captures self-sustained oscillations and is applied in various scientific disciplines, including electronics, neuroscience, biology, and economics. The van der Pol equation is transformed into a piecewise linear system to simplify the analysis of stability and controllability, which is particularly beneficial in engineering applications. This work explores the impact of increasing the number of linear segments on the system’s dynamics, focusing on the stability of the equilibria, phase portraits, and bifurcations. The findings reveal that while the bifurcation structure at critical values of the bifurcation parameter is complex, the topology of the piecewise linear model remains unaffected by an increase in the number of linear segments from three to four. This research contributes to our understanding of the dynamics of nonlinear systems with piecewise linear characteristics and has implications for the analysis and design of real-world systems exhibiting such behavior. Full article
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Figure 1

Figure 1
<p>Nullclines (<span class="html-italic">v</span>-nullcline and <span class="html-italic">w</span>-nullcline), linear line segments of <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>p</mi> <mi>w</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>v</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, and the corresponding regions of the PWL model (<a href="#FD7-axioms-14-00197" class="html-disp-formula">7</a>)–(<a href="#FD8-axioms-14-00197" class="html-disp-formula">8</a>) with <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>0.885</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>. From left to right, the slopes of <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mrow> <mi>l</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>L</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>L</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>L</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> are <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mrow> <mi>l</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mn>1</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>1.27</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mn>2</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. The line segments <math display="inline"><semantics> <msub> <mi>L</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>L</mi> <mn>2</mn> </msub> </semantics></math> join at <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mo>−</mo> <mn>0.385</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 2
<p>A persistence bifurcation occurs when <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> for system (<a href="#FD7-axioms-14-00197" class="html-disp-formula">7</a>). The top row illustrates the transition of the equilibrium on <math display="inline"><semantics> <msub> <mi>L</mi> <mrow> <mi>l</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math> from a stable node (left) to an unstable node on <math display="inline"><semantics> <msub> <mi>L</mi> <mn>1</mn> </msub> </semantics></math> (right) as <math display="inline"><semantics> <mi>λ</mi> </semantics></math> varies. Meanwhile, a large-amplitude limit cycle appears for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>&gt;</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>, traversing four distinct zones. The bottom row shows the equilibrium on <math display="inline"><semantics> <msub> <mi>L</mi> <mrow> <mi>l</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math> shifting from a stable node (left) to an unstable focus on <math display="inline"><semantics> <msub> <mi>L</mi> <mn>1</mn> </msub> </semantics></math> (right). Simultaneously, a small-amplitude limit cycle emerges when <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>&gt;</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>, passing through two specific zones, <math display="inline"><semantics> <msub> <mi>R</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>l</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math>. The representative parameter values are <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mover accent="true"> <mi>v</mi> <mo stretchy="false">^</mo> </mover> <mo>,</mo> <mover accent="true"> <mi>w</mi> <mo stretchy="false">^</mo> </mover> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mo>−</mo> <mn>0.385</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mn>1</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>1.27</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mn>2</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>0.885</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>System (<a href="#FD7-axioms-14-00197" class="html-disp-formula">7</a>) experiences a persistence bifurcation when <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. The top row illustrates the transition of the equilibrium on <math display="inline"><semantics> <msub> <mi>L</mi> <mn>2</mn> </msub> </semantics></math> from an unstable focus (left) to a stable node located on <math display="inline"><semantics> <msub> <mi>L</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math> (right) as <math display="inline"><semantics> <mi>λ</mi> </semantics></math> varies. Meanwhile, a small-amplitude limit cycle appears for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math>, passing through two zones, <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math>. The bottom row shows the equilibrium on <math display="inline"><semantics> <msub> <mi>L</mi> <mn>2</mn> </msub> </semantics></math> shifting from an unstable node (left) to a stable node on <math display="inline"><semantics> <msub> <mi>L</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math> (right). Simultaneously, a large-amplitude limit cycle emerges when <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math>, traversing four distinct zones. The representative parameter values are the same as in <a href="#axioms-14-00197-f002" class="html-fig">Figure 2</a>.</p>
Full article ">Figure 4
<p>Schematic representations of five distinct phase portraits for system (<a href="#FD7-axioms-14-00197" class="html-disp-formula">7</a>). A small-amplitude limit cycle (red curve) exists when <math display="inline"><semantics> <mi>λ</mi> </semantics></math> is close to 1, which passes through two zones <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math>. For all five cases, we take the value <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mn>1</mn> </msub> <mo>&lt;</mo> <msubsup> <mi>η</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> <mo>−</mo> </msubsup> <mo>&lt;</mo> <msub> <mi>η</mi> <mn>2</mn> </msub> <mo>&lt;</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>Schematic representations of five distinct phase portraits for system (<a href="#FD7-axioms-14-00197" class="html-disp-formula">7</a>). A large-amplitude limit cycle (red curve) exists when <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> <mo>&lt;</mo> <mi>λ</mi> <mo>&lt;</mo> <msub> <mi>v</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, which passes through four zones <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>l</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>R</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>R</mi> <mn>2</mn> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math>.</p>
Full article ">Figure 6
<p>Illustrative phase portraits for the PWL VDP models, either with three or four linear segments, are presented. The top row depicts the VDP model comprising three linear segments, while the bottom row features the model with four linear segments. The phase portraits in the first column display a limit cycle, the second column shows a continuum of homoclinic orbits, and the last column demonstrates stable equilibrium. The representative parameter values are <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mn>1</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mn>2</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>1.27</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>0.885</mn> </mrow> </semantics></math>.</p>
Full article ">
25 pages, 1039 KiB  
Article
CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis
by Ruixue Wang and Ning Zhao
Algorithms 2025, 18(3), 148; https://doi.org/10.3390/a18030148 - 5 Mar 2025
Viewed by 105
Abstract
Due to the complex operating environment of valves, when a fault occurs inside a valve, the vibration signal generated by the fault is easily affected by the environmental noise, making the extraction of fault features difficult. To address this problem, this paper proposes [...] Read more.
Due to the complex operating environment of valves, when a fault occurs inside a valve, the vibration signal generated by the fault is easily affected by the environmental noise, making the extraction of fault features difficult. To address this problem, this paper proposes a feature extraction method based on the combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Fuzzy Entropy (FN). Due to the slow convergence speed and the tendency to fall into local optimal solutions of the Hippopotamus Optimization Algorithm (HO), an improved Hippopotamus Optimization (IHO) algorithm-optimized Support Vector Machine (SVM) model for valve leakage diagnosis is introduced to further enhance the accuracy of valve leakage diagnosis. The improved Hippopotamus Optimization algorithm initializes the hippopotamus population with Tent chaotic mapping, designs an adaptive weight factor, and incorporates adaptive variation perturbation. Moreover, the performance of IHO was proven to be optimal compared to HO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Sparrow Search Algorithm (SSA) by calculating twelve test functions. Subsequently, the IHO-SVM classification model was established and applied to valve leakage diagnosis. The prediction effects of the seven models, IHO-SVM. HO-SVM, PSO-SVM, GWO-SVM, WOA-SVM, SSA-SVM, and SVM were compared and analyzed with actual data. As a result, the comparison indicated that IHO-SVM has desirable robustness and generalization, which successfully improves the classification efficiency and the recognition rate in fault diagnosis. Full article
(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
18 pages, 456 KiB  
Article
Optimal Control of an Electromechanical Energy Harvester
by Dario Lucente, Alessandro Manacorda, Andrea Plati, Alessandro Sarracino and Marco Baldovin
Entropy 2025, 27(3), 268; https://doi.org/10.3390/e27030268 - 5 Mar 2025
Viewed by 178
Abstract
Many techniques originally developed in the context of deterministic control theory have recently been applied to the quest for optimal protocols in stochastic processes. Given a system subject to environmental fluctuations, one may ask what is the best way to change its controllable [...] Read more.
