Symmetry

48 pages, 15203 KiB

Open AccessArticle

MRBMO: An Enhanced Red-Billed Blue Magpie Optimization Algorithm for Solving Numerical Optimization Challenges

by Baili Lu, Zhanxi Xie, Junhao Wei, Yanzhao Gu, Yuzheng Yan, Zikun Li, Shirou Pan, Ngai Cheong, Ying Chen and Ruishen Zhou

Symmetry 2025, 17(8), 1295; https://doi.org/10.3390/sym17081295 (registering DOI) - 11 Aug 2025

Abstract

To address the limitations of the Red-billed Blue Magpie Optimization algorithm (RBMO), such as its tendency to get trapped in local optima and its slow convergence rate, an enhanced version called MRBMO was proposed. MRBMO was improved by integrating Good Nodes Set Initialization, [...] Read more.

To address the limitations of the Red-billed Blue Magpie Optimization algorithm (RBMO), such as its tendency to get trapped in local optima and its slow convergence rate, an enhanced version called MRBMO was proposed. MRBMO was improved by integrating Good Nodes Set Initialization, an Enhanced Search-for-food Strategy, a newly designed Siege-style Attacking-prey Strategy, and Lens-Imaging Opposition-Based Learning (LIOBL). The experimental results showed that MRBMO demonstrated strong competitiveness on the CEC2005 benchmark. Among a series of advanced metaheuristic algorithms, MRBMO exhibited significant advantages in terms of convergence speed and solution accuracy. On benchmark functions with 30, 50, and 100 dimensions, the average Friedman values of MRBMO were 1.6029, 1.6601, and 1.8775, respectively, significantly outperforming other algorithms. The overall effectiveness of MRBMO on benchmark functions with 30, 50, and 100 dimensions was 95.65%, which confirmed the effectiveness of MRBMO in handling problems of different dimensions. This paper designed two types of simulation experiments to test the practicability of MRBMO. First, MRBMO was used along with other heuristic algorithms to solve four engineering design optimization problems, aiming to verify the applicability of MRBMO in engineering design optimization. Then, to overcome the shortcomings of metaheuristic algorithms in antenna S-parameter optimization problems—such as time-consuming verification processes, cumbersome operations, and complex modes—this paper adopted a test suite specifically designed for antenna S-parameter optimization, with the goal of efficiently validating the effectiveness of metaheuristic algorithms in this domain. The results demonstrated that MRBMO had significant advantages in both engineering design optimization and antenna S-parameter optimization. Full article

(This article belongs to the Section Engineering and Materials)

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19 pages, 8749 KiB

Open AccessArticle

Applying Computer Vision for the Detection and Analysis of the Condition and Operation of Street Lighting

by Sunggat Aiymbay, Ainur Zhumadillayeva, Eric T. Matson, Bakhyt Matkarimov and Bigul Mukhametzhanova

Symmetry 2025, 17(8), 1294; https://doi.org/10.3390/sym17081294 (registering DOI) - 11 Aug 2025

Abstract

Urban safety critically depends on effective street lighting systems; however, rapidly expanding cities, such as Astana, face considerable challenges in maintaining these systems due to the inefficiency, high labor intensity, and error-prone nature of conventional manual inspection methods. This necessitates an urgent shift [...] Read more.

Urban safety critically depends on effective street lighting systems; however, rapidly expanding cities, such as Astana, face considerable challenges in maintaining these systems due to the inefficiency, high labor intensity, and error-prone nature of conventional manual inspection methods. This necessitates an urgent shift toward automated, accurate, and scalable monitoring systems capable of quickly identifying malfunctioning streetlights. In response, this study introduces an advanced computer vision-based approach for automated detection and analysis of street lighting conditions. Leveraging high-resolution dashcam footage collected under diverse nighttime weather conditions, we constructed a robust dataset of 4260 carefully annotated frames highlighting streetlight poles and lamps. To significantly enhance detection accuracy, we propose the novel YOLO-CSE model, which integrates a Channel Squeeze-and-Excitation (CSE) module into the YOLO (You Only Look Once) detection architecture. The CSE module leverages the inherent symmetry of streetlight structures, such as the bilateral symmetry of poles and the radial symmetry of lamps, to dynamically recalibrate feature channels, emphasizing spatially repetitive and geometrically uniform patterns. By modifying the bottleneck layer through the addition of an extra convolutional layer and the SE block, the model learns richer, more discriminative feature representations, particularly for small or distant lamps under partial occlusion or low illumination. A comprehensive comparative analysis demonstrates that YOLO-CSE outperforms conventional YOLO variants and state-of-the-art models, achieving a mean average precision (mAP) of 0.798, recall of 0.794, precision of 0.824, and an F1 score of 0.808. The model’s symmetry-aware design enhances robustness to urban clutter (e.g., asymmetric noise from headlights or signage) while maintaining real-time efficiency. These results validate YOLO-CSE as a scalable solution for smart cities, where symmetry principles bridge geometric priors with computational efficiency in infrastructure monitoring. Full article

(This article belongs to the Special Issue Symmetry in Advancing Digital Signal and Image Processing)

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18 pages, 400 KiB

Open AccessArticle

Symmetry in the Algebra of Learning: Dual Numbers and the Jacobian in K-Nets

by Agustin Solis-Winkler, J. Raymundo Marcial-Romero and J. A. Hernández-Servín

Symmetry 2025, 17(8), 1293; https://doi.org/10.3390/sym17081293 (registering DOI) - 11 Aug 2025

Abstract

The black-box nature of deep machine learning hinders the extraction of knowledge in science. To address this issue, a proposal for a neural network (k-net) based on the Kolmogorov–Arnold Representation Theorem is presented, pursuing to be an alternative to the traditional Multilayer Perceptron. [...] Read more.

The black-box nature of deep machine learning hinders the extraction of knowledge in science. To address this issue, a proposal for a neural network (k-net) based on the Kolmogorov–Arnold Representation Theorem is presented, pursuing to be an alternative to the traditional Multilayer Perceptron. In its core, the algorithmic nature of neural networks lies in the fundamental symmetry between forward-mode and reverse-mode accumulation techniques, both of which rely on the chain rule of partial derivatives. These methods are essential for computing gradients of functions, an operation that is at the core of the training process of neural networks. Automatic differentiation addresses the need for accurate and efficient calculation of derivative values in scientific computing; procedural programs are thus transformed into the computation of the required derivatives at the same numerical arguments. This work formalizes the algebraic structure of neural network computations by framing the training process within the domain of hyperdual numbers. Specifically, it defines a Kolmogorov–Arnold-inspired neural network (k-net) using dual numbers by extending the univariate functions and their compositions that appear in the representation theorem. This approach focuses on computation of the Jacobian and the ability to implement such procedures algorithmically, without sacrificing accuracy and mathematical rigor, while exploiting the inherent symmetry of the dual number formalism. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry and Its Applications in Deep Learning and Artificial Intelligence Methods)

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21 pages, 1549 KiB

Open AccessArticle

Reinforcement Learning-Guided Particle Swarm Optimization for Multi-Objective Unmanned Aerial Vehicle Path Planning

by Wuke Li, Ying Xiong and Qi Xiong

Symmetry 2025, 17(8), 1292; https://doi.org/10.3390/sym17081292 - 11 Aug 2025

Abstract

Multi-objective Unmanned Aerial Vehicle (UAV) path planning in complex 3D environments presents a fundamental challenge requiring the simultaneous optimization of conflicting objectives such as path length, safety, altitude constraints, and smoothness. This study proposes a novel hybrid framework, termed QL-MOPSO, that integrates reinforcement [...] Read more.

