Search Results (849)

Artificial intelligence (AI) is increasingly reshaping esthetic and reconstructive plastic surgery by improving measurement accuracy, treatment planning, and prediction of surgical outcomes. This article provides a scientific overview of current AI applications, including automated image analysis, machine-learning-based outcome forecasting, and generative models for preoperative simulation. AI-driven three-dimensional morphometrics allow precise, reproducible quantification of facial and body structures, supporting more objective assessments of symmetry, proportion, and contour. Predictive algorithms trained on large clinical datasets can estimate postoperative results and complication risks with higher consistency than traditional subjective evaluation. Intraoperative AI tools, such as real-time image guidance and robotic assistance, show potential to increase procedural precision and reduce variability. Despite these advances, important limitations persist. Algorithmic bias, restricted data diversity, opaque model architectures, and unresolved ethical concerns regarding data privacy and esthetic standardization challenge widespread clinical adoption. Overall, AI offers a powerful framework for enhancing precision and reproducibility in esthetic surgery, but its safe and responsible integration will require rigorous validation, transparent methodology, and continued human oversight. Full article

The reconstruction of nodal prices and generation patterns in electricity markets with network constraints constitutes a challenging inverse analysis problem due to congestion-induced non-uniqueness and limited observability. This study introduces node symmetry analysis as a novel early indicator of locational marginal price (LMP) growth in power systems with high renewable energy penetration. Symmetric nodes, defined as nodes with identical generation cost structures and comparable network topology, exhibit near-identical price signals under uncongested conditions. In this study, the term “price” refers to the LMP obtained from the DC-OPF market-clearing model under scenarios with high renewable energy penetration. Deviations from this symmetry, quantified through price differences between symmetric node pairs (ΔLMP), serve as sensitive indicators of emerging network stress and congestion, providing early warning of peak-price events. Using DC power flow sensitivities and congestion indicators, LMPs are reconstructed in a simplified five-node test system under three scenarios: baseline operation, severe transmission congestion, and high renewable generation variability. Results show strong correlations between symmetry violations and system-wide price increases. In congested scenarios, ΔLMP exceeding €2/MWh consistently precedes peak prices by 1–2 h, demonstrating the metric’s predictive capability. Integration of storage further highlights the operational value of symmetry-based analysis, showing reductions in curtailed renewable generation and peak prices. The proposed framework offers a computationally efficient and interpretable tool for congestion diagnosis, price trend forecasting, and inverse market analysis, with potential scalability to larger AC networks and stochastic scenarios. These findings provide actionable insights for system operators, market participants, and regulators seeking to enhance flexibility, reliability, and economic efficiency in high-renewable electricity markets. Full article

Background/Objectives: Bone-anchored prostheses provide an alternative to socket prostheses, directly connecting the prosthesis to the residual limb via osseointegration. However, limited evidence exists on how spatiotemporal gait parameters and gait symmetry change over time following osseointegration in individuals with unilateral transfemoral amputation. This study aimed to examine changes in spatiotemporal and gait symmetry parameters before osseointegration and at 6 and 12 months post-surgery. Methods: Common spatiotemporal parameters were collected from six individuals with unilateral transfemoral amputation at baseline (with socket prosthesis) and at 6 and 12 months post-osseointegration using a motion analysis system. Group-level differences were assessed using repeated measures ANOVA. Gait symmetry was evaluated using selected spatiotemporal parameters. Results: Following osseointegration, individuals with unilateral transfemoral amputation experienced significant spatiotemporal changes over time. At the group level, walking velocity and stride length decreased at 6 months, with stride length increasing at 12 months. Step width and prosthetic-side step length increased at 12 months relative to 6 months, while intact-side step length decreased. Prosthetic-side toe-off timing was shorter at 12 months. Gait symmetry responses varied individually: some with poor baseline symmetry improved, while those with better baseline symmetry became more asymmetric, indicating heterogeneous outcomes. Conclusions: This study highlights longitudinal changes in gait biomechanics following osseointegration in individuals with unilateral transfemoral amputation. Gait adaptations were highly variable across individuals and time points. Future research should involve larger, more homogeneous samples and incorporate kinetic, muscle activity, and functional outcome measures to better understand the impact of bone-anchored prostheses on gait and mobility. Full article

