Search Results (800)

Background: Temporomandibular disorders are a generic term referred to clinical conditions involving the jaw muscles and temporomandibular joint with multifactorial pattern and genetic background. The aim of this observational study was to investigate the correlation between craniomandibular disorders and the presence of occlusal alterations. A clinical evaluation of the occlusal and articular status of the patients was carried out, integrating the latter with the electromyographic recording the activity of the masseter and temporalis muscles. Methods: A clinical observational study on 20 adults assessed temporomandibular disorders using DC/TMD criteria, anamnesis, clinical exams, occlusal and electromyographic analyses. Occlusion was evaluated morphologically and functionally. Electromyography tested static/dynamic muscle activity. Data were statistically analyzed using t-tests and Pearson correlation (p < 0.05). Results: Electromyographic analysis revealed significant differences between subjects with and without visual correction, suggesting that visual input influences masticatory muscle activity. Correlations emerged between occlusal asymmetries and neuromuscular parameters. These findings highlight clinical implications for mandibular function, muscle symmetry, and the potential for therapeutic rebalancing through targeted interventions. Conclusions: The study demonstrates a significant correlation between visual–motor integration and masticatory muscle efficiency. It emphasizes lateralized neuromuscular activation’s influence on occlusal contact distribution. Moreover, it identifies mandibular torsion–endfeel inverse correlation as a potential diagnostic marker for craniomandibular dysfunctions via surface electromyography. Full article

I address the challenge of extracting reliable insights from large datasets using a simplified model that illustrates how hierarchical classification can distort outcomes. The model consists of discrete pixels labeled red, blue, or white. Red and blue indicate distinct properties, while white represents unclassified or ambiguous data. A macro-color is assigned only if one color holds a strict majority among the pixels. Otherwise, the aggregate is labeled white, reflecting uncertainty. This setup mimics a percolation threshold at fifty percent. Assuming that directly accessing the various proportions from the data of colors is infeasible, I implement a hierarchical coarse-graining procedure. Elements (first pixels, then aggregates) are recursively grouped and reclassified via local majority rules, ultimately producing a single super-aggregate for which the color represents the inferred macro-property of the collection of pixels as a whole. Analytical results supported by simulations show that the process introduces additional white aggregates beyond white pixels, which could be present initially; these arise from groups lacking a clear majority, requiring arbitrary symmetry-breaking decisions to attribute a color to them. While each local resolution may appear minor and inconsequential, their repetitions introduce a growing systematic bias. Even with complete data, unavoidable asymmetries in local rules are shown to skew outcomes. This study highlights a critical limitation of recursive data reduction. Insight extraction is shaped not only by data quality but also by how local ambiguity is handled, resulting in built-in biases. Thus, the related flaws are not due to the data but to structural choices made during local aggregations. Although based on a simple model, these findings expose a high likelihood of inherent flaws in widely used hierarchical classification techniques. Full article

Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO₂ emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article

In this study, the effects of walking speed on lower limb muscle activity and gait parameters during over-ground walking were investigated in individuals with chronic stroke. Twenty-four patients with chronic stroke participated in a cross-sectional repeated-measures study, walking 20 m at three different speeds: slow (80% of self-selected speed), self-selected, and maximal speed. Surface electromyography was used to measure muscle activity in five paretic-side muscles (rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, and gluteus medius), while gait parameters, including stride length, stance and swing phases, single-limb support time, and the gait asymmetry index were assessed using a triaxial accelerometer. As walking speed increased, activity in the rectus femoris, biceps femoris, and gastrocnemius muscles significantly increased during the stance and swing phases (p < 0.05), whereas the gluteus medius activity tended to decrease. Stride length on the paretic and non-paretic sides significantly increased with faster walking speed (p < 0.05); however, no significant improvements were observed in other gait parameters or gait asymmetry. These findings suggest that although increasing walking speed enhances specific muscle activities, it does not necessarily improve overall gait quality or symmetry. Therefore, rehabilitation programs should incorporate multidimensional gait training that addresses speed and neuromuscular control factors such as balance and proprioception. Full article