Multi-objective Unmanned Aerial Vehicle (UAV) path planning in complex 3D environments presents a fundamental challenge requiring the simultaneous optimization of conflicting objectives such as path length, safety, altitude constraints, and smoothness. This study proposes a novel hybrid framework, termed QL-MOPSO, that integrates reinforcement learning with metaheuristic optimization through a three-stage hierarchical architecture. The framework employs Q-learning to generate a global guidance path in a discretized 2D grid environment using an eight-directional symmetric action space that embodies rotational symmetry at π/4 intervals, ensuring uniform exploration capabilities and unbiased path planning. A crucial intermediate stage transforms the discrete 2D path into a 3D initial trajectory, bridging the gap between discrete learning and continuous optimization domains. The MOPSO algorithm then performs fine-grained refinement in continuous 3D space, guided by a novel Q-learning path deviation objective that ensures continuous knowledge transfer throughout the optimization process. Experimental results demonstrate that the symmetric action space design yields 20.6% shorter paths compared to asymmetric alternatives, while the complete QL-MOPSO framework achieves 5% path length reduction and significantly faster convergence compared to standard MOPSO. The proposed method successfully generates Pareto-optimal solutions that balance multiple objectives while leveraging the symmetry-aware guidance mechanism to avoid local optima and accelerate convergence, offering a robust solution for complex multi-objective UAV path planning problems. Full article

(This article belongs to the Special Issue Symmetry in Chaos Theory and Applications)

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17 pages, 2642 KiB

Open AccessArticle

Exploiting Semantic-Visual Symmetry for Goal-Oriented Zero-Shot Recognition

by Haixia Zheng, Yu Zhou and Mingjie Jiang

Symmetry 2025, 17(8), 1291; https://doi.org/10.3390/sym17081291 - 11 Aug 2025

Abstract

Traditional machine learning methods only classify the instances whose classes are seen during training. In practice, many applications require to recognize the classes unknown in the training stage. In order to tackle this kind of challenging task, zero-shot learning is introduced, which incorporates [...] Read more.

Traditional machine learning methods only classify the instances whose classes are seen during training. In practice, many applications require to recognize the classes unknown in the training stage. In order to tackle this kind of challenging task, zero-shot learning is introduced, which incorporates additional semantic information to establish a semantic-visual symmetry, thereby facilitating the transfer of knowledge from known to unknown classes. Although user-defined attributes are commonly utilized to provide prior semantic information for zero-shot recognition, their importance for discrimination is not always consistent. Motivated by the observation that there exist both latent discriminative features and attributes in the images, this paper proposes a goal-oriented joint learning architecture to establish the symmetric relationships between images, attributes and categories for zero-shot learning. To be more specific, we model the latent feature and attribute spaces using the dictionary learning architecture. To learn the symmetric relationships between latent features and latent attributes, a linear transformation is applied while maintaining the semantic information. Moreover, seen-class classifiers are trained to enhance the discriminability of latent features. Extensive experiments on three representative benchmark datasets show that the proposed algorithm outperforms existing methods, highlighting the effectiveness of modeling explicit symmetry in the semantic-visual space for robust zero-shot knowledge transfer. Full article

(This article belongs to the Special Issue Asymmetry in Machine Learning)

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23 pages, 6919 KiB

Open AccessArticle

Addressing the Information Asymmetry of Fake News Detection Using Large Language Models and Emotion Embeddings

by Kirishnni Prabagar, Kogul Srikandabala, Nilaan Loganathan, Shalinka Jayatilleke, Gihan Gamage and Daswin De Silva

Symmetry 2025, 17(8), 1290; https://doi.org/10.3390/sym17081290 - 11 Aug 2025

Abstract

Fake news generation and propagation occurs in large volumes, at high speed, in diverse formats, while also being short-lived to evade detection and counteraction. Despite its role as an enabler, Artificial Intelligence (AI) has been effective at fake news detection and prediction through [...] Read more.

Fake news generation and propagation occurs in large volumes, at high speed, in diverse formats, while also being short-lived to evade detection and counteraction. Despite its role as an enabler, Artificial Intelligence (AI) has been effective at fake news detection and prediction through diverse techniques of both supervised and unsupervised machine learning. In this article, we propose a novel Artificial Intelligence (AI) approach that addresses the underexplored attribution of information asymmetry in fake news detection. This approach demonstrates how fine-tuned language models and emotion embeddings can be used to detect information asymmetry in intent, emotional framing, and linguistic complexity between content creators and content consumers. The intensity and temperature of emotion, selection of words, and the structure and relationship between words contribute to detecting this asymmetry. An empirical evaluation conducted on five benchmark datasets demonstrates the generalizability and real-time detection capabilities of the proposed AI approach. Full article

(This article belongs to the Special Issue Symmetry and Asymmetry in Large Language Models-Based Natural Language Processing)

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16 pages, 5796 KiB

Open AccessArticle

Microstructural Evolution and Mechanical Properties of an Additively Manufactured AlSi10Mg Alloy Post-Processed by Twist Equal Channel Angular Pressing

by Przemysław Snopiński, Augustine Appiah, Ondřej Hilšer and Jiři Hajnyš

Symmetry 2025, 17(8), 1289; https://doi.org/10.3390/sym17081289 - 11 Aug 2025

Abstract

This study investigates the microstructural evolution and mechanical response of an additively manufactured (PBF-LB/M) AlSi10Mg alloy subjected to severe plastic deformation via two passes of twist channel angular pressing (TCAP). Processing was conducted using Route Bc, with the first pass at 150 °C [...] Read more.

