Search Results (606)

The natural frequencies and damping characterisation of a new aerospace grade composite material were investigated using a modified impulse method combined with the half power bandwidth method, which is applicable to the structures with a low damping. The composite material of interest was unidirectional carbon fibre reinforced plastic. The tests were carried out with three identical square 4.6 mm thick plates consisting of 24 plies. The composite plates were clamped along one edge in a SignalForce shaker, which applied a sinusoidal signal generated by the signal conditioner exiting the bending modes of the plates. Laser vibrometer measurements were taken at three points on the free end so that different vibrational modes could be obtained: one measurement was taken on the longitudinal symmetry plane with the other two 35 mm on either side of the symmetry plane. The acceleration of the clamp was also recorded and integrated twice to calculate its displacement, which was then subtracted from the free end displacement. Two material orientations were tested, and the first four natural frequencies were obtained in the test. Damping was determined by the half-power bandwidth method. A linear relationship between the loss factors and frequency was observed for the first two modes but not for the other two modes, which may be related to the coupling of the modes of the plate and the shaker. The experiment was also modelled by using the Finite Element Method (FEM) and implicit solver of LS Dyna, where the simulation results for the first two modes were within 15% of the experimental results. The novelty of this paper lies in the presentation of new experimental data for the natural frequencies and damping coefficients of a newly developed composite material intended for the vibration analysis of rotating components. Full article

Early and accurate detection of brain tumors is critical for MRI-based diagnosis. Conventional convolutional neural networks often struggle to capture fine-grained details, small or boundary-ambiguous lesions, and hemispheric symmetry patterns. To address these limitations, we propose S-ResGCN, a symmetry-aware framework integrating hierarchical feature extraction, attention mechanisms, and graph-based classification. S-ResGCN employs a ResNet50 backbone to extract multi-level features, with Convolutional Block Attention Modules applied to intermediate and deep layers to enhance key information and discriminative features. Furthermore, we introduce a novel self-paired regularization to enforce feature consistency between original and horizontally flipped images, improving sensitivity to bilateral symmetric structures. Extracted features are converted into nodes and modeled as a small graph, and a graph convolutional network captures inter-node relationships to generate symmetry-aware predictions. Evaluation on three publicly available brain tumor MRI datasets demonstrates that S-ResGCN achieves average accuracies of 99.83%, 99.37% and 99.26% ± 0.16, with consistently high precision, recall, and F1-scores. These results indicate that S-ResGCN effectively captures fine-grained and symmetric tumor characteristics often overlooked by conventional models, providing a robust and efficient tool for automated, graph convolutional network. Full article

Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have been widely applied to hyperspectral image classification tasks, but both exhibit certain limitations. To address these issues, this paper proposes a multi-scale feature fusion architecture (MCGNet). Symmetry serves as the core design principle of MCGNet, where its parallel CNN-GCN branches and multi-scale fusion mechanism strike a balance between local spectral-spatial features and global graph structural dependencies, effectively reducing redundancy and enhancing generalization capabilities. The architecture comprises four modules: the Spectral Noise Suppression (SNS) module enhances the signal-to-noise ratio of spectral features; the Local Spectral Extraction (LSE) module employs deep separable convolutions to extract local spectral-spatial features; Superpixel-level Graph Convolution (SGC), performing graph convolution on superpixel graphs to precisely capture dependencies between object regions; Pixel-level Graph Convolution (PGC), constructed via adaptive sparse pixel graphs based on spectral and spatial similarity to accurately capture irregular boundaries and fine-grained non-local relationships between pixels. These modules form a symmetric, hierarchical feature learning pipeline integrated within a unified framework. Experiments on three public datasets—Indian Pine, Pavia University, and Salinas—demonstrate that MCGNet outperforms baseline methods in overall accuracy, average precision, and Kappa coefficient. This symmetric design not only enhances classification performance but also endows the model with strong theoretical interpretability and cross-dataset robustness, highlighting the significance of symmetry principles in hyperspectral image analysis. Full article

