Symmetry

26 pages, 1082 KiB

Open AccessArticle

Duality-Driven Aspect Sentiment Triplet Extraction with LLM and Iterative Reinforcement

by Xun Li, Kun Zhang and Danjie Han

Symmetry 2025, 17(5), 642; https://doi.org/10.3390/sym17050642 - 24 Apr 2025

Aspect-based sentiment triplet extraction tasks remain a long-standing challenge, which aim to achieve aspect, opinion, and sentiment polarity from sentences. Most existing methods achieve excellent performance by exploring the interactions between aspect and opinion terms. However, few studies focus on the positive impact [...] Read more.

Aspect-based sentiment triplet extraction tasks remain a long-standing challenge, which aim to achieve aspect, opinion, and sentiment polarity from sentences. Most existing methods achieve excellent performance by exploring the interactions between aspect and opinion terms. However, few studies focus on the positive impact of sentiment on triplet extraction. As sentiment acts as a key cue in triplet extraction, its role is often overlooked, thereby limiting extraction performance. This paper proposes a novel framework, duality-driven aspect sentiment triplet extraction with a large language model and iterative reinforcement, which integrates duality-driven with a large language model for the aspect sentiment triplet task. This study employs a duality-driven strategy based on symmetry to extract aspect-based sentiment triplets, fully taking into account sentiment polarity during the interaction between aspects and opinions. Moreover, this study devises a two-view prompt template for prior knowledge fusion based on large language models and employs confidence cycle iteration strategies to alleviate cascading errors. Extensive experiments show that the framework outperforms the previous state-of-the-art model. These findings demonstrate that the proposed model makes a positive impact on the aspect sentiment triplet extraction task overall. Full article

(This article belongs to the Section Computer)

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16 pages, 551 KiB

Open AccessArticle

Dual-Channel Spoofed Speech Detection Based on Graph Attention Networks

by Yun Tan, Xiaoqian Weng and Jiangzhang Zhu

Symmetry 2025, 17(5), 641; https://doi.org/10.3390/sym17050641 - 24 Apr 2025

Abstract

In the field of voice cryptography, detecting forged speech is crucial for secure communication and identity authentication. While most existing spoof detection methods rely on monaural audio, the characteristics of dual-channel signals remain underexplored. To address this, we propose a symmetrical dual-branch detection [...] Read more.

In the field of voice cryptography, detecting forged speech is crucial for secure communication and identity authentication. While most existing spoof detection methods rely on monaural audio, the characteristics of dual-channel signals remain underexplored. To address this, we propose a symmetrical dual-branch detection framework that integrates Res2Net with coordinate attention (Res2NetCA) and a dual-channel heterogeneous graph fusion module (DHGFM). The proposed architecture encodes left and right vocal tract signals into spectrogram and time-domain graphs, and it models both intra- and inter-channel time–frequency dependencies through graph attention mechanisms and fusion strategies. Experimental results on the ASVspoof2019 and ASVspoof2021 LA datasets demonstrate the superior detection performance of our method. Specifically, it achieved an EER of 1.64% and a Min-tDCF of 0.051 on ASVspoof2019, and an EER of 6.76% with a Min-tDCF of 0.3638 on ASVspoof2021, validating the effectiveness and potential of dual-channel modeling in spoofed speech detection. Full article

(This article belongs to the Special Issue Applications Based on Symmetry in Applied Cryptography)

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24 pages, 2662 KiB

Open AccessArticle

A Robust Feature-Matching Method for 3D Point Clouds via Spatial Encoding

by Han Wang, Fengxiang Wang, Ruikai Xue, Xiaokai She, Wei Kong and Genghua Huang

Symmetry 2025, 17(5), 640; https://doi.org/10.3390/sym17050640 - 24 Apr 2025

Abstract

This study addresses the challenging issues in 3D point cloud feature matching within the field of computer vision, where high data quality requirements and vulnerability to disturbances significantly impact performance. Existing methods are prone to outliers when generating feature correspondences due to noise, [...] Read more.

