Symmetry

19 pages, 509 KB

Open AccessArticle

Symmetric Equilibrium Bagging–Cascading Boosting Ensemble for Financial Risk Early Warning

by Yao Zou, Yuan Yuan, Chen Zhu and Chenhui Yu

Symmetry 2025, 17(10), 1779; https://doi.org/10.3390/sym17101779 - 21 Oct 2025

Viewed by 325

Financial risk early warning systems provide critical corporate financial status information to stakeholders, including corporate managers, investors, regulatory agencies, and other interested parties, enabling informed decision-making. This study proposes a corporate financial risk early warning model based on a bagging–cascading–boosting architecture, which can [...] Read more.

Financial risk early warning systems provide critical corporate financial status information to stakeholders, including corporate managers, investors, regulatory agencies, and other interested parties, enabling informed decision-making. This study proposes a corporate financial risk early warning model based on a bagging–cascading–boosting architecture, which can be used to predict the financial risk of a firm. The model performance is improved by integrating the residual fitting characteristics of LightGBM, the variance suppression mechanism of bagging, and the adaptive expansion ability of the cascade framework. Evaluated on 46 financial indicators from 2826 A-share-listed companies, the model demonstrates superior performance in AUC and F1-score metrics, outperforming traditional statistical methods and standalone machine-learning models. The methodological innovation lies in its tripartite mechanism: LightGBM ensures low-bias prediction, bagging controls variance, and the cascading structure dynamically adapts to data complexity, maintaining 94.09% AUC robustness, even when training data is reduced to 50%. Empirical results confirm this “ensemble-of-ensembles” framework effectively identifies Special Treatment (ST) firms, delivering early risk alerts for management while supporting investment decisions and regulatory risk mitigation. Full article

(This article belongs to the Section Computer)

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17 pages, 680 KB

Open AccessArticle

Stochastic SO(3) Lie Method for Correlation Flow

by Yasemen Ucan and Melike Bildirici

Symmetry 2025, 17(10), 1778; https://doi.org/10.3390/sym17101778 - 21 Oct 2025

Viewed by 255

Abstract

It is very important to create mathematical models for real world problems and to propose new solution methods. Today, symmetry groups and algebras are very popular in mathematical physics as well as in many fields from engineering to economics to solve mathematical models. [...] Read more.

It is very important to create mathematical models for real world problems and to propose new solution methods. Today, symmetry groups and algebras are very popular in mathematical physics as well as in many fields from engineering to economics to solve mathematical models. This paper introduces a novel methodological framework based on the SO(3) Lie method to estimate time-dependent correlation matrices (correlation flows) among three variables that have chaotic, entropy, and fractal characteristics, from 11 April 2011 to 31 December 2024 for daily data; from 10 April 2011 to 29 December 2024 for weekly data; and from April 2011 to December 2024 for monthly data. So, it develops the stochastic SO(2) Lie method into the SO(3) Lie method that aims to obtain the correlation flow for three variables with chaotic, entropy, and fractal structure. The results were obtained at three stages. Firstly, we applied entropy (Shannon, Rényi, Tsallis, Higuchi) measures, Kolmogorov–Sinai complexity, Hurst exponents, rescaled range tests, and Lyapunov exponent methods. The results of the Lyapunov exponents (Wolf, Rosenstein’s Method, Kantz’s Method) and entropy methods, and KSC found evidence of chaos, entropy, and complexity. Secondly, the stochastic differential equations which depend on S² (SO(3) Lie group) and Lie algebra to obtain the correlation flows are explained. The resulting equation was numerically solved. The correlation flows were obtained by using the defined covariance flow transformation. Finally, we ran the robustness check. Accordingly, our robustness check results showed the SO(3) Lie method produced more effective results than the standard and Spearman correlation and covariance matrix. And, this method found lower RMSE and MAPE values, greater stability, and better forecast accuracy. For daily data, the Lie method found RMSE = 0.63, MAE = 0.43, and MAPE = 5.04, RMSE = 0.78, MAE = 0.56, and MAPE = 70.28 for weekly data, and RMSE = 0.081, MAE = 0.06, and MAPE = 7.39 for monthly data. These findings indicate that the SO(3) framework provides greater robustness, lower errors, and improved forecasting performance, as well as higher sensitivity to nonlinear transitions compared to standard correlation measures. By embedding time-dependent correlation matrix into a Lie group framework inspired by physics, this paper highlights the deep structural parallels between financial markets and complex physical systems. Full article

(This article belongs to the Special Issue New Trends on the Mathematical Models and Solitons Arising in Real-World Problems, 3rd Edition)

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33 pages, 525 KB

Open AccessArticle

Limit Theorem for Kernel Estimate of the Conditional Hazard Function with Weakly Dependent Functional Data

by Abderrahmane Belguerna, Abdelkader Rassoul, Hamza Daoudi, Zouaoui Chikr Elmezouar and Fatimah Alshahrani

Symmetry 2025, 17(10), 1777; https://doi.org/10.3390/sym17101777 - 21 Oct 2025

Viewed by 177

Abstract

This paper examines the asymptotic behavior of the conditional hazard function using kernel-based methods, with particular emphasis on functional weakly dependent data. In particular, we establish the asymptotic normality of the proposed estimator when the covariate follows a functional quasi-associated process. This contribution [...] Read more.

This paper examines the asymptotic behavior of the conditional hazard function using kernel-based methods, with particular emphasis on functional weakly dependent data. In particular, we establish the asymptotic normality of the proposed estimator when the covariate follows a functional quasi-associated process. This contribution extends the scope of nonparametric inference under weak dependence within the framework of functional data analysis. The estimator is constructed through kernel smoothing techniques inspired by the classical Nadaraya–Watson approach, and its theoretical properties are rigorously derived under appropriate regularity conditions. To evaluate its practical performance, we carried out an extensive simulation study, where finite-sample outcomes were compared with their asymptotic counterparts. The results showed the robustness and reliability of the estimator across a range of scenarios, thereby confirming the validity of the proposed limit theorem in empirical settings. Full article

(This article belongs to the Section Mathematics)

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24 pages, 648 KB

Open AccessReview

A Review of Control Sets of Linear Control Systems on Two-Dimensional Lie Groups and Applications

by Víctor Ayala, Jhon Eddy Pariapaza Mamani, William Eduardo Valdivia Hanco and María Luisa Torreblanca Todco

Symmetry 2025, 17(10), 1776; https://doi.org/10.3390/sym17101776 - 21 Oct 2025

Viewed by 201

Abstract

This review article explores the theory of control sets for linear control systems defined on two-dimensional Lie groups, with a focus on the plane

R^{2}

and the affine group

A f f_{+} (2)

. We systematically summarize recent advances, [...] Read more.

