Search Results (515)

Facial palsy affects millions worldwide. Botulinum toxin Type A (BoNT-A) is an established treatment for non-flaccid facial palsy, yet objective evidence remains limited. This study evaluates the effects of BoNT-A using AI-based tools and patient-reported outcome measures (PROMs). In this prospective observational study, patients with non-flaccid facial palsy received individualized BoNT-A injections. Exclusion criteria included age < 18, hypersensitivity to BoNT-A, or lack of follow-up. Assessments were conducted before and 3 weeks after treatment, including facial symmetry (Emotrics^®), emotion expression (FaceReader™), and PROMs (FaCE and FDI). Eleven patients (mean age 50.1 ± 18 years) were included. BoNT-A significantly improved dynamic facial symmetry: eyebrow raising (p = 0.032), smile angle (p = 0.005), and lower lip height (p = 0.042). Emotion analysis showed no significant changes. PROMs revealed improvements in social well-being (FDI, p = 0.004) and aesthetic satisfaction (FaCE, p = 0.035), while functional FDI scores remained unchanged (p = 0.406). BoNT-A improves objective symmetry and patient satisfaction in non-flaccid facial palsy. The lack of change in emotional expression may reflect improved symmetry at the cost of dynamic muscle activation. Full article

Flexible grid regulation necessitates Francis turbines to operate at heads of 120–180 m (compared to the rated head of 154.6 m), breaking the designed rotational symmetry and inducing hydraulic instabilities that threaten structural integrity and operational reliability. This study presents extensive field measurements of pressure pulsations in a 600 MW prototype Francis turbine operating at heads of 120–180 m and loads of 20–600 MW across 77 operating conditions (7 head levels × 11 load points). We strategically positioned high-precision piezoelectric pressure sensors at three critical locations—volute inlet, vaneless space, and draft tube cone—to capture the amplitude and frequency characteristics of symmetry-breaking phenomena. Advanced signal processing revealed three distinct mechanisms with characteristic pressure pulsation signatures: (1) Draft tube rotating vortex rope (RVR) represents spontaneous breaking of axial symmetry, exhibiting helical precession at 0.38 Hz (approximately 0.18 f_n, where f_n = 2.08 Hz) with maximum peak-to-peak amplitudes of 108 kPa (87% of the rated pressure p_rated = 124 kPa) at H = 180 m and P = 300 MW, demonstrating approximately 70% amplitude reduction potential through load-based operational strategies. (2) Vaneless space rotor-stator interaction (RSI) reflects periodic disruption of the combined C₂₄ × C₁₃ symmetry at the blade-passing frequency of 27.1 Hz (N_r × f_n = 13 × 2.08 Hz), reaching peak amplitudes of 164 kPa (132% p_rated) at H = 180 m and P = 150 MW, representing the most severe symmetry-breaking phenomenon. (3) Volute multi-point excitation exhibits broadband spectral characteristics (4–10 Hz) with peak amplitudes of 146 kPa (118% p_rated) under small guide vane openings. The spatial amplitude hierarchy—vaneless space (164 kPa) > volute (146 kPa) > draft tube (108 kPa)—directly correlates with the local symmetry-breaking intensity, providing quantitative evidence for the relationship between geometric symmetry disruption and hydraulic excitation magnitude. Systematic head-dependent amplitude increases of 22–43% across all monitoring locations are attributed to effects related to Euler head scaling and Reynolds number variation, with the vaneless space demonstrating the highest sensitivity (0.83 kPa/m, equivalent to 0.67% p_rated/m). The study establishes data-driven operational guidelines identifying forbidden operating regions (H = 160–180 m, P = 20–150 MW for vaneless space; H = 160–180 m, P = 250–350 MW for draft tube) and critical monitoring frequencies (0.38 Hz for RVR, 27.1 Hz for RSI), providing essential reference data for condition monitoring systems and operational optimization of large Francis turbines functioning as flexible grid-regulating units in renewable energy integration scenarios. Full article