Many techniques originally developed in the context of deterministic control theory have recently been applied to the quest for optimal protocols in stochastic processes. Given a system subject to environmental fluctuations, one may ask what is the best way to change its controllable parameters in time in order to maximize, on average, a certain reward function, while steering the system between two pre-assigned states. In this work, we study the problem of optimal control for a wide class of stochastic systems, inspired by a model of an energy harvester. The stochastic noise in this system is due to the mechanical vibrations, while the reward function is the average power extracted from them. We consider the case in which the electrical resistance of the harvester can be changed in time, and we exploit the tools of control theory to work out optimal solutions in a perturbative regime, close to the stationary state. Our results show that it is possible to design protocols that perform better than any possible solution with constant resistance. Full article
(This article belongs to the Special Issue Control of Driven Stochastic Systems: From Shortcuts to Optimality)
Show Figures

Figure 1

Figure 1
<p>Scheme of the optimal control strategy leading to the differential system Equation (13).</p>
Full article ">Figure 2
<p>Characterization of the solutions of PMP. Panels (<b>a</b>,<b>b</b>) show the intensity of the infinite discontinuities <math display="inline"><semantics> <msub> <mi>u</mi> <mn>0</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>u</mi> <mi>f</mi> </msub> </semantics></math> occurring at the beginning and at the end of the protocol in the two physically admissible solutions A and B of system (<a href="#FD31-entropy-27-00268" class="html-disp-formula">31</a>). Different choices of the stationary control <math display="inline"><semantics> <msub> <mi>u</mi> <mi>s</mi> </msub> </semantics></math>, fixing the boundary conditions (<a href="#FD37-entropy-27-00268" class="html-disp-formula">37</a>), are considered. Panels (<b>c</b>,<b>d</b>) account for the corresponding net power gain (or loss) with respect to the stationary strategy <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>=</mo> <msup> <mi>u</mi> <mo>*</mo> </msup> </mrow> </semantics></math>. The red squares refer to the value of the average power computed within the perturbative approach. Green circles are obtained by plugging the solution protocol <span class="html-italic">u</span> into the original (non-perturbative) dynamics, and computing the average power of the process (see the caption of <a href="#entropy-27-00268-f004" class="html-fig">Figure 4</a> for details). Of course, in this case the final state <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> </semantics></math> will not match the prescribed boundary condition exactly—see <a href="#entropy-27-00268-f004" class="html-fig">Figure 4</a>. The distance between the two curves is an indicator of the quality of the perturbative approximation. Finally, the blue triangles represent, for reference, the power obtained with a stationary protocol <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>=</mo> <msub> <mi>u</mi> <mi>s</mi> </msub> </mrow> </semantics></math>. Parameters: <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ζ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>.</p>
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<p>Bulk part <math display="inline"><semantics> <msub> <mi>u</mi> <mi>b</mi> </msub> </semantics></math> of the protocol, for the two solutions A (<b>a</b>) and B (<b>b</b>), as a function of time. Different boundary conditions are considered. Parameters as in <a href="#entropy-27-00268-f002" class="html-fig">Figure 2</a>.</p>
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<p>Dynamics of the system within solution B for boundary conditions fixed by <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>1.02</mn> <mspace width="0.166667em"/> <msup> <mi>u</mi> <mo>*</mo> </msup> </mrow> </semantics></math>. Panels (<b>a</b>–<b>c</b>) show the evolution of the elements of the vector <math display="inline"><semantics> <mi>σ</mi> </semantics></math> (the covariances <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <msup> <mi>v</mi> <mn>2</mn> </msup> </mfenced> </semantics></math>, <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mi>v</mi> <mi>I</mi> </mfenced> </semantics></math> and <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <msup> <mi>I</mi> <mn>2</mn> </msup> </mfenced> </semantics></math>), as computed in the perturbative approach (red solid curves). By inserting the solution protocol <span class="html-italic">u</span>, shown in panel (<b>d</b>), back into the original dynamics (<a href="#FD33-entropy-27-00268" class="html-disp-formula">33</a>), it is possible to compute the true behavior of the system under the prescribed protocol (computation has been carried out using an explicit Runge–Kutta integration scheme of 4-th order): this evolution is represented by the blue dashed curves in panels (<b>a</b>–<b>c</b>). While it is expected that the two sets of curves do not overlap, the fact that they stay close is a consistency check on our perturbative approximation. Parameters as in <a href="#entropy-27-00268-f002" class="html-fig">Figure 2</a>.</p>
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18 pages, 4562 KiB  
Article
Multirhythmicity, Synchronization, and Noise-Induced Dynamic Diversity in a Discrete Population Model with Competition
by Lev Ryashko, Anna Otman and Irina Bashkirtseva
Mathematics 2025, 13(5), 857; https://doi.org/10.3390/math13050857 - 5 Mar 2025
Viewed by 52
Abstract
The problem of mathematical modeling and analysis of stochastic phenomena in population systems with competition is considered. This problem is investigated based on a discrete system of two populations modeled by the Ricker map. We study the dependence of the joint dynamic behavior [...] Read more.
The problem of mathematical modeling and analysis of stochastic phenomena in population systems with competition is considered. This problem is investigated based on a discrete system of two populations modeled by the Ricker map. We study the dependence of the joint dynamic behavior on the parameters of the growth rate and competition intensity. It is shown that, due to multistability, random perturbations can transfer the population system from one attractor to another, generating stochastic P-bifurcations and transformations of synchronization modes. The effectiveness of a mathematical approach, based on the stochastic sensitivity technique and the confidence domain method, in the parametric analysis of these stochastic effects is demonstrated. For monostability zones, the phenomenon of stochastic generation of the phantom attractor is found, in which the system enters the trigger mode with alternating transitions between states of almost complete extinction of one or the other population. It is shown that the noise-induced effects are accompanied by stochastic D-bifurcations with transitions from order to chaos. Full article
(This article belongs to the Section E3: Mathematical Biology)
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Figure 1

Figure 1
<p>Bifurcation diagram of an isolated population.</p>
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<p>Variability of dynamics in system (<a href="#FD2-mathematics-13-00857" class="html-disp-formula">2</a>): (<b>a</b>) Bifurcation diagram of diagonal attractors for three values of <math display="inline"><semantics> <mi>ρ</mi> </semantics></math>. (<b>b</b>) <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>A</mi> <mo>,</mo> <mi>ρ</mi> <mo>)</mo> </mrow> </semantics></math>-parametric diagram of mono- and multistability zones. Bifurcation diagrams (<b>c</b>,<b>d</b>) and Lyapunov exponents (<b>e</b>) of coexisting attractors for system (<a href="#FD2-mathematics-13-00857" class="html-disp-formula">2</a>) with <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.03</mn> </mrow> </semantics></math>. Here, <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>7.389</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>8.927</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>12.306</mn> <mo>,</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>12.496</mn> </mrow> </semantics></math>.</p>
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<p>Attractors of system (<a href="#FD2-mathematics-13-00857" class="html-disp-formula">2</a>) with <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.03</mn> </mrow> </semantics></math> and their basins for (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>12.45</mn> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>13</mn> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, (<b>f</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>16</mn> </mrow> </semantics></math>, (<b>g</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>18</mn> </mrow> </semantics></math>, (<b>h</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Random states of system (<a href="#FD3-mathematics-13-00857" class="html-disp-formula">3</a>) with <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.03</mn> <mo>,</mo> <mspace width="0.166667em"/> <mi>ε</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>, and confidence ellipses around deterministic attractors for (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p>
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<p>Stochastic deformations of distributions of random states in system (<a href="#FD3-mathematics-13-00857" class="html-disp-formula">3</a>) with <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.03</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> as noise intensity <math display="inline"><semantics> <mi>ε</mi> </semantics></math> increases: <span class="html-italic">u</span>-coordinates of random states <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>1001</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>1001</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>1100</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>1100</mn> </msub> <mo>)</mo> </mrow> </mrow> </semantics></math> of solutions starting at the deterministic anti-phase 2-cycle <math display="inline"><semantics> <mi mathvariant="script">B</mi> </semantics></math> (<b>a</b>) and the in-phase 2-cycle <math display="inline"><semantics> <mi mathvariant="script">R</mi> </semantics></math> (<b>b</b>). Here, the synchronization indicator <span class="html-italic">z</span> is shown in green.</p>