Inspired by the concept of symmetry in functional representation, complex nonlinear relationships can be decomposed into combinations of lower-dimensional functions, providing an interpretable framework for modeling high-dimensional systems. With the continuous growth of road traffic volume in China and the rapid acceleration of urbanization, traffic safety issues have become increasingly prominent. To address the limitations of traditional traffic accident prediction models—including insufficient spatial information representation, weak nonlinear fitting capability, and poor interpretability—this study proposes an improved Kolmogorov–Arnold Networks (KANs) model. Specifically, a spatial embedding module, a multi-scale spline mechanism, and a residual connection structure are incorporated into the original KAN framework to enhance its ability to capture spatial heterogeneity and complex nonlinear relationships in traffic accident data. Experimental results demonstrate that the improved KAN model achieves a 2.38% increase in the coefficient of determination, while reducing the mean absolute deviation and mean squared prediction error by 24.89% and 34.69%, respectively, indicating a significant improvement in both prediction accuracy and model stability. Furthermore, the proposed model enhances interpretability by visualizing variable relationships through spline functions, enabling intuitive analysis of nonlinear effects. Overall, the improved KAN model exhibits strong capability in modeling spatially non-stationary and nonlinear structures, making it a promising tool for macroscopic traffic safety modeling with substantial application potential and practical value. Full article

Gibson’s concept of optic flow established that perception is grounded in lawful structure generated by action. However, no formal mechanical framework has described the invariant structure of action-generated kinesthetic information during skilled manipulation. This study introduces haptic flow as a screw-theoretic invariant defined by the coupled rotational–translational organization of a body–object system. Motion capture data from a two-case comparison (one proficient and one novice golfer) were analyzed using instantaneous screw axes (ISA), pitch evolution, and cylindroid geometry derived from a linear line-complex formulation. The proficient golfer exhibited (1) progressive convergence of ISAs toward a coherent bundle, (2) stabilization of screw pitch through impact, and (3) co-cylindrical alignment of harmonic screws consistent with inertial–restoring conjugacy. In contrast, the novice golfer showed fragmented ISA organization and elevated pitch variability. These differences were descriptive rather than inferential and do not imply population-level generalization. The findings suggest that skilled manipulation is characterized by stabilization of symmetry-bearing screw invariants rather than by independent joint control. Interpreted ecologically, haptic flow is proposed as a mechanically specified candidate invariant generated by lawful body–object coupling. The present study establishes a geometric framework for quantifying such invariants while identifying the need for cross-task and perceptual validation. Full article

Optimizing the control parameters of an islanded microgrid with active load integration presents a challenging operational research problem since current methodologies frequently fail to reach the ideal balance or symmetry between transient response, stability, and efficiency. The conventional methods, such as the canonical Particle Swarm Optimization (PSO), have settling time and voltage ripple minimization constraints, indicating possible improvement scopes. This research addresses this gap by employing advanced metaheuristic algorithms such as Accelerated Particle Swarm Optimization (APSO), Accelerated Particle Swarm Optimization with variable α (APSO α), Accelerated Particle Swarm Optimization with Normal Distribution (APSO_G), Rayleigh Distribution Accelerated Particle Swarm Optimization (RDAPSO), Rayleigh Distribution Accelerated Particle Swarm Optimization with variable α (RDAPSO α), and the Dragonfly Algorithm (DA). The algorithms were tested for their performance by using CEC Standard Benchmark functions from 2017, 2019, and 2022, providing a basis for rigorous and symmetrical testing and validation. The optimized RDAPSO α algorithm showed a significant reduction in voltage ripple, which was reduced from 4 V to 0.47 V, with an 88.25% reduction. It also showed a 46.32% improvement in settling time, which was reduced from 184.2 ms to 98.9 ms compared to PSO. A detailed statistical analysis was conducted to enhance the reliability and symmetry of the outcomes using Multivariate Analysis of Variance (MANOVA), the Mann–Whitney U test, the Friedman test, and the Bonferroni test. The results show that RDAPSO α offers a significant edge over the rest of the algorithms, with improvements that can be declared statistically superior in optimizing microgrids with improved symmetry in performance. Full article