In order to address challenges in small object recognition for remote sensing imagery—including high model complexity, overfitting with small samples, and insufficient cross-scenario generalization—this study proposes CAGM-Seg, a lightweight recognition model integrating multi-attention mechanisms. The model systematically enhances the U-Net architecture: First, the encoder adopts a pre-trained MobileNetV3-Large as the backbone network, incorporating a coordinate attention mechanism to strengthen spatial localization of min targets. Second, an attention gating module is introduced in skip connections to achieve adaptive fusion of cross-level features. Finally, the decoder fully employs depthwise separable convolutions to significantly reduce model parameters. This design embodies a symmetry-aware philosophy, which is reflected in two aspects: the structural symmetry between the encoder and decoder facilitates multi-scale feature fusion, while the coordinate attention mechanism performs symmetric decomposition of spatial context (i.e., along height and width directions) to enhance the perception of geometrically regular small targets. Regarding training strategy, a hybrid loss function combining Dice Loss and Focal Loss, coupled with the AdamW optimizer, effectively enhances the model’s sensitivity to small objects while suppressing overfitting. Experimental results on the Xingtai black and odorous water body identification task demonstrate that CAGM-Seg outperforms comparison models in key metrics including precision (97.85%), recall (98.08%), and intersection-over-union (96.01%). Specifically, its intersection-over-union surpassed SegNeXt by 11.24 percentage points and PIDNet by 8.55 percentage points; its F1 score exceeded SegFormer by 2.51 percentage points. Regarding model efficiency, CAGM-Seg features a total of 3.489 million parameters, with 517,000 trainable parameters—approximately 80% fewer than the baseline U-Net—achieving a favorable balance between recognition accuracy and computational efficiency. Further cross-task validation demonstrates the model’s robust cross-scenario adaptability: it achieves 82.77% intersection-over-union and 90.57% F1 score in landslide detection, while maintaining 87.72% precision and 86.48% F1 score in cloud detection. The main contribution of this work is the effective resolution of key challenges in few-shot remote sensing small-object recognition—notably inadequate feature extraction and limited model generalization—via the strategic integration of multi-level attention mechanisms within a lightweight architecture. The resulting model, CAGM-Seg, establishes an innovative technical framework for real-time image interpretation under edge-computing constraints, demonstrating strong potential for practical deployment in environmental monitoring and disaster early warning systems. Full article

In this study, we investigate a nonlinear Schrödinger equation relevant to the evolution of optical beams in weakly nonlocal media. Utilizing the modified F-expansion method, we construct a variety of novel soliton solutions, including dark, bright, and wave solitons. These solutions are illustrated through comprehensive graphical simulations, including 2D contour plots and 3D surface profiles, to highlight their structural dynamics and propagation behavior. The effects of the temporal parameter on soliton formation and evolution are thoroughly analyzed, demonstrating its role in modulating soliton shape and stability. To further explore the system’s dynamics, chaos and sensitivity theories are employed, revealing the presence of complex chaotic behavior under perturbations. The outcomes underscore the versatility and richness of the present model in describing nonlinear wave phenomena. This work contributes to the theoretical understanding of soliton dynamics in weakly nonlocal nonlinear optical systems and supports advancements in photonic technologies. This study reports a novel soliton structure for the weak nonlocal cubic–quantic NLSE and also details the comprehensive chaotic and sensitivity analysis that represents the unexplored dynamical behavior of the model. This study further demonstrates how the underlying nonlinear structures, along with the novel solitons and chaotic dynamics, reflect key symmetry properties of the weakly nonlocal cubic–quintic Schrödinger model. These results enhanced the theoretical framework of the nonlocal nonlinear optics and offer potential implications in photonic waveguides, pulse shape, and optical communication systems. Full article

Based on a review of the current literature reflecting the results obtained in the field of synthesis and studying of neuromorphic materials and complex networks of various natures, a new interpretation of the term “sociomorphic materials” is proposed, aligning with philosophical views on the nature of complex systems. It is shown that the current level of research in the field of complex systems allows for the formulation and resolution of the problem of finding general regularities inherent to such systems, regardless of their nature. The basis for this is, among other things, the principle of dialectical symmetry, which treats information as a dialectical category paired with the category of matter. In the foreseeable future, sociomorphic materials may serve as a tool for simulating processes occurring in society, which is itself inherently a complex system. Furthermore, such systems could act as a “mediator” enabling direct contact with the transpersonal level of information processing—resources of which remain largely untapped. The relevance of establishing such contact is emphasized. The paper also discusses the chemical mechanisms that support the transition from neuromorphic to sociomorphic materials. Full article