This review article explores the theory of control sets for linear control systems defined on two-dimensional Lie groups, with a focus on the plane

R^{2}

and the affine group

A f f_{+} (2)

. We systematically summarize recent advances, emphasizing how the geometric and algebraic structures inherent in low-dimensional Lie groups influence the formation, shape, and properties of control sets—maximal regions where controllability is maintained. Control sets with non-empty interiors are of particular interest as they characterize regions where the system can be steered between states via bounded inputs. The review highlights key results concerning the existence, uniqueness, and boundedness of these sets, including criteria based on the Ad-rank condition and orbit analysis. We also underscore the central role of the symmetry properties of Lie groups, which facilitate the systematic classification and description of control sets, linking the abstract mathematical framework to concrete, physically motivated applications. To illustrate the practical relevance of the theory, we present examples from mechanics, motion planning, and neuroscience, demonstrating how control sets naturally emerge in diverse domains. Overall, this work aims to deepen the understanding of controllability regions in low-dimensional Lie group systems and to foster future research that bridges geometric control theory with applied problems. Full article

(This article belongs to the Special Issue Symmetries in Dynamical Systems and Control Theory)

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24 pages, 2742 KB

Open AccessArticle

Capturing the Asymmetry of Pitting Corrosion: An Interpretable Prediction Model Based on Attention-CNN

by Xiaohai Ran and Changfeng Wang

Symmetry 2025, 17(10), 1775; https://doi.org/10.3390/sym17101775 - 21 Oct 2025

Viewed by 272

Abstract

Fossil fuels are crucial to the global energy supply, with pipelines being a vital transportation method. However, these vital assets are highly susceptible to pitting corrosion, an insidious form of degradation that can lead to catastrophic failures. Unlike uniform corrosion, which represents a [...] Read more.

Fossil fuels are crucial to the global energy supply, with pipelines being a vital transportation method. However, these vital assets are highly susceptible to pitting corrosion, an insidious form of degradation that can lead to catastrophic failures. Unlike uniform corrosion, which represents a symmetric form of material loss, pitting corrosion is a highly asymmetric and localized phenomenon. The inherent complexity and asymmetry of this process make its prediction a significant challenge. To address this, this study presents SSA-CNN-Attention, a deep learning model specifically designed to analyze the complex, nonlinear interactions among environmental factors. The model employs a Convolutional Neural Network (CNN) to extract local features, while a crucial attention mechanism allows it to asymmetrically weight the importance of these features, enhancing its ability to recognize intricate interactions. Additionally, the Sparrow Search Algorithm (SSA) optimizes the model’s hyperparameters for improved accuracy and stability. Furthermore, a post hoc interpretability analysis using the LIME framework validates that the model’s learned feature relationships are consistent with established corrosion science, revealing how the model accounts for the asymmetric influence of key variables. The experimental results demonstrate that the proposed model reduces mean squared error (MSE) by 61.3% and mean absolute error (MAE) by 26.6%, while improving the coefficient of determination (R²) by 28.2% compared to traditional CNNs. These findings highlight the model’s superior performance in predicting a fundamentally asymmetric process and provide valuable insights into the underlying corrosion mechanisms. Full article

(This article belongs to the Section Computer)

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21 pages, 4777 KB

Open AccessArticle

Processing the Sensor Signal in a PI Control System Using an Adaptive Filter Based on Fuzzy Logic

by Jarosław Joostberens, Aurelia Rybak and Aleksandra Rybak

Symmetry 2025, 17(10), 1774; https://doi.org/10.3390/sym17101774 - 21 Oct 2025

Viewed by 218

Abstract

This paper presents an adaptive fuzzy filter applied to processing a signal from a voltage sensor fed to the input of an object in an automatic temperature control system with a PI controller. (1) The research goal was to develop an algorithm for [...] Read more.

This paper presents an adaptive fuzzy filter applied to processing a signal from a voltage sensor fed to the input of an object in an automatic temperature control system with a PI controller. (1) The research goal was to develop an algorithm for processing the signal from an RMS voltage sensor, measured at the terminals of a heating element in a temperature control system with a PI controller, in a way that ensures good dynamic properties while maintaining an appropriate level of accuracy. (2) The paper presents a method for designing an adaptive fuzzy filter by synthesizing a first-order low-pass infinite impulse response (IIR) filter and a fuzzy model of the dependence of this filter parameter value on the modulus of the derivative of the measured quantity. The application of a model with a symmetric input and output structure and a modified fuzzy model with asymmetry resulting from the uneven distribution of modal values of singleton fuzzy sets at the output was shown. The innovation in the proposed solution is the use of a signal from a PI controller to determine the derivative module of the measured quantity and, using a fuzzy model, linking its instantaneous value with a digital filter parameter in the measurement chain with a sensor monitoring the signal at the input of the controlled object. It is demonstrated that the signal generated by the PI controller can be used in a control system to continuously determine the modulus of the time derivative of the signal measured at the input of the controlled object, also indicating the limitations of this method. The signal from the PI controller can also be used to select filter parameters. In such a situation, it can be treated as a reference signal representing the useful signal. The mean square error (MSE) was adopted as the criterion for matching the signal at the filter output to the reference signal. (3) Based on a comparative analysis of the results of using an adaptive fuzzy filter with a classic first-order IIR filter with an optimal parameter in the MSE sense, it was found that using a fuzzy filter yields better results, regardless of the structure of the fuzzy model used (symmetric or asymmetric). (4) The paper demonstrates that in the tested temperature control system, introducing a simple fuzzy model with one input characterized by three fuzzy sets, relating the modulus of the derivative of the signal developed by the PI controller to the value of the first-order IIR filter parameter, into the voltage sensor signal-processing algorithm gave significantly better results than using a first-order IIR filter with a constant optimal parameter in terms of MSE. The best results were obtained using a fuzzy model in which an intentional asymmetry in the modal values of the output fuzzy sets was introduced. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Fuzzy Control)

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22 pages, 9604 KB

Open AccessArticle

Elliptic Functions and Advanced Analysis of Soliton Solutions for the Dullin–Gottwald–Holm Dynamical Equation with Applications of Mathematical Methods

by Syed T. R. Rizvi, Ibtehal Alazman, Nimra and Aly R. Seadawy

Symmetry 2025, 17(10), 1773; https://doi.org/10.3390/sym17101773 - 21 Oct 2025

Viewed by 219

Abstract

We studied traveling-wave solutions of the Dullin–Gottwald–Holm (DGH) equation via a sub-ODE construction. Under explicit algebraic constraints, the approach yielded closed-form families—bell-shaped, hyperbolic (

sech / \tanh

), Jacobi-elliptic function (JEF), Weierstrass-elliptic function (WEF), periodic, and rational—and classified their symmetry properties. Optical solitons [...] Read more.