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<p>Stochastic “anti-phase→in-phase” transitions in system (<a href="#FD3-mathematics-13-00857" class="html-disp-formula">3</a>) with <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.03</mn> <mo>,</mo> <mspace width="0.166667em"/> <mi>A</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>: (<b>a</b>) Time series of <span class="html-italic">x</span> (blue) and <span class="html-italic">y</span> (red) coordinates, and synchronization indicator <span class="html-italic">z</span> (green) of solutions starting at the anti-phase 2-cycle <math display="inline"><semantics> <mi mathvariant="script">B</mi> </semantics></math>. (<b>b</b>) An enlarged fragment. (<b>c</b>) Confidence ellipses around states of coexisting in-phase 2-cycle <math display="inline"><semantics> <mi mathvariant="script">R</mi> </semantics></math> (red) and anti-phase 2-cycle <math display="inline"><semantics> <mi mathvariant="script">B</mi> </semantics></math> (blue). Confidence ellipses are plotted using dashed lines for <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> and solid lines for <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>.</p>
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<p>Stochastic system (<a href="#FD2-mathematics-13-00857" class="html-disp-formula">2</a>) with <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.03</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>12.45</mn> </mrow> </semantics></math>: (<b>a</b>) <span class="html-italic">u</span>-coordinates (blue) of stochastic states and synchronization indicator <span class="html-italic">z</span> (green) versus <math display="inline"><semantics> <mi>ε</mi> </semantics></math> for solutions starting at the anti-phase 2-cycle (top) and in-phase 4-cycle (bottom); (<b>b</b>) backward stochastic <span class="html-italic">P</span>-bifurcations. Here, the probability density function <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </semantics></math> of projections <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>=</mo> <mrow> <mo>(</mo> <mi>x</mi> <mo>−</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>/</mo> <msqrt> <mn>2</mn> </msqrt> </mrow> </semantics></math> of random states of solutions starting at the 4-cycle <math display="inline"><semantics> <mi mathvariant="script">G</mi> </semantics></math> is shown for different values of noise intensity; (<b>c</b>) confidence ellipses around states of the anti-phase 2-cycle for <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> (solid) and <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math> (dashed).</p>
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<p>Stochastic system (<a href="#FD2-mathematics-13-00857" class="html-disp-formula">2</a>) with <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.03</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>13</mn> </mrow> </semantics></math>: (<b>a</b>) <span class="html-italic">u</span>-coordinates (blue) of random states of solutions starting at the in-phase 4-cycle and synchronization indicator <span class="html-italic">z</span> (green); (<b>b</b>) stochastic <span class="html-italic">P</span>-bifurcations. Here, the probability density functions <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </semantics></math> of projections <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>=</mo> <mrow> <mo>(</mo> <mi>x</mi> <mo>−</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>/</mo> <msqrt> <mn>2</mn> </msqrt> </mrow> </semantics></math> of the coordinates of random states of solutions starting at the diagonal 4-cycle <math display="inline"><semantics> <mi mathvariant="script">R</mi> </semantics></math> are shown for different values of the noise intensity; (<b>c</b>) confidence ellipses for <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> around the states of coexisting in-phase 4-cycles.</p>
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<p>Stochastic system (<a href="#FD2-mathematics-13-00857" class="html-disp-formula">2</a>) with <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.03</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>: <span class="html-italic">u</span>-coordinates of random states of solutions starting (<b>a</b>) at the in-phase chaos <math display="inline"><semantics> <mi mathvariant="script">G</mi> </semantics></math> (green), (<b>b</b>) at the in-phase chaos <math display="inline"><semantics> <mi mathvariant="script">P</mi> </semantics></math> (purple), and (<b>c</b>) at the anti-phase 2-torus <math display="inline"><semantics> <mi mathvariant="script">B</mi> </semantics></math> (blue). Here, the synchronization indicator <span class="html-italic">z</span> is shown in red.</p>
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<p>Synchronization in stochastic system (<a href="#FD2-mathematics-13-00857" class="html-disp-formula">2</a>). Plots of mean values of the synchronization indicator <span class="html-italic">z</span>: (<b>a</b>) for different <span class="html-italic">A</span> versus <math display="inline"><semantics> <mi>ε</mi> </semantics></math>, (<b>b</b>) in the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>A</mi> <mo>,</mo> <mi>ε</mi> <mo>)</mo> </mrow> </semantics></math>-plane.</p>
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<p>Stochastic generation of phantom attractors in system (<a href="#FD3-mathematics-13-00857" class="html-disp-formula">3</a>) with <math display="inline"><semantics> <mrow> <mspace width="0.277778em"/> <mi>ρ</mi> <mo>=</mo> <mn>0.99</mn> </mrow> </semantics></math> for (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>. Here, random states of system (<a href="#FD3-mathematics-13-00857" class="html-disp-formula">3</a>) solutions starting at the deterministic attractors after the transient process (left) and time series (right) are shown for different values of the noise intensity <math display="inline"><semantics> <mi>ε</mi> </semantics></math>.</p>
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<p>Probability density functions <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </semantics></math> for system (<a href="#FD3-mathematics-13-00857" class="html-disp-formula">3</a>) with (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p>
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<p>Stochastic generation of the trigger regime with temporary near-extinction in system (<a href="#FD3-mathematics-13-00857" class="html-disp-formula">3</a>) with <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.99</mn> </mrow> </semantics></math> and (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>15.5</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>16</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>. Here, random states of system (<a href="#FD3-mathematics-13-00857" class="html-disp-formula">3</a>) solutions starting at the deterministic attractors after the transient process (left) and time series (right) are plotted for several values of the noise intensity <math display="inline"><semantics> <mi>ε</mi> </semantics></math>.</p>
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<p>Probability of temporary near-extinction: the population size is below the threshold of 0.01.</p>
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<p>Mean values of time intervals <math display="inline"><semantics> <mi>τ</mi> </semantics></math> for which the stochastic system (<a href="#FD3-mathematics-13-00857" class="html-disp-formula">3</a>) resides in the mode of near-extinction, where the population size is below the threshold <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>.</p>
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<p>Largest Lyapunov exponent.</p>
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14 pages, 3896 KiB  
Article
Multi-Peak Photovoltaic Maximum Power Point Tracking Method Based on Honey Badger Algorithm Under Localized Shading Conditions
by Qianjin Gui, Lei Wang, Chao Ding, Wenfa Xu, Xiaoyang Li, Feilong Yu and Haisen Wang
Energies 2025, 18(5), 1258; https://doi.org/10.3390/en18051258 - 4 Mar 2025
Viewed by 131
Abstract
The P-V and I-V curves of photovoltaic (PV) strings show multiple peaks when exposed to partial shading conditions (PSCs). The traditional maximum power point tracking (MPPT) method cannot track the global maximum power point (GMPP) due to the multi-peak characteristics, power fluctuation, and [...] Read more.
The P-V and I-V curves of photovoltaic (PV) strings show multiple peaks when exposed to partial shading conditions (PSCs). The traditional maximum power point tracking (MPPT) method cannot track the global maximum power point (GMPP) due to the multi-peak characteristics, power fluctuation, and tracking speed. In this paper, a multi-peak PV MPPT method based on the honey badger algorithm (HBA) is proposed to track the GMPP in a localized shading environment. The performance of this method is also compared and analyzed with the traditional MPPT methods based on the perturbation observation (P&O) method and Particle Swarm Optimization (PSO) algorithm. The experimental results have proven that, compared with the MPPT methods based on P&O and PSO, the proposed multi-peak MPPT method based on the HBA algorithm has a faster tracking speed, higher tracking accuracy, and fewer iterations. Full article
(This article belongs to the Special Issue Power Electronic and Power Conversion Systems for Renewable Energy)
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Figure 1
<p>Schematic diagram of multi-panel series PV power generation system.</p>
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<p>P-V and I-V curve of output characteristics of series-connected PV strings in localized shading.</p>
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<p>Variation of operating point when DC bus is connected to the high-voltage side.</p>
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<p>Flow chart of multi-peak PV MPPT control based on HBA.</p>
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<p>Simulation waveforms of PSO, MCA, and the proposed method for (<b>a</b>) duty ratio; (<b>b</b>) PV power.</p>
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<p>Experimental platform.</p>
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<p>Power−voltage curve of PV string output under environmental condition 1.</p>
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<p>Power−voltage curve of PV string output under environmental condition 2.</p>
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<p>Tracking process of three methods under environmental condition 1. (<b>a</b>) P&amp;O. (<b>b</b>) PSO. (<b>c</b>) HBA.</p>
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<p>Tracking process of three methods under environmental condition 2. (<b>a</b>) P&amp;O. (<b>b</b>) PSO. (<b>c</b>) HBA.</p>
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<p>Tracking process of three methods when the environment changes. (<b>a</b>) P&amp;O. (<b>b</b>) PSO. (<b>c</b>) HBA.</p>
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26 pages, 6719 KiB  
Article
Sketch-Guided Topology Optimization with Enhanced Diversity for Innovative Structural Design
by Siyu Zhu, Jie Hu, Jin Qi, Lingyu Wang, Jing Guo, Jin Ma and Guoniu Zhu
Appl. Sci. 2025, 15(5), 2753; https://doi.org/10.3390/app15052753 - 4 Mar 2025
Viewed by 91
Abstract
Topology optimization (TO) is a powerful generative design tool for innovative structural design, capable of optimizing material distribution to generate structures with superior performance. However, current topology optimization algorithms mostly target a single objective and are highly dependent on the problem definition parameters, [...] Read more.
