Mathematics

41 pages, 3112 KB

Open AccessFeature PaperArticle

A Bird’s-Eye View on a New Stochastic Interpretation of Quantum Mechanics

by Olavo L. Silva Filho and Marcello Ferreira

Mathematics 2025, 13(21), 3571; https://doi.org/10.3390/math13213571 - 6 Nov 2025

Viewed by 581

Since the early twentieth century, quantum mechanics has sought an interpretation that offers a consistent worldview. In the course of that, many proposals were advanced, but all of them introduce, at some point, interpretation elements (semantics) that find no correlate in the formalism [...] Read more.

Since the early twentieth century, quantum mechanics has sought an interpretation that offers a consistent worldview. In the course of that, many proposals were advanced, but all of them introduce, at some point, interpretation elements (semantics) that find no correlate in the formalism (syntactics). This distance from semantics and syntactics is one of the major reasons for finding so abstruse and diverse interpretations of the formalism. To overcome this issue, we propose an alternative stochastic interpretation, based exclusively on the formal structure of the Schrödinger equation, without resorting to external assumptions such as the collapse of the wave function or the role of the observer. We present four (mathematically equivalent) mathematical derivations of the Schrödinger equation based on four constructs: characteristic function, Boltzmann entropy, Central Limit Theorem (CLT), and Langevin equation. All of them resort to axioms already interpreted and offer complementary perspectives to the quantum formalism. The results show the possibility of deriving the Schrödinger equation from well-defined probabilistic principles and that the wave function represents a probability amplitude in the configuration space, with dispersions linked to the CLT. It is concluded that quantum mechanics has a stochastic support, originating from the separation between particle and field subsystems, allowing an objective description of quantum behavior as a mean-field theory, analogous, but not equal, to Brownian motion, without the need for arbitrary ontological entities. Full article

(This article belongs to the Special Issue Advances in Mathematics for Quantum Mechanics)

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20 pages, 3054 KB

Open AccessArticle

Tri-Invariance Contrastive Framework for Robust Unsupervised Person Re-Identification

by Lei Wang, Chengang Liu, Xiaoxiao Wang, Weidong Gao, Xuejian Ge and Shunjie Zhu

Mathematics 2025, 13(21), 3570; https://doi.org/10.3390/math13213570 - 6 Nov 2025

Viewed by 359

Abstract

Unsupervised person re-identification (Re-ID) has been proven very effective and it boosts the performance in learning representations from unlabeled data in the dataset. Most current methods have good accuracy, but there are two main problems. First, clustering often generates noisy labels. Second, features [...] Read more.

Unsupervised person re-identification (Re-ID) has been proven very effective and it boosts the performance in learning representations from unlabeled data in the dataset. Most current methods have good accuracy, but there are two main problems. First, clustering often generates noisy labels. Second, features can change because of different camera styles. Noisy labels causes incorrect optimization, which reduces the accuracy of the model. The latter results in inaccurate prediction for samples within the same category that have been captured by different cameras. Despite the significant variations inherent in the vast source data, the principles of invariance and symmetry remain crucial for effective feature recognition. In this paper, we propose a method called Invariance Constraint Contrast Learning (ICCL) to address these two problems. Specifically, we introduce center invariance and instance invariance to reduce the effect of noisy samples. We also use camera invariance to handle feature changes caused by different cameras. Center invariance and instance invariance help decrease the impact of noise. Camera invariance improves the classification accuracy by using a camera-aware classification strategy. We test our method on three common large-scale Re-ID datasets. It clearly improves the accuracy of unsupervised person Re-ID. Specifically, our approach demonstrates its effectiveness by improving mAP by 3.5% on Market-1501, 1.3% on MSMT17 and 3.5% on CUHK03 over state-of-the-art methods. Full article

(This article belongs to the Special Issue Mathematical Computation for Pattern Recognition and Computer Vision)

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19 pages, 5377 KB

Open AccessArticle

LEMSOFT: Leveraging Extraction Method and Soft Voting for Android Malware Detection

by Qiang Han, Zhichao Shi, Yao Li and Tao Zhang

Mathematics 2025, 13(21), 3569; https://doi.org/10.3390/math13213569 - 6 Nov 2025

Viewed by 344

Abstract

The pervasive spread of Android malware poses significant threats to users and systems worldwide. In most existing studies, differences in feature importance are often overlooked, and the calculation of feature weights is conducted independently of the classification model. In this paper, we propose [...] Read more.

The pervasive spread of Android malware poses significant threats to users and systems worldwide. In most existing studies, differences in feature importance are often overlooked, and the calculation of feature weights is conducted independently of the classification model. In this paper, we propose an Android malware detection method, Leveraging Extraction Method and Soft Voting classification (LEMSOFT). This approach includes a novel preprocessing module, lexical occurrence ratio-based filtering (LORF), and an improved Soft Voting mechanism optimized through genetic algorithms. We introduce LORF to evaluate and enhance the significance of permissions, API calls, and opcodes. Each type of feature is then independently classified using tailored machine learning models. To integrate the outputs of these classifiers, this paper proposes an innovative soft voting mechanism that improves prediction accuracy for encountered applications by assigning weights through a genetic algorithm. Our solution outperforms the baseline methods we studied, as evidenced by the evaluation of 5560 malicious and 8340 benign applications, with an average accuracy of 99.89%. The efficacy of our methodology is demonstrated through extensive experiments, showcasing significant improvements in detection rates compared to state-of-the-art (SOTA) methods. Full article

(This article belongs to the Special Issue Securing Software Through Mathematics and Domain-Specific Knowledge: Innovations and Applications)

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28 pages, 1494 KB

Open AccessArticle

Hydrodynamic Performance Analysis of an MR Damper in Valve Mode Characterized by the Mason Number

by Juan P. Escandón, Juan R. Gómez, René O. Vargas, Edson M. Jimenez and Rubén Mil-Martínez

Mathematics 2025, 13(21), 3568; https://doi.org/10.3390/math13213568 - 6 Nov 2025

Viewed by 451

Abstract

This work analyzes the hydrodynamic behavior of a magnetorheological valve, considering the microscopic fluid characteristics to generate a damper force. The magnetorheological fluid is composed of ferromagnetic particles dispersed in a non-magnetic carrier fluid, whose mechanical resistance depends on the magnetic field intensity. [...] Read more.