This study investigates the microstructural evolution and mechanical response of an additively manufactured (PBF-LB/M) AlSi10Mg alloy subjected to severe plastic deformation via two passes of twist channel angular pressing (TCAP). Processing was conducted using Route Bc, with the first pass at 150 °C and the second at 250 °C. For the first time, the evolution from the initial hierarchical AM structure to a refined state was characterized in high-fidelity detail using a novel EBSD detector. The two-pass process transformed the initial structure into a heterogeneous, bimodal microstructure existing in a non-equilibrium state, characterized by a high fraction of low-angle grain boundaries (63%) and significant internal lattice distortion. The mechanical properties were dictated by the processing temperature: a single pass at 150 °C induced work hardening, increasing the yield strength from 450 MPa to 482 MPa. Conversely, the second pass at an elevated temperature of 250 °C promoted significant dynamic recovery. This led to a decrease in yield strength to 422 MPa but concurrently resulted in a substantial increase in ultimate compressive strength to 731 MPa. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry and Novel Nanomaterials: Preparation, Characterizations, and Applications)

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4 pages, 171 KiB

Open AccessEditorial

Special Issue: Electron Diffraction and Structural Imaging—Volume I

by Partha Pratim Das, Arturo Ponce-Pedraza, Enrico Mugnaioli and Stavros Nicolopoulos

Symmetry 2025, 17(8), 1288; https://doi.org/10.3390/sym17081288 - 11 Aug 2025

Abstract

In recent years, electron diffraction (ED) and structural imaging have undergone a major resurgence in the scientific community, driven by continuous advancements in transmission electron microscopy (TEM) instrumentation, such as Cs correctors, direct detection cameras and automation, and the development or expansion of [...] Read more.

In recent years, electron diffraction (ED) and structural imaging have undergone a major resurgence in the scientific community, driven by continuous advancements in transmission electron microscopy (TEM) instrumentation, such as Cs correctors, direct detection cameras and automation, and the development or expansion of analytical methods, such as cryo-EM, beam precession, 4D Scanning Electron Diffraction, 3D electron diffraction, 4D-STEM, and ptychography [...] Full article

(This article belongs to the Special Issue Electron Diffraction and Structural Imaging)

4 pages, 172 KiB

Open AccessEditorial

Special Issue: Electron Diffraction and Structural Imaging—Volume II

by Partha Pratim Das, Arturo Ponce-Pedraza, Enrico Mugnaioli and Stavros Nicolopoulos

Symmetry 2025, 17(8), 1287; https://doi.org/10.3390/sym17081287 - 11 Aug 2025

Abstract

Following the success of the first edition of our Special Issue “Electron Diffraction and Structural Imaging”, we present Volume II, featuring new and innovative contributions that further expand the scope and depth of this rapidly evolving field [...] Full article

(This article belongs to the Special Issue Electron Diffraction and Structural Imaging II)

25 pages, 4087 KiB

Open AccessReview

Progress in High-Entropy Alloy-Based Microwave Absorbing Materials

by Chengkun Ma and Yuying Zhang

Symmetry 2025, 17(8), 1286; https://doi.org/10.3390/sym17081286 - 10 Aug 2025

Abstract

The rational design of high-performance microwave absorbers with broadband coverage, superior attenuation, and environmental durability is critical for addressing challenges in both defense and civilian technologies. High-entropy alloys (HEAs) exhibit atomic-scale asymmetric arrangements, demonstrating exceptional potential for microwave absorption through their unique lattice [...] Read more.

The rational design of high-performance microwave absorbers with broadband coverage, superior attenuation, and environmental durability is critical for addressing challenges in both defense and civilian technologies. High-entropy alloys (HEAs) exhibit atomic-scale asymmetric arrangements, demonstrating exceptional potential for microwave absorption through their unique lattice distortion, high entropy, sluggish diffusion, and “cocktail effect”. This critical review article provides an overview of the progress made in the development and understanding of HEA-based microwave absorbing materials. Initially, the microwave dissipation mechanisms for HEAs were analyzed, where atomic-scale distortions enhance polarization loss and broaden resonance bandwidth. Subsequently, key synthesis techniques like mechanical alloying and carbothermal shock are discussed, highlighting non-equilibrium processing for phase engineering. Building on these foundations, the discussion then progresses to evaluate four principal material design approaches: (1) compositionally-tuned powders, (2) multifunctional core–shell structures, (3) phase-controlled architectures, and (4) two-dimensional/porous configurations, each demonstrating distinct performance advantages. Finally, the discussion concludes by addressing current challenges in quantitative property modeling and industrial scalability while outlining future directions, including machine learning-assisted design and flexible integration, providing comprehensive guidance for developing next-generation high-performance microwave absorbing materials. Full article

(This article belongs to the Section Engineering and Materials)

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18 pages, 7574 KiB

Open AccessArticle

Compact Four-Port Axial Symmetry UWB MIMO Antenna Array with Bandwidth Enhancement Using Reactive Stub Loading

by José Alfredo Tirado-Méndez, Hildeberto Jardón-Aguilar, Roberto Linares-Miranda, Ruben Flores-Leal, Alberto Vasquez-Toledo, Ricardo Gomez-Villanueva and Angel Perez-Miguel

Symmetry 2025, 17(8), 1285; https://doi.org/10.3390/sym17081285 - 10 Aug 2025

Abstract

This work presents the use of a novel impedance coupling technique and electrical length increase by using stub loading placed from the radiator to the ground plane. This method is applied to the design of a small four-element ultrawideband (UWB) MIMO antenna arranged [...] Read more.

This work presents the use of a novel impedance coupling technique and electrical length increase by using stub loading placed from the radiator to the ground plane. This method is applied to the design of a small four-element ultrawideband (UWB) MIMO antenna arranged in axial symmetry to achieve a compact array size while obtaining a bandwidth starting from a very low cutoff frequency compared to a conventional radiator operating at the same frequency. The four-element MIMO antenna, with an operational bandwidth of 1.9 GHz to 30 GHz, is based on a wideband monopole with a semicircular geometry, fed by a coplanar structure and an L-shaped half-ground plane section. To increase the electrical length of the structure and achieve a compact antenna design, reactive stub loading is introduced, placing it on the backside of the substrate, located orthogonally between the radiator and the L-shaped ground plane, obtaining a small-sized configuration. The axial symmetry is employed to increase the antennas’ isolation by taking advantage of the orthogonal positioning and making the radiated fields have a low correlation. The antenna array footprint measures 48 mm × 48 mm, corresponding to 0.3λ₀ × 0.3λ₀ at the lower cutoff frequency. The array exhibits a low envelope correlation coefficient (ECC) of around 0.033 at 2 GHz, and less than 0.001 at the rest of the bandwidth; a diversity gain (DG) of approximately 10; a stable total active reflection coefficient (TARC) below −10 dB; interport isolation between 20 and 40 dB; and an average gain of 2.8 dBi. Full article

(This article belongs to the Special Issue Innovations in Passive and Active Microwave Circuits for Advanced Communication and Sensing Systems: Exploiting Symmetry for Enhanced Performance)

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13 pages, 1900 KiB

Open AccessArticle

Symmetric Taper Fiber Cleaving for Centered Waist-Inserted FPI: Temperature-Compensated High-Sensitivity Strain Sensor

by Xuntao Yu, Weijie Kong, Yunfeng Zhang, Hongqi Yuan, Jingwei Lv, Chao Liu, Miao Liu and Paul K. Chu

Symmetry 2025, 17(8), 1284; https://doi.org/10.3390/sym17081284 - 10 Aug 2025

Abstract

A highly sensitive Fabry–Pérot interferometer (FPI) is fabricated via symmetric taper fiber cleaving and centered waist-inserted assembly, a design where geometric symmetry is fundamental to the sensor’s performance. The FPI is fabricated by simple and cost-effective techniques, including fiber cleaving, splicing, and tapering. [...] Read more.