Topology optimization (TO) is a powerful generative design tool for innovative structural design, capable of optimizing material distribution to generate structures with superior performance. However, current topology optimization algorithms mostly target a single objective and are highly dependent on the problem definition parameters, causing two critical issues: limited human controllability and solution diversity. These issues often lead to burdensome design iterations and insufficient design exploration. This paper proposes a multi-solution TO framework to address them. Human designers express their stylistic preferences for structures through sketches which are decomposed into stroke and closed-shape elements to flexibly guide each TO process. Sketch-based constraints are integrated with Fourier mapping-based length-scale control to enhance human controllability. Solution diversity is achieved by perturbing Fourier mapping frequencies and load conditions in the neural implicit TO framework. Adaptive parallel scale adjustment is incorporated to reduce the computational cost for design exploration. Using the structural design of a wheel spoke as a case study, the mechanical performance and diversity of the generated TO solutions as well as the effectiveness of human control are analyzed both qualitatively and quantitatively. The results reveal that the sketch-based constraints and length-scale control have distinct control effects on structural features and have different impacts on the mechanical performance and diversity, thereby enabling fine-grained and flexible human controllability to better balance conflicting objectives. Full article
(This article belongs to the Special Issue Computer-Aided Design in Mechanical Engineering)
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Figure 1
<p>Overview of proposed multi-solution TO framework with human controllability.</p>
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<p>Definition of design field, boundary, and load conditions.</p>
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<p>Summary of experiment design.</p>
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<p>Loss curves and intermediate solutions during the iterative TO process.</p>
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<p>Generated innovative structural designs constrained by sketch image 1.</p>
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<p>Generated innovative structural designs constrained by sketch image 2.</p>
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<p>Generated innovative structural designs constrained by sketch images 3–6.</p>
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<p>Illustration of 2D-to-3D conversion steps.</p>
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<p>Innovative design exploration results for automobile wheel spokes under sketch-based constraints. Randomly perturbed hyper-parameters include <math display="inline"><semantics> <mrow> <mi>d</mi> <mi>v</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>l</mi> <mo movablelimits="true" form="prefix">min</mo> </msub> </semantics></math>, and <math display="inline"><semantics> <mi mathvariant="bold-italic">f</mi> </semantics></math>. (<b>a</b>) Guided by sketch 1. (<b>b</b>) Guided by sketch 2. (<b>c</b>) Guided by sketch 3. (<b>d</b>) Guided by sketch 4.</p>
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<p>Experimental results of human controllability by integrating <math display="inline"><semantics> <msub> <mi>l</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <mi>λ</mi> </semantics></math>.</p>
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<p>Ablation study of <math display="inline"><semantics> <msub> <mi>β</mi> <mrow> <mi>I</mi> <mi>n</mi> <mi>c</mi> </mrow> </msub> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <msub> <mi>l</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math> (<b>c</b>) in terms of compliance.</p>
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<p>Ablation study of <math display="inline"><semantics> <msub> <mi>β</mi> <mrow> <mi>I</mi> <mi>n</mi> <mi>c</mi> </mrow> </msub> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <msub> <mi>l</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math> (<b>c</b>) in terms of volume.</p>
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<p>Ablation study of <math display="inline"><semantics> <msub> <mi>β</mi> <mrow> <mi>I</mi> <mi>n</mi> <mi>c</mi> </mrow> </msub> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <msub> <mi>l</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math> (<b>c</b>) in terms of <math display="inline"><semantics> <mover> <mrow> <mi>S</mi> <mi>S</mi> <mi>I</mi> <mi>M</mi> </mrow> <mo>¯</mo> </mover> </semantics></math>.</p>
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<p>Ablation study of <math display="inline"><semantics> <msub> <mi>β</mi> <mrow> <mi>I</mi> <mi>n</mi> <mi>c</mi> </mrow> </msub> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <msub> <mi>l</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math> (<b>c</b>) in terms of <math display="inline"><semantics> <mover> <mrow> <mi>M</mi> <mi>A</mi> <mi>E</mi> </mrow> <mo>¯</mo> </mover> </semantics></math>.</p>
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<p>Unreasonable generated structures due to conflicts between sketch-based constraints, volume constraint, and compliance.</p>
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<p>Examples of recognized regions of conflict.</p>
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25 pages, 10436 KiB  
Article
Effects of the Geomagnetic Superstorms of 10–11 May 2024 and 7–11 October 2024 on the Ionosphere and Plasmasphere
by Viviane Pierrard, Tobias G. W. Verhulst, Jean-Marie Chevalier, Nicolas Bergeot and Alexandre Winant
Atmosphere 2025, 16(3), 299; https://doi.org/10.3390/atmos16030299 - 4 Mar 2025
Viewed by 152
Abstract
On 10 May 2024 at 17 h:07 UTC, the simultaneous arrival of several solar coronal mass ejections (CMEs) generated the strongest geomagnetic storm of the last twenty years, with a minimum Dst = −412 nT, usually referred to as the Mother’s Day event. [...] Read more.