This work analyzes the hydrodynamic behavior of a magnetorheological valve, considering the microscopic fluid characteristics to generate a damper force. The magnetorheological fluid is composed of ferromagnetic particles dispersed in a non-magnetic carrier fluid, whose mechanical resistance depends on the magnetic field intensity. In the absence of a magnetic field, the magnetorheological fluid behaves as a liquid whose viscosity depends on the particle volume fraction. Conversely, the presence of a magnetic field generates particle chain-like structures that inhibit fluid motion, thereby regulating flow in the control valve. The mathematical model employs the continuity and momentum equations, the Bingham model, and the boundary conditions at the solid–liquid interfaces to determine the flow field. The results show the fluid hydrodynamic response under different flow conditions depending on dimensionless parameters such as the pressure gradient, the field-independent viscosity, the yield stress, the particle volume fraction, the Bingham number, the Mason number, and the critical Mason number. For a pressure gradient of

Γ = - 10

, the flow rate inside the valve (with particle volume fraction

ϕ = 0.2

) results in

{\bar{Q}}_{T, x} = 0.34

,

0.06

, and 0 when the magnetic field is 80, 120, and 160 kA m⁻¹, respectively. Likewise, when the magnetic field increases from 80 to 160 kA m⁻¹, the damping capacity increases by

88 %

when

ϕ = 0.2

and

128 %

when

ϕ = 0.3

compared to the Newtonian viscous damping. This work contributes to our understanding of semi-active damping devices for flow control. Full article

(This article belongs to the Special Issue Engineering Thermodynamics and Fluid Mechanics)

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25 pages, 415 KB

Open AccessArticle

Compactness of the Complex Green Operator on C¹ Pseudoconvex Boundaries in Stein Manifolds

by Abdullah Alahmari, Emad Solouma, Marin Marin, A. F. Aljohani and Sayed Saber

Mathematics 2025, 13(21), 3567; https://doi.org/10.3390/math13213567 - 6 Nov 2025

Viewed by 290

Abstract

We study compactness for the complex Green operator

G_{q}

associated with the Kohn Laplacian

□_{b}

on boundaries of pseudoconvex domains in Stein manifolds. Let

Ω ⋐ X

be a bounded pseudoconvex domain in a Stein manifold X of complex dimension n [...] Read more.

We study compactness for the complex Green operator

G_{q}

associated with the Kohn Laplacian

□_{b}

on boundaries of pseudoconvex domains in Stein manifolds. Let

Ω ⋐ X

be a bounded pseudoconvex domain in a Stein manifold X of complex dimension n with

C^{1}

boundary. For

1 \leq q \leq n - 2

, we first prove a compactness theorem under weak potential-theoretic hypotheses: if

b Ω

satisfies weak

(P_{q})

and weak

(P_{n - 1 - q})

, then

G_{q}

and

G_{n - 1 - q}

are compact on

L_{p, q}^{2} (b Ω)

. This extends known

C^{\infty}

results in

C^{n}

to the minimal regularity

C^{1}

and to the Stein setting. On locally convexifiable

C^{1}

boundaries, we obtain a full characterization: compactness of

G_{q}

is equivalent to simultaneous compactness of

G_{q}

and

G_{n - 1 - q}

, to compactness of the

\bar{\partial}

-Neumann operators

N_{q}

and

N_{n - 1 - q}

in the interior, to weak

(P_{q})

and

(P_{n - 1 - q})

, and to the absence of (germs of) complex varieties of dimensions q and

n - 1 - q

on

b Ω

. A key ingredient is an annulus compactness transfer on

Ω_{+} = Ω_{2} ∖ \bar{Ω_{1}}

, which yields compactness of

N_{q}^{Ω_{+}}

from weak

(P)

near each boundary component and allows us to build compact

{\bar{\partial}}_{b}

-solution operators via jump formulas. Consequences include the following: compact canonical solution operators for

{\bar{\partial}}_{b}

, compact resolvent for

□_{b}

on the orthogonal complement of its harmonic space (hence discrete spectrum and finite-dimensional harmonic forms), equivalence between compactness and standard compactness estimates, closed range and

L^{2}

Hodge decompositions, trace-class heat flow, stability under

C^{1}

boundary perturbations, vanishing essential norms, Sobolev mapping (and gains under subellipticity), and compactness of Bergman-type commutators when

q = 1

. Full article

(This article belongs to the Special Issue Recent Developments in Theoretical and Applied Mathematics—In Memory of Prof. Dr. Ravi P. Agarwal)

23 pages, 1405 KB

Open AccessArticle

Long-Term Behavior of Lotka–Volterra Model with Lévy Jump in Countable State-Dependent Environments

by Huijie Ji, Ping Yu, Hongxia Sun and Yuhang Zhen

Mathematics 2025, 13(21), 3566; https://doi.org/10.3390/math13213566 - 6 Nov 2025

Viewed by 322

Abstract

In this study, we analyze a multi-species mutualistic Lotka–Volterra model with Lévy jumps and regime-switching. A defining feature of the work lies in modeling the random environment through state-dependent switching in an infinite countable state space. Our main objective is to establish the [...] Read more.

In this study, we analyze a multi-species mutualistic Lotka–Volterra model with Lévy jumps and regime-switching. A defining feature of the work lies in modeling the random environment through state-dependent switching in an infinite countable state space. Our main objective is to establish the sufficient conditions of the extinction and stochastic permanence of the model. First, we analyze the existence and uniqueness of the model’s solution, followed by an examination of the solution’s stochastic ultimate boundedness. Moreover, the challenges arising from state-dependent switching are addressed using the stochastic comparison method. Due to the presence of the jump component, more complex conditions are required to achieve a finite partition of the countably infinite space. Furthermore, the M-matrix theory is also used to obtain the stochastic permanence property. Finally, two specific examples are provided to illustrate the conclusions in this paper. Full article

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18 pages, 255 KB

Open AccessArticle

New Characterizations of SEP Elements in a Ring with Involution

by Xiaoming Li and Junchao Wei

Mathematics 2025, 13(21), 3565; https://doi.org/10.3390/math13213565 - 6 Nov 2025

Viewed by 233

Abstract

The characterization of SEP elements is a classical problem in generalized inverse theory. Most existing characterizations are formulated in terms of specific algebraic identities. This paper proposes a new approach based on polynomial equations with parameters to characterize SEP elements. This framework provides [...] Read more.

The characterization of SEP elements is a classical problem in generalized inverse theory. Most existing characterizations are formulated in terms of specific algebraic identities. This paper proposes a new approach based on polynomial equations with parameters to characterize SEP elements. This framework provides an alternative characterization and, more importantly, naturally clarifies the structural relationships between SEP elements and three other types of elements: square rootable elements, invertible elements, and involutional projection elements. Full article

(This article belongs to the Special Issue Recent Advances in Generalized Inverses and Matrix Theory)

26 pages, 3421 KB

Open AccessArticle

A Heuristic Approach to Minimize Age of Information for Wirelessly Charging Unmanned Aerial Vehicles in Unmanned Data Collection Systems

by Zhengying Cai, Yingjing Fang, Zeya Liu, Cancan He, Shulan Huang and Guoqiang Gong

Mathematics 2025, 13(21), 3564; https://doi.org/10.3390/math13213564 - 6 Nov 2025

Viewed by 275

Abstract

Wirelessly charging unmanned aerial vehicles (WCUAVs) can complete charging tasks without human intervention and may help us efficiently collect various types of geographically dispersed data in unmanned data collection systems (UDCSs). However, the limited number of wireless charging stations and longer wireless charging [...] Read more.

