Search Results (9,999)

In the early faults of transformer windings, there are obvious variation characteristics of the spatial leakage magnetic field. Taking the leakage magnetic field as the fault characteristic quantity can establish an active defense system for transformer defects and faults, thereby increasing the service life of the equipment. However, the installation method of the optical fiber leakage magnetic field sensor, the principle of leakage magnetic field protection, the research and development of the protection device, and the dynamic model testing of the protection device are all key links in realizing the leakage magnetic field monitoring and active defense system. This paper first analyzes the symmetry of the winding leakage magnetic field, proposes invasive and non-invasive installation methods for optical fiber sensors based on different application scenarios, presents the principle of leakage magnetic field differential protection, and develops a protection device. The feasibility of the protection scheme proposed in this paper was verified through dynamic model experiments, and the early fault active defense system was put into actual on-site operation. Full article

Background/Objectives: Archery is a technical sport involving repetitive and asymmetrical movements that requires trunk stability to enable good performance of the upper extremities. Being an asymmetrical sport, imbalances between sides might appear in the abdominal and back muscles. To assess trunk muscle function and symmetry in young competitive archers. Methods: Analyzing pre-season screening evaluation tests from medical files. This included an ultrasound examination of back and abdominal muscles (transverse abdominus and internal oblique) during rest and contraction and trunk muscle clinical strength tests. Results: Data on 15 elite archery athletes (mean age 17.2 (±2.7) years) were included. No athletes reported low back pain. No differences were found between the dominant and non-dominant sides in all outcome measurements (absolute thickness and percentage difference). Internal oblique muscle thickness during rest and contraction for the dominant side was higher in males compared with females (p < 0.05). The back muscles were more symmetrical than the abdominal muscles. Conclusions: Despite the asymmetrical functional demands of sport archery, young athletes displayed trunk muscle symmetry, particularly in their back muscles. While some variability in abdominal muscle asymmetry was observed, these differences were not statistically significant. Full article

In the nonlocal Almgren problem, the goal is to investigate the convexity of a minimizer under a mass constraint via a nonlocal free energy generated with a nonlocal perimeter and convex potential. In this paper, the main result is a quantitative stability theorem for the nonlocal free energy under a symmetry assumption on the potential. In addition, several results that involve uniqueness, non-existence, and moduli estimates from the theory of crystals are also proven in the nonlocal context. Full article

Symmetry and asymmetry between past and future knowledge are at the heart of continual learning. Deep neural networks typically lose the temporal symmetry that would preserve earlier knowledge when the network is trained sequentially, a phenomenon known as catastrophic forgetting. Dynamically expandable networks (DENs) attempt to restore symmetry by allocating a dedicated module—such as a feature extractor or a task token—for every new task while freezing all previously learned modules. Although this strategy yields high average accuracy, we observe a pronounced asymmetry: earlier tasks still degrade over time, indicating that frozen modules alone do not guarantee knowledge conservation. Moreover, feature bias, arising from the imbalance between old and new samples, further exacerbates the forgetting issue. This raises a fundamental challenge: how can multiple feature extractors be coordinated more effectively to mitigate catastrophic forgetting while enabling the robust acquisition of new tasks? To address this challenge, we propose two asymmetric, contrastive auxiliary losses that exploit rich information from previous tasks to guide new task learning. Specifically, our approach integrates features extracted by both frozen and current modules to reinforce task boundaries while facilitating the learning process. In addition, we introduce a feature adjustment mechanism to alleviate the bias caused by class imbalance. Extensive experiments on benchmarks, including DyTox and MCG, demonstrate that our approach reduces catastrophic forgetting and achieves state-of-the-art performance on ImageNet-100. Full article