A highly sensitive Fabry–Pérot interferometer (FPI) is fabricated via symmetric taper fiber cleaving and centered waist-inserted assembly, a design where geometric symmetry is fundamental to the sensor’s performance. The FPI is fabricated by simple and cost-effective techniques, including fiber cleaving, splicing, and tapering. Due to the ultra-long cantilever beam with an effective length of 2.33 mm and the ultra-short Fabry–Pérot (FP) cavity with an actual length of 13.98 μm, the sensor exhibits an ultra-high strain sensitivity of 544.57 pm/με in experimental results. The sensor boasts a small temperature sensitivity of 1.02 pm/°C and a cross-temperature sensitivity of 0.0019 µε/°C in the temperature range of 25–200 °C. Furthermore, the sensor has good stability and repeatability. Owing to the symmetry-enhanced design, simple fabrication process, high strain sensitivity, as well as a stable, linearly proportional response over an extensive strain regime, the device has large potential in various sensing applications. Full article

(This article belongs to the Section Engineering and Materials)

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31 pages, 8113 KiB

Open AccessArticle

An Autoencoder-like Non-Negative Matrix Factorization with Structure Regularization Algorithm for Clustering

by Haiyan Gao and Ling Zhong

Symmetry 2025, 17(8), 1283; https://doi.org/10.3390/sym17081283 - 10 Aug 2025

Abstract

Clustering plays a crucial role in data mining and knowledge discovery, where non-negative matrix factorization (NMF) has attracted widespread attention due to its effective data representation and dimensionality reduction capabilities. However, standard NMF has inherent limitations when processing sampled data embedded in low-dimensional [...] Read more.

Clustering plays a crucial role in data mining and knowledge discovery, where non-negative matrix factorization (NMF) has attracted widespread attention due to its effective data representation and dimensionality reduction capabilities. However, standard NMF has inherent limitations when processing sampled data embedded in low-dimensional manifold structures within high-dimensional ambient spaces, failing to effectively capture the complex structural information hidden in feature manifolds and sampling manifolds, and neglecting the learning of global structures. To address these issues, a novel structure regularization autoencoder-like non-negative matrix factorization for clustering (SRANMF) is proposed. Firstly, based on the non-negative symmetric encoder-decoder framework, we construct an autoencoder-like NMF model to enhance the characterization ability of latent information in data. Then, by fully considering high-order neighborhood relationships in the data, an optimal graph regularization strategy is introduced to preserve multi-order topological information structures. Additionally, principal component analysis (PCA) is employed to measure global data structures by maximizing the variance of projected data. Comparative experiments on 11 benchmark datasets demonstrate that SRANMF exhibits excellent clustering performance. Specifically, on the large-scale complex datasets MNIST and COIL100, the clustering evaluation metrics improved by an average of 35.31% and 46.17% (ACC) and 47.12% and 18.10% (NMI), respectively. Full article

(This article belongs to the Section Computer)

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16 pages, 1117 KiB

Open AccessArticle

Uncertainty-Aware Prediction of Mixing Enthalpy in Binary Alloys with Symmetry-Augmented Embeddings

by Roman Dębski, Władysław Gąsior, Wojciech Gierlotka and Adam Dębski

Symmetry 2025, 17(8), 1282; https://doi.org/10.3390/sym17081282 - 9 Aug 2025

Viewed by 93

Abstract

The modeling of the enthalpy of mixing in binary alloys is essential to thermodynamic assessments and computational alloy design, particularly in data-scarce systems where experimental measurements are limited or incomplete. In this work, we propose a machine learning framework for the prediction of [...] Read more.

The modeling of the enthalpy of mixing in binary alloys is essential to thermodynamic assessments and computational alloy design, particularly in data-scarce systems where experimental measurements are limited or incomplete. In this work, we propose a machine learning framework for the prediction of mixing enthalpy in binary alloys under conditions of limited data availability. The method integrates symmetry-augmented embeddings, which enforce physical invariances such as element permutation and compositional mirroring, ensuring consistency across chemically equivalent representations and capturing chemically meaningful similarities between elements, thereby supporting knowledge transfer across alloy systems. To account for data uncertainty and improve trust in predictions, we incorporate Bayesian neural networks, enabling the estimation of predictive confidence, especially in composition ranges lacking experimental data. The model is trained jointly across multiple binary alloy systems, allowing it to share structural insights and improve prediction quality in data-limited concentration intervals. The method achieves a reduction in mean absolute error by more than a factor of eight compared with the classical Miedema model (0.53 kJ·mol⁻¹ vs. 4.27 kJ·mol⁻¹) while maintaining consistent accuracy even when trained on only 25% of the experimental measurements, confirming its robustness thanks to cross-alloy knowledge transfer and symmetry-based data augmentation. We evaluate the method on a benchmark dataset containing both fully and partially characterized binary alloy systems and demonstrate its effectiveness in interpolating and extrapolating enthalpy values while providing reliable uncertainty estimates. The results highlight the value of incorporating domain-specific symmetries and uncertainty-aware learning in data-driven material modeling and suggest that this approach can support predictive thermodynamic assessments even in under-sampled systems. Full article

(This article belongs to the Special Issue Symmetry Application in Metals and Alloys)

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18 pages, 2337 KiB

Open AccessArticle

Foldable/Deployable Spherical Mechanisms Based on Regular Polygons

by Raffaele Di Gregorio

Symmetry 2025, 17(8), 1281; https://doi.org/10.3390/sym17081281 - 9 Aug 2025

Viewed by 95

Abstract

The possibility of satisfying special geometric conditions, either through their architecture or through their configuration, makes a mechanism acquire changeable motion characteristics (kinematotropic or metamorphic behavior, multi-mode operation capability, etc.) that are of interest. Aligning revolute (R)-pair axes is one of such special [...] Read more.