Wirelessly charging unmanned aerial vehicles (WCUAVs) can complete charging tasks without human intervention and may help us efficiently collect various types of geographically dispersed data in unmanned data collection systems (UDCSs). However, the limited number of wireless charging stations and longer wireless charging times also pose challenges to minimizing the Age of Information (AoI). Here, we provide a heuristic method to minimize AoI for WCUAVs. Firstly, the problem of minimizing AoI is modeled as a trajectory optimization problem with nonlinear constraints involving n sensor nodes, a data center, and a limited number of wireless charging stations. Secondly, to solve this NP-hard problem, an improved artificial plant community (APC) approach is proposed, including a single-WCUAV architecture and a multi-WCUAV architecture. Thirdly, a benchmark test set is designed, and benchmark experiments are conducted. When the number of WCUAVs increased from 1 to 2, the total flight distance increased by 12.011% and the average AoI decreased by 45.674%. When the number of WCUAVs increased from 1 to 10, the total flight distance increased by 87.667% and the average AoI decreased by 78.641%. The experimental results show that the proposed APC algorithm can effectively solve AoI minimization challenges of WCUAVs and is superior to other baseline algorithms with a maximum improvement of 9.791% in average AoI. Due to its simple calculation and efficient solution, it is promising to deploy the APC algorithm on the edge computing platform of WCUAVs. Full article

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15 pages, 369 KB

Open AccessArticle

Certain Subclasses of Bi-Univalent Functions Involving Caputo Fractional Derivatives with Bounded Boundary Rotation

by Abbas Kareem Wanas, Mohammad El-Ityan, Adel Salim Tayyah and Adriana Catas

Mathematics 2025, 13(21), 3563; https://doi.org/10.3390/math13213563 - 6 Nov 2025

Viewed by 276

Abstract

In this paper, we introduce and investigate new subclasses of analytic bi-univalent functions defined via Caputo fractional derivatives with boundary rotation constraints. Utilizing the generalized operator

C_{ȷ}^{ϱ}

, which encompasses and extends classical operators such as the Salagean differential operator and [...] Read more.

In this paper, we introduce and investigate new subclasses of analytic bi-univalent functions defined via Caputo fractional derivatives with boundary rotation constraints. Utilizing the generalized operator

C_{ȷ}^{ϱ}

, which encompasses and extends classical operators such as the Salagean differential operator and the Libera–Bernardi integral operator, we establish sharp coefficient estimates for the initial Taylor Maclaurin coefficients of functions within these subclasses. Furthermore, we derive Fekete–Szegö-type inequalities that provide bounds on the second and third coefficients and their linear combinations involving a real parameter. Our approach leverages subordination principles through analytic functions associated with the classes

T_{ς} (ξ)

and

R_{Ω}^{ȷ, ϱ} (ϑ, ς, ξ)

, allowing a unified treatment of fractional differential operators in geometric function theory. The results generalize several known cases and open avenues for further exploration in fractional calculus applied to analytic function theory. Full article

(This article belongs to the Special Issue Advances in Nonlinear Differential Equations with Applications)

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15 pages, 390 KB

Open AccessArticle

Covariate-Adjusted Precision Matrix Estimation Under Lower Polynomial Moment Assumption

by Shuwei Hu

Mathematics 2025, 13(21), 3562; https://doi.org/10.3390/math13213562 - 6 Nov 2025

Viewed by 283

Abstract

Multiple regression analysis has a wide range of applications. The analysis of error structures in regression model

Y = Γ X + Z

has also attracted much attention. This paper focuses on large-scale precision matrix of the error vector that only has lower [...] Read more.

Multiple regression analysis has a wide range of applications. The analysis of error structures in regression model

Y = Γ X + Z

has also attracted much attention. This paper focuses on large-scale precision matrix of the error vector that only has lower polynomial moments. We mainly study upper bounds of the proposed estimator under different norms in term of the probability estimation. It is shown that our estimator achieves the same optimal convergence order as under Gaussian assumption on the data. Simulation experiments further validate that our method has advantages. Full article

(This article belongs to the Section D1: Probability and Statistics)

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12 pages, 258 KB

Open AccessArticle

A New Hardy–Hilbert-Type Integral Inequality Involving General Homogeneous Kernel and Two Derivative Functions of Higher Order

by Bicheng Yang, Shanhe Wu and Xianyong Huang

Mathematics 2025, 13(21), 3561; https://doi.org/10.3390/math13213561 - 6 Nov 2025

Viewed by 289

Abstract

In this paper, by introducing a general homogeneous kernel function and several parameters, we establish a new Hardy–Hilbert-type integral inequality involving two derivative functions of higher-order. For the resulting inequality, we determine the equivalent conditions of the best possible constant factor related to [...] Read more.

In this paper, by introducing a general homogeneous kernel function and several parameters, we establish a new Hardy–Hilbert-type integral inequality involving two derivative functions of higher-order. For the resulting inequality, we determine the equivalent conditions of the best possible constant factor related to the parameters. As applications, we demonstrate that a lot of new Hardy–Hilbert-type integral inequalities can be derived by choosing specific homogeneous kernel functions. Full article

(This article belongs to the Section C3: Real Analysis)

20 pages, 2056 KB

Open AccessArticle

A New 5D Chaotic Supply Chain System with Transport Lag: Modeling, Bifurcation Analysis, Offset Boosting Control and Synchronization

by Muhamad Deni Johansyah, Khaled Benkouider, Sundarapandian Vaidyanathan, Aceng Sambas and Chittineni Aruna

Mathematics 2025, 13(21), 3560; https://doi.org/10.3390/math13213560 - 6 Nov 2025

Viewed by 378

Abstract

This paper introduces an enhanced five-dimensional Chaotic Supply Chain Model (5DCSCM) by incorporating a transport lag variable into a previously established four-dimensional model. The newly added differential equation in the transit dynamics of the supply chain model captures the inherent lag between customer [...] Read more.