Diaphragm springs, as critical components in commercial vehicle clutch assemblies, directly determine the clutch’s working performance. The design of diaphragm springs, which possess a distinct symmetrical structure that underpins their mechanical behavior, centers on obtaining the large-end nonlinear load–displacement curve—a typical large deformation-induced nonlinear problem. Traditional design relies on the A-L formula, but studies show finite element analysis (FEA) yields results closer to actual measurements. This study established an FEA model of the diaphragm spring’s disc spring (excluding separation fingers) and validated its correctness by comparing it with the A-L formula. Then, using FEA on models with separation fingers, it analyzed factors influencing the large-end load–displacement characteristics. Leveraging the inherent symmetry of the diaphragm spring structure, particularly the symmetrical distribution of separation fingers, the analysis process efficiently captures uniform mechanical responses during deformation, while this symmetric arrangement also ensures balanced load distribution during clutch operation, a critical factor for stabilizing the load–displacement curve. Results indicate the separation finger root is a key factor, with larger root holes, square holes (compared to circular ones), and more separation fingers reducing stiffness to effectively adjust the curve; in contrast, the tip and length of separation fingers have little impact, making the latter unsuitable for design adjustments. Full article

With the advancement of artificial intelligence, Agricultural Expert Systems (AESs) show great potential in enhancing agricultural management efficiency and resource utilization. Accurate extraction of semantic features from agricultural short texts is fundamental to enabling key functions such as intelligent question answering, semantic retrieval, and decision support. However, existing single-structure deep neural networks struggle to capture the hierarchical linguistic patterns and contextual dependencies inherent in domain-specific texts. To address this limitation, we propose a hybrid deep learning model—Bidirectional Encoder Recurrent Neural Network (BERNN)—which combines a domain-specific pre-trained Transformer encoder (AgQsBERT) with a Bidirectional Long Short-Term Memory (BiLSTM) network. AgQsBERT generates contextualized word embeddings by leveraging domain-specific pretraining, effectively capturing the semantics of agricultural terminology. These embeddings are then passed to the BiLSTM, which models sequential dependencies in both directions, enhancing the model’s understanding of contextual flow and word disambiguation. Importantly, the bidirectional nature of the BiLSTM introduces a form of architectural symmetry, allowing the model to process input in both forward and backward directions. This symmetric design enables balanced context modeling, which improves the understanding of fragmented and ambiguous phrases frequently encountered in agricultural texts. The synergy between semantic abstraction from AgQsBERT and symmetric contextual modeling from BiLSTM significantly enhances the expressiveness and generalizability of the model. Evaluated on a self-constructed agricultural question dataset with 110,647 annotated samples, BERNN achieved a classification accuracy of 97.19%, surpassing the baseline by 3.2%. Cross-domain validation on the Tsinghua News dataset further demonstrates its robust generalization capability. This architecture provides a powerful foundation for intelligent agricultural question-answering systems, semantic retrieval, and decision support within smart agriculture applications. Full article

This research evaluates the suitability of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) for improving financial return predictions across 15 major worldwide stock indices. The proposed method uses graph modeling to represent financial index relationships which enables the detection of symmetric market dependencies including mutual spillover effects and bidirectional influence patterns. The symmetric network structures become most important during financial instability because market interdependencies strengthen at such times. The evaluation process compares these models against XGBoost and Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) traditional forecasting approaches. The results of 30 time-series cross-validation experiments show that GNN models produce lower RMSE and MAE values, especially during financial crises and recovery phases and volatile market periods. The models show reduced advantages when markets remain stable. The research demonstrates that graph-based forecasting models which incorporate symmetry effectively detect complex financial relationships which leads to important implications for investment strategies and financial risk management and global economic forecasting. Full article

Let G be a connected graph. The Steiner k-Wiener index $S{W}_k\left(G\right)$ of graph G is defined as $S{W}_k\left(G\right)={\displaystyle \sum _{S\subseteq V\left(G\right),\left|S\right|=k}}{d}_G\left(S\right)$, where $d_G\left(S\right)$ represents the minimum size of a connected subgraph of G that connects S. Using some graph operations, we obtain the minimum value and the second minimum value of the Steiner k-Wiener index for cacti with order n and t cycles, and we characterize the corresponding extremal graphs by exploiting structural symmetries. Full article