Background: Anterior cruciate ligament reconstruction (ACLr) often leads to asymmetries between limbs, with variable return-to-performance rates in athletes. The single-leg countermovement jump (SLCMJ) is commonly used to assess postoperative knee function. However, limited research has explored deficits specifically during the unweighting phase of the jump. Methods: This study assessed 53 recreational athletes (11 females, 42 males) between 6 and 9 months post-ACLr using a dual force plate system (1000 Hz). Each participant performed three maximal-effort SLCMJs per limb. Outcome measures included jump height, negative peak velocity, minimum force, and center of mass (COM) displacement. Paired t-tests and Wilcoxon tests were used to compare the ACLr limb with the contralateral limb. Results: Compared to the healthy limb, the ACLr limb showed significantly lower negative peak velocity (−0.80 ± 0.40 m/s vs. −0.94 ± 0.40 m/s, p < 0.001), higher minimum force (36.75 ± 17.88 kg vs. 32.05 ± 17.25 kg, p < 0.001), and reduced COM displacement (−17.62 ± 6.25 cm vs. −19.73 ± 5.34 cm, p = 0.014). Eccentric phase duration did not differ significantly. Conclusions: Athletes post-ACLr demonstrate altered neuromuscular control during the early SLCMJ phase. These findings highlight the importance of rehabilitation strategies targeting eccentric strength and symmetry restoration. Full article

Many challenges in solving large graph coloring through parallel strategies remain unresolved. Previous algorithms based on Pregel-like frameworks, such as Apache Giraph, encounter parallelism bottlenecks due to sequential execution and the need for a full graph traversal in certain stages. Additionally, GPU-based algorithms face the dilemma of costly and time-consuming processing when moving complex graph applications to GPU architectures. In this study, we propose Spardex, a novel parallel and distributed graph coloring optimization algorithm designed to overcome and avoid these challenges. We design a symmetry-driven optimization approach wherein the EdgePartition1D strategy in GraphX induces partitioning asymmetry, leading to overlapping locally symmetric regions. This structure is leveraged through asymmetric partitioning and symmetric reassembly to reduce the search space. A two-stage pipeline consisting of partitioned repaint and core conflict detection is developed, enabling the precise correction of conflicts without traversing the entire graph as in previous algorithms. We also integrate symmetry principles from combinatorial optimization into a distributed computing framework, demonstrating that leveraging locally symmetric subproblems can significantly enhance the efficiency of large-scale graph coloring. Combined with Spark-specific optimizations such as AQE skew join optimization, all these techniques contribute to an efficient parallel graph coloring optimization in Spardex. We conducted experiments using the Aliyun Cloud platform. The results demonstrate that Spardex achieves a reduction of 8–72% in the number of colors and a speedup of 1.13–10.27 times over concurrent algorithms. Full article

This study evaluates a novel high-density polyethylene (HDPE) browlift peg for brow fixation in endoscopic forehead lifting, assessing its safety, effectiveness, and aesthetic outcomes while contextualizing its use through a narrative review of existing techniques. Twenty-nine consecutive female patients underwent bilateral endoscopic brow lifts using a custom-shaped HDPE peg inserted into the frontal bone via a small paramedian incision. Outcomes included postoperative brow symmetry, defined as ≤2 mm asymmetry, and documentation of complications. The mean patient age was 62.1 years, with an average follow-up of 12.3 months. All patients achieved symmetric brow positioning within 2 mm. No cases of implant extrusion, wound dehiscence, or permanent nerve injury occurred. Minor complications included one case each of transient paresthesia, localized incision infection not involving the implant, and a palpable implant removed in-office under local anesthesia. A parallel narrative review highlighted common limitations in brow fixation strategies—namely, implant palpability, risk of relapse, cost, and invasiveness. These findings suggest that the HDPE peg is a safe, customizable, and cost-effective alternative for brow fixation, offering durable aesthetic results with minimal complications and potential value in aesthetic and oculoplastic surgery. Full article

Maintaining body symmetry in sports characterized by high lateralization is crucial for optimizing long-term athletic performance and mitigating injury risk. This study aimed to evaluate the extent of morphological asymmetry in anthropometric features among elite professional fencers. Additionally, the presence of functional asymmetry and its associations with morphological asymmetry were assessed. Thirty-two Polish adult female fencers, aged 18–33 yrs, were examined. Data collection involved a questionnaire survey, anthropometric measurements, calculation of anthropological indices, and assessment of functional asymmetry. For the 24 bilateral anthropometric features, small differences were found in seven characteristics: foot length, subscapular skinfold thickness, upper arm circumference, minimum and maximum forearm circumference, upper limb length, and arm circumference in tension. Morphological asymmetry index did not exceed 5%. Left-sided lateralization of either the upper or lower limbs was associated with significantly high asymmetry, specifically indicating larger minimum forearm circumferences in the right limb. Continuous, individualized monitoring of morphological asymmetry and its direction in athletes is essential, demanding concurrent consideration of functional lateralization. This ongoing assessment establishes a critical baseline for evaluating training adaptations, reducing injury susceptibility, and optimizing rehabilitation strategies. Deeper investigation of symmetry within non-dominant limbs is warranted to enhance our understanding. Full article