In industrial acoustic imaging, microphone array design is often limited by the strong frequency dependence of array performance, the high computational cost of optimization, and the expense of deploying large numbers of microphones. Most existing optimization-based methods require simultaneous optimization of all array elements, resulting in long design times and limited flexibility in controlling element count. To overcome these limitations, this paper proposes a fast and iterative microphone array design method using equilateral triangular subarrays as basic units. Instead of optimizing the entire array at once, the proposed method incrementally adds subarrays, and in each iteration, a genetic algorithm optimizes only the placement of the newly added subarray for a specified target frequency. By exploiting the rotational symmetry of the equilateral triangular subarrays and the geometric characteristics of the array point spread function, the number of optimization variables and the computational domain are significantly reduced, enabling efficient array design. The proposed method allows frequency-specific performance optimization while providing direct control over the number of array elements, achieving a practical balance between imaging performance and hardware cost. Comparative results show that arrays designed using this method generally exhibit improved main lobe width and sidelobe level performance near the target frequencies compared with several classical array configurations. Full article

We present a covariant geometric extension of General Relativity formulated within a controlled effective field theory framework. The gravitational action is supplemented by curvature-dependent operators parametrized by three coefficients $\alpha$, $\beta$, and $\gamma$, chosen such that the resulting field equations remain second order in time derivatives and free of Ostrogradsky instabilities. In a homogeneous and isotropic cosmological background, the modified dynamics generically replaces the classical Big Bang singularity with a smooth, nonsingular bounce driven by a repulsive curvature core proportional to $a^{-6}$. A distinctive feature of the framework is the presence of a geometric slip term proportional to $\dot{H}$, which encodes curvature-memory effects at the level of the background evolution without introducing additional propagating degrees of freedom. This term dynamically correlates the expansion rate with its temporal variation, leading to effective ultraviolet damping and enhanced dynamical stability across the high-curvature regime. As a consequence, the cosmological solutions admit the definition of an intrinsic relational time variable that is strictly monotonic throughout the evolution, including across the bounce. The emergent temporal ordering arises purely from geometric dynamics and does not rely on matter clocks, nonlocality, or fundamental violations of time-reversal or CPT symmetry. We discuss the consistency of the framework within its effective field theory domain of validity and comment on its implications for the conceptual problems of singularity resolution and the emergence of time in cosmology. Full article

Complex dynamical systems are characterized by inherent nonlinearity, high dimensionality, spatiotemporal uncertainty, and implicit symmetry, posing fundamental challenges for their mathematical modeling and multi-step evolution prediction. For example, wind power exhibits strong randomness, intermittency, and latent temporal symmetry. To address this, this paper proposes a symmetry-guided deep generative model, the bi-directional recurrent generative adversarial network (BDR-GAN), for the multi-step rolling prediction of such systems. The BDR-GAN formalizes multi-step evolution as a conditional probability distribution learning problem. It systematically integrates three forms of symmetry to enhance modeling validity: bi-directional temporal symmetry captured by a BiLSTM-based generator, structural symmetry within the adversarial learning framework between the generator and a 1D-CNN discriminator, and rolling symmetry enabled by a recursive prediction strategy that supports cyclic state updates. Theoretical analysis demonstrates that this symmetry-embedded adversarial mechanism enables BDR-GAN to effectively approximate the underlying dynamic operators and the conditional distribution of future states, improving the learned model’s generalization. Experimental validation on wind power datasets confirms the framework’s superiority. Compared to benchmark models, BDR-GAN achieves superior prediction accuracy (e.g., RMSE 0.236, MAPE 5.12%), provides reliable uncertainty quantification (PICP 95.5%), and exhibits enhanced robustness against noise and variability. This work provides a generalizable, symmetry-guided modeling framework for the multi-step evolution of complex dynamical systems, offering theoretical and technical support for high-precision prediction in critical applications such as wind power integration and smart grid operation. Full article