This study presents the synthesis and comprehensive characterization of tin dioxide nanoparticles (SnO₂NPs). SnO₂NPs were obtained using a conventional wet-chemistry route and an environmentally friendly green-chemistry approach employing plant extracts from rooibos leaves (Aspalathus linearis), pomegranate seeds (Punica granatum), and kiwifruit peels (family Actinidiaceae). The thermal stability and decomposition profiles were analyzed by thermogravimetric analysis (TGA), while their structural and physicochemical properties were investigated using X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), ultraviolet–visible (UV–Vis) spectroscopy, dynamic light scattering (DLS), Raman spectroscopy, and attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy. Transmission electron microscopy (TEM) confirmed the nanoscale morphology and uniformity of the obtained particles. The photocatalytic activity of SnO₂NPs was evaluated via the degradation of methyl orange (MeO) under UV irradiation, revealing that nanoparticles synthesized using rooibos extract exhibited the highest efficiency (68% degradation within 180 min). Furthermore, surface-enhanced Raman scattering (SERS) spectroscopy was employed to study the adsorption behavior of L-phenylalanine (L-Phe) on the SnO₂NP surface. To the best of our knowledge, this is the first report demonstrating the use of pure SnO₂ nanoparticles as SERS substrates for biologically active, low-symmetry molecules. The calculated enhancement factor (EF) reached up to two orders of magnitude (10²), comparable to other transition metal-based nanostructures. These findings highlight the potential of SnO₂NPs as multifunctional materials for biomedical and sensing applications, bridging nanotechnology and regenerative medicine. Full article

Rhythm disturbances during human exercise represent a critical challenge for both physiological monitoring and athlete safety. To address this, a structure-enhanced β-TCVAE framework was proposed that derives a Rhythm Disturbance Index (RDI) from multimodal wearable sensor signals. RDI demonstrated a strong correlation with bilateral imbalance (r = 0.838, R² = 0.702) and achieved high discriminative performance (ROC-AUC = 0.823). Importantly, its weak and non-significant correlation with heart rate (r = 0.0569, p > 0.05) supported independence from cardiovascular load, underscoring its specificity to motor rhythm rather than systemic exertion. Analyses conducted on multimodal datasets further validated the robustness of this correlation, showing that RDI consistently aligns with disruptions in locomotor symmetry even after controlling for heart rate. This quantifiable coupling between rhythmic instability and symmetry loss positions RDI as a dual correlational indicator, sensitively reflecting both neuromuscular rhythm irregularities and axial imbalance. Such dual insight enables continuous and objective monitoring of locomotor quality, empowering coaches, clinicians, and sports scientists to tailor training strategies, optimize performance, and reduce the risk of injury. By integrating advanced variational reasoning with real-time wearable sensing, the proposed framework offers an evidence-based step forward in precision monitoring and risk assessment for athletes. Full article

This paper studies the information interaction process in Bayesian theorem-based swarm systems. Through theoretical analysis, model construction, and simulation experiments, it explores how Bayesian decision-making utilizes information cascades to update its state step by step in group information interaction. The system operates within a theoretical framework where an underlying symmetry governs the dynamic combination of prior knowledge, neighbor information, and target guidance, leading to spontaneous aggregation behavior similar to biological swarms. A key embodiment of this symmetry is the action–reaction force parity between agents, which ensures local stability.The simulation results show that groups with different prior information exhibit a multi-stage convergence characteristic, which reveals that within each iteration step, the agent adheres to the rules for information-symmetric communication and interaction. This dynamic behavior is a true reflection of natural biological populations and provides theoretical support for practical applications such as traffic management and robot collaboration. Full article

This paper proves a new lemma that characterizes controllability for linear Volterra control systems and shows that the usual controllability assumption for the variational linearized system near an optimal pair is superfluous. Building on this, it establishes a Pontryagin-type maximum principle for Volterra optimal control with general control and state constraints (fixed terminal constraints and time-dependent state bounds), where the cost combines a terminal term with a state-dependent and integral term. Using the Dubovitskii–Milyutin framework, we construct conic approximations for the cost, dynamics, and constraints and derive necessary optimality conditions under mild regularity: (i) a classical adjoint system when only terminal constraints are present and (ii) a Stieltjes-type adjoint with a non-negative Borel measure when pathwise state constraints are active. Furthermore, under convexity of the cost functional and linear Volterra dynamics, the maximum principle becomes a sufficient criterion for global optimality (recovering the classical sufficiency in the differential case). The differential case recovers the classical PMP, and an SIR example illustrates the results. A key theme is symmetry/duality: the adjoint differentiates in the state while the maximum condition differentiates in the control, reflecting operator transposition and the primal–dual geometry of Dubovitskii–Milyutin cones. Full article