This study addresses the challenging issues in 3D point cloud feature matching within the field of computer vision, where high data quality requirements and vulnerability to disturbances significantly impact performance. Existing methods are prone to outliers when generating feature correspondences due to noise, sampling deviations, symmetric structure, and other factors. To improve the robustness of point cloud feature matching, this paper proposes a novel 3D spatial encoding (3DSE) method that incorporates compact geometric constraints. Our method encodes the spatial layout of matching feature points by quantifying the order of appearance of matching points, and combines rigidity constraints to iteratively eliminate the least consistent matching pairs with the remaining point pairs, thereby sorting the initial matching set. The 3DSE algorithm was evaluated on multiple datasets, including simulated data, self-collected data, and public datasets, which cover data from both LiDAR and Kinect sensors. The comparison results with existing techniques demonstrate that 3DSE exhibits superior performance and robustness in handling noise, sparse point clouds, and changes in data modalities. The application of the proposed method significantly enhances the point cloud registration process, showing promising potential for 3D reconstruction, model-driven 3D object recognition, and pose estimation of non-cooperative targets. Full article

(This article belongs to the Special Issue Studies of Optoelectronics in Symmetry)

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19 pages, 6567 KiB

Open AccessArticle

Performance Evaluation of ARIMA, Autoformer, and Symmetric LSTNFCL Models for Traffic Accident Emergency Prediction

by Honglei Wei, Qingbiao Song, Chonghong Dan, Zhou He, Haoran Li and Maowu Pu

Symmetry 2025, 17(5), 639; https://doi.org/10.3390/sym17050639 - 23 Apr 2025

Abstract

The prediction of pre-hospital medical emergencies based on historical timing data holds significant potential for enhancing individual safety. In this study, we constructed ARIMA (Autoregressive Integrated Moving Average Model), Autoformer, and the structurally symmetric deep learning model LSTNFCL (Long-Short-Term Network with Conv2Former, CBAM [...] Read more.

The prediction of pre-hospital medical emergencies based on historical timing data holds significant potential for enhancing individual safety. In this study, we constructed ARIMA (Autoregressive Integrated Moving Average Model), Autoformer, and the structurally symmetric deep learning model LSTNFCL (Long-Short-Term Network with Conv2Former, CBAM (Convolutional Block Attention Module), and LSTM) using the time-period symmetric pre-hospital traffic accident medical emergency calls in Chengdu from 1 January 2022 to 31 December 2023. We systematically evaluated the prediction efficiency of the three models on pre-hospital traffic accident medical emergency call demand. The experiments show the following: The MAE (mean absolute error) of Autoformer is 0.849 and the RMSE (root mean square error) is 0.922, but its inference takes longer; LSTNFCL achieves competitive performance with an MAE of 1.681 and an RMSE of 3.301 under a lightweight architecture by integrating LSTM (Long Short-Term Memory), Conv2Former, and CBAM modules. It demonstrates notably superior computational efficiency and reasoning time compared to ARIMA. Furthermore, it surpasses Autoformer in terms of efficiency and reasoning time. Ablation experiments demonstrate that the Conv2Former module reduces the MSE (mean squared error) by 21%, while the CBAM module further optimizes the MAPE (mean absolute percentage error) to 0.891%. This study provides a prediction scheme that balances accuracy and real-time performance for pre-hospital emergency systems, and the results show that Autoformer is suitable for offline high-precision scenarios, while LSTNFCL is more suitable for real-time prediction needs with resource constraints. Focusing on the public safety, this paper offers an effective early prediction method for the pre-hospital emergency medical system. Full article

(This article belongs to the Section Mathematics)

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44 pages, 8130 KiB

Open AccessArticle

Classification-Based Q-Value Estimation for Continuous Actor-Critic Reinforcement Learning

by Chayoung Kim

Symmetry 2025, 17(5), 638; https://doi.org/10.3390/sym17050638 - 23 Apr 2025

Abstract

Stable Q-value estimation is critical for effective policy learning in deep reinforcement learning (DRL), especially continuous control tasks. Traditional algorithms like Soft Actor-Critic (SAC) and Twin Delayed Deep Deterministic (TD3) policy gradients rely on Mean Squared Error (MSE) loss for Q-value approximation, which [...] Read more.