This paper introduces an enhanced five-dimensional Chaotic Supply Chain Model (5DCSCM) by incorporating a transport lag variable into a previously established four-dimensional model. The newly added differential equation in the transit dynamics of the supply chain model captures the inherent lag between customer demand and the physical response in transportation, modeled as a first-order transport lag system. Through comprehensive numerical simulations, the influence of various system parameters—including customer demand rate, delivery efficiency, information distortion, contingency reserve, safety stock, and transportation lag—are examined. The study utilizes bifurcation diagrams and a Lyapunov Exponent (LE) to investigate tran-sitions between periodic and chaotic behavior. Additionally, the model is extended with offset boosting control, allowing for controlled amplitude adjustment without altering the underlying chaotic dynamics. Offset boosting control (OBC) is useful in chaotic supply chain systems because it stabilizes inventory and order fluctuations by counter-acting the amplification of small disturbances, reducing the bullwhip effect, and im-proving overall system reliability and responsiveness. As an application, integral sliding mode control (ISMC) technique has been applied to achieve complete synchronization between a pair of the 5DCSCM. Synchronization based on ISMC is useful in chaotic supply chain systems because it ensures robust coordination between different tiers, suppresses chaos-induced fluctuations, and maintains stable inventory and order patterns even under disturbances and uncertainties. Full article

(This article belongs to the Special Issue Advanced Computational Modeling and Optimization in Operations Research and Supply Chain Management)

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34 pages, 1584 KB

Open AccessArticle

Cost Optimization in a GI/M/2/N Queue with Heterogeneous Servers, Working Vacations, and Impatient Customers via the Bat Algorithm

by Abdelhak Guendouzi and Salim Bouzebda

Mathematics 2025, 13(21), 3559; https://doi.org/10.3390/math13213559 - 6 Nov 2025

Cited by 1 | Viewed by 363

Abstract

This paper analyzes a finite-capacity

G I / M / 2 / N

queue with two heterogeneous servers operating under a multiple working-vacation policy, Bernoulli feedback, and customer impatience. Using the supplementary-variable technique in tandem with a tailored recursive scheme, we derive the [...] Read more.

This paper analyzes a finite-capacity

G I / M / 2 / N

queue with two heterogeneous servers operating under a multiple working-vacation policy, Bernoulli feedback, and customer impatience. Using the supplementary-variable technique in tandem with a tailored recursive scheme, we derive the stationary distributions of the system size as observed at pre-arrival instants and at arbitrary epochs. From these, we obtain explicit expressions for key performance metrics, including blocking probability, average reneging rate, mean queue length, mean sojourn time, throughput, and server utilizations. We then embed these metrics in an economic cost function and determine service-rate settings that minimize the total expected cost via the Bat Algorithm. Numerical experiments implemented in R validate the analysis and quantify the managerial impact of the vacation, feedback, and impatience parameters through sensitivity studies. The framework accommodates general renewal arrivals (

G I

), thereby extending classical (

M / M / 2 / N

) results to more realistic input processes while preserving computational tractability. Beyond methodological interest, the results yield actionable design guidance: (i) they separate Palm and time-stationary viewpoints cleanly under non-Poisson input, (ii) they retain heterogeneity throughout all formulas, and (iii) they provide a cost–optimization pipeline that can be deployed with routine numerical effort. Methodologically, we (i) characterize the generator of the augmented piecewise–deterministic Markov process and prove the existence/uniqueness of the stationary law on the finite state space, (ii) derive an explicit Palm–time conversion formula valid for non-Poisson input, (iii) show that the boundary-value recursion for the Laplace–Stieltjes transforms runs in linear time

O (N)

and is numerically stable, and (iv) provide influence-function (IPA) sensitivities of performance metrics with respect to

(μ_{1}, μ_{2}, ν, α, ϕ, β)

. Full article

(This article belongs to the Section D1: Probability and Statistics)

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18 pages, 7393 KB

Open AccessArticle

Dynamic Event-Triggered Adaptive Broad Learning for a Two-Degree-of-Freedom Helicopter System with Prescribed Performance

by Liang Cao, Yexin Mo, Wei Xiao, Kaili Feng, Zhongzhen Wu and Xiangli Li

Mathematics 2025, 13(21), 3558; https://doi.org/10.3390/math13213558 - 6 Nov 2025

Viewed by 263

Abstract

This study proposes an adaptive broad learning strategy for a two-degree-of-freedom helicopter system based on specified performance and dynamic event-triggered. First, broad learning is employed to approximate system uncertainties. Compared to radial basis function neural networks, broad learning achieves this by increasing the [...] Read more.

This study proposes an adaptive broad learning strategy for a two-degree-of-freedom helicopter system based on specified performance and dynamic event-triggered. First, broad learning is employed to approximate system uncertainties. Compared to radial basis function neural networks, broad learning achieves this by increasing the number of nodes, enabling it to approximate system uncertainties with smaller tracking errors. At the same time, an error transformation method is employed to guarantee that the tracking error adheres to a predefined performance function. Furthermore, a dynamic event-triggering mechanism reduces communication overhead and prevents the Zeno effect. Subsequent Lyapunov-based stability analysis confirms that the system exhibits semi-global consistency, stability, and boundedness. In addition, simulation results verify the proposed control strategy’s effectiveness and robustness. Full article

(This article belongs to the Special Issue Deep Learning and Adaptive Control, 3rd Edition)

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34 pages, 26061 KB

Open AccessArticle

An Analysis of the Mechanism and Mode Evolution for Blockchain-Empowered Research Credit Supervision Based on Prospect Theory: A Case from China

by Gang Li, Zhihuang Zhao, Ruirui Chai and Mengjiao Zhu

Mathematics 2025, 13(21), 3557; https://doi.org/10.3390/math13213557 - 6 Nov 2025

Viewed by 384

Abstract

The crisis of research integrity triggered by academic misconduct, such as scientific fraud and paper retractions, has emerged as a critical issue demanding urgent resolution within the academic community. Blockchain (BC), with its core features of distributed ledger, peer-to-peer transmission, consensus mechanisms, timestamps, [...] Read more.

The crisis of research integrity triggered by academic misconduct, such as scientific fraud and paper retractions, has emerged as a critical issue demanding urgent resolution within the academic community. Blockchain (BC), with its core features of distributed ledger, peer-to-peer transmission, consensus mechanisms, timestamps, and smart contracts, offers novel technical solutions for research institutions seeking efficient models of research credit supervision. By incorporating the psychological factors of risk perception among decision-makers and the dynamic evolution of behavioral decision-making, and drawing on prospect theory, this study has constructed an evolutionary game model involving researchers, scientific research institutions, and governmental entities to examine BC-enabled research credit supervision. This model analyzes the key determinants influencing scientific research institutions’ adoption of blockchain regulation (BC regulation), elucidates the behavioral characteristics and boundary conditions of research integrity among researchers under this new regulatory paradigm, and reveals the dynamic evolutionary trajectory of collaborative supervision between governments and scientific research institutions. The findings indicate the following: (1) Compared to traditional regulation, the BC regulation demonstrates superior regulatory effectiveness at equivalent levels of researcher integrity and misconduct costs, as well as under identical settings for reputational loss and penalties. (2) In addition to cost considerations and government subsidies, factors such as loss aversion coefficient, risk preference coefficient, and privacy breach losses are critical in influencing research institutions’ decisions to implement BC regulation. (3) The evolution of blockchain-empowered regulatory models encompasses three distinct evolutionary patterns. This study provides a theoretical foundation and a simulation case to optimize regulatory strategy formulation and resource allocation, thereby enhancing the effectiveness of research credit supervision. Full article