Air quality prediction is a critical and complex time-series forecasting task, where traditional linear models often struggle to capture non-stationary, multiscale temporal dynamics. While recent advances in deep learning have improved predictive performance, challenges remain in interpretability, component disentanglement, and structured modeling. In this work, we propose a symmetric decomposition–modeling–evaluation framework tailored for air quality forecasting. Our method decomposes air quality time series into three complementary components: trend, periodicity, and fluctuation, based on their structural characteristics and symmetry relationships. Each component is modeled using lightweight, component-specific linear modules that preserve interpretability and computational efficiency. Importantly, the framework explicitly leverages the symmetrical properties between components to inform both model design and multiscale interaction. To evaluate component-level prediction quality, we introduce dual series-wise metrics that assess temporal correlation and distributional symmetry, addressing the limitations of conventional point-wise error metrics in capturing sequence-level consistency. Experimental results on real-world AQI datasets and other public time-series benchmarks demonstrate that our approach achieves competitive forecasting accuracy. On the Beijing dataset, our method achieved an MAE of 0.445 at the 96 h prediction horizon, outperforming the best baseline model, PatchFormer, with an MAE of 0.464. On the Tianjin dataset, our method achieved an MAE of 0.466 compared to the best competing model’s MAE of 0.481. Across multiple datasets, our approach consistently outperformed traditional methods, with improvements in MAE ranging from 0.019 to 0.086, demonstrating its effectiveness in capturing complex temporal patterns while enhancing interpretability through symmetry-aware modeling and evaluation. Full article

Aim of the Study: To determine whether adding a lateral extra-articular tenodesis (LET) to primary anterior cruciate ligament reconstruction (ACLR) lowers graft-failure risk and improves functional recovery in competitive athletes with high-grade pivot-shift. Methods: Multicentre retrospective cohort with 1:1 propensity-score matching (age, sex, sport, graft, centre). Competitive athletes with pivot-shift grade ≥ 2 who underwent primary ACLR with hamstring or bone–patellar tendon–bone (BPTB) autografts (2018–2024) were eligible. The primary outcome was graft failure within 24 months (composite of revision ACLR, symptomatic rotatory laxity with pivot-shift ≥ 2 plus KT-1000 > 5 mm, or MRI-confirmed rupture). Time-to-event was summarised with Kaplan–Meier (KM) curves and log-rank tests. Secondary outcomes included residual rotatory laxity and functional performance (single-leg hop, side hop, Y-Balance) analysed as the proportion achieving Limb Symmetry Index ≥ 90% at 6 and 24 months and as continuous LSI means. Two-sided α = 0.05; secondary outcomes were prespecified without multiplicity adjustment. Results: Of 1368 ACL reconstructions screened, 97 eligible athletes were identified; 92 were analysed after matching (46 isolated ACLR; 46 ACLR + LET; mean follow-up 30.0 ± 4.2 months). KM survival at 24 months was 95.7% after ACLR + LET versus 82.6% after isolated ACLR (log-rank p = 0.046). The absolute risk reduction was 13.0% (Number Needed to Treat 8; 95% CI 4→∞). In graft-type subgroups, failures were 6/32 vs. 1/30 for hamstring and 2/14 vs. 1/16 for BPTB (ACLR vs. ACLR + LET, respectively); there was no evidence of interaction (Breslow–Day p = 0.56). At 6 months, a higher proportion of ACLR + LET athletes achieved LSI ≥ 90% across tests—single-leg hop 77.8% vs. 40.9% (p = 0.0005), side hop 62.2% vs. 34.9% (p = 0.012), Y-Balance 84.4% vs. 59.1% (p = 0.010), with a larger mean LSI (between-group differences +8.2 to +9.1, all p < 0.001). By 24 months, threshold attainment largely converged (all p ≥ 0.06), while mean LSI differences persisted but were smaller (+3.9 to +4.9, all p ≤ 0.001). Conclusion: In competitive athletes with high-grade pivot-shift undergoing accelerated, criteria-based rehabilitation, adding LET to primary ACLR was associated with lower graft-failure risk and earlier functional symmetry, with consistent effects across hamstring and BPTB autografts. Given the observational design, causal inference is limited; confirmation in randomized and longer-term studies is warranted. Full article