Accurate stock price forecasting is crucial for supporting informed investment decisions, effective risk management, and the identification of profitable market opportunities. Financial time series present considerable challenges for prediction due to their complex, nonlinear dynamics and sensitivity to a wide range of economic factors. Although various statistical methods have been developed to model the multidimensional relationships inherent in such datasets, advancements in big data technologies have greatly facilitated the recording, analysis, and interpretation of large-scale financial data, thereby accelerating the adoption of deep learning (DL) algorithms in this domain. In the present study, RNN-, LSTM-, and GRU-based models were developed to forecast the closing prices of two airline stocks, with hyperparameter optimization conducted via the Bayesian optimization algorithm. The dataset consisted of daily closing prices of THYAO and PGSUS stocks obtained from Yahoo Finance. Comparative analysis demonstrated that the GRU model yielded the highest accuracy for THYAO stock price prediction, achieving a MAPE of 3.05% and an RMSE of 3.195, whereas for PGSUS, the model achieved a MAPE of 3.97% and an RMSE of 3.232. Beyond its empirical contribution, this study also emphasizes the conceptual relevance of symmetry in financial forecasting. The proposed deep learning framework captures the balanced relationships and nonlinear interactions inherent in stock market behavior, reflecting both symmetry and asymmetry in market responses to economic factors. Full article

The symmetry between different representation spaces plays a crucial role in effectively modeling complex multimodal data. To address the challenge of equipment knowledge graphs containing hierarchical relationships that cannot be fully represented in a single space, this study proposes UMEAD, an unsupervised multimodal entity alignment method based on dual-space embeddings. The method simultaneously learns graph embeddings in both Euclidean and hyperbolic spaces, forming a structural symmetry where the Euclidean space captures local regularities and the hyperbolic space models global hierarchies. Their complementarity achieves a balanced and symmetric representation of multimodal knowledge. An adaptive feature fusion strategy is further employed to dynamically weight semantic and visual modalities, enhancing the symmetry and complementarity between different modalities. To reduce reliance on scarce pre-aligned data, pseudo seed instances are generated from multimodal features, and an iterative constraint mechanism progressively enlarges the training set, enabling unsupervised alignment. Experiments on public datasets, including EMMEAD, FB15K-DB15K, and FB15K-YAGO15K, demonstrate that the combination of dual-space embeddings, adaptive fusion, and iterative constraints significantly improves alignment accuracy. In summary, the proposed method reduces dependence on pre-aligned data, strengthens multimodal and structural alignment, and its symmetric embedding and fusion design offers a promising approach for the construction and application of multimodal knowledge graphs in the equipment domain. Full article

A comprehensive comparative study is presented of three approaches for color morphological processing: the proposed bitmixing transformation, lexicographic ordering, and conventional marginal processing. The novel bitmixing method converts RGB channels into a single 24-bit scalar representation through bit-interleaving, preserving both color information and spatial ordering relationships. This enables the direct application of grayscale morphological operators while generally preserving structural, chromatic, and visual relationships in processed objects. Under appropriate conditions—such as when objects exhibit clear color-based boundaries and bilateral symmetry—the method tends to maintain these symmetries more consistently than marginal or lexicographic alternatives. Experimental evaluation shows that the bitmixing approach achieves a competitive balance between color preservation and computational efficiency. In specific scenarios involving color-defined regions and symmetric structures, it demonstrates modest advantages over marginal and lexicographic methods. These findings suggest that the proposed method can serve as a viable alternative in applications where color fidelity, structural coherence, and symmetry preservation are desirable, though its benefits are context-dependent. Full article