(This article belongs to the Section D2: Operations Research and Fuzzy Decision Making)

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20 pages, 515 KB

Open AccessArticle

Estimating Climate Risk Exposure in the U.S. Insurance Sector Using Factor Model and EVT

by Olanrewaju Oluwadamilare Olaniyan

Mathematics 2025, 13(21), 3556; https://doi.org/10.3390/math13213556 - 6 Nov 2025

Viewed by 640

Abstract

This study examines the exposure of the U.S. insurance sector to climate-related risks using a two-step approach combining factor modeling and Extreme Value Theory. The analysis first constructs a climate risk factor from transition-sensitive sectors and estimates its impact on the SPDR S&P [...] Read more.

This study examines the exposure of the U.S. insurance sector to climate-related risks using a two-step approach combining factor modeling and Extreme Value Theory. The analysis first constructs a climate risk factor from transition-sensitive sectors and estimates its impact on the SPDR S&P Insurance ETF using a standard factor model. The resulting residual, termed Insurance Climate Risk, isolates climate-driven excess returns by controlling for market-wide effects. To assess the sector’s sensitivity to extreme events, the study applies both the Peaks Over Threshold method using the Generalized Pareto Distribution and the Block Maxima Method using the Generalized Extreme Value distribution. The findings reveal statistically significant climate sensitivity, especially in daily and weekly data, and confirm the presence of heavy tails in the loss distribution. VaR and CVaR estimates indicate heightened risk over longer horizons and under block maxima modeling. Notably, peak over threshold daily returns yield a 95% VaR of 1.33% and CVaR of 2.28%, while block maxima CVaR exceeds 5%. These results show the importance of incorporating tail-risk-aware metrics in insurance risk management and highlight the growing influence of climate-related financial shocks. Full article

(This article belongs to the Special Issue New Advances in Mathematical Economics and Financial Modelling)

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19 pages, 1742 KB

Open AccessFeature PaperArticle

Analysis of a Markovian Queueing Model with an Alternating Server and Queue-Length-Based Threshold Control

by Doo Il Choi and Dae-Eun Lim

Mathematics 2025, 13(21), 3555; https://doi.org/10.3390/math13213555 - 6 Nov 2025

Cited by 1 | Viewed by 445

Abstract

This paper analyzes a finite-capacity Markovian queueing system with two customer types, each assigned to a separate buffer, and a single alternating server whose service priority is dynamically controlled by a queue-length-based threshold policy. The arrivals of both customer types follow independent Poisson [...] Read more.

This paper analyzes a finite-capacity Markovian queueing system with two customer types, each assigned to a separate buffer, and a single alternating server whose service priority is dynamically controlled by a queue-length-based threshold policy. The arrivals of both customer types follow independent Poisson processes, and the service times are generally distributed. The server alternates between the two buffers, granting service priority to buffer 1 when its queue length exceeds a specified threshold immediately after service completion; otherwise, buffer 2 receives priority. Once buffer 1 gains priority, it retains it until it becomes empty, with all priority transitions occurring non-preemptively. We develop an embedded Markov chain model to derive the joint queue length distribution at departure epochs and employ supplementary variable techniques to analyze the system performance at arbitrary times. This study provides explicit expressions for key performance measures, including blocking probabilities and average queue lengths, and demonstrates the effectiveness of threshold-based control in balancing service quality between customer classes. Numerical examples illustrate the impact of buffer capacities and threshold settings on system performance and offer practical insights into the design of adaptive scheduling policies in telecommunications, cloud computing, and healthcare systems. Full article

(This article belongs to the Special Issue Advances in Queueing Theory and Applications)

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19 pages, 465 KB

Open AccessFeature PaperArticle

Spectral Geometry of the Primes

by Douglas F. Watson

Mathematics 2025, 13(21), 3554; https://doi.org/10.3390/math13213554 - 5 Nov 2025

Viewed by 757

Abstract

We construct a family of self-adjoint operators on the prime numbers whose entries depend on pairwise arithmetic divergences, replacing geometric distance with number-theoretic dissimilarity. The resulting spectra encode how coherence propagates through the prime sequence and define an emergent arithmetic geometry. From these [...] Read more.

We construct a family of self-adjoint operators on the prime numbers whose entries depend on pairwise arithmetic divergences, replacing geometric distance with number-theoretic dissimilarity. The resulting spectra encode how coherence propagates through the prime sequence and define an emergent arithmetic geometry. From these spectra we extract observables such as the heat trace, entropy, and eigenvalue growth, which reveal persistent spectral compression): eigenvalues grow sublinearly, entropy scales slowly, and the inferred dimension remains strictly below one. This rigidity appears across logarithmic, entropic, and fractal-type kernels, reflecting intrinsic arithmetic constraints. Analytically, we show that for the unnormalized Laplacian, the continuum limit of its squared Hamiltonian corresponds to the one-dimensional bi-Laplacian, whose heat trace follows a short-time scaling proportional to

t^{- 1 / 4}

. Under the spectral dimension convention

d_{s} = - 2 d \log Θ / d \log t

, this result produces

d_{s} = 1 / 2

directly from first principles, without fitting or external hypotheses. This value signifies maximal spectral compression and the absence of classical diffusion, indicating that arithmetic sparsity enforces a coherence-limited, non-Euclidean geometry linking spectral and number-theoretic structure. Full article

(This article belongs to the Section E4: Mathematical Physics)

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23 pages, 2907 KB

Open AccessArticle

Embedding Public Opinion in Sustainable Urban Infrastructure Planning: A Fuzzy–Grey Multi-Criteria Decision-Making Framework

by Hezheng Mao and Yicheng Chu

Mathematics 2025, 13(21), 3553; https://doi.org/10.3390/math13213553 - 5 Nov 2025

Viewed by 420

Abstract

Urban infrastructure planning is central to advancing sustainable cities, but project success increasingly depends on public acceptance as well as technical, economic, and environmental performance. This study develops a fuzzy–grey multi-criteria decision-making (MCDM) framework that embeds public opinion as a formal evaluation dimension. [...] Read more.