We studied traveling-wave solutions of the Dullin–Gottwald–Holm (DGH) equation via a sub-ODE construction. Under explicit algebraic constraints, the approach yielded closed-form families—bell-shaped, hyperbolic (

sech / \tanh

), Jacobi-elliptic function (JEF), Weierstrass-elliptic function (WEF), periodic, and rational—and classified their symmetry properties. Optical solitons (bright and dark) arose as limiting cases of the elliptic solutions. We specified the parameter regimes that produced each profile and illustrated representative solutions with 2D/3D plots to highlight symmetry. The results provide a unified, reproducible procedure for generating solitary and periodic DGH waves and expand the catalog of exact solutions for this model. Full article

(This article belongs to the Special Issue Computational Mathematics and Its Applications in Numerical Analysis)

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28 pages, 1103 KB

Open AccessArticle

An Efficient and Effective Model for Preserving Privacy Data in Location-Based Graphs

by Surapon Riyana and Nattapon Harnsamut

Symmetry 2025, 17(10), 1772; https://doi.org/10.3390/sym17101772 - 21 Oct 2025

Viewed by 253

Abstract

Location-based services (LBSs), which are used for navigation, tracking, and mapping across digital devices and social platforms, establish a user’s position and deliver tailored experiences. Collecting and sharing such trajectory datasets with analysts for business purposes raises critical privacy concerns, as both symmetry [...] Read more.

Location-based services (LBSs), which are used for navigation, tracking, and mapping across digital devices and social platforms, establish a user’s position and deliver tailored experiences. Collecting and sharing such trajectory datasets with analysts for business purposes raises critical privacy concerns, as both symmetry in recurring behavior mobility patterns and asymmetry in irregular movement mobility patterns in sensitive locations collectively expose highly identifiable information, resulting in re-identification risks, trajectory disclosure, and location inference. In response, several privacy preservation models have been proposed, including k-anonymity, l-diversity, t-closeness, LKC-privacy, differential privacy, and location-based approaches. However, these models still exhibit privacy issues, including sensitive location inference (e.g., hospitals, pawnshops, prisons, safe houses), disclosure from duplicate trajectories revealing sensitive places, and the re-identification of unique locations such as homes, condominiums, and offices. Efforts to address these issues often lead to utility loss and computational complexity. To overcome these limitations, we propose a new (

ξ

,

ϵ

)-privacy model that combines data generalization and suppression with sliding windows and R-Tree structures, where sliding windows partition large trajectory graphs into simplified subgraphs, R-Trees provide hierarchical indexing for spatial generalization, and suppression removes highly identifiable locations. The model addresses both symmetry and asymmetry in mobility patterns by balancing generalization and suppression to protect privacy while maintaining data utility. Symmetry-driven mechanisms that enhance resistance to inference attacks and support data confidentiality, integrity, and availability are core requirements of cryptography and information security. An experimental evaluation on the City80k and Metro100k datasets confirms that the (

ξ

,

ϵ

)-privacy model addresses privacy issues with reduced utility loss and efficient scalability, while validating robustness through relative error across query types in diverse analytical scenarios. The findings provide evidence of the model’s practicality for large-scale location data, confirming its relevance to secure computation, data protection, and information security applications. Full article

(This article belongs to the Special Issue Applications Based on Symmetry in Cryptography and Information Security)

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23 pages, 1262 KB

Open AccessArticle

A Symmetry-Enhanced Secure and Traceable Data Sharing Model Based on Decentralized Information Flow Control for the End–Edge–Cloud Paradigm

by Jintian Lu, Chengzhi Yu, Menglong Qi, Han Luo, Jie Tian and Jianfeng Li

Symmetry 2025, 17(10), 1771; https://doi.org/10.3390/sym17101771 - 21 Oct 2025

Viewed by 297

Abstract

The End–Edge–Cloud (EEC) paradigm hierarchically orchestrates Internet of Things (IoT) devices, edge nodes, and cloud, optimizing system performance for both delay-sensitive data and compute-intensive processing tasks. Securing IoT data sharing in the EEC-driven paradigm while maintaining data traceability poses critical challenges. In this [...] Read more.

The End–Edge–Cloud (EEC) paradigm hierarchically orchestrates Internet of Things (IoT) devices, edge nodes, and cloud, optimizing system performance for both delay-sensitive data and compute-intensive processing tasks. Securing IoT data sharing in the EEC-driven paradigm while maintaining data traceability poses critical challenges. In this paper we propose STDSM, a symmetry-enhanced secure and traceable data sharing model for the EEC-driven data sharing paradigm. STDSM enables IoT data owners to share data securely by attaching symmetric security labels (for secrecy and integrity) to their data. This mechanism symmetrically controls both data outflow and inflow. Furthermore, STDSM can also track data user identity. Subsequently, the security properties of STDSM, including data confidentiality, integrity, and identity traceability, are formally verified; the verification takes 280 ms, using a novel approach that combines High-Level Petri Net modeling with the satisfiability modulo theories library and the Z3 solver. In addition, our experimental results show that STDSM reduces time overhead by up to 15% while providing enhanced traceability. Full article

(This article belongs to the Section Computer)

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23 pages, 8417 KB

Open AccessArticle

A Skewness-Based Density Metric and Deep Learning Framework for Point Cloud Analysis: Detection of Non-Uniform Regions and Boundary Extraction

by Cheng Li, Xianghong Hua, Wenbo Wang and Pengju Tian

Symmetry 2025, 17(10), 1770; https://doi.org/10.3390/sym17101770 - 20 Oct 2025

Viewed by 249

Abstract

This paper redefines point cloud density by utilizing statistical skewness derived from the geometric relationships between points and their local centroids. By comparing with a symmetric uniform reference model, this method can efficiently describe distribution patterns and detect non-uniform regions. Furthermore, a deep [...] Read more.

This paper redefines point cloud density by utilizing statistical skewness derived from the geometric relationships between points and their local centroids. By comparing with a symmetric uniform reference model, this method can efficiently describe distribution patterns and detect non-uniform regions. Furthermore, a deep learning model trained on these skewness features achieves 85.96% accuracy in automated boundary extraction, significantly reducing omission errors compared to conventional density-based methods. The proposed framework offers an effective solution for automated point cloud segmentation and modeling. Full article

(This article belongs to the Section Computer)

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11 pages, 3423 KB

Open AccessArticle

High-Precision Digital Time-Interval Measurement in Dual-Comb Systems via Adaptive Signal Processing and Centroid Localization

by Ganbin Lu, Dongrui Yu, Ziyue Zhang, Yang Xie, Yufei Zhang, Zhongyuan Fu, Sifei Chen, Lin Xiao, Ziyang Chen, Bin Luo and Hong Guo

Symmetry 2025, 17(10), 1769; https://doi.org/10.3390/sym17101769 - 20 Oct 2025

Viewed by 347

Abstract

Time and frequency standards constitute fundamental requirements for diverse applications spanning daily life technologies to advanced scientific research. Among precision time dissemination methods, microwave-clock-based dual comb time transfer has emerged as a promising approach that achieves ultra-precise time interval measurements through linear optical [...] Read more.