Stable Q-value estimation is critical for effective policy learning in deep reinforcement learning (DRL), especially continuous control tasks. Traditional algorithms like Soft Actor-Critic (SAC) and Twin Delayed Deep Deterministic (TD3) policy gradients rely on Mean Squared Error (MSE) loss for Q-value approximation, which may cause instability due to misestimation and overestimation biases. Although distributional reinforcement learning (RL) algorithms like C51 have improved robustness in discrete action spaces, their application to continuous control remains computationally expensive owing to distribution projection needs. To address this, we propose a classification-based Q-value learning method that reformulates Q-value estimation as a classification problem rather than a regression task. Replacing MSE loss with cross-entropy (CE) and Kullback–Leibler (KL) divergence losses, the proposed method improves learning stability and mitigates overestimation errors. Our statistical analysis across 30 independent runs shows that the approach achieves an approximately 10% lower Q-value estimation error in the pendulum environment and a 40–60% reduced training time compared to SAC and Continuous Twin Delayed Distributed Deep Deterministic (CTD4) Policy Gradient. Experimental results on OpenAI Gym benchmark environments demonstrate that our approach, with up to 77% fewer parameters, outperforms the SAC and CTD4 policy gradients regarding training stability and convergence speed, while maintaining a competitive final policy performance. Full article

(This article belongs to the Special Issue Symmetry in Intelligent Algorithms)

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25 pages, 5253 KiB

Open AccessArticle

Thermal Performance Analysis of Integrated Energy Management System for Mold Cooling/Heat Pump/Material Preheating of Injection-Molding Machine

by Yuxuan Tang, Hemin Hu, Yumei Ding, Tao Wang, Pengcheng Xie and Weimin Yang

Symmetry 2025, 17(5), 637; https://doi.org/10.3390/sym17050637 - 23 Apr 2025

Abstract

The material in the mold of the injection-molding machine releases significant latent heat of solidification during the cooling process. The efficient recovery and utilization of this waste heat is crucial for improving energy efficiency. A novel integrated energy management system for mold cooling/heat [...] Read more.

The material in the mold of the injection-molding machine releases significant latent heat of solidification during the cooling process. The efficient recovery and utilization of this waste heat is crucial for improving energy efficiency. A novel integrated energy management system for mold cooling/heat pump/material preheating is proposed in this paper. Taking the symmetrical thermodynamic performance of the heat pump components as the basis and optimizing the system configurations, four system configurations were investigated: MC/BHP/MPCC, MC/RHP/MPCC, MC/HP/MP-IEMS, and MC/DCHP/MP-IEMS, utilizing EBSILON software. The performance of the systems was evaluated through the coefficient of performance (COP) and whole cycle energy efficiency (η). The T-q, T-s, and P-h diagrams were analyzed. It was found that, under comparative operating conditions, both the MC/HP/MP-IEMS and MC/DCHP/MP-IEMS systems exhibited significantly higher COP and η than the MC/BHP/MPCC and MC/RHP/MPCC systems. MC/HP/MP-IEMS achieves a COP of 13.66 and η of 22.09. Similarly, MC/DCHP/MP-IEMS achieves a COP of 14.00 and η of 22.53. The paper optimizes the other three systems using MC/BHP/MPCC as the comparison condition. Optimal cycle performances are achieved with COP and η values of 9, 16, 16, and 9, 26, 25, respectively. A comparison of the thermodynamic performance of five different refrigerants revealed that R123 and R245fa have superior overall performance. This study provides theoretical support for the engineering implementation of integrated energy management systems for injection-molding machines. Full article

(This article belongs to the Section Engineering and Materials)

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18 pages, 5462 KiB

Open AccessArticle

A Composite Linear Active Disturbance Rejection Control-Sliding Mode Control Strategy with Nominal Model Compensation for Precision Motion Tracking in Semiconductor Die Attach Machines

by Huairong Chen, Yonghong Zhang, Wen Li, Xiang Zhang and Weiming Liang

Symmetry 2025, 17(5), 636; https://doi.org/10.3390/sym17050636 - 23 Apr 2025

Abstract

In this paper, the concept of symmetry is utilized to design the composite controller for the die attach machine’s motion platform—that is, the construction and the solution of the nominal model-based composite controller design approach are symmetrical. With escalating demands for ultra-high-speed operations [...] Read more.

In this paper, the concept of symmetry is utilized to design the composite controller for the die attach machine’s motion platform—that is, the construction and the solution of the nominal model-based composite controller design approach are symmetrical. With escalating demands for ultra-high-speed operations and microscale positioning accuracy (<5 μm) in semiconductor manufacturing, motion platforms face critical challenges, including high-speed instability, positioning jitter, and insufficient disturbance rejection. To address these limitations, a composite control strategy integrating nominal model-based linear active disturbance rejection control (NMLADRC) with sliding mode control (SMC) is developed. The synergistic interaction ensures the concurrent realization of robust tracking accuracy and rapid transient convergence. Simulation results demonstrate significant improvements over conventional PI control, LADRC, and NMLADRC. The phase lag is reduced by 50.04%, 36.34%, and 23.07%, respectively, while positioning time within ±5 μm accuracy threshold is shortened by 44.00%, 56.31%, and 31.51% when tracking the executed motion profile. The composite controller substantially enhances motion control precision, strengthens disturbance rejection capability, and improves system stability during high-speed operations. These advancements highlight the method’s strong practical applicability in precision motion control systems requiring both rapid response and microscale positioning accuracy. Full article