Urban infrastructure planning is central to advancing sustainable cities, but project success increasingly depends on public acceptance as well as technical, economic, and environmental performance. This study develops a fuzzy–grey multi-criteria decision-making (MCDM) framework that embeds public opinion as a formal evaluation dimension. A novel POI, derived from online discourse data, integrates multi-dimensional emotions, polarization, and participation intensity to capture societal legitimacy. The framework employs entropy weighting and applies three established MCDM methods: TOPSIS, VIKOR, and EDAS, to evaluate project alternatives under uncertainty and incomplete information. An empirical case study in Nanjing demonstrates that incorporating Public Opinion Index (POI) significantly alters decision outcomes: the ecological park gained priority due to strong public support, while the wastewater treatment plant declined in ranking despite environmental benefits. These results underscore the decisive role of societal legitimacy in shaping sustainable infrastructure decisions. The framework contributes to sustainable urban planning by providing a replicable tool for balancing technical feasibility, environmental responsibility, and social acceptance in future infrastructure projects. Full article

(This article belongs to the Special Issue Applications of Operations Research and Decision Making)

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57 pages, 3573 KB

Open AccessArticle

Estimating the Expected Time to Enter and Leave a Common Target Area in Robotic Swarms

by Yuri Tavares dos Passos and Leandro Soriano Marcolino

Mathematics 2025, 13(21), 3552; https://doi.org/10.3390/math13213552 - 5 Nov 2025

Viewed by 442

Abstract

Coordination algorithms are required to minimise congestion when every robot in a robotic swarm has a common target area to visit. Some of these algorithms use artificial potential fields to enable path planning to become distributed and local. An efficiency measure for comparing [...] Read more.

Coordination algorithms are required to minimise congestion when every robot in a robotic swarm has a common target area to visit. Some of these algorithms use artificial potential fields to enable path planning to become distributed and local. An efficiency measure for comparing them is the time to complete a task in relation to the number of individuals in the swarm. To compare distinct solutions as the swarm grows, experiments with different numbers of robots must be simulated to form a plot of the function of the task completion time versus the number of robots or other parameters. Nevertheless, plotting it for many robots through simulation is time-consuming. Additionally, the inference of a global swarm behaviour as the task completion time from the local individual robot motion controller based on potential fields and other dynamical variables is intractable and requires experimental analysis. Based on that, equations are presented and compared with simulation data for estimating the expected task completion time of state-of-the-art algorithms, robots using only attractive and repulsive force fields and mixed teams for the common target area problem in robotic swarms with not only the number of robots as input but also environment- and algorithm-related global variables, such as the size of the common target area and the working area, average speed and average distance between the robots. This paper is a fundamental first step to start a discussion on how better approximations can be achieved and which mathematical theories about local-to-global analysis are better suited to this problem. Full article

(This article belongs to the Special Issue Advances in Intelligent Control Theory and Robotics)

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23 pages, 1880 KB

Open AccessArticle

A Data-Driven Framework for Flight Delay Propagation Forecasting During Extreme Weather

by Jiuxia Guo, Jingyuan Li, Jiang Yuan, Yungui Yang and Zihao Ren

Mathematics 2025, 13(21), 3551; https://doi.org/10.3390/math13213551 - 5 Nov 2025

Viewed by 711

Abstract

Flight delays during extreme weather events exhibit spatio-temporal propagation and cascading effects, posing serious challenges to the resilience of aviation systems. Existing prediction approaches often neglect dynamic dependencies across flight chains and struggle to model sparse extreme events. This study develops a data-driven [...] Read more.

Flight delays during extreme weather events exhibit spatio-temporal propagation and cascading effects, posing serious challenges to the resilience of aviation systems. Existing prediction approaches often neglect dynamic dependencies across flight chains and struggle to model sparse extreme events. This study develops a data-driven framework that explicitly models delay propagation paths, incorporates historical scenario retrieval to capture rare disruption patterns, and integrates meteorological, airport operational, and flight-specific information through multi-source fusion. Using U.S. flight operations and weather records, the framework demonstrates clear advantages over established baselines in extreme-delay scenarios, achieving a MAE of 3.23 min, an RMSE of 6.25 min, and an R² of 0.92—improving by 8.8%, 26.0%, and 5.75% compared to the best benchmark. Ablation studies confirm the contribution of the propagation modeling, historical retrieval, and multi-source integration modules, while cross-airport evaluations reveal consistent accuracy at both major hubs (e.g., Atlanta, Chicago O’Hare) and regional airports (e.g., Kona, Anchorage). These findings demonstrate that the proposed framework enables reliable forecasting of delay propagation under complex weather conditions, providing valuable support for proactive departure management and enhancing the resilience of aviation operations. Full article

(This article belongs to the Special Issue Optimization and Machine Learning-Based Methods in Air Traffic Management and Aeronautical Domains)

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12 pages, 956 KB

Open AccessArticle

Impact of Vertical Magnetic Field on Onset of Instability of a Casson Fluid Saturated Porous Layer: A Nonlinear Theory

by S. Suresh Kumar Raju, Fatemah H. H. Al Mukahal, Hasan Mulki and Saleh Mahmoud

Mathematics 2025, 13(21), 3550; https://doi.org/10.3390/math13213550 - 5 Nov 2025

Viewed by 231

Abstract

This study examines the stability and instability of a Casson fluid in a horizontal porous medium with magnetic effect using linear and global theories. Both linear and nonlinear analyses are conducted using the normal modes. The study proves that the linear and nonlinear [...] Read more.

This study examines the stability and instability of a Casson fluid in a horizontal porous medium with magnetic effect using linear and global theories. Both linear and nonlinear analyses are conducted using the normal modes. The study proves that the linear and nonlinear stability thresholds coincide. Two different methodologies were used to solve the system of equations. The eigenvalue problem for linear and global theories were solved using a Galerkin scheme and bvp4c routine in MATLAB. The results show that the Casson parameter destabilizes the flow, while the solutal Rayleigh number and Darcy number stabilize it. Full article

(This article belongs to the Special Issue Advances and Applications in Computational Fluid Dynamics)

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13 pages, 774 KB

Open AccessFeature PaperArticle

A Space–Time Collocation Method Using the Method of Particular Solutions with Polynomial Basis Functions for Solving the Fisher–KPP Equation

by Thir Dangal, Balaram Khatri Ghimire and Anup Lamichhane

Mathematics 2025, 13(21), 3549; https://doi.org/10.3390/math13213549 - 5 Nov 2025

Viewed by 292

Abstract

The method of particular solutions (MPS) has been widely applied for solving various types of partial differential equations. In this paper, the space–time collocation technique is implemented using MPS with polynomial basis functions (MPS-PBF) to solve the nonlinear Fisher–KPP (Kolmogorov–Petrovsky–Piskunov) equation in both [...] Read more.