Time and frequency standards constitute fundamental requirements for diverse applications spanning daily life technologies to advanced scientific research. Among precision time dissemination methods, microwave-clock-based dual comb time transfer has emerged as a promising approach that achieves ultra-precise time interval measurements through linear optical sampling. However, conventional peak detection methodologies employed in such systems exhibit critical limitations: vulnerability to amplitude noise interference and inherent accuracy constraints imposed by analog sampling rates. To address these challenges, we present a novel digital time differential measurement paradigm integrating three key algorithmic innovations: (1) adaptive signal detection and extraction protocols, (2) multi-stage noise suppression processing, and (3) optimized centroid determination techniques. This comprehensive digital processing framework significantly enhances both measurement stability and operational efficiency, demonstrating single-shot temporal resolution at 17.6 fs stability levels. Our method establishes new capabilities for high-precision time-frequency transfer applications requiring robust noise immunity and enhanced sampling dynamics. Full article

(This article belongs to the Section Physics)

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20 pages, 20080 KB

Open AccessArticle

Symmetric Combined Convolution with Convolutional Long Short-Term Memory for Monaural Speech Enhancement

by Yang Xian, Yujin Fu, Peixu Xing, Hongwei Tao and Yang Sun

Symmetry 2025, 17(10), 1768; https://doi.org/10.3390/sym17101768 - 20 Oct 2025

Viewed by 280

Abstract

Deep neural network-based approaches have obtained remarkable progress in monaural speech enhancement. Nevertheless, current cutting-edge approaches remain vulnerable to complex acoustic scenarios. We propose a Symmetric Combined Convolution Network with ConvLSTM (SCCN) for monaural speech enhancement. Specifically, the Combined Convolution Block utilizes parallel [...] Read more.

Deep neural network-based approaches have obtained remarkable progress in monaural speech enhancement. Nevertheless, current cutting-edge approaches remain vulnerable to complex acoustic scenarios. We propose a Symmetric Combined Convolution Network with ConvLSTM (SCCN) for monaural speech enhancement. Specifically, the Combined Convolution Block utilizes parallel convolution branches, including standard convolution and two different depthwise separable convolutions, to reinforce feature extraction in depthwise and channelwise. Similarly, Combined Deconvolution Blocks are stacked to construct the convolutional decoder. Moreover, we introduce the exponentially increasing dilation between convolutional kernel elements in the encoder and decoder, which expands receptive fields. Meanwhile, the grouped ConvLSTM layers are exploited to extract the interdependency of spatial and temporal information. The experimental results demonstrate that the proposed SCCN method obtains on average 86.00% in STOI and 2.43 in PESQ, which outperforms the state-of-the-art baseline methods, confirming the effectiveness in enhancing speech quality. Full article

(This article belongs to the Section Computer)

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16 pages, 343 KB

Open AccessArticle

Soliton Geometry of Modified Gravity Models Engaged with Strange Quark Matter Fluid and Penrose Singularity Theorem

by Mohd Danish Siddiqi and Fatemah Mofarreh

Symmetry 2025, 17(10), 1767; https://doi.org/10.3390/sym17101767 - 20 Oct 2025

Viewed by 280

Abstract

The nature of the

F (R, T)

-gravity in conjunction with the quark matter fluid

(Q M F)

is examined in this research note. In the

F (R, T)

-gravity framework, we derive the equation [...] Read more.

The nature of the

F (R, T)

-gravity in conjunction with the quark matter fluid

(Q M F)

is examined in this research note. In the

F (R, T)

-gravity framework, we derive the equation of state for the

Q M F

in the form of:

F (R, T) = F_{1} (R) + F_{2} (T)

and the model of

F (R)

-gravity. We also discuss how the quark matter supports the Ricci solitons with a conformal vector field in

F (R, T)

-gravity. In this continuing work, we give estimates for the pressure and quark density in the phantom barrier period and the radiation epoch, respectively. Additionally, we use Ricci solitons to identify several black hole prospects and energy requirements for quark matter fluid spacetime (

Q M F

-spacetime) connected with

F (R, T)

-gravity. Furthermore, in the

F (R, T)

-gravity model connected with

Q M F

, we also discuss some applications of the Penrose singularity theorem in terms of Ricci solitons with a conformal vector field. Finally, we deduce the Schrödinger Equation using the equation of state of the

F (R, T)

-gravity model connected with

Q M F

, and we uncover some constraints that imply the existence of compact quark stars of the

I a

-supernova type in the

Q M F

-spacetime with

F (R, T)

-gravity. Full article

(This article belongs to the Section Mathematics)

19 pages, 607 KB

Open AccessArticle

The Stability of Linear Control Systems on Low-Dimensional Lie Groups

by Víctor Ayala, William Eduardo Valdivia Hanco, Jhon Eddy Pariapaza Mamani and María Luisa Torreblanca Todco

Symmetry 2025, 17(10), 1766; https://doi.org/10.3390/sym17101766 - 20 Oct 2025

Viewed by 236

Abstract

This work investigates the stability analysis of linear control systems defined on Lie groups, with a particular focus on low-dimensional cases. Unlike their Euclidean counterparts, such systems evolve on manifolds with non-Euclidean geometry, where trajectories respect the group’s intrinsic symmetries. Stability notions, such [...] Read more.

This work investigates the stability analysis of linear control systems defined on Lie groups, with a particular focus on low-dimensional cases. Unlike their Euclidean counterparts, such systems evolve on manifolds with non-Euclidean geometry, where trajectories respect the group’s intrinsic symmetries. Stability notions, such as inner asymptotic, inner, and input–output (BIBO) stability, are studied. The qualitative behavior of solutions is shown to depend critically on the spectral decomposition of derivations associated with the drift, and on the algebraic structure of the underlying Lie algebra. We study two classes of examples in detail: Abelian and solvable two-dimensional Lie groups, and the three-dimensional nilpotent Heisenberg group. These settings, while mathematically tractable, retain essential features of non-commutativity, geometric non-linearity, and sub-Riemannian geometry, making them canonical models in control theory. The results highlight the interplay between algebraic properties, invariant submanifolds, and trajectory behavior, offering insights applicable to robotic motion planning, quantum control, and signal processing. Full article

(This article belongs to the Special Issue Symmetries in Dynamical Systems and Control Theory)

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23 pages, 309 KB

Open AccessArticle

On Symmetric Aspects of Operator Pair Inequalities in Hilbert Spaces via Pečarić’s Theorem with Applications

by Najla Altwaijry and Silvestru Sever Dragomir

Symmetry 2025, 17(10), 1765; https://doi.org/10.3390/sym17101765 - 20 Oct 2025

Viewed by 194

Abstract

In this paper, we establish several new operator inequalities for generalizations of the joint numerical radius and joint operator norm for pairs of operators in complex Hilbert spaces, as well as for the classical numerical radius of a single operator. One of our [...] Read more.