(This article belongs to the Section Engineering and Materials)

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21 pages, 4335 KiB

Open AccessArticle

Advancing Decision-Making in AI Through Bayesian Inference and Probabilistic Graphical Models

by Mohammed Atef Abdallah

Symmetry 2025, 17(5), 635; https://doi.org/10.3390/sym17050635 - 23 Apr 2025

Abstract

The navigation of autonomous vehicles should be accurate and reliable to navigate safely in changing and unpredictable conditions. This paper proposes an advanced autonomous vehicle navigation framework that integrates probabilistic graphical models, Markov Chain Monte Carlo methods, and Bayesian optimization to enable reliable, [...] Read more.

The navigation of autonomous vehicles should be accurate and reliable to navigate safely in changing and unpredictable conditions. This paper proposes an advanced autonomous vehicle navigation framework that integrates probabilistic graphical models, Markov Chain Monte Carlo methods, and Bayesian optimization to enable reliable, real-time decision-making in uncertain environments. Due to dynamic and unpredictable surroundings, autonomous navigation is highly challenged in uncertainty quantification and adaptive parameter tuning. By leveraging PGMs, the framework can first determine probabilistic dependencies between critical variables, i.e., nodes and edges, such as vehicle speed, obstacle proximity, and environmental factors, to create a robust foundation for situational awareness. Then, Bayesian inference is obtained using MCMC: the system updates its real-time beliefs as new sensor data become available. The inference layer allows adaptation to unexpected obstacles by revising trajectories or controlling a vehicle’s speed while improving safety and reliability. Finally, Bayesian optimization fine-tunes key parameters within the system, such as sensor thresholds and control variables, maximizing efficiency without exhaustive manual tuning of these parameters. Using a multi-sensor data source with images, LiDAR, radar, and annotated environmental features, the Lyft Level 5 Perception Dataset tested real-world navigation scenarios against the framework. This proposed framework’s accuracy was around 99.01% and signified good decision-making capabilities for an autonomous vehicle navigating through complex environments with reliable performance. The autonomous vehicle system is also intended to provide improved safety and flexibility in complex environments, promising the development of more resilient and dependable AI-driven solutions for navigation. Full article

(This article belongs to the Section Mathematics)

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12 pages, 235 KiB

Open AccessArticle

Casimir Effect and the Cosmological Constant

by Jaume Giné

Symmetry 2025, 17(5), 634; https://doi.org/10.3390/sym17050634 - 23 Apr 2025

Abstract

Any quantum theory of gravity at the quantum gravity scale has the expectation of the existence of a minimal observable length. It is also expected that this fundamental length has a principal role in nature at the quantum gravity scale. From the uncertainty [...] Read more.

Any quantum theory of gravity at the quantum gravity scale has the expectation of the existence of a minimal observable length. It is also expected that this fundamental length has a principal role in nature at the quantum gravity scale. From the uncertainty principle that influences the quantum measurement process, the existence of a minimal measurable length can be heuristically deduced. The existence of this minimal measurable length leads to an apparent discretization of spacetime, as distinguishing below this minimal length becomes impossible. In topologically non-trivial cosmological models, the Casimir effect is significant since it alters the spectrum of vacuum fluctuations and leads to a non-zero Casimir energy density. This suggests that the topology of the Universe could influence its vacuum energy, potentially affecting its expansion dynamics. In this sense, the Casimir effect could contribute to the observed acceleration of the Universe’s expansion. Here, we use the Casimir effect to determine the value of the electromagnetic zero-point energy in the Universe, applying it to the regions outside and inside the Universe horizon or Hubble horizon and assuming the existence of this minimal length. The Casimir effect is directly related to the boundary conditions imposed by the geometry and symmetries of the Hubble horizon. The agreement of the obtained value with the observed cosmological constant is not exact and therefore the contribution of non-electromagnetic radiation (gravitational effects) must be take into account. Full article

(This article belongs to the Section Physics)

35 pages, 18520 KiB

Open AccessArticle

Optimizing Legal Text Summarization Through Dynamic Retrieval-Augmented Generation and Domain-Specific Adaptation

by S Ajay Mukund and K. S. Easwarakumar

Symmetry 2025, 17(5), 633; https://doi.org/10.3390/sym17050633 - 23 Apr 2025

Abstract

Legal text summarization presents distinct challenges due to the intricate and domain-specific nature of legal language. This paper introduces a novel framework integrating dynamic Retrieval-Augmented Generation (RAG) with domain-specific adaptation to enhance the accuracy and contextual relevance of legal document summaries. The proposed [...] Read more.