On 10 May 2024 at 17 h:07 UTC, the simultaneous arrival of several solar coronal mass ejections (CMEs) generated the strongest geomagnetic storm of the last twenty years, with a minimum Dst = −412 nT, usually referred to as the Mother’s Day event. On 10 October 2024, the second strongest event of solar cycle 25 appeared with a Dst = −335 nT, preceded on 8 October by an event with a Dst = −153 nT. In the present work, with measurements of the vertical total electron content and with ionosonde observations from Europe, USA, and South Korea, we show that the ionization of the upper atmosphere shortly increased at the arrival of the CME for these different events, followed by a fast decrease at all latitudes. The ionization remained very low for more than a full day. While the recovery started at the beginning of the second day after the onset for both events in October, the sudden recovery in the middle of the second day on 12 May is much more unusual. The analysis of the observations at different latitudes and longitudes shows that the causes of the ionization variations during the superstorms were mainly due to strong perturbations in the ionospheric F layer, amplified by the plasmasphere’s influence on the vertical total electron content (VTEC). The erosion of the plasmasphere during these two strong events led to a plasmapause located at exceptionally low radial distances smaller than 2 Re (Earth’s radii) in the post-midnight sector and a rotating plume in the afternoon–dusk sector clearly visible in the BSPM plasmasphere model. It took several days after the storms to recover normal ionization rates. Full article
(This article belongs to the Special Issue Ionospheric Disturbances and Space Weather)
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Figure 1

Figure 1
<p>Examples of spread-F condition ((<b>left</b>), recorded by IC437 on 11 May 2024 at 20:15:00 UTC) and particle-induced Es layer ((<b>right</b>), recorded by DB049 on 11 May at 00:50:02 UTC). Note that the height (vertical axis) ranges from 0 to 800 km in both cases, but the frequencies (horizontal axis) range from 1 to 12 MHz in the left panel and from 1 to 9 MHz in the right panel.</p>
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<p>Solar wind density n (<b>top</b>), bulk velocity v (<b>2nd panel</b>), pressure P (<b>3rd panel</b>), proton temperature T (<b>4th panel</b>), and the southward component of the Interplanetary Magnetic Field Bz (<b>bottom panel</b>) from 1 to 24 May 2024, observed by OMNI at 1 AU.</p>
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<p>Geomagnetic activity indices of Bartels Kp (<b>top panel</b>), Ap in nT (<b>2nd panel</b>), Disturbed Storm Time Dst in nT (<b>3rd panel</b>), daily solar activity indices F10.7, solar <span class="html-italic">radio flux</span> at 10.7 cm (<b>4th panel</b>), and intensity of the Lyman alpha line in the solar spectrum (<b>5th panel</b>) from 1 to 24 May 2024. The (<b>bottom panel</b>) illustrates Dst from 1 January 2000 to 31 January 2025. The two analyzed events are indicated by the red vertical lines.</p>
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<p>VTEC time series at 3 locations in Europe from 9 to 14 May 2024. The (<b>left panel</b>) shows the time evolution of the vertical total electron content (VTEC) (in red) at three locations illustrated in the (<b>right panel</b>) on a map of Europe: (a) in the northern part of Europe (61° N, 5° E), (b) above Brussels (50.5° N, 4.5° E), and (c) in North Africa (36° N, 5° E).</p>
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<p>VTEC maps, differences with expected behaviour and variability over Europe on 11 May 2024 at 20:20 UTC. (<b>Top</b>): VTEC maps estimated in real-time. The dots represent the VTEC data used for the interpolation. (<b>Bottom left</b>): differences between VTEC maps and the expected VTEC (median over the past 15 days). (<b>Bottom right</b>): the VTEC variability reflecting the ionospheric state variations during the 5 min time span of the interpolation.</p>
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<p>Peak electron densities for F<sub>2</sub> (red) and F<sub>1</sub> (blue) obtained from the ionosonde measurements at Juliusruh ((<b>top</b>), JR055), Dourbes ((<b>middle</b>), DB049), and Ebre ((<b>bottom</b>), EB040), from 10 May 2024 0:00 to 13 May 2024 0:00 UTC.</p>
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<p>Peak electron densities for the F<sub>2</sub> (red) and F<sub>1</sub> (blue) obtained from the ionosonde measurements at Millstone Hill ((<b>top</b>), MHJ45) and I-Cheon ((<b>bottom</b>), IC437) from 10 May 2024 0:00 to 13 May 2024 0:00 UTC.</p>
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<p>Electron density obtained with the BSPM coupled with the ionosphere [<a href="#B25-atmosphere-16-00299" class="html-bibr">25</a>] on 10 May 2024 at 17:00 UTC (before the storm, (<b>top</b>) panels) and 11 May 2024 at 5:00 UTC (after the storm, (<b>bottom</b>) panels). For the two cases, the plasmasphere (orange region) is illustrated in the equatorial plane (<b>left</b>) and in the meridian plane (<b>right</b>). The Bartels geomagnetic index Kp from 1 day before to 1 day after the simulated day is shown in the top panels, with a red dashed line to indicate the exact illustrated time.</p>
Full article ">Figure 8 Cont.
<p>Electron density obtained with the BSPM coupled with the ionosphere [<a href="#B25-atmosphere-16-00299" class="html-bibr">25</a>] on 10 May 2024 at 17:00 UTC (before the storm, (<b>top</b>) panels) and 11 May 2024 at 5:00 UTC (after the storm, (<b>bottom</b>) panels). For the two cases, the plasmasphere (orange region) is illustrated in the equatorial plane (<b>left</b>) and in the meridian plane (<b>right</b>). The Bartels geomagnetic index Kp from 1 day before to 1 day after the simulated day is shown in the top panels, with a red dashed line to indicate the exact illustrated time.</p>
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<p>(<b>Top panel</b>): midnight plasmapause proxy derived from the magnetic and plasma observations of the low-Earth orbiting Swarm satellites from 9 to 12 May 2024. (<b>Bottom panel</b>): observed Bartels geomagnetic activity Kp index. (<a href="https://swe.ssa.esa.int/elte-plasma-federated" target="_blank">https://swe.ssa.esa.int/elte-plasma-federated</a>, accessed on 5 November 2024).</p>
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<p>Solar wind density n, velocity v, temperature T, the southward component of the interplanetary magnetic field Bz, and geomagnetic indices Kp and Dst from 7 to 14 October 2024.</p>
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<p>VTEC time series at the same 3 locations as in <a href="#atmosphere-16-00299-f004" class="html-fig">Figure 4</a> from 7 to 13 October 2024 included. (<b>a</b>) in the northern part of Europe (61° N, 5° E), (<b>b</b>) above Brussels (50.5° N, 4.5° E), and (<b>c</b>) in North Africa (36° N, 5° E).</p>
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<p>VTEC maps, differences with expected behaviour and variability over Europe on 10 October 2024 at 21:10 UTC. (<b>Top</b>): VTEC maps estimated in real-time. The dots represent the VTEC data used for the interpolation. (<b>Bottom left</b>): differences between VTEC maps and the expected VTEC (median over the past 15 days). (<b>Bottom right</b>): the VTEC variability reflecting the ionospheric state variations during the 5 min time span of the interpolation.</p>
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<p>Peak electron densities for F<sub>2</sub> (red) and F<sub>1</sub> (blue) for the JR055 (<b>top</b>), DB049 (<b>middle</b>), and EB040 (<b>bottom</b>) ionosondes from 10 to 13 October 2024 0:00 UTC. Note that the maximum density (vertical axis) here is 2.5 × 10<sup>6</sup> (instead of 1.5 × 10<sup>6</sup> as in <a href="#atmosphere-16-00299-f006" class="html-fig">Figure 6</a> and <a href="#atmosphere-16-00299-f007" class="html-fig">Figure 7</a>).</p>
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<p>Time series of <span class="html-italic">foF</span><sub>1</sub> (blue) and <span class="html-italic">foF</span><sub>2</sub> (red) observed by the MHJ45 and IC437 ionosondes during the 11 October storm. Notice that the vertical axes are again extended to 2.5 × 10<sup>6</sup>.</p>
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<p>Electron density obtained with the BSPM coupled with the ionosphere on 10 October 2024 at 12:00 UTC (before the superstorm, (<b>top panels</b>)) and 10 October 2024 at 22:00 UTC (during the superstorm, (<b>bottom panels</b>)). The red dashed line on the observed Kp top panels indicates the two illustrated times.</p>
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<p>Midnight plasmapause proxy derived from the magnetic and plasma observation of the low-Earth orbiting Swarm satellites (like in <a href="#atmosphere-16-00299-f009" class="html-fig">Figure 9</a>) from 9 to 12 October 2024.</p>
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37 pages, 2141 KiB  
Article
Cavity Instabilities in a High-Speed Low-Pressure Turbine Stage
by Lorenzo Da Valle, Antonino Federico Maria Torre, Filippo Merli, Bogdan Cezar Cernat and Sergio Lavagnoli
Int. J. Turbomach. Propuls. Power 2025, 10(1), 4; https://doi.org/10.3390/ijtpp10010004 - 4 Mar 2025
Viewed by 139
Abstract
This study investigates the time-resolved aerodynamics in the cavity regions of a full-scale, high-speed, low-pressure turbine stage representative of geared turbofan engines. The turbine stage is tested in the von Karman Institute’s short-duration rotating facility at different purge rates (PR) injected through the [...] Read more.