Accurate gait assessment is essential for managing pathological locomotion, especially in elderly patients recovering from hip joint surgeries. Inertial measurement units (IMUs) provide real-time, objective data in clinical settings. This study examined pelvic oscillations in sagittal, frontal, and transverse planes using a wearable IMU system in two groups: Group A (n = 15, osteosynthesis metallica) and Group B (n = 34, arthroplasty), all over age 65. Gait analysis was conducted during assisted and unassisted walking. In the frontal plane, both groups showed statistically significant improvements: Group A from 46.4% to 75.2% (p = 0.001) and Group B from 52.6% to 72.2% (p = 0.001), reflecting enhanced lateral stability. In the transverse plane, Group A improved significantly from 47.7% to 80.2% (p = 0.001), while Group B showed a non-significant increase from 73.0% to 80.5% (p = 0.068). Sagittal plane changes were not statistically significant (Group A: 68.8% to 71.1%, p = 0.313; Group B: 76.4% to 69.1%, p = 0.065). These improvements correspond to better pelvic symmetry and postural control, which are critical for a safe and stable gait. Improvements were more pronounced during unassisted walking, indicating better pelvic control. These results confirm the clinical utility of IMUs in capturing subtle gait asymmetries and monitoring recovery progress. The findings support their use in tailoring rehabilitation strategies, particularly for enhancing frontal and transverse pelvic stability in elderly orthopedic patients. Full article

The symmetry of the interparticle interaction potential (IIP) plays a critical role in determining the thermodynamic and transport properties of solids. This study investigates the isolated effect of IIP asymmetry on thermalization. Asymmetry and nonlinearity are typically intertwined. To isolate the effect of asymmetry, we introduce a one-dimensional asymmetric harmonic (AH) model whose IIP possesses asymmetry but no nonlinearity, evidenced by energy-independent vibrational frequencies. Extensive numerical simulations confirm a power-law relationship between thermalization time (T_eq) and perturbation strength for the AH chain, revealing an exponent larger than the previously observed inverse-square law in the thermodynamic limit. Upon adding symmetric quartic nonlinearity into the AH model, we systematically study thermalization under combined asymmetry and nonlinearity. Matthiessen’s rule provides a good estimate of T_eq in this case. Our results demonstrate that asymmetry plays a distinct role in enhancing higher-order effects and governing relaxation dynamics. Full article

Background: Lokomat-assisted robotic rehabilitation is increasingly used for gait restoration in patients with spinal cord injury (SCI). However, the objective evaluation of treatment effectiveness through biomechanical parameters and machine learning approaches remains underexplored. Methods: This study analyzed data from 29 SCI patients undergoing Lokomat-based rehabilitation. A dataset of 46 variables including range of motion (L-ROM), joint stiffness (L-STIFF), and muscular force (L-FORCE) was examined using statistical methods (paired t-test, ANOVA, and ordinary least squares regression), clustering techniques (k-means), dimensionality reduction (t-SNE), and anomaly detection (Isolation Forest). Predictive modeling was applied to assess the influence of age, speed, body weight, body weight support, and exercise duration on biomechanical outcomes. Results: No statistically significant asymmetries were found between left and right limb measurements, indicating functional symmetry post-treatment (p > 0.05). Clustering analysis revealed a weak structure among patient groups (Silhouette score ≈ 0.31). Isolation Forest identified minimal anomalies in stiffness data, supporting treatment consistency. Regression models showed that body weight and body weight support significantly influenced joint stiffness (p < 0.01), explaining up to 60% of the variance in outcomes. Conclusions: Lokomat-assisted robotic rehabilitation demonstrates high functional symmetry and biomechanical consistency in SCI patients. Machine learning methods provided meaningful insight into the structure and predictability of outcomes, highlighting the clinical value of weight and support parameters in tailoring recovery protocols. Full article