In this paper, we establish several new operator inequalities for generalizations of the joint numerical radius and joint operator norm for pairs of operators in complex Hilbert spaces, as well as for the classical numerical radius of a single operator. One of our main tools is the well-known Pečarić’s Theorem. As applications, we derive a series of power inequalities for the operator norm and for the generalized numerical radius, which refine and generalize a number of existing results in the literature. Our approach considers two key symmetric pairings: the Cartesian decomposition

(R (U), I (U))

and the operator-adjoint pair

(U, U^{⋆})

. Full article

(This article belongs to the Section Mathematics)

22 pages, 1811 KB

Open AccessArticle

Hierarchical Construction of Fuzzy Signature Models for Non-Destructive Assessment of Masonry Strength

by András Kaszás, Vanda O. Pomezanski and László T. Kóczy

Symmetry 2025, 17(10), 1764; https://doi.org/10.3390/sym17101764 - 19 Oct 2025

Viewed by 277

Abstract

Non-destructive testing methods are essential in civil engineering applications, such as evaluating the compressive strength of masonry. This paper presents a fuzzy signature model based on non-destructive in situ measurements and visual inspection, applying weighted geometric mean aggregation in the signature vertices determined [...] Read more.

Non-destructive testing methods are essential in civil engineering applications, such as evaluating the compressive strength of masonry. This paper presents a fuzzy signature model based on non-destructive in situ measurements and visual inspection, applying weighted geometric mean aggregation in the signature vertices determined by experts. The weights of the aggregation terms were optimized using the Monte Carlo method, genetic algorithm and particle swarm algorithm to ensure that the evaluation by the signature aligned with the results of destructive tests performed on existing masonry. The results of the methods were compared for single and multiple assembled masonry structures using the same objective function. All three methods provided relatively high confidence in finding the extreme values of the objective function on a generated dataset, which accounted for the correlations observed in actual measurements. Accordingly, validation based on real data yielded the expected results, thus demonstrating the model’s suitability for practical application. This study assessed the inherent, analyzing whether symmetric or asymmetric weight distributions affected evaluation consistency. While symmetric weighting simplified aggregation, asymmetry allowed local structural irregularities to be highlighted. In addition, the cost analysis of the optimization methods revealed a disparity in computational cost increments between the two approaches. The presented work outlines the advantages of the different methods and their applicability to structural assessment. Full article

(This article belongs to the Section Engineering and Materials)

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15 pages, 5150 KB

Open AccessArticle

Insulator Defect Detection Algorithm Based on Improved YOLO11s in Snowy Weather Environment

by Ziwei Ding, Song Deng and Qingsheng Liu

Symmetry 2025, 17(10), 1763; https://doi.org/10.3390/sym17101763 - 19 Oct 2025

Viewed by 335

Abstract

The intelligent transformation of power systems necessitates robust insulator condition detection to ensure grid safety. Existing methods, primarily reliant on manual inspection or conventional image processing, suffer significantly degraded target identification and detection efficiency under extreme weather conditions such as heavy snowfall. To [...] Read more.

The intelligent transformation of power systems necessitates robust insulator condition detection to ensure grid safety. Existing methods, primarily reliant on manual inspection or conventional image processing, suffer significantly degraded target identification and detection efficiency under extreme weather conditions such as heavy snowfall. To address this challenge, this paper proposes an enhanced YOLO11s detection framework integrated with image restoration technology, specifically targeting insulator defect identification in snowy environments. First, data augmentation and a FocalNet-based snow removal algorithm effectively enhance image resolution under snow conditions, enabling the construction of a high-quality training dataset. Next, the model architecture incorporates a dynamic snake convolution module to strengthen the perception of tubular structural features, while the MPDIoU loss function optimizes bounding box localization accuracy and recall. Comparative experiments demonstrate that the optimized framework significantly improves overall detection performance under complex weather compared to the baseline model. Furthermore, it exhibits clear advantages over current mainstream detection models. This approach provides a novel technical solution for monitoring power equipment conditions in extreme weather, offering significant practical value for ensuring reliable grid operation. Full article

(This article belongs to the Special Issue Symmetry and Asymmetry in Data Analysis)

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15 pages, 653 KB

Open AccessArticle

Basic Vaidya White Hole Evaporation Process

by Qingyao Zhang

Symmetry 2025, 17(10), 1762; https://doi.org/10.3390/sym17101762 - 18 Oct 2025

Viewed by 278

Abstract

We developed a self-consistent double-null description of an evaporating white-hole spacetime by embedding the outgoing Vaidya solution in a coordinate system that remains regular across the future horizon. Starting from the radiation-coordinate form, we specialize in retarded time so that a monotonically decreasing [...] Read more.

We developed a self-consistent double-null description of an evaporating white-hole spacetime by embedding the outgoing Vaidya solution in a coordinate system that remains regular across the future horizon. Starting from the radiation-coordinate form, we specialize in retarded time so that a monotonically decreasing mass function

M (u)

encodes outgoing positive-energy flux. Expressing the metric in null coordinates

(u, v)

, Einstein’s equations for a single-directional null-dust stress–energy tensor,

T_{u u} = ρ (u)

, then reduce to one first-order PDE for the areal radius:

\partial_{v} r = B (u) (1 - 2 M (u) / r)

. Its integral,

r + 2 M (u) ln | r - 2 M (u) | = v - C (u)

, defines an implicit foliation of outgoing null cones. The metric coefficient follows algebraically as

f (u, v) = 1 - 2 M (u) / r

. Residual gauge freedom in

B (u)

and

C (u)

is fixed so that u matches the Bondi retarded time at null infinity, while v remains analytic at the apparent horizon, generalizing the Kruskal prescription to dynamical mass loss. In the limit

M (u) \to M

, the construction reduces to the familiar Eddington–Finkelstein and Kruskal forms. Our solution, therefore, provides a compact analytic framework for studying white-hole evaporation, Hawking-like energy fluxes, and back-reaction in spherically symmetric settings without encountering coordinate singularities. Full article

(This article belongs to the Special Issue Advances in Black Holes, Symmetry and Chaos)

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18 pages, 5967 KB

Open AccessArticle

Effect of Rotational Speed Fluctuation Parameters on Dynamic Characteristics of Angular Contact Ball Bearings

by Haibin He, Jun Feng, Zuoxiang Zhu, Jinmei Guo and Guohu Luo

Symmetry 2025, 17(10), 1761; https://doi.org/10.3390/sym17101761 - 18 Oct 2025

Viewed by 288

Abstract

The fluctuation in the rotational speed of the inner ring can lead to significant instability in the motion of both the inner ring and the cage of rolling bearings. This instability seriously impacts the operational performance and service life of the bearings. In [...] Read more.