The Hodgkin–Huxley equations have successfully described neuronal excitability for over seventy years, yet their mathematical structure remains empirically justified rather than theoretically explained. Why are gating variables bounded between 0 and 1? Why does sodium conductance depend on m³h rather than other combinations? Why does potassium depend on n⁴? Why do all rate functions contain exponential voltage dependencies? Why are the kinetics first-order? We demonstrate that these structural features arise naturally from three fundamental physical symmetries governing ion channel dynamics: the compactness of conformational state space, the scaling invariance of membrane conductance, and temporal translation invariance. Using Lie group theory, we show that these symmetries uniquely determine a mathematical structure in which: (1) gating variables are necessarily bounded, (2) voltage dependencies must be exponential, (3) exponents must be integers, and (4) kinetics must be first-order. The Hodgkin–Huxley equations, rather than mere empirical fits, emerge from fundamental symmetry principles. This framework establishes that neural electrophysiology obeys the same theoretical principles as modern physics, where symmetries constrain the form of dynamical equations. It further provides a principled basis for interpreting deviations from classical behavior as manifestations of additional symmetries or symmetry breaking. Full article

Quaternion Fractional Moment (QFM) descriptors are widely used in geometric pattern recognition due to their ability to encode multi-channel image information and exhibit invariance properties. However, their robustness under real-world acquisition variability, particularly photometric noise, remains insufficiently understood. Based on the Lipschitz stability theorem, which defines a strong, linear form of stability for dynamical systems, applied to one of our previous works, this article improves upon it by introducing a robustness-driven analysis framework that models feature extraction as a stochastic process, where bounded spatio-temporal perturbations generate multiple descriptor realizations for each pattern. Descriptor robustness is directly quantified in feature space using a novel normalized dispersion stability metric. Furthermore, a Lipschitz stability theorem is formally established and proved, providing theoretical guarantees of descriptor robustness under bounded perturbations. Experiments conducted on Moroccan–Andalusian geometric patterns with p4m and p6m symmetry groups demonstrate that the proposed framework achieves high intrinsic stability (${\sigma }_{norm}$ = 0.042 ± 0.010), while preserving state-of-the-art classification performance (Macro-F1 = 0.589 vs. 0.570 under σ = 0.05 noise). These results confirm that robustness is an intrinsic and measurable property of the descriptor, independent of classifier performance. The proposed framework provides both theoretical and methodological support for reliable geometric pattern recognition in cultural heritage imaging under real-world conditions. Full article

Reduced arm swing frequently occurs in older adults and is associated with declined gait performance. Experimental studies have demonstrated that restricting arm swing decreases stride length and walking speed, whereas deliberately increasing arm swing can improve these gait parameters. This study evaluated whether a wearable haptic feedback system could effectively increase arm-swing amplitude and assess its effects on spatiotemporal gait outcomes during overground walking. Using a within-subject repeated-measures design, twelve community-dwelling older adults (6 males/6 females; $75.8\pm 6.5$ years) completed three no-feedback conditions (Baseline, Exaggerated, Fast) and six feedback conditions varying Direction (Forward, Backward, Combined) and target Magnitude (+100%, +200% of the Baseline). The arm-swing angle was estimated in real time from upper-arm inertial measurement unit (IMU) sensors; targets were defined for peak Forward flexion and/or peak Backward extension, and vibrotactile cues were delivered when the corresponding peak failed to reach the target. The arm range of motion (ROM) increased significantly across conditions, with the largest increase during Feedback (+229%), exceeding Exaggerated (+120%) and Fast (+64%) (all $p<0.001$). Walking speed and stride length also increased during Feedback relative to the Baseline ($p<0.001$). Within feedback conditions, the arm ROM showed independent main effects of the Direction and Magnitude, whereas gait outcomes were primarily influenced by Direction. Arm-swing symmetry was largely preserved, with the smallest variability during Feedback. These findings support the feasibility of vibrotactile feedback to enhance arm swing and improve gait outcomes in older adults. Full article

Metaheuristic methods for three-dimensional (3D) unmanned aerial vehicle (UAV) path planning often suffer from premature convergence and reduced accuracy in complex high-dimensional spaces, in which waypoint-based decision variables exhibit structured dependencies and segment-level regularities. In a symmetry-inspired operational sense, these regularities can be interpreted as exploitable dependency patterns across path segments and permutation invariance among homogeneous UAVs, which are often overlooked by standard algorithms. The paper proposes an enhanced dung beetle optimizer (LEDBO) that integrates interaction-aware variable handling, adaptive role regulation, and a fitness-state-driven hybrid search mechanism. Correlation-based variable grouping clusters dependent waypoints into segments to exploit statistical dependency patterns among waypoint-coordinate variables and enhance local refinement. A three-level adaptive role-regulation scheme adjusts search behaviors according to convergence status and population diversity, thereby mitigating stagnation. Meanwhile, a fitness-state-driven hybrid engine combines Nelder–Mead local refinement with Lévy-flight global exploration to balance exploitation and exploration across stages. Experiments on the CEC2017 benchmark suite and complex 3D UAV path-planning simulations demonstrate that LEDBO achieves better solution quality, convergence behavior, and robustness than representative metaheuristics, producing smoother, shorter, and safer trajectories. The results suggest that incorporating interaction-aware variable grouping and adaptive search regulation can improve UAV path planning and related high-dimensional continuous optimization tasks. Full article