The symmetry-breaking vibrational response of a gun muzzle, induced by the thermo–mechanical coupling effect under continuous firing, is a critical factor degrading shooting accuracy. This study investigates the nonlinear influence of chamber pressure variation on this asymmetric dynamic response. A thermo–mechanically coupled interaction model between a 5.8 mm bullet and its barrel is established using nonlinear finite element methods, incorporating experimentally measured pressure data. The kinematic state of the muzzle under a heated barrel condition (after 90 rounds) is systematically analyzed across five chamber pressure levels (90% to 110% of standard). The results reveal a highly nonlinear relationship between chamber pressure and muzzle vibration. Surprisingly, the maximum values for comprehensive radial displacement (10.601 × 10⁻³ mm), velocity (0.327 m/s), acceleration (11.083 m/s²), swing angle (0.192 mrad), and swing angular velocity (9.166 rad/s) occurred at the 100% standard pressure, not the highest pressure. Reducing the pressure to 90% of the standard effectively suppressed these asymmetric vibrations, with magnitudes declining by 84.28% to 95.49%. This indicates that the symmetry of the muzzle’s dynamic state is disrupted under thermal effects, and strategically lowering chamber pressure can restore a more symmetric and stable launch attitude, thereby enhancing accuracy. This study elucidates the nonlinear correlation mechanism between pressure and thermally induced asymmetric vibration, providing a novel perspective for optimizing the accuracy of rapid-fire weapons based on symmetry principles. Full article

To address the challenges of poor adaptability to spatial heterogeneity, easy breakage of amplitude–phase coupling relationships, and insufficient physical consistency in complex optical wavefield reconstruction, this paper proposes the DdONN-PINNs hybrid framework. Focused on preserving the intrinsic symmetries of wave physics, the framework achieves deep integration of optical neural networks and physics-informed information. Centered on an architecture of “SIREN shared encoding–domain-specific output”, it utilizes the periodic activation property of SIREN encoders to maintain the spatial symmetry of wavefield distribution, incorporates learnable Fourier diffraction layers to model physical propagation processes, and adopts native complex-domain modeling to avoid splitting the real and imaginary parts of complex amplitudes—effectively adapting to spatial heterogeneity while fully preserving amplitude-phase coupling in wavefields. Validated on rogue wavefields governed by the Nonlinear Schrödinger Equation (NLSE), experimental results demonstrate that DdONN-PINNs achieve an amplitude Mean Squared Error (MSE) of $2.94\times {10}^{-3}$ and a phase MSE of $5.86\times {10}^{-4}$, outperforming non-domain-decomposed models and ReLU-activated variants significantly. Robustness analysis shows stable reconstruction performance even at a noise level of $\sigma =0.1$. This framework provides a balanced solution for wavefield reconstruction that integrates precision, physical interpretability, and robustness, with potential applications in fiber-optic communication and ocean optics. Full article

Complex associations among production cost factors, multi-source cost information silos, and opaque transmission mechanisms of hidden costs in open-pit coal mining were addressed. The production process—including drilling, blasting, excavation, transportation, and dumping—was taken as the application context. A corpus of 103 open-pit coal mining standards and related research documents was constructed. Eleven entity types and twelve relationship types were defined. Dynamic word vectors were obtained through transformer (BERT) pre-training. The optimal entity tag sequence was labeled using a bidirectional long short-term memory–conditional random field (BiLSTM–CRF) 9 model. A total of 3995 entities and 6035 relationships were identified, forming a symmetry-aware knowledge graph for open-pit coal mining costs based on the BERT–BiLSTM–CRF model. The results showed that, among nine entity types, including Parameters, the F1-scores all exceeded 60%, indicating more accurate entity recognition compared to conventional methods. Knowledge embedding was performed using the TransH inference algorithm, which outperformed traditional models in all reasoning metrics, with a Hits@10 of 0.636. This verifies its strong capability in capturing complex causal paths among cost factors, making it suitable for practical cost optimization. On this basis, a symmetry-aware BERT–BiLSTM–CRF knowledge graph of open-pit coal mining costs was constructed. Knowledge embedding was then performed with the TransH inference algorithm, and latent relationships among cost factors were mined. Finally, a knowledge-graph-based cost factor identification system was developed. The system lists, for each cost item, the influencing factors and their importance ranking, analyzes variations in relevant factors, and provides decision support. Full article