Understanding and improving web performance is essential for enhancing user experience, yet existing approaches remain largely correlation-based and lack causal interpretability. To address this limitation, we propose a causal-inference-driven framework for diagnosing and optimizing user-centric Web Vitals such as Largest Contentful Paint (LCP), First Input Delay (FID), and Cumulative Layout Shift (CLS). Our contributions are threefold. (1) We construct a comprehensive feature representation that captures Document Object Model (DOM) structure, resource loading behaviors, rendering characteristics, and JavaScript execution, integrating browser-level domain knowledge into the modeling pipeline. (2) We introduce a hybrid causal discovery method that combines constraint-based reasoning with differentiable score-based learning to estimate high-dimensional causal structures reflecting real rendering processes. (3) We develop a causal-effect-based intervention optimization module that leverages counterfactual reasoning to identify actionable modifications for performance improvement. Our framework further leverages structural symmetries inherent in rendering processes, using repeated layout patterns and invariant dependency flows to reduce redundancy and strengthen the stability and identifiability of causal discovery. Extensive experiments on HTTP Archive, Chrome UX Report (CrUX), and a synthetic ground truth dataset demonstrate that our framework achieves higher causal accuracy, more stable predictive performance, more effective intervention recommendations, and improved interpretability compared with existing rule-based, statistical, and machine learning baselines. These results highlight the potential of causality-aware analysis for practical web performance optimization. Full article

Driven by 5G/6G and the Internet of Things (IoT), wireless sensor networks (WSNs) are confronted with core challenges such as limited energy constraints, unbalanced resource allocation, and security vulnerabilities. To address these, WSNs are integrated with cognitive radio networks (CRNs) to alleviate spectrum scarcity, and reconfigurable intelligent surfaces (RIS) are adopted to enhance performance, but traditional passive RIS suffers from “double fading” (signal path loss from transmitter to RIS and RIS to receiver), which undermines WSNs’ energy efficiency and the physical layer security (PLS) (e.g., secrecy rate, SR) of primary users (PUs) in CRNs. This study leverages symmetry to develop an active RIS framework for WSN-integrated CRNs, constructing a tripartite collaborative model where symmetric beamforming and resource allocation improve WSN connectivity, reduce energy consumption, and strengthen PLS. Specifically, three symmetry types—resource allocation symmetry, beamforming structure symmetry, and RIS reflection matrix symmetry—are formalized mathematically. These symmetries reduce the degrees of freedom in optimization (e.g., cutting precoding complexity by ~50%) and enhance the directionality of green interference, while ensuring balanced resource use for WSN nodes. The core objective is to minimize total transmit power while satisfying constraints of PU SR, secondary user (SU) quality-of-service (QoS), and PU interference temperature, achieved by converting non-convex SR constraints into solvable second-order cone (SOC) forms and using an alternating optimization algorithm to iteratively refine CBS/PBS precoding matrices and active RIS reflection matrices, with active RIS generating directional “green interference” to suppress eavesdroppers without artificial noise, avoiding redundant energy use. Simulations validate its adaptability to WSN scenarios: 50% lower transmit power than RIS-free schemes (with four CBS antennas), 37.5–40% power savings as active RIS elements increase to 60, and a 40% lower power growth slope in multi-user WSN scenarios, providing a symmetry-aided, low-power solution for secure and efficient WSN-integrated CRNs to advance intelligent WSNs. Full article

Microchannels can create disturbed flow patterns by altering pressure gradients, shear forces, and flow symmetry, which are essential in the design of microfluidic devices and, hence, blood-contacting devices. The effect of asymmetric stenosis on pressure, wall shear stress, and velocity in rectangular and circular microchannels with same operating conditions was analyzed in this study using three-dimensional (3D) steady laminar computational fluid dynamics (CFD) simulations. Asymmetric flow patterns induced by asymmetric stenosis are of particular importance and remain underexplored, especially in the context of multiple constrictions. This is, to our knowledge, is the first systematic CFD comparison of multiple asymmetric stenoses in circular microchannels directly contrasted with rectangular and single-stenosis cases under identical settings. Several parameters, such as wall shear stress (WSS), pressure, and velocity distributions, were analyzed in various stenotic and non-stenotic geometries. These microchannel models, while not reflecting real blood vessels themselves nor exhibiting wall compliance, pulsatility, or non-Newtonian rheology, replicate important mechanical characteristics of stenosis-mediated flow disturbance. Single and multiple asymmetric stenoses create flow patterns that are similar to those of vascular pathologies. For this reason, these channels should be considered as simplified device-scale models of vascular phenomena as opposed to realistic, in vitro vascular models. The results showed that asymmetric stenosis creates asymmetric velocity peaks and elevated WSS, which are more evident in the case of circular configurations with double asymmetric stenosis. The findings will help design microfluidic devices that mimic unstable flow characteristics that occur in stenotic conditions, and assist in testing clinical devices. In this study, two fabrication-ready microchannel designs under fixed operating conditions (identical inlet velocity and fluid properties) that reflect common microfluidic use were compared. Consequently, all pressure, velocity, and WSS outcomes are interpreted as device-scale responses under fixed velocity, rather than a fundamental isolation of cross-section shape, which would require matched hydraulic diameters or flow rates. This study is explicitly device-oriented, representing a fixed operating point rather than a strict geometric isolation. Accordingly, the results are also expressed with dimensionless loss coefficients (Ktot and Klocal) to enable scale-independent, device-level comparison. Full article