The possibility of satisfying special geometric conditions, either through their architecture or through their configuration, makes a mechanism acquire changeable motion characteristics (kinematotropic or metamorphic behavior, multi-mode operation capability, etc.) that are of interest. Aligning revolute (R)-pair axes is one of such special conditions. In spherical linkages, only R-pairs, whose axes share a common intersection (spherical motion center (SMC)), are present. Investigating how R-pair axes can become collinear in a spherical mechanism leads to the identification of those that exhibit changeable motion features. This approach is adopted here to select non-redundant spherical mechanisms coming from regular polygons that are foldable/deployable and have a wide enough workspace for performing motion tasks. This analysis shows that the ones with hexagonal architecture prevail over the others. These results are exploitable in many contexts related to field robotics (aerospace, machines for construction sites, deployable antennas, etc.) Full article

(This article belongs to the Special Issue Advances in Mechanics of Rigid and Flexible Systems: Mathematical Models, Numerical Modelling, Experiments, Symmetry and Applications)

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19 pages, 4719 KiB

Open AccessArticle

Laser Stripe Segmentation Network Based on Evidential Uncertainty Theory Modeling Fine-Tuning Optimization Symmetric Algorithm

by Chenbo Shi, Delin Wang, Xiangyu Zhang, Chun Zhang, Jia Yan, Changsheng Zhu and Xiaobing Feng

Symmetry 2025, 17(8), 1280; https://doi.org/10.3390/sym17081280 - 9 Aug 2025

Viewed by 165

Abstract

In welding applications, line-structured-light vision is widely used for seam tracking, but intense noise from arc glow, spatter, smoke, and reflections makes reliable laser-stripe segmentation difficult. To address these challenges, we propose EUFNet, an uncertainty-driven symmetrical two-stage segmentation network for precise stripe extraction [...] Read more.

In welding applications, line-structured-light vision is widely used for seam tracking, but intense noise from arc glow, spatter, smoke, and reflections makes reliable laser-stripe segmentation difficult. To address these challenges, we propose EUFNet, an uncertainty-driven symmetrical two-stage segmentation network for precise stripe extraction under real-world welding conditions. In the first stage, a lightweight backbone generates a coarse stripe mask and a pixel-wise uncertainty map; in the second stage, a functionally mirrored refinement network uses this uncertainty map to symmetrically guide fine-tuning of the same image regions, thereby preserving stripe continuity. We further employ an uncertainty-weighted loss that treats ambiguous pixels and their corresponding evidence in a one-to-one, symmetric manner. Evaluated on a large-scale dataset of 3100 annotated welding images, EUFNet achieves a mean IoU of 89.3% and a mean accuracy of 95.9% at 236.7 FPS (compared to U-Net’s 82.5% mean IoU and 90.2% mean accuracy), significantly outperforming existing approaches in both accuracy and real-time performance. Moreover, EUFNet generalizes effectively to the public WLSD benchmark, surpassing state-of-the-art baselines in both accuracy and speed. These results confirm that a structurally and functionally symmetric, uncertainty-driven two-stage refinement strategy—combined with targeted loss design and efficient feature integration—yields high-precision, real-time performance for automated welding vision. Full article

(This article belongs to the Topic Intelligent Optimization Algorithm: Theory and Applications)

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41 pages, 800 KiB

Open AccessReview

Bridging Classic Operations Research and Artificial Intelligence for Network Optimization in the 6G Era: A Review

by Pablo Adasme, Ali Dehghan Firoozabadi and Enrique San Juan

Symmetry 2025, 17(8), 1279; https://doi.org/10.3390/sym17081279 - 9 Aug 2025

Viewed by 225

Abstract

This paper comprehensively reviews how operations research and optimization procedures are applied to address challenges in wireless network communications. Key challenges such as network topology design, dynamic task scheduling, and multi-objective resource allocation are examined and systematically categorized. The revision focuses on literature [...] Read more.

This paper comprehensively reviews how operations research and optimization procedures are applied to address challenges in wireless network communications. Key challenges such as network topology design, dynamic task scheduling, and multi-objective resource allocation are examined and systematically categorized. The revision focuses on literature published between 2023 and 2025, and covers topics such as flow optimization and routing, resource allocation and scheduling, mobile and wireless network management, network resilience and robustness, and energy efficiency. The works are selected using a methodological approach ranging from the exact optimization methods, such as mixed-integer programming, to heuristic/metaheuristic strategies and machine-learning-based techniques. It is reported a comparative analysis in terms of computational efficiency, scalability, and practical applicability. The main contribution is to highlight current research gaps and open challenges, with particular emphasis on the integration of operations research and artificial intelligence, especially in problems modeled using graphs and network structures. Full article

(This article belongs to the Special Issue Navigating the Frontiers of 6G Wireless Communication Networks and Beyond)

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17 pages, 1934 KiB

Open AccessArticle

Transition of Natural Convection in Liquid Metal Within an Annular Enclosure with Various Angular Partitions

by Takuya Masuda and Toshio Tagawa

Symmetry 2025, 17(8), 1278; https://doi.org/10.3390/sym17081278 - 9 Aug 2025

Viewed by 132

Abstract

This study investigates natural convection of liquid metal in an annular enclosure with a square cross-section using three-dimensional numerical simulations. Liquid metals, with low Prandtl numbers, exhibit oscillatory transitions at lower Rayleigh numbers than conventional fluids. While previous studies focused on full-circle domains [...] Read more.

This study investigates natural convection of liquid metal in an annular enclosure with a square cross-section using three-dimensional numerical simulations. Liquid metals, with low Prandtl numbers, exhibit oscillatory transitions at lower Rayleigh numbers than conventional fluids. While previous studies focused on full-circle domains where steady or irregular flows were observed, this work examines the effect of angular partitions on flow dynamics. The results reveal that periodic three-dimensional oscillatory flows arise in domains with specific angular sizes, such as quarter circles, whereas full-circle domains produce irregular or steady flows. Angular wave numbers vary spatially and temporally during transitional growth. The emergence of half-symmetric oscillatory modes highlights the role of symmetry constraints imposed by the geometry and boundary conditions. These transitions are closely tied to symmetry breaking and mode selection. A linear stability perspective helps clarify the critical factors that determine the transition type. These findings underscore that angular segmentation and periodic boundary conditions are essential for sustaining regular oscillatory convection. This study contributes to the understanding of symmetry-governed convection transitions in low-Prandtl-number fluids and has potential implications for industrial processes, such as semiconductor crystal growth, where flow uniformity and thermal stability are crucial. Full article

(This article belongs to the Section Engineering and Materials)

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22 pages, 936 KiB

Open AccessArticle

Insights into IF-Geodetic Convexity in Intuitionistic Fuzzy Graphs: Harnessing the IF-Geodetic Wiener Index for Global Human Trading Analysis and IF-Geodetic Cover for Gateway Node Identification

by A. M. Anto, R. Rajeshkumar, Ligi E. Preshiba and V. Mary Mettilda Rose

Symmetry 2025, 17(8), 1277; https://doi.org/10.3390/sym17081277 - 8 Aug 2025

Viewed by 89

Abstract

To offer a viewpoint on convexity and connectedness inside intuitionistic fuzzy graphs (IFGs), the paper is devoted to the study of intuitionistic fuzzy geodetic convexity. The paper introduces an algorithm for precise identification and characterization of geodetic pathways in IFGs, supported by a [...] Read more.