This study investigates the time-resolved aerodynamics in the cavity regions of a full-scale, high-speed, low-pressure turbine stage representative of geared turbofan engines. The turbine stage is tested in the von Karman Institute’s short-duration rotating facility at different purge rates (PR) injected through the upstream hub cavity. Spectra from the shroud and downstream hub cavity show perturbations linked to blade passing frequency and rotor speed. Asynchronous flow structures associated with ingress/egress mechanisms are observed in the rim seal of the purged cavity. At 0% PR, the ingress region extends to the entire rim seal, and pressure fluctuations are maximized. At 1% PR, the instability is suppressed and the cavity is sealed. At 0.5%, the rim-seal instability exhibits multiple peaks in the spectra, each corresponding to a state of the instability. Kelvin–Helmholtz instabilities are identified as trigger mechanisms. A novel technique based on the properties of the cross-power spectral density is developed to determine the speed and wavelength of the rotating structures, achieving higher precision than the commonly used cross-correlation method. Moreover, unlike the standard methodology, this approach allows researchers to calculate the structure characteristics for all the instability states. Spectral analysis at the turbine outlet shows that rim-seal-induced instabilities propagate into regions occupied by secondary flows. A methodology is proposed to quantify the magnitude of the induced fluctuations, showing that the interaction with secondary flows amplifies the instability at the stage outlet. Full article
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Figure 1

Figure 1
<p>Schematic representation of the SPLEEN stage cross-section (<b>a</b>). The right-hand figures show the positions of the fast-response pressure transducers in the upstream purge cavity (<b>b</b>), downstream purge cavity (<b>c</b>), and shroud cavity (<b>d</b>).</p>
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<p>Effect of the time window on the average PSD distributions.</p>
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<p>(<b>a</b>) shows a schematic representation of the cavity modes, adapted from Beard et al. [<a href="#B31-ijtpp-10-00004" class="html-bibr">31</a>]. (<b>b</b>) shows a synthetic example of an ideal time lag plot.</p>
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<p>(<b>a</b>) shows the synthetic signals, <math display="inline"><semantics> <msub> <mi>x</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>x</mi> <mn>2</mn> </msub> </semantics></math>, employed in the demonstration of the CPSD properties. (<b>b</b>) shows the cross-correlation computed between the two signals.</p>
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<p>In the upper row, (<b>a</b>) shows the PSD of the synthetic signals, <math display="inline"><semantics> <msub> <mi>x</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>x</mi> <mn>2</mn> </msub> </semantics></math>, while (<b>b</b>) depicts the modulus of their CPSD. In the lower row, (<b>c</b>) shows their coherence, while (<b>d</b>) displays the phase of their CPSD.</p>
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<p>(<b>a</b>) shows the phase lag plot generated from the analysis of the BPF signal band by applying the CPSD methodology. (<b>b</b>) shows the value of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>−</mo> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math> computed for possible regression lines.</p>
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<p>Average PSD distributions under NP conditions for different sensors at the same radii.</p>
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<p>Average PSD distributions at different purge conditions for fast-response pressure sensors located at different radial positions of the upstream purge cavity region.</p>
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<p>CPSD distributions of the modulus (<b>a</b>) and phase (<b>b</b>) for a combination of sensors during a single test. The modulus is normalized with respect to the strongest peak, while the phase is normalized by <math display="inline"><semantics> <mrow> <mn>2</mn> <mi>π</mi> </mrow> </semantics></math> [i.e., <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>12</mn> </msub> <mo>=</mo> <msub> <mi>ϕ</mi> <mn>12</mn> </msub> <mo>/</mo> <mrow> <mo>(</mo> <mn>2</mn> <mi>π</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> ].</p>
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<p>The left-hand figures show the evolution of the PSD distributions during a single test for two sensors <math display="inline"><semantics> <msub> <mi>s</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>s</mi> <mn>2</mn> </msub> </semantics></math>, separated by an angle of <math display="inline"><semantics> <msub> <mi>α</mi> <mn>12</mn> </msub> </semantics></math>. On the right, (<b>d</b>) presents the spectra computed over the entire test (covering windows 1 to 3).</p>
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<p>(<b>a</b>) shows the CPSD phase collected over multiple tests and sub-windows at the CPSD modulus peaks location. (<b>b</b>,<b>c</b>) presents the pdfs computed for the coefficients of the phase law (<math display="inline"><semantics> <msub> <mi>m</mi> <mn>12</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>q</mi> <mrow> <mn>0</mn> <mo>−</mo> <mn>12</mn> </mrow> </msub> </semantics></math>). (<b>d</b>) show the phase lag pdf, <math display="inline"><semantics> <msub> <mi>p</mi> <mn>12</mn> </msub> </semantics></math>, computed at the 15 EO peak.</p>
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<p>(<b>a</b>) shows the phase plot lag for the 15 EO frequency peak. (<b>b</b>,<b>c</b>) show the distributions of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>−</mo> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math> and normalized <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>r</mi> </mrow> </msub> </semantics></math>, respectively. (<b>d</b>) shows the pdf of the number of structures.</p>
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<p>(<b>a</b>) shows two pressure measurements (1 revolution) pass-band-filtered over the rim-seal instability band. (<b>b</b>) shows their cross-correlation and the location of the three most energetic peaks.</p>
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<p>Time lag plots populated considering the first (<b>a</b>) and the first two (<b>b</b>) strongest peaks of the cross-correlation distribution. Best regression computed considering the centroids of the clusters (no bootstrapping procedure applied yet).</p>
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<p>Phase lag (<b>a</b>) and time lag (<b>b</b>) plots obtained in Plane 2 (hub, <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>P</mi> <mi>S</mi> <mi>D</mi> </mrow> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math> EO). The uncertainty of the phase and time delays is expressed by the horizontal error bars (95% CI).</p>
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<p>Average PSD distributions at different purge conditions for fast-response pressure sensors located at different radial positions of the downstream purge cavity region.</p>
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<p>Average PSD distributions at different purge conditions for fast-response pressure sensors located at different radial positions of the shroud cavity region.</p>
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<p>PSD distributions measured during the same tests by one of the lateral holes of the FR4H probe in Plane 3 (<b>a</b>), and by a fixed sensor in the upstream purge cavity (<b>b</b>).</p>
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<p>Average PSD distributions measured by a sensor of the FR4H probe (down sensor) and at the upper lip of the upstream purge cavity under NP (<b>a</b>) and HP (<b>b</b>) conditions.</p>
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<p>(<b>a</b>) displays the methodology applied to estimate the contribution of the distributed unsteadiness. (<b>b</b>,<b>c</b>) show the contributions of rim-seal instability and distributed unsteadiness to the spectra under HP and NP conditions, respectively.</p>
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22 pages, 4239 KiB  
Article
How Natural Regeneration After Severe Disturbance Affects Ecosystem Services Provision of Andean Forest Soils at Contrasting Timescales
by Juan Ortiz, Marcelo Panichini, Pablo Neira, Carlos Henríquez-Castillo, Rocio E. Gallardo Jara, Rodrigo Rodriguez, Ana Mutis, Camila Ramos, Winfred Espejo, Ramiro Puc-Kauil, Erik Zagal, Neal Stolpe, Mauricio Schoebitz, Marco Sandoval and Francis Dube
Forests 2025, 16(3), 456; https://doi.org/10.3390/f16030456 - 4 Mar 2025
Viewed by 199
Abstract
Chile holds ~50% of temperate forests in the Southern Hemisphere, thus constituting a genetic–ecological heritage. However, intense anthropogenic pressures have been inducing distinct forest structural-regeneration patterns. Accordingly, we evaluated 22 soil properties at 0–5 and 5–20 cm depths in two protected sites, with [...] Read more.
Chile holds ~50% of temperate forests in the Southern Hemisphere, thus constituting a genetic–ecological heritage. However, intense anthropogenic pressures have been inducing distinct forest structural-regeneration patterns. Accordingly, we evaluated 22 soil properties at 0–5 and 5–20 cm depths in two protected sites, with similar perturbation records but contrasting post-disturbance regeneration stages: long-term secondary forest (~50 y) (SECFORST) (dominated by Chusquea sp.-understory) and a short-term forest after disturbance (~5 y) (FADIST) within a Nothofagus spp. forest to determine the potential of these soils to promote nutrient availability, water cycling, soil organic carbon (SOC) sequestration (CO2→SOC), and microbiome. Results detected 93 correlations (r ≥ 0.80); however, no significant differences (p < 0.05) in physical or chemical properties, except for infiltration velocity (+27.97%), penetration resistance (−23%), SOC (+5.64%), and % Al saturation (+5.64%) relative to SECFORST, and a consistent trend of suitable values 0–5 > 5–20 cm were estimated. The SOC→CO2 capacity reached 4.2 ± 0.5 (FADIST) and 2.7 ± 0.2 Mg C y−1 (SECFORST) and only microbial abundance shifts were observed. These findings provide relevant insights on belowground resilience, evidenced by similar ecosystem services provision capacities over time, which may be influenced progressively by opportunistic Chusquea sp. Full article
(This article belongs to the Special Issue How Does Forest Management Affect Soil Dynamics?)