In this study, numerical simulations were performed, and leaks in viscoelastic pipelines were detected. Based on the transient flow equations derived from the continuity and momentum equations, the Kelvin–Voigt model was used to describe the viscoelastic constitutive relationship and derive the strain equation, further establishing a one-dimensional transient flow model for viscoelastic pipelines. A frequency-domain analysis of the transient flow was performed by deriving the Fourier transform and transfer matrix. An inverse problem analysis method for transient flow leak detection was proposed to identify the leak location and rate by minimizing the objective function. To verify the effectiveness of the proposed model, an experimental platform was built, and the pressure head frequency-domain data under working conditions of no leak, experimental leak, and simulated leak were compared. The results showed that the experimental data were consistent with the simulated data under leakage conditions, thus proving that the model was accurate and reliable. Under leak-free conditions, the frequency-domain characteristics of transient pressure waves exhibit significant symmetrical features, whereas when a leak exists in the pipeline, the leak point acts as a localized non-uniform disturbance source, disrupting the symmetry of the frequency-domain characteristics. Moreover, the leak point can be determined by the difference in the peak heights between the no-leak and leak conditions, and the leak parameters can be accurately identified using the inverse problem method. Full article

Forecasting real-world time series in domains with strong event sensitivity and regional variability poses unique challenges, as predictive models must account for sudden disruptions, heterogeneous contextual factors, and structural differences across locations. In tackling these challenges, we draw on the concept of symmetry that refers to the balance and invariance patterns across temporal, multimodal, and structural dimensions, which help reveal consistent relationships and recurring patterns within complex systems. This study is based on two multimodal datasets covering 12 tourist regions and more than 3 years of records, ensuring robustness and practical relevance of the results. In many applications, such as monitoring economic indicators, assessing operational performance, or predicting demand patterns, short-term fluctuations are often triggered by discrete events, policy changes, or external incidents, which conventional statistical and deep learning approaches struggle to model effectively. To address these limitations, we propose an event-aware multimodal time-series forecasting framework with graph-based regional transfer built upon an enhanced PatchTST backbone. The framework unifies multimodal feature extraction, event-sensitive temporal reasoning, and graph-based structural adaptation. Unlike Informer, Autoformer, FEDformer, or PatchTST, our model explicitly addresses naive multimodal fusion, event-agnostic modeling, and weak cross-regional transfer by introducing an event-aware Multimodal Encoder, a Temporal Event Reasoner, and a Multiscale Graph Module. Experiments on diverse multi-region multimodal datasets demonstrate that our method achieves substantial improvements over eight state-of-the-art baselines in forecasting accuracy, event response modeling, and transfer efficiency. Specifically, our model achieves a 15.06% improvement in the event recovery index, a 15.1% reduction in MAE, and a 19.7% decrease in event response error compared to PatchTST, highlighting its empirical impact on tourism event economics forecasting. Full article

Conventional hip disarticulation prostheses often require amputees to produce limited leg-lifting torque through exaggerated pelvic motion, resulting in complex control and pronounced gait abnormalities. To overcome the limitations, we present a mapping control strategy for a powered hip disarticulation prosthesis aimed at improving gait symmetry. A quaternion-based method was implemented to capture hip joint kinematics, while a gated recurrent unit (GRU) neural network was trained to model the kinematic relationship between the intact and prosthetic limbs, enabling biomimetic trajectory control. Validation experiments showed that trajectory similarity between predicted and actual motions increased with walking speed, reaching 98.12% at 3.0 km/h. Comparative walking tests revealed an 84.00% improvement in hip flexion angle with the powered prosthesis over conventional designs. Notable improvements in gait symmetry were observed: stride symmetry (measured by SI and RII) improved by 23.21% and 19.28%, respectively, while hip trajectory symmetry increased by 68.07% (SI) and 47.59% (RII). These results confirm that the GRU-based kinematic mapping model offers robust trajectory prediction and that the powered prosthesis significantly enhances gait symmetry, delivering more natural and biomimetic motion. Full article