The method of particular solutions (MPS) has been widely applied for solving various types of partial differential equations. In this paper, the space–time collocation technique is implemented using MPS with polynomial basis functions (MPS-PBF) to solve the nonlinear Fisher–KPP (Kolmogorov–Petrovsky–Piskunov) equation in both one and two dimensions. The Picard iteration method is used to deal with the nonlinearity of the problem. Four numerical examples are provided, and their results are compared with established methods to demonstrate the effectiveness of the proposed scheme. Full article

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26 pages, 3775 KB

Open AccessArticle

Expanding the Scope: Modified Fortescue Theory for Frequency Unbalance Resolution in Control Strategies

by Karim Mansouri, Brahim Metoui, Cristian Nichita, Amr Yousef and Ezzeddine Touti

Mathematics 2025, 13(21), 3548; https://doi.org/10.3390/math13213548 - 5 Nov 2025

Viewed by 383

Abstract

To analyze unbalanced electrical systems, the mathematical technique “symmetrical components method” developed by Charles LeGeyt Fortescue in the early 20th century has been very successful in this field. By decomposing three-phase systems into three symmetrical components: positive sequence, negative sequence, and zero sequence, [...] Read more.

To analyze unbalanced electrical systems, the mathematical technique “symmetrical components method” developed by Charles LeGeyt Fortescue in the early 20th century has been very successful in this field. By decomposing three-phase systems into three symmetrical components: positive sequence, negative sequence, and zero sequence, the Fortescue theory provides an important analyzing method. It allows for the calculation of these symmetrical components, which helps in understanding and addressing issues related to unbalance in amplitude within electrical systems. This theory deals only with amplitude unbalances in electrical systems to analyze and solve those problems. Since this technique is limited only to amplitude unbalance, our objective is to propose a modified Fortescue theory, which will resolve frequency unbalance problems in electrical systems. The new balanced components, at the conclusion of this new theory, will be used as references to be assigned in the adopted control strategies in a subsequent research paper. Full article

(This article belongs to the Special Issue Advanced Mathematical Approaches to Engineering and Computational Problems, 2nd Edition)

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31 pages, 2197 KB

Open AccessArticle

A Case Study of a Transportation Company Modeled as a Scheduling Problem

by Cristina Tobar-Fernández, Ana Dolores López-Sánchez and Jesús Sánchez-Oro

Mathematics 2025, 13(21), 3547; https://doi.org/10.3390/math13213547 - 5 Nov 2025

Viewed by 707

Abstract

This case study tackles a real-world problem of a transportation company that is modeled as a scheduling optimization problem. The main goal of the considered problem is to schedule the maximum number of jobs that must be performed by vehicles over a specific [...] Read more.

This case study tackles a real-world problem of a transportation company that is modeled as a scheduling optimization problem. The main goal of the considered problem is to schedule the maximum number of jobs that must be performed by vehicles over a specific planning horizon in order to minimize the total operational costs. Here, each customer request corresponds to a job composed of multiple operations, such as loading, unloading, and mandatory jobs, each associated with a specific location and time window. Once a job is allocated to a vehicle, all its operations must be executed by that same vehicle within their designated time constraints. Due to the imposed limitations, not every job can feasibly be scheduled. To address this challenge, two distinct methodologies are proposed. The first, a Holistic approach, solves the entire problem formulation using a black-box optimizer, serving as a comprehensive benchmark. The second, a Divide-and-Conquer approach, combines a heuristic greedy algorithm with a binary linear programming, decomposing the problem into sequential subproblems. Both approaches are implemented using the solver Hexaly. A comparative analysis is conducted under different scenarios and problem settings to highlight the advantages and drawbacks of each approach. The results show that the Divide-and-Conquer approach significantly improves computational efficiency, reducing time by up to 99% and vehicle usage by around 15–20% compared to the Holistic method. On the other hand, the Holistic method better ensures that mandatory jobs are completed, although at the cost of more resources. Full article

(This article belongs to the Special Issue Advancements in Applied Mathematics for Economic Data Analytics: Models, Methods, and Applications)

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25 pages, 10678 KB

Open AccessArticle

Dynamics of Soliton Solutions to Nonlinear Dynamical Equations in Mathematical Physics: Application of Neural Network-Based Symbolic Methods

by Jan Muhammad, Aljethi Reem Abdullah, Fengping Yao and Usman Younas

Mathematics 2025, 13(21), 3546; https://doi.org/10.3390/math13213546 - 5 Nov 2025

Viewed by 446

Abstract

While recent advances have successfully integrated neural networks with physical models to derive numerical solutions, there remains a compelling need to obtain exact analytical solutions. The ability to extract closed-form expressions from these models would provide deeper theoretical insights and enhanced predictive capabilities, [...] Read more.

While recent advances have successfully integrated neural networks with physical models to derive numerical solutions, there remains a compelling need to obtain exact analytical solutions. The ability to extract closed-form expressions from these models would provide deeper theoretical insights and enhanced predictive capabilities, complementing existing computational techniques. In this paper, we study the nonlinear Gardner equation and the (2+1)-dimensional Zabolotskaya–Khokhlov model, both of which are fundamental nonlinear wave equations with broad applications in various physical contexts. The proposed models have applications in fluid dynamics, describing shallow water waves, internal waves in stratified fluids, and the propagation of nonlinear acoustic beams. This study integrates a modified generalized Riccati equation mapping approach and a novel generalized

(\frac{G^{'}}{G})

-expansion method with neural networks for obtaining exact solutions for the suggested nonlinear models. Researchers are currently investigating potential applications of these neural networks to enhance our understanding of complex physical processes and to develop new analytical techniques. The proposed strategies incorporate the solutions of the Riccati problem into neural networks. Neural networks are multi-layer computing approaches including activation and weight functions among neurons in input, hidden, and output layers. Here, the solutions of the Riccati equation are allocated to each neuron in the first hidden layer; thus, new trial functions are established. We evaluate the suggested models, which lead to the construction of exact solutions in different forms, such as kink, dark, bright, singular, and combined solitons, as well as hyperbolic and periodic solutions, in order to verify the mathematical framework of the applied methods. The dynamic properties of certain wave-related solutions have been shown using various three-dimensional, two-dimensional, and contour visualizations. This paper introduces a novel framework for addressing nonlinear partial differential equations, with significant potential applications in various scientific and engineering domains. Full article

(This article belongs to the Special Issue New Trends in Nonlinear Dynamics and Nonautonomous Solitons)

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29 pages, 3379 KB

Open AccessArticle

Robust OTFS Detection via MMSE-DFE Equalization for ISAC in Doubly Dispersive Channels

by Khaled Ramadan, Ibrahim Aqeel and Emad S. Hassan

Mathematics 2025, 13(21), 3545; https://doi.org/10.3390/math13213545 - 5 Nov 2025

Viewed by 442

Abstract

This paper presents a detailed performance evaluation of a proposed Orthogonal Time Frequency Space (OTFS) system for Integrated Sensing and Communications (ISAC) in doubly dispersive wireless channels, characterized by both delay and Doppler spreads. The system is benchmarked against conventional Orthogonal Frequency Division [...] Read more.