Laser Speckle Imaging (LSI) is a non-invasive optical technique used to assess biological activity by detecting dynamic variations in speckle patterns. These patterns exhibit statistical symmetry in static regions, while biological activity induces symmetry breaking that can be captured through the Graphic Absolute Value of Differences (GAVD), producing the activity map $I_{GAVD}$. This work evaluates the effect of four contrast enhancement algorithms: Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Multiscale Morphological Contrast Enhancement (MMCE), and Multiscale Top-Hat Transform with an Open-Close Close-Open (OCCO) filter, applied to intermediate LSI images, with the final activity map used for quantitative evaluation. Each method represents a distinct enhancement paradigm: HE and CLAHE are histogram-based techniques for global and local contrast adjustment, whereas MMCE and OCCO-MTH are morphological approaches that emphasize structural preservation and local detail enhancement. The dataset consisted of images of Phaseolus vulgaris (SP) and Lactuca sativa (SL) seeds. Evaluation was conducted through expert visual inspection and quantitative analysis using contrast, entropy, spatial frequency (SF), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and contrast improvement ratio (CIR). All metrics were computed on $I_{GAVD}$ activity maps, which reflect bioactivity through the disruption of statistical symmetry. Non-parametric statistical tests (Friedman, aligned Friedman, and Quade) revealed that CLAHE and MMCE significantly improved image quality compared to the original images ($p<0.05$). Among the evaluated algorithms, CLAHE increased global contrast by approximately 25% and entropy by 6% relative to the original speckle frames, enhancing the visibility of bioactive regions. MMCE achieved the highest bioactivity contrast ratio (CIR = 0.64), while OCCO-MTH provided the best structural fidelity (SSIM = 0.91) and noise suppression (PSNR = 30.7 dB). These results demonstrate that suitable contrast enhancement can substantially improve the interpretability of LSI activity maps without altering acquisition hardware. This finding is particularly relevant for experimental applications aiming to maximize information quality without modifying acquisition hardware. Full article

This article reflects on the methodological and emotional dimensions of conducting ethnographic research on online misogyny through technology-mediated encounters. Drawing on research about online misogyny against climate justice activists, I explore how this format, following interlocutors’ preferences, enabled forms of connection and nuance that would not have been possible if we had met in person. I argue that it afforded participants greater control over the interview environment and the visibility they granted me, reshaping power relations, enabling more symmetrical interactions, and opening up spaces for reflections on online misogyny within a wider constellation of struggles. Methodologically, the paper examines both the opportunities and risks of technology-mediated conversations when researching sensitive topics, including the potential for over-disclosure and the danger of reproducing the power dynamics we seek to avoid. Reflecting on my own positionality as a woman researching online misogyny “at home”, I discuss how embracing my own vulnerability became part of building rapport, while continuous “emotional management” was necessary to process the emotional weight of the fieldwork. I suggest that technology-mediated interactions should not be seen as a replacement for face-to-face research but as a context-sensitive method that, in certain cases, uniquely facilitates engagement with sensitive topics and vulnerable interlocutors. Full article

Strategic foresight often involves navigating deeply uncertain futures through scenario-based reasoning. Traditional methods rely on static narratives or probabilistic models, which fail to capture the dynamic, interacting, and nonexclusive nature of competing futures. This paper introduces a quantum-inspired framework for scenario interaction grounded in the mathematical formalism of superposition, interference, and contextuality. Agents are modeled as epistemic learners who iteratively update their preferences across multiple narrative pathways, which are treated as basis states in a conceptual Hilbert space. The simulation combines reinforcement learning with stochastic imitation, producing emergent distributions that resemble quantum-like collapse under feedback. Central to the model is the emergence of symmetries in the scenario structure and learning dynamics. Agents begin with neutral priors, facing a balanced reward landscape, epistemic and normative symmetry that is gradually broken through adaptive behavior. However, statistical symmetries persist at the ensemble level, as agents maintain partial preferences and oscillate among futures. These layered symmetries reflect both the cognitive realism of foresight practices and the mathematical tractability of quantum-inspired systems. The proposed model bridges strategic foresight, game-theoretic interaction, and quantum cognition, and offers a novel computational lens to study how futures are constructed, selected, and stabilized under uncertainty. Full article