Background: Foot morphology plays a central role in musculoskeletal development during childhood. Variations in the medial longitudinal arch may influence walking mechanics, and excess body weight can further affect plantar structure and gait. Objective: This study examined the relationship between foot type, body mass index (BMI), and gait function in school-aged children, with particular focus on gait symmetry as a sensitive marker. Methods: Ninety-eight children aged 8–16 years were evaluated. Foot type was classified using a pressure platform, and gait was assessed with a wearable sensor. Outcomes included gait symmetry, walking speed, cadence, Timed Up and Go (TUG), and Six-Minute Walk Distance (6MWD). Results: Mixed bilateral foot patterns were observed in 46 of the 98 participants (47%). Significant associations were found between foot type, BMI, and gait symmetry (p < 0.01), while other mobility measures (speed, cadence, TUG, 6MWD) remained stable across groups. Children with normal bilateral feet showed the best gait symmetry, whereas mixed patterns had the lowest. Conclusions: Gait symmetry is a sensitive indicator of functional imbalance in schoolchildren and is strongly influenced by both foot morphology and body weight. Incorporating plantar assessment and BMI monitoring into routine pediatric evaluations may help clinicians identify children at risk for long-term musculoskeletal problems at an early stage. Full article

Background/Objectives: Orthognathic surgery represents a surgical modality for the correction of craniofacial skeletal deformities. These procedures help achieve stable occlusion and improve facial symmetry, which in turn enhances functional outcomes and overall quality of life. However, to date, no consensus has been reached regarding whether orthognathic surgery also induces changes in the relationship of articular surfaces within the temporomandibular joints (TMJs). The primary objective of this study was to conduct a systematic review of research evaluating joint space dimensions based on CBCT imaging performed before and after orthognathic surgery. Methods: A comprehensive literature search was carried out across four electronic databases: PubMed, Web of Science, Cochrane Library, and Scopus. Two independent reviewers screened titles and abstracts according to predefined inclusion criteria. Eligible studies were subjected to critical appraisal, and relevant data were systematically extracted and summarized in tabular form. Results: Fourteen studies published between 2010 and 2024 met the inclusion criteria. In all studies, CBCT-based joint space measurements were conducted at least twice once preoperatively and once postoperatively, across a total of 527 patients included in the review. Conclusions: The synthesized evidence suggests that orthognathic surgery produces measurable modifications in the spatial relationship of TMJ articular surfaces. Nonetheless, the clinical relevance of these alterations appears to be modulated by several variables, including the surgical technique employed and the patient’s individual adaptive capacity. Full article

The purpose of few-shot relation extraction (RE) is to recognize the relationship between specific entity pairs in text when there are a limited number of labeled samples. A few-shot RE method based on a prototype network, which constructs relation prototypes by relying on the support set to assign labels to query samples, inherently leverages the symmetry between support and query processing. Although these methods have achieved remarkable results, they still face challenges such as the misjudging of noisy samples or outliers, as well as distinguishing semantic similarity relations. To address the aforementioned challenges, we propose a novel semantic enhanced prototype network, which can integrate the semantic information of relations more effectively to promote more expressive representations of instances and relation prototypes, so as to improve the performance of the few-shot RE. Firstly, we design a prompt encoder to uniformly process different prompt templates for instance and relation information, and then utilize the powerful semantic understanding and generation capabilities of large language models (LLMs) to obtain precise semantic representations of instances, their prototypes, and conceptual prototypes. Secondly, graph attention learning techniques are introduced to effectively extract specific-relation features between conceptual prototypes and isomorphic instances while maintaining structural symmetry. Meanwhile, a prototype-level contrastive learning strategy with bidirectional feature symmetry is proposed to predict query instances by integrating the interpretable features of conceptual prototypes and the intra-class shared features captured by instance prototypes. In addition, a clustering loss function was designed to guide the model to learn a discriminative metric space with improved relational symmetry, effectively improving the accuracy of the model’s relationship recognition. Finally, the experimental results on the FewRel1.0 and FewRel2.0 datasets show that the proposed approach delivers improved performance compared to existing advanced models in the task of few-shot RE. Full article