Legal text summarization presents distinct challenges due to the intricate and domain-specific nature of legal language. This paper introduces a novel framework integrating dynamic Retrieval-Augmented Generation (RAG) with domain-specific adaptation to enhance the accuracy and contextual relevance of legal document summaries. The proposed Dynamic Legal RAG system achieves a vital form of symmetry between information retrieval and content generation, ensuring that retrieved legal knowledge is both comprehensive and precise. Using the BM25 retriever with top-3 chunk selection, the system optimizes relevance and efficiency, minimizing redundancy while maximizing legally pertinent content. with top-3 chunk selection, the system optimizes relevance and efficiency, minimizing redundancy while maximizing legally pertinent content. A key design feature is the compression ratio constraint (0.05 to 0.5), maintaining structural symmetry between the original judgment and its summary by balancing representation and information density. Extensive evaluations establish BM25 as the most effective retriever, striking an optimal balance between precision and recall. A comparative analysis of transformer-based (Decoder-only) models—DeepSeek-7B, LLaMA 2-7B, and LLaMA 3.1-8B—demonstrates that LLaMA 3.1-8B, enriched with Legal Named Entity Recognition (NER) and the Dynamic RAG system, achieves superior performance with a BERTScore of 0.89. This study lays a strong foundation for future research in hybrid retrieval models, adaptive chunking strategies, and legal-specific evaluation metrics, with practical implications for case law analysis and automated legal drafting. Full article

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23 pages, 4365 KiB

Open AccessArticle

Multi-Stage Stochastic Planning for Transmission Systems Considering Dynamic Frequency Stability

by Xiaoming Luan, Huadong Sun, Guoliang Zhao, Guangyao Qiao, Zhengang Lu, Yunfei Xu, Weiguo Li and Tao Zheng

Symmetry 2025, 17(5), 632; https://doi.org/10.3390/sym17050632 - 23 Apr 2025

Abstract

The increasing penetration of renewable generations (RGs) poses significant challenges to the operation of transmission power systems, especially for not only steady-stage uncertainties but also dynamic-state stability. Hence, the symmetry of RGs and synchronous generations (SGs) should be improved by a proper planning [...] Read more.

The increasing penetration of renewable generations (RGs) poses significant challenges to the operation of transmission power systems, especially for not only steady-stage uncertainties but also dynamic-state stability. Hence, the symmetry of RGs and synchronous generations (SGs) should be improved by a proper planning strategy. For this purpose, a multi-stage stochastic planning method is proposed for transmission power systems (TPSs) considering dynamic frequency stability. Specifically, the progressive uncertainties of RGs over stages are addressed with a multi-stage stochastic method with nonanticipativity constraints. In this scheme, the uncertainty is represented by probability-weighted scenarios, and the planning decisions are only dependent on current realizations of RG uncertainties according to nonanticipativity constraints to improve planning efficiency. Moreover, the dynamic frequency stability constraints are derived considering the frequency regulation strategy of RGs, and these nonconvex constraints are further linearized with high accuracy by a maximal affine function-based method to improve the compatibility with the planning model. Subsequently, a progressive hedging algorithm (PHA) is designed to reduce the computational burden by decomposing the multi-scenario high-dimensional model into multiple small-scale models. Finally, the effectiveness of the proposed method, as well as its superiority over existing approaches, are validated through numerical experiments. Full article

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13 pages, 455 KiB

Open AccessArticle

Quantification of Foot Drop Stimulator Effects on Post-Stroke Hemiplegic Gait: A Cyclogram-Based Evaluation of Inter-Limb Gait Symmetry

by Flavia Marrone, Maira Jaqueline da Cunha, Serena Cerfoglio, Massimiliano Pau, Micaela Porta, Bruno Leban, Marco Tarabini, Manuela Galli, Aline Souza Pagnussat and Veronica Cimolin

Symmetry 2025, 17(5), 631; https://doi.org/10.3390/sym17050631 - 22 Apr 2025

Abstract

Post-stroke hemiplegia often leads to gait asymmetry, mobility reduction, and increased fall risk. Foot Drop Stimulation (FDS) is used in rehabilitation to improve dorsiflexion and gait patterns. Through cyclogram-based analysis, this retrospective study evaluated the effectiveness of FDS in enhancing inter-limb gait symmetry [...] Read more.