The fluctuation in the rotational speed of the inner ring can lead to significant instability in the motion of both the inner ring and the cage of rolling bearings. This instability seriously impacts the operational performance and service life of the bearings. In this paper, a nonlinear dynamic model of a fully flexible angular contact ball bearing was established by comprehensively considering various nonlinear factors, including elastic contact relationships, internal collisions, friction, and clearance. The dynamic characteristics of the inner ring and cage under sinusoidal rotational speed fluctuations were studied. The effects of amplitude and frequency of rotational speed fluctuation of the inner ring on the motion stability of the inner ring and cage were analyzed. The results show that a greater the fluctuation amplitude leads to a higher the fluctuation amplitude in the cage’s rotational speed curve, while a higher fluctuation frequency correlates with an increased frequency in the cage’s rotational speed curve. These results indicate that increases in both the amplitude and frequency of rotational speed fluctuations result in more pronounced oscillations of the inner ring. The validity of the model was confirmed by comparing the LS-DYNA results with the analytical results and experimental results. The research findings can provide a theoretical foundation for enhancing motion stability and optimizing design of the bearings. Full article

(This article belongs to the Section Engineering and Materials)

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13 pages, 275 KB

Open AccessArticle

Generalized Gamma Frailty and Symmetric Normal Random Effects Model for Repeated Time-to-Event Data

by Kai Liu, Yan Qiao Wang, Xiaojun Zhu and Narayanaswamy Balakrishnan

Symmetry 2025, 17(10), 1760; https://doi.org/10.3390/sym17101760 - 17 Oct 2025

Viewed by 269

Abstract

Clustered time-to-event data are quite common in survival analysis and finding a suitable model to account for dispersion as well as censoring is an important issue. In this article, we present a flexible model for repeated, overdispersed time-to-event data with right-censoring. We present [...] Read more.

Clustered time-to-event data are quite common in survival analysis and finding a suitable model to account for dispersion as well as censoring is an important issue. In this article, we present a flexible model for repeated, overdispersed time-to-event data with right-censoring. We present here a general model by incorporating generalized gamma and normal random effects in a Weibull distribution to accommodate overdispersion and data hierarchies, respectively. The normal random effect has the property of being symmetrical, which means its probability density function is symmetric around its mean. While the random effects are symmetrically distributed, the resulting frailty model is asymmetric in its survival function because the random effects enter the model multiplicatively via the hazard function, and the exponentiation of a symmetric normal variable leads to lognormal distribution, which is right-skewed. Due to the intractable integrals involved in the likelihood function and its derivatives, the Monte Carlo approach is used to approximate the involved integrals. The maximum likelihood estimates of the parameters in the model are then numerically determined. An extensive simulation study is then conducted to evaluate the performance of the proposed model and the method of inference developed here. Finally, the usefulness of the model is demonstrated by analyzing a data on recurrent asthma attacks in children and a recurrent bladder data set known in the survival analysis literature. Full article

(This article belongs to the Special Issue Skewed (Asymmetrical) Probability Distributions and Applications Across Disciplines, Fourth Edition)

14 pages, 3159 KB

Open AccessArticle

Optimal Function Study of One-Cycle Control with Embedded Composite Function for Boost Converters

by Lei Wang, Lidan Chen, Wei Ma and Jubao Li

Symmetry 2025, 17(10), 1759; https://doi.org/10.3390/sym17101759 - 17 Oct 2025

Viewed by 236

Abstract

One-cycle control (OCC) is prized in power converter applications for its rapid dynamic response and effective disturbance suppression. While its core principle relies on the symmetry of the volt-second value of the inductor in each cycle, recent research shows that embedding a composite [...] Read more.

One-cycle control (OCC) is prized in power converter applications for its rapid dynamic response and effective disturbance suppression. While its core principle relies on the symmetry of the volt-second value of the inductor in each cycle, recent research shows that embedding a composite function can significantly expand the stable parameter domain of conventional OCC. This paper seeks to identify the optimal function for this enhancement. The logarithmic and arc-tangent functions are selected based on the required characteristics and analyzed using a state-space average model. Analysis of the stability boundaries demonstrates that with

l n (u + 1)

embedded, the stability region of

u_{r e f}

is effectively enlarged to more than

4 u_{i n}

. With

\tan^{- 1} u

embedded, the stability region of

u_{r e f}

is effectively enlarged to infinity. Therefore, embedding

\tan^{- 1} u

can achieve optimal results, so it is considered the optimal function. This conclusion is conclusively validated by both simulation and experimental results. Full article

(This article belongs to the Section Mathematics)

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26 pages, 7405 KB

Open AccessArticle

An Efficient Task Scheduling Framework for Large-Scale 3D Reconstruction in Multi-UAV Edge-Intelligence Systems

by Yu Xia, Xueyong Xu, Yuhang Xu, Anmin Li, Jinchen Wang, Chenchen Fu and Weiwei Wu

Symmetry 2025, 17(10), 1758; https://doi.org/10.3390/sym17101758 - 17 Oct 2025

Viewed by 360

Abstract

With the rapid development of edge-intelligence systems, multi-UAV platforms have become vital for large-scale 3D reconstruction. However, efficient task scheduling remains a critical challenge due to constraints on UAV energy, communication range, and the need for balanced workload distribution. To address these issues, [...] Read more.

With the rapid development of edge-intelligence systems, multi-UAV platforms have become vital for large-scale 3D reconstruction. However, efficient task scheduling remains a critical challenge due to constraints on UAV energy, communication range, and the need for balanced workload distribution. To address these issues, this paper presents a novel, centralized two-stage task scheduling framework. In the first stage, the framework partitions the target area into communication-feasible subregions by applying cell decomposition that accounts for no-fly zones and workload. It then models the subregion allocation as a Capacitated Vehicle Routing Problem (CVRP) with an added balancing constraint to optimize the traversal sequence for each operational sortie. In the second stage, a time-efficient, scan-based heuristic algorithm allocates viewpoints among UAVs to ensure workload balance, minimizing the mission completion time. Extensive simulations demonstrate that our proposed approach achieves superior performance in workload balance, path efficiency, and reconstruction quality. Overall, this work provides a scalable and energy-aware solution for centralized multi-UAV 3D reconstruction, highlighting an effective approach to ensure cooperation and efficiency in complex multi-agent systems. Full article

(This article belongs to the Special Issue Symmetry and Asymmetry in Embedded Systems)

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21 pages, 3232 KB

Open AccessArticle

PGF-Net: A Symmetric Cutting Path Generation and Fitting Optimization Method for Pig Carcasses Under Multi-Medium Interference

by Lei Cai, Jin Luo and Pengtao Ban

Symmetry 2025, 17(10), 1757; https://doi.org/10.3390/sym17101757 - 17 Oct 2025

Viewed by 300

Abstract

In the automated cutting process of pork carcasses, asymmetric cutting path planning is critical. However, various substances on the carcass surface, such as blood stains and fascia, severely interfere with the separation boundaries between fresh meat and bones, significantly reducing the accuracy of [...] Read more.