To offer a viewpoint on convexity and connectedness inside intuitionistic fuzzy graphs (IFGs), the paper is devoted to the study of intuitionistic fuzzy geodetic convexity. The paper introduces an algorithm for precise identification and characterization of geodetic pathways in IFGs, supported by a Python program. Various properties of IF-geodetic convex sets such as IF-internal and IF-boundary vertices are obtained. Furthermore, this work introduces and characterizes the concepts of geodetic IF-cover, geodetic IF-basis, and geodetic IF-number. Additionally, the study develops the IF-geodetic Wiener index. The scope of the work explores the application of IF-geodetic cover in wireless mesh networks, focusing on the identification of gateway nodes, where symmetry in connectivity patterns enhances network efficiency. A practical implementation of the IF-geodetic Wiener index method in global human trading analysis underscores the real-world implications of the developed concepts, where the efficiency and interpretability of fuzzy geodetic measures are improved by symmetry in network topologies and trade patterns. Full article

(This article belongs to the Special Issue Advances in Graph Theory Ⅱ)

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20 pages, 1554 KiB

Open AccessArticle

Structure of Odd-A Ag Isotopes Studied via Algebraic Approaches

by Stanimir Kisyov and Stefan Lalkovski

Symmetry 2025, 17(8), 1276; https://doi.org/10.3390/sym17081276 - 8 Aug 2025

Viewed by 67

Abstract

The structure of the odd-A silver isotopes

{}^{103 - 115}{Ag}

is discussed within the frame of the interacting boson–fermion model (IBFM). An overview of their key properties is presented, with a particular attention paid to the “J-1 anomaly”, represented [...] Read more.

The structure of the odd-A silver isotopes

{}^{103 - 115}{Ag}

is discussed within the frame of the interacting boson–fermion model (IBFM). An overview of their key properties is presented, with a particular attention paid to the “J-1 anomaly”, represented by an abnormal ordering of the lowest

{7/2}^{+}

and

{9/2}^{+}

states. By examining previously published data and newly performed calculations, it is demonstrated that the experimentally known level schemes and electromagnetic properties of

{}^{103 - 115}{Ag}

can be reproduced well within IBFM-1 by using a consistent set of model parameters. The contribution of different single-particle orbitals to the structure of the lowest-lying excited nuclear states in

{}^{103 - 115}{Ag}

is discussed. Given that the J-1 anomaly brings down the

{7/2}^{+}

level from the

j^{- 3}

multiplet to energies, which can be thermally populated in hot stellar environments, the importance of low-lying excited states in odd-A silver isotopes for astrophysical processes is outlined. Full article

(This article belongs to the Special Issue Feature Papers in 'Physics' Section 2025)

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26 pages, 1965 KiB

Open AccessArticle

Formal Verification of Solidity Smart Contracts via Automata Theory

by Meihua Xiao, Yangping Xu, Yongtuo Zhang, Ke Yang, Sufen Yan and Li Cen

Symmetry 2025, 17(8), 1275; https://doi.org/10.3390/sym17081275 - 8 Aug 2025

Viewed by 156

Abstract

Smart contracts, as a critical application of blockchain technology, significantly enhance its programmability and scalability, offering broad application prospects. However, frequent security incidents have resulted in substantial economic losses and diminished user trust, making security issues a key challenge for further development. Since [...] Read more.

Smart contracts, as a critical application of blockchain technology, significantly enhance its programmability and scalability, offering broad application prospects. However, frequent security incidents have resulted in substantial economic losses and diminished user trust, making security issues a key challenge for further development. Since smart contracts cannot be modified after deployment, flaws in their design or implementation may lead to severe consequences. Therefore, rigorous pre-deployment verification of their correctness is particularly crucial. This paper explores the symmetry in control flows and state transitions of Solidity smart contracts and leverages this inherent structural symmetry to develop a normalized state transition model based on a finite state machine. The FSM model is subsequently formalized into a Promela model with the Spin model checker. By integrating manually defined Linear Temporal Logic formulas with those generated by Smart Pulse, the Promela model is formally verified in Spin to ensure the correctness and security of smart contracts. This approach establishes a systematic verification framework, providing effective support to enhance the reliability and security of smart contracts. Full article

(This article belongs to the Section Computer)

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22 pages, 686 KiB

Open AccessArticle

Enhancing Commentary Strategies for Guandan: A Study of LLMs in Game Commentary Generation

by Jiayi Su, Meiling Tao, Xuechen Liang, Yangfan He, Yiling Tao and Miao Zhang

Symmetry 2025, 17(8), 1274; https://doi.org/10.3390/sym17081274 - 8 Aug 2025

Viewed by 109

Abstract

Recent advancements in large language models (LLMs) have unlocked the potential for generating high-quality game commentary. However, producing insightful and engaging commentary for complex games with incomplete information remains a significant challenge. In this paper, we introduce a novel commentary method that combines [...] Read more.

Recent advancements in large language models (LLMs) have unlocked the potential for generating high-quality game commentary. However, producing insightful and engaging commentary for complex games with incomplete information remains a significant challenge. In this paper, we introduce a novel commentary method that combines reinforcement learning (RL) and LLMs, tailored specifically for the Chinese card game Guandan. Our system leverages RL to generate intricate card-playing scenarios and employs LLMs to generate corresponding commentary text, effectively emulating the strategic analysis and narrative prowess of professional commentators. The framework comprises a state commentary guide, a Theory of Mind (ToM)-based strategy analyzer, and a style retrieval module, which seamlessly collaborate to deliver detailed and context-relevant game commentary in the Chinese language environment. We empower LLMs with ToM capabilities and refine both retrieval and information filtering mechanisms. This facilitates the generation of personalized commentary content. Our experimental results demonstrate a significant improvement in the system’s effectiveness in generating accurate, coherent, and engaging commentary when applied to open-source LLMs, surpassing GPT-4 across multiple evaluation metrics. Full article

(This article belongs to the Section Computer)

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21 pages, 426 KiB

Open AccessArticle

Symmetry-Oriented Dynamic Routing Planning Algorithm for Reliable Map Fusion in Distributed UAV Communication Networks

by Mingyun Xia and Ruiyun Xie

Symmetry 2025, 17(8), 1273; https://doi.org/10.3390/sym17081273 - 8 Aug 2025

Viewed by 84

Abstract

To enable distributed target searches by unmanned aerial vehicle (UAV) swarms, it is essential to collaboratively process multi-source sensing data and construct a globally consistent map. In response to the challenges posed by constrained communication and multi-hop transmission delays, this paper proposes a [...] Read more.