Background: In maxillofacial reconstruction, even small inaccuracies can compromise aesthetics, function, and safety. Surgeons currently rely on preoperative imaging; however, recent advances in intraoperative imaging now provide three-dimensional, real-time guidance, possibly enhancing surgical outcomes. This review evaluates the current application of intraoperative imaging in maxillary and mandibular surgery including its impact on accuracy, efficiency, and outcomes. Methods: Two separate systematic reviews (PROSPERO CRD420251125497, CRD420251124600), analyzing maxillary and mandibular repair were conducted through Cochrane, Medline, Embase, and Web of Science. Both reviews adhered to the PRISMA guidelines. Inclusion criteria encompassed intraoperative digital imaging or navigation in maxillary or mandibular surgery. Studies without human subjects, intraoperative imaging, or the surgery of interest were excluded. Bias was assessed with NIH Quality Assessment. Results: A combined total of 795 publications were screened, with 35 studies ultimately included in this review, encompassing 1643 patients. Techniques included intraoperative computed tomography (CT) (n = 12, 34.3%), stereotactic navigation (n = 16, 45.7%), augmented reality (n = 2, 5.7%), ultrasound, fluoroscopy, infrared stereoscopic and electromagnetic (n = 1, 2.9%, each). The most common indication for surgery was fracture repair. Reporting was heterogeneous, with variable metrics and reporting for accuracy, complications, and revisions. Overall, cone-beam CT (CBCT) and stereotactic navigation both demonstrated significant restoration of normal symmetry, and stereotactic navigation enabled accuracy of <2 mm. CBCT added the shortest amount of time intraoperatively, ranging from 1 to 20 min. Reporting on long-term outcomes was heterogeneous. Conclusions: A variety of intraoperative imaging and navigation techniques are being applied in maxillofacial surgery. However, inconsistent reporting metrics, small study size, and study feasibility-focused study design limit meaningful comparison across technologies. Rigorous prospective studies with standardized outcome measures are needed to further define their clinical value and guide adoption. Full article

Electricity spot prices jointly encode network physics and strategic bidding outcomes. In a well-functioning market, nodal and temporal price patterns tend to remain approximately invariant under mild perturbations-exhibiting symmetry-preserving regularities in distribution shape, spatial gradients, and temporal variation. Conversely, congestion binding, net-load stress, and abnormal bidding can induce symmetry breaking, manifested as heavy tails, mean shifts, and localized price discontinuities. This study develops a symmetry-guided and explainable diagnostic framework to identify price anomalies and attribute their dominant drivers. First, representative anomaly types (spike and mean shift) are defined using statistically and operationally motivated criteria, together with robustness checks across alternative thresholds. Second, principal component analysis is applied to construct compact, anomaly-specific feature sets, filtering weakly related variables while retaining system stress, congestion proxies, and renewable-induced variability indicators. Third, leveraging the optimization structure of market clearing and the associated KKT conditions, we characterize the price–feature linkage as a piecewise mapping and quantify each feature’s contribution via a sampling-based influence scoring procedure, yielding a ranked causal attribution. Case studies on a regional day-ahead spot market dataset demonstrate that the proposed framework achieves high consistency with expert assessments, with traceability accuracy exceeding 85% overall and particularly strong performance for spike-type anomalies. The method reduces reliance on purely manual diagnosis and black-box learning, and provides symmetry-oriented, actionable evidence for market surveillance and renewable-friendly flexibility and congestion management design. The proposed framework enables transparent identification of dominant structural drivers underlying different types of electricity price anomalies, linking observed price signals to market-clearing mechanisms. The results provide actionable diagnostic insights for market monitoring and regulatory assessment in electricity markets with high renewable penetration. Full article