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Figure 1
<p>Approaching maps illustrating study site. (<b>A</b>) national map of central-south Chile, highlighting Ñuble Region in orange, (<b>B</b>) regional map of Ñuble, and the location of the Ranchillo Alto site in southern part of the region, (<b>C</b>) localization of the protected area Ranchillo Alto and the position of the FAD<sub>IST</sub> and SEC<sub>FORST</sub> analyzed in this study.</p>
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<p>Photographs of the study area, (<b>A</b>) original degraded site overview, (<b>B</b>) FAD<sub>IST</sub>, and (<b>C</b>) SEC<sub>FORST</sub>. Photo credits: F. Dube.</p>
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<p>Heat map illustrating Spearman’s correlation coefficients among the evaluated physical and chemical properties. The symbols * and ** represent <span class="html-italic">p</span>-values below 0.05 and 0.01, respectively. Reddish tones correspond to negative correlations, blue tones refer to positive correlations, and color intensity represents levels of correlation.</p>
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<p>Composition of microbial communities in degraded and non-degraded soils at different depths. (<b>A</b>) Bacterial community and (<b>B</b>) fungal community. Bars represent the relative abundance (%) of different microbial classes in degraded soils at 20 cm (FAD<sub>ist</sub>20) and 5 cm (FAD<sub>ist</sub>5) depths, and in non-degraded soils at 20 cm (SEC<sub>forst</sub>20) and 5 cm (SEC<sub>forst</sub>5) depths. Different microbial classes are indicated by specific colors, as shown in the legend. Differences in the abundance and diversity of microbial classes reflect the influence of both soil degradation and depth.</p>
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<p>The figure shows a comparison of bacterial and fungal communities across soil samples with different levels of organic management (FAD<sub>IST</sub> and SEC<sub>FORST</sub>). Panels (<b>A</b>,<b>B</b>) display the distribution of bacterial and fungal communities, respectively, categorized by their energy sources, biogeochemical cycles, trophic modes, and guilds, with color intensity reflecting the percentage of each functional group. Panels (<b>C</b>,<b>D</b>) present heatmaps illustrating the correlations between microbial community functions (bacterial and fungal) and soil characteristics, with color gradients indicating the strength and direction of these correlations (red for positive and blue for negative). These analyses highlight how varying soil management practices influence the composition and functional dynamics of microbial communities in agricultural soils.</p>
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<p>Relationship between bacterial orders and soil properties. The symbols * and *** represent <span class="html-italic">p</span>-values below 0.05 and 0.01, respectively.</p>
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30 pages, 4601 KiB  
Article
Finding Multiple Optimal Solutions to an Integer Linear Program by Random Perturbations of Its Objective Function
by Noah Schulhof, Pattara Sukprasert, Eytan Ruppin, Samir Khuller and Alejandro A. Schäffer
Algorithms 2025, 18(3), 140; https://doi.org/10.3390/a18030140 - 4 Mar 2025
Viewed by 137
Abstract
Integer linear programs (ILPs) and mixed integer programs (MIPs) often have multiple distinct optimal solutions, yet the widely used Gurobi optimization solver returns certain solutions at disproportionately high frequencies. This behavior is disadvantageous, as, in fields such as biomedicine, the identification and analysis [...] Read more.
Integer linear programs (ILPs) and mixed integer programs (MIPs) often have multiple distinct optimal solutions, yet the widely used Gurobi optimization solver returns certain solutions at disproportionately high frequencies. This behavior is disadvantageous, as, in fields such as biomedicine, the identification and analysis of distinct optima yields valuable domain-specific insights that inform future research directions. In the present work, we introduce MORSE (Multiple Optima via Random Sampling and careful choice of the parameter Epsilon), a randomized, parallelizable algorithm to efficiently generate multiple optima for ILPs. MORSE maps multiplicative perturbations to the coefficients in an instance’s objective function, generating a modified instance that retains an optimum of the original problem. We formalize and prove the above claim in some practical conditions. Furthermore, we prove that for 0/1 selection problems, MORSE finds each distinct optimum with equal probability. We evaluate MORSE using two measures; the number of distinct optima found in r independent runs, and the diversity of the list (with repetitions) of solutions by average pairwise Hamming distance and Shannon entropy. Using these metrics, we provide empirical results demonstrating that MORSE outperforms the Gurobi method and unweighted variations of the MORSE method on a set of 20 Mixed Integer Programming Library (MIPLIB) instances and on a combinatorial optimization problem in cancer genomics. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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<p>Average normalized pairwise Hamming distance (<span class="html-italic">y</span>-axis) vs. run number (<span class="html-italic">x</span>-axis) for MIPLIB instance <tt>noswot</tt>.</p>
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<p>Average normalized pairwise Hamming distance (<span class="html-italic">y</span>-axis) vs. run number (<span class="html-italic">x</span>-axis) for MIPLIB instance <tt>p0033</tt>.</p>
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<p>Average normalized pairwise Hamming distance (<span class="html-italic">y</span>-axis) vs. run number (<span class="html-italic">x</span>-axis) for MIPLIB instance <tt>lp4l</tt>.</p>
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<p>Tabular results. Average normalized pairwise Hamming distance (<span class="html-italic">y</span>-axis) vs. run number (<span class="html-italic">x</span>-axis) for tested MIPLIB instances with the 10 highest maximum average normalized pairwise Hamming distances. The plots are arranged in descending order of maximum average normalized pairwise Hamming distance. Please note that the <span class="html-italic">y</span>-axis starts above 0 for some panels.</p>
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<p>Tabular results. Average normalized pairwise Hamming distance (<span class="html-italic">y</span>-axis) vs. run number (<span class="html-italic">x</span>-axis) for tested MIPLIB instances with the 10 lowest maximum average normalized pairwise Hamming distances. The plots are arranged in descending order of maximum average normalized pairwise Hamming distance. Please note that the <span class="html-italic">y</span>-axis starts above 0 for some panels.</p>
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<p>Number of distinct optima vs. run number for MIPLIB instance <tt>vpm1</tt>. While we cannot find all optima in the allotted 1000 runs, we find 998 distinct optima with MORSE and 573 distinct optima with the uniform weights method.</p>
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<p>Number of distinct optima vs. run number for MIPLIB instance <tt>stein15</tt>. With MORSE, we find 264 distinct optima in the allotted 500 runs, while we find one optimum with the uniform weights method.</p>
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<p>Number of Distinct Optima vs. Run Number for MIPLIB instance <tt>pigeon-10</tt>. With MORSE, we find 10 distinct optima in 23 runs, while 70 runs are needed to find 10 distinct optima using the uniform weights method.</p>
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<p>Full MIPLIB results: number of distinct optima vs. run number for all MIPLIB instances tested. The plots are arranged in descending order of maximum number of unique optima found.</p>
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<p>Number of distinct optima found (<span class="html-italic">y</span>-axis) vs. run number (<span class="html-italic">x</span>-axis) for <tt>GSE103322</tt> (brain cancer) 1269-gene dataset, lower bound = 0.8, upper bound = 0.1. With MORSE, we find all 5 distinct optima in 9 runs. With uniform weights, we find these optima in 397 runs.</p>
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<p>Number of distinct optima found (<span class="html-italic">y</span>-axis) vs. run number (<span class="html-italic">x</span>-axis) for <tt>GSE147082</tt> (ovarian cancer) 1269-gene dataset, lower bound = 0.8, upper bound = 0.1. With MORSE, we find all 360 distinct optima in 959 runs. With uniform weights, we find 215 optima in 1000 runs.</p>
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<p>Tabular results. Number of distinct optima found (<span class="html-italic">y</span>-axis) vs. run number (<span class="html-italic">x</span>-axis) for all 1269-gene cancer datasets tested, lower bound = 0.8, upper bound = 0.1. For GSE127465 (lower row, center), the results for MORSE (red) and uniform weights (blue) are identical.</p>
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<p>Gene solution Shannon entropy values for datasets with feasible solutions at lower bound = 0.8, upper bound = 0.1. The plots are arranged in descending order of the maximum Shannon entropy. The Shannon entropy for MORSE runs exceeds that of uniform weights runs for all datasets used.</p>
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<p>Full MIPLIB results, Shannon entropy vs. optimization method for all MIPLIB instances tested. The plots are arranged in descending order of maximum Shannon entropy.</p>
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<p>Full MIPLIB results, average Shannon entropy vs. optimization method for all MIPLIB instances tested. The plots are arranged in descending order of maximum average Shannon entropy.</p>
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11 pages, 626 KiB  
Article
Reactive Balance in Adolescent Idiopathic Scoliosis: A Prospective Motion Analysis Study
by Ria Paradkar, Christina Regan, Kathie Bernhardt, Kenton R. Kaufman, Todd A. Milbrandt and A. Noelle Larson
J. Clin. Med. 2025, 14(5), 1715; https://doi.org/10.3390/jcm14051715 - 4 Mar 2025
Viewed by 143
Abstract
Background/Objectives: Traditional fusion leads to a loss of spine mobility across the fused vertebrae. Vertebral body tethering (VBT) was developed with the goal of increasing flexibility and maintaining some spinal mobility. However, it is not known if the additional mobility leads to [...] Read more.