Air pollution poses a threat to public health, ecosystem stability, and sustainable development. Accurate air quality prediction is essential for environmental protection and achieving sustainability. This study proposes a symmetry-driven hybrid framework that integrates an Improved Triangulation Topology Aggregation Optimizer (ITTAO) with a Stable Long Short-Term Memory (sLSTM) network and an attention mechanism to achieve high-precision air quality prediction. Three enhancement strategies are introduced to improve the optimization capability of the TTAO algorithm. Experiments with CEC2017 standard functions validate the ITTAO algorithm’s superior convergence and global search ability. ITTAO then optimizes the hyperparameters of the sLSTM-Attention model, resulting in the ITTAO-sLSTM-Attention model. Four air quality datasets from diverse regions in China verify the model’s performance, demonstrating that the proposed model outperforms seven swarm intelligence-optimized sLSTM-Attention models and six machine learning models. Compared to the LSTM model, ITTAO-sLSTM-Attention reduces RMSE by 23.47%, 13.23%, 19.69%, and 26.46% across four cities, confirming its enhanced accuracy and generalization. Finally, an interactive air quality prediction system based on the ITTAO-sLSTM-Attention model and PyQt is developed, offering a user-friendly tool for air quality prediction. Full article

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article

In this study, we propose a novel hybrid metaheuristic framework that leverages symmetry awareness to enhance the performance of multi-objective optimization in renewable energy systems. Motivated by the repetitive and structured patterns commonly found in solar, wind, and demand profiles, we introduce a symmetry-guided variant of the NSGA-II algorithm, enriched with a customized crossover operator that detects and exploits symmetrical patterns in candidate solutions. To further accelerate convergence and reduce computational cost, we integrate a surrogate modeling strategy using machine learning to approximate fitness evaluations in later generations. Our experimental evaluation, based on a synthetic dataset simulating one week (168 h) of operation in a hybrid solar–wind power system, incorporating realistic diurnal patterns in generation and demand, demonstrates the proposed method’s superiority over baseline NSGA-II in terms of solution diversity, convergence, and runtime efficiency. The results highlight the importance of integrating domain-specific structure—such as temporal symmetry—into the design of metaheuristics for sustainable energy applications. This approach opens new avenues for scalable, intelligent optimization in complex energy environments. Full article

Nicotine is a highly used addictive substance that has recently also become available through electronic cigarettes. Here we present a study of nicotine from e-cigarette liquids through reversed-phase (RP) and hydrophilic interaction (HILIC) liquid chromatography. Multiple aqueous mobile-phase additives are considered for the RP mechanism, focusing on chaotropic agents, mobile-phase concentrations and mixing ratios, and column temperature. Sample preparation was conducted by toluene liquid–liquid extraction of e-cigarette liquids diluted with aqueous 25 mM NaHCO₃/Na₂CO₃. Optimal RP results for retention and peak symmetry were obtained using aqueous 0.1% formic acid and 20 mM ammonium hexafluorophosphate with 0.1% formic acid in acetonitrile, using a gradient profile with a C18 column, exploited at 40 °C and a 1.5 mL/min flow rate. A dilute-and-shoot alternative with automated flow reversal after isocratic elution is presented. For HILIC, aqueous 100 mM ammonium formate and 0.1% formic acid in acetonitrile were used as mobile-phase components, using a gradient profile, on a Thermo Scientific™ Acclaim™ Mixed-Mode HILIC-1 column, operated at 25 °C with a 1 mL/min flow rate. UV detection was at 260 nm. Absolute limits of quantitation in the 1 μg/mL range were obtained for all tested alternatives, with 1 μL injection volumes. Full article

Facial features are critical visual indicators for understanding what a person is experiencing, providing valuable insights into their emotions and physical states. However, accurately detecting these features under diverse conditions remains a significant challenge, especially in computationally constrained environments. This paper presents a facial feature extraction method designed to identify regions of interest for detecting facial cues, with a focus on improving the accuracy of eye and mouth detection. Addressing the limitations of standard Haar cascade classifiers, particularly in challenging scenarios such as droopy eyes, red eyes, and droopy mouths, this method introduces a correction algorithm rooted in normal human facial anatomy, emphasizing symmetry and consistent feature placement. By integrating this correction algorithm with a feature-based refinement process, the proposed approach enhances detection accuracy from 67.22% to 96.11%. Through this method, the accurate detection of facial features like the eyes and mouth is significantly improved, offering a lightweight and efficient solution for real-time applications while maintaining computational efficiency. Full article