Post-stroke hemiplegia often leads to gait asymmetry, mobility reduction, and increased fall risk. Foot Drop Stimulation (FDS) is used in rehabilitation to improve dorsiflexion and gait patterns. Through cyclogram-based analysis, this retrospective study evaluated the effectiveness of FDS in enhancing inter-limb gait symmetry in 21 post-stroke hemiplegic individuals following 10 sessions of treadmill training combined with FDS. Participants underwent 3D gait analysis pre- and post-intervention, performed by means of optical motion capture system, from which spatiotemporal and cyclogram features of the hip, knee, and ankle were computed. FDS was found to significantly improve dynamic range of motion (ROM) of the affected side at hip (+5%) and knee (+9%) joints. Cyclogram analysis showed that FDS reduced inter-limb hip asymmetry (orientation: 13.35° to 10.65°, Trend Symmetry Index: 19.09° to 15.46°), though no improvements were observed at the ankle. FDS with treadmill training improved hip and knee symmetry, supporting cyclogram-based assessments for gait rehabilitation and highlighting the need for targeted ankle interventions. Further research is needed to explore long-term effects and optimize rehabilitation strategies. Full article

(This article belongs to the Section Life Sciences)

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19 pages, 1772 KiB

Open AccessArticle

Analysis of Near-Polar and Near-Circular Periodic Orbits Around the Moon with J₂, C₂₂ and Third-Body Perturbations

by Xingbo Xu

Symmetry 2025, 17(5), 630; https://doi.org/10.3390/sym17050630 - 22 Apr 2025

Abstract

In the Moon–Earth elliptic restricted three-body problem, near-polar and near-circular lunar-type periodic orbits are numerically continued from Keplerian circular orbits using Broyden’s method with line search. The Hamiltonian system, expressed in Cartesian coordinates, is treated via the symplectic scaling method. The radii of [...] Read more.

In the Moon–Earth elliptic restricted three-body problem, near-polar and near-circular lunar-type periodic orbits are numerically continued from Keplerian circular orbits using Broyden’s method with line search. The Hamiltonian system, expressed in Cartesian coordinates, is treated via the symplectic scaling method. The radii of the initial Keplerian circular orbits are then scaled and normalized. For cases in which the integer ratios

{j / k}

of the mean motions between the inner and outer orbits are within the range

[9, 150]

, some periodic orbits of the elliptic restricted three-body problem are investigated. For the middle-altitude cases with

j / k \in [38, 70]

, the perturbations due to

J_{2}

and

C_{22}

are incorporated, and some new near-polar periodic orbits are computed. The orbital dynamics of these near-polar, near-circular periodic orbits are well characterized by the first-order double-averaged system in the Poincaré–Delaunay elements. Linear stability is assessed through characteristic multipliers derived from the fundamental solution matrix of the linear varational system. Stability indices are computed for both the near-polar and planar near-circular periodic orbits across the range

j / k \in [9, 50]

. Full article

(This article belongs to the Section Mathematics)

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20 pages, 3728 KiB

Open AccessArticle

Adaptive Switching Control of Voltage Source Converters in Renewable Energy Station Based on Operating Short Circuit Ratio

by Zhan Zhang, Huangqing Xiao, Wenze Liu and Ying Huang

Symmetry 2025, 17(5), 629; https://doi.org/10.3390/sym17050629 - 22 Apr 2025

Abstract

By integrating the grid-following control and grid-forming control, the adaptability of grid-connected converters to the grid impedance fluctuation can be effectively improved, and a stable operation in a wide short circuit ratio range can be realized. The existing fusion control schemes focus on [...] Read more.