In the automated cutting process of pork carcasses, asymmetric cutting path planning is critical. However, various substances on the carcass surface, such as blood stains and fascia, severely interfere with the separation boundaries between fresh meat and bones, significantly reducing the accuracy of asymmetric cutting path planning. To address these issues, this paper proposes a method for generating and fitting optimized cutting paths for pork carcasses (PGF-Net). Specifically, this method comprises a cutting path generation module that integrates multi-scale boundary features and a cutting path fitting optimization module. The cutting path generation module extracts asymmetric boundary information by enhancing attention to boundaries across different regions, identifies key cutting points, and generates a coarse cutting path. The cutting path fitting optimization module then performs fitting optimization on the generated key cutting points to ultimately produce a refined asymmetric cutting path. Experimental results demonstrate that PGF-Net achieves mean root mean square errors of 0.4212 cm, 0.4651 cm, and 0.5313 cm across three cutting paths on six different pork carcass images. Findings confirm that this method enhances the yield of premium meat cuts while reducing tool wear costs. It provides an innovative technological solution for automated meat processing, holding significant industrial application value. Full article

(This article belongs to the Section Computer)

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23 pages, 1945 KB

Open AccessArticle

A Symmetry-Informed Multimodal LLM-Driven Approach to Robotic Object Manipulation: Lowering Entry Barriers in Mechatronics Education

by Jorge Gudiño-Lau, Miguel Durán-Fonseca, Luis E. Anido-Rifón and Pedro C. Santana-Mancilla

Symmetry 2025, 17(10), 1756; https://doi.org/10.3390/sym17101756 - 17 Oct 2025

Viewed by 418

Abstract

The integration of Large Language Models (LLMs), particularly Visual Language Models (VLMs), into robotics promises more intuitive human–robot interactions; however, challenges remain in efficiently translating high-level commands into precise physical actions. This paper presents a novel architecture for vision-based object manipulation that leverages [...] Read more.

The integration of Large Language Models (LLMs), particularly Visual Language Models (VLMs), into robotics promises more intuitive human–robot interactions; however, challenges remain in efficiently translating high-level commands into precise physical actions. This paper presents a novel architecture for vision-based object manipulation that leverages a VLM’s reasoning capabilities while incorporating symmetry principles to enhance operational efficiency. Implemented on a Yahboom DOFBOT educational robot with a Jetson Nano platform, our system introduces a prompt-based framework that uniquely embeds symmetry-related cues to streamline feature extraction and object detection from visual data. This methodology, which utilizes few-shot learning, enables the VLM to generate more accurate and contextually relevant commands for manipulation tasks by efficiently interpreting the symmetric and asymmetric features of objects. The experimental results in controlled scenarios demonstrate that our symmetry-informed approach significantly improves the robot’s interaction efficiency and decision-making accuracy compared to generic prompting strategies. This work contributes a robust method for integrating fundamental vision principles into modern generative AI workflows for robotics. Furthermore, its implementation on an accessible educational platform shows its potential to simplify complex robotics concepts for engineering education and research. Full article

(This article belongs to the Special Issue Symmetry and Asymmetry in Object Detection, Object Tracking, and Behaviour Understanding)

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20 pages, 436 KB

Open AccessArticle

Numerical Solutions for Fractional Bagley–Torvik Equation with Integral Boundary Conditions

by Xueling Liu, Jing Huang, Junlin Li and Yufeng Zhang

Symmetry 2025, 17(10), 1755; https://doi.org/10.3390/sym17101755 - 17 Oct 2025

Viewed by 341

Abstract

The Bagley–Torvik equation (BTE) is an important model in mathematical physics and mechanics, but obtaining its analytical solution remains challenging. For its numerical treatment, the presence of composite functions in the generalized BTE poses additional difficulties, and efficient approaches for handling nonlinear terms [...] Read more.

The Bagley–Torvik equation (BTE) is an important model in mathematical physics and mechanics, but obtaining its analytical solution remains challenging. For its numerical treatment, the presence of composite functions in the generalized BTE poses additional difficulties, and efficient approaches for handling nonlinear terms are still lacking in the literature. This study proposes an improved numerical method for the fractional BTE with integral boundary conditions. By employing an integration technique, the original problem is transformed into a weakly singular Fredholm–Hammerstein (F–H) integral equation of the second kind. To address the nonlinear terms, an enhanced piecewise Taylor expansion scheme is developed to construct the discrete form, while the uniqueness of the solution is proven using the contraction mapping theorem in Banach spaces. The convergence and error analyses are rigorously carried out, and numerical experiments confirm the accuracy and efficiency of the proposed approach. Full article

(This article belongs to the Section Mathematics)

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23 pages, 9496 KB

Open AccessArticle

Symmetry-Aware LSTM-Based Effective Connectivity Framework for Identifying MCI Progression and Reversion with Resting-State fMRI

by Bowen Sun, Lei Wang, Mengqi Gao, Ziyu Fan and Tongpo Zhang

Symmetry 2025, 17(10), 1754; https://doi.org/10.3390/sym17101754 - 17 Oct 2025

Viewed by 308

Abstract

Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates [...] Read more.

Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates a healthy control–AD difference template (HAD) with a large-scale Granger causality algorithm based on long short-term memory networks (LSTM-lsGC) to construct effective connectivity (EC) networks. By applying principal component analysis for dimensionality reduction, modeling dynamic sequences with LSTM, and estimating EC matrices through Granger causality, the framework captures both symmetrical and asymmetrical connectivity, providing a refined characterization of the network alterations underlying MCI progression and reversion. Leveraging graph-theoretical features, our method achieved an MCI subtype classification accuracy of 84.92% (AUC = 0.84) across three subgroups and 90.86% when distinguishing rMCI from pMCI. Moreover, key brain regions, including the precentral gyrus, hippocampus, and cerebellum, were identified as being associated with MCI progression. Overall, by developing a symmetry-aware effective connectivity framework that simultaneously investigates both MCI progression and reversion, this study bridges a critical gap and offers a promising tool for early detection and dynamic disease characterization. Full article

(This article belongs to the Section Computer)

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24 pages, 2473 KB

Open AccessArticle

An Approximate Solution for M/G/1 Queues with Pure Mixture Service Time Distributions

by Melik Koyuncu and Nuşin Uncu

Symmetry 2025, 17(10), 1753; https://doi.org/10.3390/sym17101753 - 17 Oct 2025

Viewed by 413

Abstract

This study introduces an approximate solution for the M/G/1 queueing model in scenarios where the service time distribution follows a pure mixture distribution. The derivation of the proposed approximation leverages the analytical tractability of the variance for certain mixture distributions. By incorporating this [...] Read more.