To enable distributed target searches by unmanned aerial vehicle (UAV) swarms, it is essential to collaboratively process multi-source sensing data and construct a globally consistent map. In response to the challenges posed by constrained communication and multi-hop transmission delays, this paper proposes a symmetry-oriented dynamic routing planning algorithm for reliable map fusion. The algorithm introduces a framework for the transmission and fusion of local perception maps, formulating routing tasks as an integer programming problem to determine latency-minimized transmission paths. When packet loss occurs, a dynamic re-routing strategy is triggered to ensure the continued reliability of the fusion process. The routing design preserves latency symmetry, aiming to keep transmission delays under packet loss conditions close to those under ideal, lossless scenarios. To improve scalability in large-scale UAV swarms, an approximate algorithm based on L-step forward prediction is further introduced to reduce computational complexity. The simulation results demonstrate that the proposed algorithm achieves low latency, strong robustness, and stable performance under varying communication conditions. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Wireless Communication and Sensor Networks II)

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23 pages, 2557 KiB

Open AccessArticle

Computer Simulation Everywhere: Mapping Fifteen Years Evolutionary Expansion of Discrete-Event Simulation and Integration with Digital Twin and Generative Artificial Intelligence

by Ikpe Justice Akpan and Godwin Esukuku Etti

Symmetry 2025, 17(8), 1272; https://doi.org/10.3390/sym17081272 - 8 Aug 2025

Viewed by 219

Abstract

Discrete-event simulation (DES) as an operations research (OR) technique has continued to evolve since its inception in the 1950s. DES evolution mirrors the advances in computer science (hardware and software, processing speed, and advanced information visualization capabilities). DES overcame the initial usability obstacles [...] Read more.

Discrete-event simulation (DES) as an operations research (OR) technique has continued to evolve since its inception in the 1950s. DES evolution mirrors the advances in computer science (hardware and software, processing speed, and advanced information visualization capabilities). DES overcame the initial usability obstacles and lack of efficacy challenges in the early 2000s to remain a popular OR tool of “last resort.” Using bibliographic data from SCOPUS, this study undertakes a science mapping of the DES literature and evaluates its evolution and expansion in the past fifteen years. The results show asymmetrical but positive yearly literature output; broadened DES adoption in diverse fields; and sustained relevance as a potent OR method for tackling old, new, and emerging operations and production issues. The thematic analysis identifies DES as an essential tool that integrates and enhances digital twin technology in Industry 4.0, playing a central role in enabling digital transformation processes that have swept the industrial space in manufacturing, logistics, healthcare, and other sectors. DES integration with generative/artificial intelligence (GenAI/AI) provides a great potential to revolutionize modeling and simulation activities, tasks, and processes. Future studies will explore more ways to integrate GenAI tools in DES. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Operations Research)

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29 pages, 4460 KiB

Open AccessArticle

Dimensional and Numerical Approach to Heat Transfer in Structural Elements with a Symmetrical Cross Section

by Betti Bolló, Ioan Száva, Ildikó-Renáta Száva, Teofil-Florin Gălățanu, Károly Jármai and Denisa-Elena Muntean

Symmetry 2025, 17(8), 1271; https://doi.org/10.3390/sym17081271 - 8 Aug 2025

Viewed by 188

Abstract

The structures of buildings employ elements with symmetrical cross sections (columns have two axes of symmetry, and connecting beams have at least one), leading to symmetrical states of stress and deformation under the action of mechanical and thermal loads. Thermal stresses, resulting from [...] Read more.

The structures of buildings employ elements with symmetrical cross sections (columns have two axes of symmetry, and connecting beams have at least one), leading to symmetrical states of stress and deformation under the action of mechanical and thermal loads. Thermal stresses, resulting from temperature variations and fires, must be taken into account during calculations. Thus, it is important to perform theoretical and experimental studies on the propagation of heat flux during fires. Experimental investigations on prototypes can be replaced by investigations into attached, reduced-scale models. With the help of the model law (ML), deduced by dimensional approaches, the results obtained by the model can be extrapolated to the prototype. In the present article, the Szirtes’ Modern Dimensional Analysis (MDA) method is proposed; this is a simple, reliable, and repeatable dimensional approach. By applying the MDA to both the structural elements and model of an entire industrial hall, in terms of thermal field propagation, the authors demonstrate the undeniable effectiveness of the method in these construction calculations. MDA enables the efficient and easy analysis of thermal states of the homologous points of the prototype, even for spatial structures. Full article

(This article belongs to the Section Physics)

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35 pages, 7005 KiB

Open AccessArticle

Research on Load Forecasting Prediction Model Based on Modified Sand Cat Swarm Optimization and SelfAttention TCN

by Haotong Han, Jishen Peng, Jun Ma, Hao Liu and Shanglin Liu

Symmetry 2025, 17(8), 1270; https://doi.org/10.3390/sym17081270 - 8 Aug 2025

Viewed by 212

Abstract

The core structure of modern power systems reflects a fundamental symmetry between electricity supply and demand, and accurate load forecasting is essential for maintaining this dynamic balance. To improve the accuracy of short-term load forecasting in power systems, this paper proposes a novel [...] Read more.

The core structure of modern power systems reflects a fundamental symmetry between electricity supply and demand, and accurate load forecasting is essential for maintaining this dynamic balance. To improve the accuracy of short-term load forecasting in power systems, this paper proposes a novel model that combines a Multi-Strategy Improved Sand Cat Swarm Optimization algorithm (MSCSO) with a Self-Attention Temporal Convolutional Network (SA TCN). The model constructs efficient input features through data denoising, correlation filtering, and dimensionality reduction using UMAP. MSCSO integrates Uniform Tent Chaos Mapping, a sensitivity enhancement mechanism, and Lévy flight to optimize key parameters of the SA TCN, ensuring symmetrical exploration and stable convergence in the solution space. The self-attention mechanism exhibits structural symmetry when processing each position in the input sequence and does not rely on fixed positional order, enabling the model to more effectively capture long-term dependencies and preserve the symmetry of the sequence structure—demonstrating its advantage in symmetry-based modeling. Experimental results on historical load data from Panama show that the proposed model achieves excellent forecasting accuracy (RMSE = 24.7072, MAE = 17.5225, R² = 0.9830), highlighting its innovation and applicability in symmetrical system environments. Full article

(This article belongs to the Section Engineering and Materials)

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23 pages, 4350 KiB

Open AccessArticle

Gardens Fire Detection Based on the Symmetrical SSS-YOLOv8 Network

by Bo Liu, Junhua Wang, Qing An, Yanglu Wan, Jianing Zhou and Xijiang Chen

Symmetry 2025, 17(8), 1269; https://doi.org/10.3390/sym17081269 - 8 Aug 2025

Viewed by 192

Abstract

Fire detection primarily relies on sensors such as smoke detectors, heat detectors, and flame detectors. However, due to cost constraints, it is impractical to deploy such a large number of sensors for fire detection in outdoor gardens and landscapes. To address this challenge [...] Read more.