By integrating the grid-following control and grid-forming control, the adaptability of grid-connected converters to the grid impedance fluctuation can be effectively improved, and a stable operation in a wide short circuit ratio range can be realized. The existing fusion control schemes focus on the influence of the short circuit ratio on the stability of the converter, ignoring the influence of the active power fluctuation of the renewable energy in the design of the fusion scheme. In order to improve this shortcoming, an adaptive switching control of voltage source converters in the renewable energy station is proposed in this paper. Based on the oscillation characteristics of the grid-following converter and the grid-forming converter, this method selects the operating short circuit ratio as the switching index of the grid-following mode and the grid-forming mode. Compared with the current switching schemes based on the short circuit ratio, the operating short circuit ratio replaces the rated capacity of the station with the active output of the station, so it can more reasonably reflect changes in stability caused by active power fluctuations and then give the appropriate switching command, which means that unnecessary switching can be reduced and the control mode can be correctly converted to enhance stability when the system state changes. Full article

(This article belongs to the Section Engineering and Materials)

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15 pages, 1354 KiB

Open AccessArticle

Harnessing AI for Cyber Defense: Honeypot-Driven Intrusion Detection Systems

by Eman Alatawi and Umar Albalawi

Symmetry 2025, 17(5), 628; https://doi.org/10.3390/sym17050628 - 22 Apr 2025

Abstract

Anomaly detection is essential in cybersecurity for identifying abnormal activities, a requirement that has grown increasingly critical with the complexity of cyberthreats. This study leverages the BPF-Extended Tracking Honeypot (BETH) dataset, a comprehensive resource designed to benchmark robustness in detecting anomalous behavior in [...] Read more.

Anomaly detection is essential in cybersecurity for identifying abnormal activities, a requirement that has grown increasingly critical with the complexity of cyberthreats. This study leverages the BPF-Extended Tracking Honeypot (BETH) dataset, a comprehensive resource designed to benchmark robustness in detecting anomalous behavior in kernel-level process and network logs. The symmetry of the proposed system lies in its ability to identify balanced and consistent patterns within kernel-level process logs, which form the foundation for accurately distinguishing anomalies. This study focuses on anomaly detection in kernel-level process logs by introducing an enhanced Isolation Forest (iForest) model, which is integrated into a structured framework that includes exploratory data analysis (EDA), data pre-processing, model training, validation, and evaluation. The proposed approach achieves a significant performance improvement in the anomaly detection results, with an area under the receiver operating characteristic curve (AUROC) score of 0.917—an approximate 7.88% increase over the baseline model’s AUROC of 0.850. Additionally, the model demonstrates high precision (99.57%), F1-score (91.69%), and accuracy (86.03%), effectively minimizing false positives while maintaining balanced detection capabilities. These results underscore the role of leveraging symmetry in designing advanced intrusion detection systems, offering a structured and efficient solution for identifying cyberthreats. Full article

(This article belongs to the Section Computer)

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12 pages, 2359 KiB

Open AccessArticle

Efficient Parallel Ray Tracing Algorithm for Electromagnetic Scattering in Inhomogeneous Plasma Using Graphic Processing Unit

by Yijing Wang, Xinbo He and Bing Wei

Symmetry 2025, 17(4), 627; https://doi.org/10.3390/sym17040627 - 21 Apr 2025

Abstract

This paper presents a parallel ray tracing (RT) algorithm based on a graphic processing unit (GPU) applied to electromagnetic scattering calculations in an inhomogeneous plasma to enhance the computational efficiency of the algorithm. The proposed algorithm utilizes a fourth-order Runge–Kutta method to solve [...] Read more.

This paper presents a parallel ray tracing (RT) algorithm based on a graphic processing unit (GPU) applied to electromagnetic scattering calculations in an inhomogeneous plasma to enhance the computational efficiency of the algorithm. The proposed algorithm utilizes a fourth-order Runge–Kutta method to solve the Haselgrove equation to track ray paths within the inhomogeneous plasma and implements parallel processing of the RT procedure using GPU. By independently assigning single threads to the rays originating from the vertices and the midpoints of each triangulated ray tube, a substantial number of rays are traced in parallel to reduce the algorithm runtime. The results indicate that the parallel RT algorithm based on GPU significantly enhances computation efficiency in inhomogeneous plasma while maintaining accuracy. Full article

(This article belongs to the Section Physics)

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25 pages, 6931 KiB

Open AccessArticle

Dynamic Evolution Method and Symmetric Consistency Analysis for Big Data-Oriented Software Architecture Based on Extended Bigraph

by Chaoze Lu and Qifeng Zou

Symmetry 2025, 17(4), 626; https://doi.org/10.3390/sym17040626 - 21 Apr 2025

Abstract

With the development of artificial intelligence technology, there are increasingly high requirements for processing big data systems. Big data systems have undergone rapid evolution in response to changing demands. Due to the complex structural connections and dispersed component positions of big data processing [...] Read more.