Background/Objectives: Traditional fusion leads to a loss of spine mobility across the fused vertebrae. Vertebral body tethering (VBT) was developed with the goal of increasing flexibility and maintaining some spinal mobility. However, it is not known if the additional mobility leads to significant functional improvement. This prospective motion analysis study evaluates functional outcomes, specifically gait stability, in pre-operative, post-fusion, and post-VBT patients by using postural perturbations on a treadmill. Methods: Overall, 79 subjects underwent a computer-controlled treadmill study with postural perturbations, which simulated trips and slips. The subjects were harnessed for safety. Overall, 21 subjects were healthy controls, 18 patients were at least one-year post-VBT, 15 patients were at least one-year post-fusion, and 25 were pre-operative scoliosis patients. Subject weight, height, and treadmill acceleration were recorded and used to determine anteroposterior single (ASSTs, PSSTs) and multiple (AMSTs, PMSTs) stepping thresholds to describe the maximum torque a patient could withstand before failing to recover from the simulated trip. Independent t-tests were run to compare groups under the advice of a master statistician with expertise in orthopedic surgery. Results: Pre-operative scoliosis patients had lower PSSTs than healthy controls (uncorrected p = 0.036). No significant differences were observed between pre-operative and post-operative groups for both fusion and VBT. There was no significant difference in ASST, AMST, or PMST between any of the groups. Conclusions: The lower PSST in pre-operative scoliosis patients compared to healthy controls may reflect impaired reactive balance and potentially increased fall risk. Interestingly, there was no significant difference in reactive balance measures between pre-operative and post-operative scoliosis patients or between post-fusion and post-VBT patients. Full article
(This article belongs to the Section Orthopedics)
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<p>Computer-controlled treadmill simulation setup with patient in harness.</p>
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<p>Control vs. pre-op scoliosis posterior single-stepping threshold.</p>
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14 pages, 678 KiB  
Article
Sustainability of a Three-Species Predator–Prey Model in Tumor-Immune Dynamics with Periodic Treatment
by Avan Al-Saffar and Eun-jin Kim
Entropy 2025, 27(3), 264; https://doi.org/10.3390/e27030264 - 3 Mar 2025
Viewed by 205
Abstract
Using a tumor-immune growth model, we investigate how immunotherapy affects its dynamical characteristics. Specifically, we extend the prey–predator model of tumor cells and immune cells by including periodic immunotherapy, the nonlinear damping of cancer cells, and the dynamics of a healthy cell population, [...] Read more.
Using a tumor-immune growth model, we investigate how immunotherapy affects its dynamical characteristics. Specifically, we extend the prey–predator model of tumor cells and immune cells by including periodic immunotherapy, the nonlinear damping of cancer cells, and the dynamics of a healthy cell population, and investigate the effects of the model parameters. The ideal value of immunotherapy, which promotes the growth of immune (and healthy) cells while contributing to the elimination or control of the cancer cells, is determined by using Fisher information as a measure of variability throughout our study. Full article
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<p>Phase portrait and time evolution of Equations (<a href="#FD3-entropy-27-00264" class="html-disp-formula">3</a>) for various <math display="inline"><semantics> <msub> <mi>a</mi> <mn>23</mn> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>r</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>. Green indicates immune cells, red indicates healthy cells, and a blue line indicates cancer.</p>
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<p>Average values as a function of periodic amplitude, with all parameter values fixed, <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>r</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>a</mi> <mn>23</mn> </msub> </semantics></math> changed.</p>
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<p>Time evolution of <span class="html-italic">x</span> using different <math display="inline"><semantics> <msub> <mi>a</mi> <mn>23</mn> </msub> </semantics></math> values, <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>r</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>For <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>r</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics></math> and fixed other parameter values, <math display="inline"><semantics> <msub> <mi>F</mi> <mi>T</mi> </msub> </semantics></math> is shown against the amplitude. In <a href="#entropy-27-00264-f002" class="html-fig">Figure 2</a> and <a href="#entropy-27-00264-f003" class="html-fig">Figure 3</a>, a peak is observed at <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mn>23</mn> </msub> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>, which is also the value at which the cancer cells start to disappear.</p>
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<p>For <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>r</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>, populations’ average values are shown against the amplitude <math display="inline"><semantics> <msub> <mi>a</mi> <mn>23</mn> </msub> </semantics></math>, with <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> being different and the other parameter values fixed. A red line indicates healthy cells, a green line indicates immune cells, and a blue line indicates cancer cells.</p>
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<p>The amplitude <math display="inline"><semantics> <msub> <mi>a</mi> <mn>23</mn> </msub> </semantics></math> against <math display="inline"><semantics> <msub> <mi>F</mi> <mi>T</mi> </msub> </semantics></math> for <math display="inline"><semantics> <mrow> <mo stretchy="false">Ω</mo> <mo>=</mo> <msub> <mo stretchy="false">Ω</mo> <mn>1</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>r</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>: At <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mn>23</mn> </msub> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>, a peak is shown, which is also the value at which the cancer cells begin to disappear in <a href="#entropy-27-00264-f005" class="html-fig">Figure 5</a>.</p>
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<p>To show the dynamics of the model with fixed parameter values as <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mn>23</mn> </msub> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo stretchy="false">Ω</mo> <mo>=</mo> <msub> <mo stretchy="false">Ω</mo> <mn>1</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mrow> <mo>[</mo> <msub> <mi>u</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>r</mi> <mn>0</mn> </msub> <mo>]</mo> </mrow> <mo>=</mo> <mrow> <mo>[</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>]</mo> </mrow> </mrow> </semantics></math>, we utilize different values of <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math>. Red indicates healthy cells, green indicates immune cells, and blue indicates cancer cells.</p>
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<p>As a function of <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math>, we show the average of each species in various colors, while the other parameters remain unchanged. The average values show no changes, indicating that our model is independent of <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math>.</p>
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25 pages, 378 KiB  
Article
The Intrinsic Exceptional Point: A Challenge in Quantum Theory
by Miloslav Znojil
Foundations 2025, 5(1), 8; https://doi.org/10.3390/foundations5010008 - 1 Mar 2025
Viewed by 148
Abstract
In spite of its unbroken PT symmetry, the popular imaginary cubic oscillator Hamiltonian H(IC)=p2+ix3 does not satisfy all of the necessary postulates of quantum mechanics. This failure is due to the “intrinsic [...] Read more.
In spite of its unbroken PT symmetry, the popular imaginary cubic oscillator Hamiltonian H(IC)=p2+ix3 does not satisfy all of the necessary postulates of quantum mechanics. This failure is due to the “intrinsic exceptional point” (IEP) features of H(IC) and, in particular, to the phenomenon of a high-energy asymptotic parallelization of its bound-state-mimicking eigenvectors. In this paper, it is argued that the operator H(IC) (and the like) can only be interpreted as a manifestly unphysical, singular IEP limit of a hypothetical one-parametric family of certain standard quantum Hamiltonians. For explanation, ample use is made of perturbation theory and of multiple analogies between IEPs and conventional Kato’s exceptional points. Full article
(This article belongs to the Section Physical Sciences)
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