Fire detection primarily relies on sensors such as smoke detectors, heat detectors, and flame detectors. However, due to cost constraints, it is impractical to deploy such a large number of sensors for fire detection in outdoor gardens and landscapes. To address this challenge and aiming to enhance fire detection accuracy in gardens while achieving lightweight design, this paper proposes an improved symmetry SSS-YOLOv8 model for lightweight fire detection in garden video surveillance. Firstly, the SPDConv layer from ShuffleNetV2 is used to preserve flame or smoke information, combined with the Conv_Maxpool layer to reduce computational complexity. Subsequently, the SE module is introduced into the backbone feature extraction network to enhance features specific to fire and smoke. ShuffleNetV2 and the SE module are configured into a symmetric local network structure to enhance the extraction of flame or smoke features. Finally, WIoU is introduced as the bounding box regression loss function to further ensure the detection performance of the symmetry SSS-YOLOv8 model. Experimental results demonstrate that the improved symmetry SSS-YOLOv8 model achieves precision and recall rates for garden flame and smoke detection both exceeding 0.70. Compared to the YOLOv8n model, it exhibits a 2.1 percentage point increase in mAP, while its parameter is only 1.99 M, reduced to 65.7% of the original model. The proposed model demonstrates superior detection accuracy for garden fires compared to other YOLO series models of the same type, as well as different types of SSD and Faster R-CNN models. Full article

(This article belongs to the Section Computer)

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25 pages, 6900 KiB

Open AccessArticle

Detection of Trends and Anomalies with MACD and RSI Market Indicators for Temperature and Precipitation

by Yunus Ziya Kaya

Symmetry 2025, 17(8), 1268; https://doi.org/10.3390/sym17081268 - 8 Aug 2025

Viewed by 224

Abstract

The changes in climatological variables are a critical concern for climatologists, hydrologists, and water resources managers. In the face of global climate change, a more profound understanding of the recent changes in climatological conditions of a specific region is becoming increasingly urgent. To [...] Read more.

The changes in climatological variables are a critical concern for climatologists, hydrologists, and water resources managers. In the face of global climate change, a more profound understanding of the recent changes in climatological conditions of a specific region is becoming increasingly urgent. To this end, hydro-climatological time series are being investigated in various ways, from traditional approaches to state-of-the-art techniques. This manuscript investigates the trend changes of surface temperature and total precipitation hydro-climatological parameters over a long period, using two of the most popular market price trend detection indicators, MACD and RSI. The RSI indicator evaluation methodology has been modified for the hydro-climatological time series. Minimum, maximum, mean surface temperatures, and precipitation parameters were analyzed. The length of the data sets is 122 years, starting in 1901 and ending in 2022. The application of these indicators to the mentioned parameters underscores their potential as powerful tools in the detection of climatological trends and trend variability over time, highlighting the need for proactive climate management strategies. The results revealed that the MACD and RSI indicators are effective tools not only for trend detection but also for determining climatological anomalies. These tools can be used to complement traditional statistical trend analysis. Moreover, their visual capabilities allow the methods to offer a more comprehensive understanding of climate management strategies. Full article

(This article belongs to the Topic Modeling, Stability, and Control of Dynamic Systems and Their Applications)

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31 pages, 2889 KiB

Open AccessArticle

Multi-Team Agile Software Project Scheduling Using Dual-Indicator Group Learning Particle Swarm Optimization

by Jiangyi Shi, Hui Lou, Xiaoning Shen and Jiyong Xu

Symmetry 2025, 17(8), 1267; https://doi.org/10.3390/sym17081267 - 8 Aug 2025

Viewed by 219

Abstract

Core problems in agile software project scheduling, such as resource-constrained balancing and iteration cycle optimization, embody the pursuit of symmetry. Simultaneously, optimization algorithms find extensive applications in symmetry problems, for example, in graphs and pattern recognition. Considering the cooperation among multiple teams and [...] Read more.

Core problems in agile software project scheduling, such as resource-constrained balancing and iteration cycle optimization, embody the pursuit of symmetry. Simultaneously, optimization algorithms find extensive applications in symmetry problems, for example, in graphs and pattern recognition. Considering the cooperation among multiple teams and environmental changes in complex agile software development, a dynamic periodic scheduling model for multi-team agile software project is constructed, which includes three tightly coupled sub-problems, namely user story selection, user story-development team allocation, and task-employee allocation. To solve the model, a group learning particle swarm optimization algorithm is proposed, which includes three novel strategies. First, the population is divided into four groups based on dual indicators of objective values and potential values. Second, different learning objects are selected according to the characteristic of each group so that the search diversity can be improved. Third, to react to the environmental changes and enhance the mining ability, heuristic population initialization and local search strategies are designed by utilizing the problem-specific information. Systematic experimental results on 13 instances indicate that compared with the state-of-the-art algorithms, the proposed algorithm is able to provide a schedule with better precision for the project manager in each sprint of the agile development. Full article

(This article belongs to the Section Computer)

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20 pages, 780 KiB

Open AccessArticle

A Semantic Behavioral Sequence-Based Approach to Trajectory Privacy Protection

by Ji Xi, Weiqi Zhang, Zhengwang Xia, Li Zhao and Huawei Tao

Symmetry 2025, 17(8), 1266; https://doi.org/10.3390/sym17081266 - 7 Aug 2025

Viewed by 190

Abstract

Trajectory data contain numerous sensitive attributes. Unauthorized disclosure of precise user trajectory information generates persistent privacy and security concerns that significantly impact daily life. Most existing trajectory privacy protection schemes focus on geographic trajectories while neglecting the critical importance of semantic trajectories, resulting [...] Read more.

Trajectory data contain numerous sensitive attributes. Unauthorized disclosure of precise user trajectory information generates persistent privacy and security concerns that significantly impact daily life. Most existing trajectory privacy protection schemes focus on geographic trajectories while neglecting the critical importance of semantic trajectories, resulting in ongoing privacy vulnerabilities. To address this limitation, we propose the Semantic Behavior Sequence-based Trajectory Privacy Protection method (SBS-TPP). Our approach integrates short-term and long-term behavioral patterns within a user behavior modeling layer to identify user preferences. A dual-model framework (geographic and semantic) generates noise-injected trajectories with maximized noise potential. This methodology applies symmetric noise addition to both geographic trajectory fragments and semantic trajectory segments, optimizing trajectory data utility while ensuring robust protection of sensitive information. The SBS-TPP framework operates in the following two phases: firstly, behavior modeling, which comprises interest extraction from behavioral trajectory sequences, and secondly, noise generation, which creates synthetic noise locations with maximal semantic expectation from original locations, yielding privacy-enhanced trajectories for publication. Experimental results demonstrate that our interest extraction model achieves 93.7% accuracy while maintaining 81.6% data utility under strict privacy guarantees. The proposed method significantly enhances data usability and enables effective recommendation services without compromising user privacy or security. Full article

(This article belongs to the Section Computer)

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