This study introduces an approximate solution for the M/G/1 queueing model in scenarios where the service time distribution follows a pure mixture distribution. The derivation of the proposed approximation leverages the analytical tractability of the variance for certain mixture distributions. By incorporating this variance into the Pollaczek–Khinchine equation, an approximate closed-form expression for the M/G/1 queue is obtained. The formulation is extended to service-time distributions composed of two or more components, specifically Gamma, Gaussian, and Beta mixtures. To assess the accuracy of the proposed approach, a discrete-event simulation of an M/G/1 system was conducted using random variates generated from these mixture distributions. The comparative analysis reveals that the approximation yields results in close agreement with simulation outputs, with particularly high accuracy observed for Gaussian mixture cases. Full article

(This article belongs to the Section Mathematics)

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26 pages, 5646 KB

Open AccessArticle

A Symmetry-Aware BAS for Improved Fuzzy Intra-Class Distance-Based Image Segmentation

by Yazhi Wang, Lei Ding and Qing Zhang

Symmetry 2025, 17(10), 1752; https://doi.org/10.3390/sym17101752 - 17 Oct 2025

Viewed by 285

Abstract

At present, the Beetle Antennae Search (BAS) algorithm has achieved remarkable success in image segmentation. However, when dealing with some complex image segmentation problems, particularly in the context of instance segmentation, which aims to identify and delineate each distinct object of interest, even [...] Read more.

At present, the Beetle Antennae Search (BAS) algorithm has achieved remarkable success in image segmentation. However, when dealing with some complex image segmentation problems, particularly in the context of instance segmentation, which aims to identify and delineate each distinct object of interest, even within the same semantic class, there are problems such as poor optimization performance, slow convergence speed, and low stability. Therefore, to address the challenges of instance segmentation, an improved image segmentation model is proposed, and a novel BAS algorithm called the Crossover and Mutation Beetle Antennae Search (CMBAS) algorithm is designed to optimize it. The core of our approach treats instance segmentation as a sophisticated clustering problem, where each cluster center corresponds to a unique object instance. Firstly, an improved intra-class distance based on fuzzy membership weighting is designed to enhance the compactness of individual instances. Secondly, to quantify the genetic potential of individuals through their fitness performance, CMBAS uses an adaptive crossover rate mechanism based on fitness ranking and establishes a ranking-driven crossover probability allocation model. Thirdly, to guide individuals to evolve towards excellence, CMBAS uses a strategy for individual mutation of longicorn beetle antennae based on DE/current-to-best/1. Furthermore, the symmetry-aware adaptive crossover and mutation operations enhance the balance between exploration and exploitation, leading to more robust and consistent instance-level segmentation results. Experimental results on five typical benchmark functions demonstrate that CMBAS achieves superior accuracy and stability compared to the BAGWO, BAS, GWO, PSO, GA, Jaya, and FA algorithms. In image segmentation applications, CMBAS exhibits exceptional instance segmentation performance, including an enhanced ability to distinguish between adjacent or overlapping objects of the same class, resulting in smoother and more continuous instance boundaries, clearer segmented targets, and excellent convergence performance. Full article

(This article belongs to the Special Issue Symmetry and Asymmetry in Intelligent Control and Computing)

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21 pages, 5358 KB

Open AccessArticle

Predefined Time Transient Coordination Control of Power-Split Hybrid Electric Vehicle Based on Adaptive Extended State Observer

by Hongdang Zhang, Hongtu Yang, Fengjiao Zhang and Yanyan Zuo

Symmetry 2025, 17(10), 1751; https://doi.org/10.3390/sym17101751 - 16 Oct 2025

Viewed by 229

Abstract

This paper proposes a predefined time transient coordinated control strategy based on an adaptive nonlinear extended state observer (ANLESO) to address the adaptability challenges of mode transition control in power-split hybrid electric vehicles (PS-HEVs). Firstly, building upon a conventional dynamic coordinated control framework, [...] Read more.

This paper proposes a predefined time transient coordinated control strategy based on an adaptive nonlinear extended state observer (ANLESO) to address the adaptability challenges of mode transition control in power-split hybrid electric vehicles (PS-HEVs). Firstly, building upon a conventional dynamic coordinated control framework, the influence of varying acceleration conditions and external disturbances on mode transition performance is analyzed. To enhance disturbance estimation under both positive and negative as well as large and small errors, an ANLESO is developed, which not only improves the speed and accuracy of disturbance observation but also guarantees symmetric convergence performance with respect to estimation errors. Subsequently, a predefined time feedback controller is developed based on the theory of predefined time control. Theoretical stability analysis demonstrates that the convergence time of the system is independent of the initial state and can be guaranteed within a predefined time. Finally, the feasibility and superiority of the proposed control strategy are validated through Hardware-in-the-Loop (HIL) testing and vehicle experimentation. The results show that, compared with PID control based on a linear expansion state observer, the proposed strategy reduces the mode transition time by 45.7% and mitigates drivability shock by 59.2%. Full article

(This article belongs to the Section Engineering and Materials)

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27 pages, 5252 KB

Open AccessArticle

Experimental Study and Model Construction on Pressure Drop Characteristics of Horizontal Annulus

by Yanchao Sun, Gengxin Shi, Shaokun Bi, Peng Wang, Panliang Liu, Jinxiang Wang and Bin Yang

Symmetry 2025, 17(10), 1750; https://doi.org/10.3390/sym17101750 - 16 Oct 2025

Viewed by 294

Abstract

Horizontal annular flow channels are widely applied in various fields, including thermal engineering, drilling engineering, and food engineering. Investigating their internal flow patterns is crucial for optimizing pipeline design, selecting appropriate equipment, and understanding the sedimentation and migration modes of multiphase flows within [...] Read more.

Horizontal annular flow channels are widely applied in various fields, including thermal engineering, drilling engineering, and food engineering. Investigating their internal flow patterns is crucial for optimizing pipeline design, selecting appropriate equipment, and understanding the sedimentation and migration modes of multiphase flows within annular geometries. In practical engineering applications, the operational conditions of annular flow channels during gas drilling are the most complex, involving parameters such as eccentricity, rotation, surface roughness, and multiphase flow interactions. This study focuses on the flow characteristics of horizontal annular channels under real-world engineering conditions, examining variations in operational parameters. The pressure drop in annular pipelines is influenced by factors such as flow velocity, eccentricity, and rotational speed, exhibiting complex variation patterns. However, previous studies have not fully considered the impact of rough wellbore walls and the interactions among various factors. Employing experimental methods, this research analyzes the pressure drop characteristics within annular geometries. The results reveal that surface roughness significantly affects pressure drop, with the inner pipe’s roughness having a greater impact when the outer pipe surface is rough compared to when it is smooth. An increase in eccentricity substantially reduces pressure drop, with both positive and negative eccentricities demonstrating symmetric pressure drop patterns. Moreover, a significant positive correlation exists between the total rough area of the annular channel and pressure drop. Furthermore, this study establishes a predictive model through dimensional analysis. Unlike existing models, this new model incorporates the influences of both roughness and eccentricity, achieving a prediction accuracy of over 99%. This research confirms the critical role of roughness in annular flow systems and provides practical implications for selecting more reliable pump power equipment in engineering fields. Full article

(This article belongs to the Section Engineering and Materials)

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