This paper presents a detailed performance evaluation of a proposed Orthogonal Time Frequency Space (OTFS) system for Integrated Sensing and Communications (ISAC) in doubly dispersive wireless channels, characterized by both delay and Doppler spreads. The system is benchmarked against conventional Orthogonal Frequency Division Multiplexing (OFDM) schemes with Linear Minimum Mean Square Error (LMMSE) and Minimum Mean Square Error Decision Feedback Equalizer (MMSE-DFE) receivers. Through extensive simulations, the paper assesses Bit Error Rate (BER) and throughput performance under various Signal-to-Noise Ratios (SNRs), channel estimation error percentages, and multipath conditions. Results indicate that the proposed OTFS system is highly suitable for ISAC scenarios due to its delay-Doppler domain resilience and robustness to mobility, delivering superior BER performance, e.g.,

1.25 \times 10^{- 5}

at 20 dB SNR with 0% estimation error, compared to

1.10 \times 10^{- 3}

for OFDM-LMMSE. It also sustains 64 Mbps throughput under ideal conditions, though it shows sensitivity under severe estimation errors and rich multipath. In contrast, OFDM with LMMSE demonstrates smaller performance variation, maintaining over 61 Mbps throughput even at 100% estimation error and 15 scattered path components. These results suggest that OTFS is an effective waveform for ISAC when accurate channel estimation is available, while the corresponding OFDM with MMSE-DFE remains a robust fallback in highly uncertain environments. Full article

(This article belongs to the Special Issue Computational Methods in Wireless Communication)

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22 pages, 574 KB

Open AccessArticle

Resource Allocation and Energy Harvesting in UAV-Assisted Full-Duplex Cooperative NOMA Systems

by Turki Essa Alharbi

Mathematics 2025, 13(21), 3544; https://doi.org/10.3390/math13213544 - 5 Nov 2025

Viewed by 460

Abstract

Unmanned aerial vehicles (UAVs) are a promising technology for future sixth-generation (6G) wireless networks. They are airborne vehicles that act either as as flying relays or base stations (BS) to provide the line-of-sight (LOS) transmission, enable wide-area coverage, and increase the spectral efficiency. [...] Read more.

Unmanned aerial vehicles (UAVs) are a promising technology for future sixth-generation (6G) wireless networks. They are airborne vehicles that act either as as flying relays or base stations (BS) to provide the line-of-sight (LOS) transmission, enable wide-area coverage, and increase the spectral efficiency. In this work, a UAV is employed to forward information from the BS to distant users using a decode-and-forward (DF) protocol. The BS serves ground users through UAV by employing non-orthogonal multiple access (NOMA). The UAV relay will be wirelessly powered and harvests energy from the BS by applying a simultaneous wireless information and power transfer (SWIPT) technique. To further improve overall performance, the near user will act as a full-duplex (FD) relay to forward the far user’s information by applying cooperative non-orthogonal multiple access (C-NOMA). The proposed scheme considers a practical detection order using a feasible successive interference cancellation (SIC) operation. Additionally, a relay power control method is introduced for the near user to guarantee a reliable cooperative link. In the proposed scheme, a low-complexity closed-form power allocation is derived to maximize the minimum achievable rate. Numerical results demonstrate that the power allocation scheme significantly improves the far user’s rate performance, and the proposed scheme guarantees a higher target rate and outperforms the conventional NOMA, half-duplex (HD) C-NOMA, and FD C-NOMA with fixed power allocation (FPA) and fractional transmit power allocation (FTPA) schemes. Full article

(This article belongs to the Special Issue Computational Methods in Wireless Communication)

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12 pages, 402 KB

Open AccessFeature PaperArticle

Pull-Based Output Rate Control of a Flexible Job Shop in a Multi-Shop Production Chain

by Wei Weng, Meimei Zheng and Jiuchun Ren

Mathematics 2025, 13(21), 3543; https://doi.org/10.3390/math13213543 - 5 Nov 2025

Viewed by 335

Abstract

This paper addresses the problem where optimizing a single production shop within a production chain may not improve the overall performance of the entire chain. To overcome this and synchronize the efficiency of each shop, methods are proposed to align the output rate [...] Read more.

This paper addresses the problem where optimizing a single production shop within a production chain may not improve the overall performance of the entire chain. To overcome this and synchronize the efficiency of each shop, methods are proposed to align the output rate of an upstream shop with the limited intake rate of its downstream shop. In the proposed methods, the output rate of the upstream shop is used to guide job scheduling, processing, and resource allocation in the shop. Simulation results from a real-world case study demonstrate that implementing this pull-based system reduces job earliness and tardiness by over 90% in the tested factory, where the upstream shop is a flexible job shop, leading to lower inventory costs, idling costs, and labor costs. Full article

(This article belongs to the Special Issue Data-Driven Algorithms for Optimal Decision Making in Logistics and Supply Chain Management)

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22 pages, 1161 KB

Open AccessArticle

Data-Driven Optimal Treatment Combination Regimes for Multiple Stressors Controlling for Multiple Adverse Effects

by Kiran Shrestha, Edward L. Boone and Ryad Ghanam

Mathematics 2025, 13(21), 3542; https://doi.org/10.3390/math13213542 - 4 Nov 2025

Viewed by 323

Abstract

Combination drug treatment plays a central role in addressing complex diseases by enhancing the therapeutic benefit while mitigating adverse effects. However, determining optimal dose levels remains challenging due to additive drug effects, competing safety constraints, and the scarcity of reliable data in clinical [...] Read more.

Combination drug treatment plays a central role in addressing complex diseases by enhancing the therapeutic benefit while mitigating adverse effects. However, determining optimal dose levels remains challenging due to additive drug effects, competing safety constraints, and the scarcity of reliable data in clinical and experimental settings. This paper develops a data-driven robust optimization framework for combination dose selection under uncertainty. The proposed approach integrates posterior sampling via Markov Chain Monte Carlo with convex hull-based and mean-based filtration methods to generate, evaluate, and refine candidate optimal solutions. By embedding uncertainty quantification into the optimization process, the framework systematically balances therapeutic efficacy against the risk of adverse effects, yielding risk-averse yet effective dose strategies. Numerical experiments using exponential dose–response models and the ED50 criterion demonstrate that convex hull-based methods consistently produce feasible solutions, while mean-based approaches are prone to infeasibility except in limited cases. Among hull methods, balance-oriented filtration (BOF) achieves the best balance between performance and conservativeness, closely approximating the benchmark solution under moderate levels of uncertainty for models with additive drug effects. These findings highlight the advantages of robust optimization for dose selection in settings where data are limited, variability is high, and risk management is essential. Full article

(This article belongs to the Section D: Statistics and Operational Research)

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