With the development of artificial intelligence technology, there are increasingly high requirements for processing big data systems. Big data systems have undergone rapid evolution in response to changing demands. Due to the complex structural connections and dispersed component positions of big data processing systems, traditional formal methods find it difficult to dynamically model their structure and position simultaneously. To address this issue, this study proposes a formal modeling framework that extends Bigraph to support the dynamic evolution of big data software architecture. This model is capable of verifying the symmetry consistency of structural connections and component positions in evolutionary systems and evaluating them through real-life case studies of banking big data systems. The results confirmed its correctness and practical feasibility. Full article

(This article belongs to the Section Computer)

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81 pages, 2075 KiB

Open AccessReview

A Comprehensive Review on Solving the System of Equations AX = C and XB = D

by Qing-Wen Wang, Zi-Han Gao and Jia-Le Gao

Symmetry 2025, 17(4), 625; https://doi.org/10.3390/sym17040625 - 21 Apr 2025

Abstract

This survey provides a review of the theoretical research on the classic system of matrix equations

A X = C

and

X B = D

, which has wide-ranging applications across fields such as control theory, optimization, image processing, and robotics. The paper [...] Read more.

This survey provides a review of the theoretical research on the classic system of matrix equations

A X = C

and

X B = D

, which has wide-ranging applications across fields such as control theory, optimization, image processing, and robotics. The paper discusses various solution methods for the system, focusing on specialized approaches, including generalized inverse methods, matrix decomposition techniques, and solutions in the forms of Hermitian, extreme rank, reflexive, and conjugate solutions. Additionally, specialized solving methods for specific algebraic structures, such as Hilbert spaces, Hilbert

C^{*}

-modules, and quaternions, are presented. The paper explores the existence conditions and explicit expressions for these solutions, along with examples of their application in color images. Full article

(This article belongs to the Special Issue Mathematics: Feature Papers 2025)

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24 pages, 6825 KiB

Open AccessArticle

Numerical Analysis on Cooling Performances for Connectors Using Immersion Cooling in Ultra-Fast Chargers for Electric Vehicles

by Seong-Guk Hwang, Moo-Yeon Lee and Beom-Seok Ko

Symmetry 2025, 17(4), 624; https://doi.org/10.3390/sym17040624 - 20 Apr 2025

Abstract

The increasing demand for ultra-fast charging in electric vehicles (EVs) necessitates advancements in thermal management strategies to mitigate Joule heating, which arises due to electrical resistance in charging connectors and cable cores. This study presents a numerical analysis of immersion cooling performance for [...] Read more.

The increasing demand for ultra-fast charging in electric vehicles (EVs) necessitates advancements in thermal management strategies to mitigate Joule heating, which arises due to electrical resistance in charging connectors and cable cores. This study presents a numerical analysis of immersion cooling performance for ultra-fast chargers under realistic charging conditions. The simulated results are validated by experiments with a maximum deviation of 5.5% at 600 A and 700 A currents. The novelty of this work lies in the consideration of a realistic charging cable length of 5 m, the evaluation of temperature characteristics in the charger connector, and the analysis of geometric symmetry in the charging cable and coolant configuration to ensure uniform heat distribution. Key operating conditions were systematically analyzed, including applied currents, ambient temperatures, coolant flow rates, cable core cross-sectional areas, and different types of coolants. Results indicate that increasing the applied current from 400 A to 800 A raised the connector temperature from 60.73 °C to 97.33 °C. As the ambient temperature increased from 20 °C to 50 °C, the connector temperature rose significantly from 42.71 °C to 74.99 °C, while the maximum cable core temperature increased from 65.26 °C to 100.61 °C. Increasing the cable core cross-sectional area from 20 mm² to 30 mm² reduced the connector temperature from 77.20 °C to 74.99 °C. Meanwhile, increasing the coolant flow rate from 2 LPM to 5 LPM had a negligible effect on the connector temperature. Among the three tested coolants, Novec 7500 exhibited the highest cooling efficiency, achieving the lowest contact temperature (74.76 °C) and the highest performance evaluation criteria (PEC) value of 3.8. This study provides valuable guidelines for enhancing symmetry-driven thermal management systems and demonstrates the potential of immersion cooling to improve efficiency, safety, and operational reliability in next-generation high-power EV chargers. Full article

(This article belongs to the Special Issue Symmetry in Power Systems and Thermal Engineering)

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