Structural magnetic resonance imaging (sMRI) is a vital tool for diagnosing neurological brain diseases. However, sMRI scans often show significant structural changes only in limited brain regions due to localised atrophy, making the identification of discriminative features a key challenge. Importantly, the human brain exhibits inherent bilateral symmetry, and deviations from this symmetry—such as asymmetric atrophy—are strong indicators of early Alzheimer’s disease (AD). Patch-based methods help capture local brain changes for early AD diagnosis, but they often struggle with fixed-size limitations, potentially missing subtle asymmetries or broader contextual cues. To address these limitations, we propose a novel augmented reality (AR)-enhanced patch-level explainable deep learning (ARE-PaLED) system. It includes an adaptive multi-scale patch extraction network (AMPEN) to adjust patch sizes based on anatomical characteristics and spatial context, as well as an informative patch selection algorithm (IPSA) to identify discriminative patches, including those reflecting asymmetry patterns associated with AD; additionally, an AR module is proposed for future immersive explainability, complementing the patch-level interpretation framework. Evaluated on 1862 subjects from the ADNI and AIBL datasets, the framework achieved an accuracy of 92.5% (AD vs. NC) and 85.9% (AD vs. MCI). The proposed ARE-PaLED demonstrates potential as an interpretable and immersive diagnostic aid for sMRI-based AD diagnosis, supporting the interpretation of model predictions for AD diagnosis. Full article

Background and Objectives: This study aims to investigate the relationship between isokinetic knee strength and competition performance in elite male ski mountaineering sprint athletes and to identify strength parameters that predict performance and contribute to injury prevention. Materials and Methods: Thirteen male athletes participating in the Ski Mountaineering Turkey Cup final stage were included. Isokinetic knee flexion (FLX) and extension (EXT) strength of dominant (DM) and non-dominant (NDM) legs were measured at angular velocities of 60°/s and 180°/s using the DIERS-Myolin Isometric Muscle Strength Analysis System. Competition performance was evaluated using the ISMF scoring system. Data were analyzed using SPSS 26.0 with Pearson correlation and multiple regression analyses after normality, linearity, and homoscedasticity checks. Results: Strong positive correlations were found between hamstring strength at high angular velocities (180°/s) and performance (DM FLX: r = 0.809; NDM FLX: r = 0.880). Extension strength showed moderate correlations at low velocities (60°/s) (DM EXT: r = 0.677; NDM EXT: r = 0.699). Regression analysis revealed that DM FLX at 180°/s and DM EXT at 60°/s explained 49% of performance variance (Adj. R² = 0.498). For NDM legs, only 180°/s FLX was a significant predictor (β = 1.468). Conclusions: High-velocity hamstring strength plays a critical role in ski mountaineering sprint performance, particularly during sudden directional changes and dynamic balance. Quadriceps strength at low velocities contributes to prolonged climbing phases. Moreover, identifying and addressing bilateral strength asymmetries may support injury prevention strategies in elite ski mountaineering athletes. These findings provide scientific support for designing training programs targeting explosive hamstring strength, bilateral symmetry, and injury risk reduction, essential for optimizing performance in the 2026 Winter Olympics sprint discipline. Full article

This study investigates the solution structure of the nonlinear Rayleigh oscillator equation through two widely used semi-analytical techniques: the Laplace–Adomian Decomposition Method (LADM) and the Homotopy Perturbation Method (HPM). The Rayleigh oscillator exhibits inherent asymmetry in its nonlinear damping term, which disrupts the time-reversal symmetry present in linear oscillatory systems. Applying the LADM and HPM, we derive approximate solutions for the Rayleigh oscillator. Due to the absence of exact analytical solutions in the literature, these approximations are benchmarked against high-precision numerical results obtained using Mathematica’s NDSolve function. We perform a detailed error analysis across different damping parameter values ε and time intervals. Our results reveal how the asymmetric damping influences the accuracy and convergence behavior of each method. This study highlights the role of nonlinear asymmetry in shaping the solution dynamics and provides insight into the suitability of the LADM and HPM under varying conditions. Full article

In the digital economy, information asymmetry among individuals, data users, and governments limits the full realization of personal data value. To address this, “symmetry enhancement” strategies aim to reduce information gaps, enabling more balanced decision-making and facilitating efficient data flow. This study establishes a tripartite evolutionary game model based on personal data collection and development, conducts simulations using MATLAB R2024a, and proposes countermeasures based on equilibrium analysis and simulation results. The results highlight that individual participation is pivotal, influenced by perceived benefits, management costs, and privacy risks. Meanwhile, data users’ compliance hinges on economic incentives and regulatory burdens, with excessive costs potentially discouraging adherence. Governments must carefully weigh social benefits against regulatory expenditures. Based on these findings, this paper proposes the following recommendations: use personal data application scenarios as a guide, rely on the construction of personal trustworthy data spaces, explore and improve personal data revenue distribution mechanisms, strengthen the management of data users, and promote the maximization of personal data value through multi-party collaborative ecological incentives. Full article