Search Results (1,555)

This work introduces a non-blind watermarking framework for color images to address tamper detection, particularly under geometric transformations. The proposed scheme fuses two watermarks, a personal signature and a biometric fingerprint, into a unified composite watermark embedded into the chrominance component of the cover image using a multi-level transform domain approach, discrete wavelet transforms (DWTs), discrete cosine transforms (DCTs), and singular value decomposition (SVD). By leveraging the rotation-invariant properties of scale-invariant feature transform (SIFT) and oriented FAST and rotated BRIEF (ORB) descriptors, the framework ensures robust tamper detection without requiring alignment, thus mitigating the limitations of conventional detection techniques vulnerable to transformation-induced tamper obfuscation (TITO). Extensive experimentation demonstrates that the method maintains high perceptual fidelity, achieving PSNR values ranging from 50 to 55 dB for embedding strength factor μ (0.01–0.04) and SSIM indices near 1 across multiple benchmark images. Furthermore, the scheme exhibits notable resilience to a range of image processing attacks and geometric distortion. Comparative evaluation reveals its superiority over existing grayscale, color, SIFT-based and DWT-DCT-SVD-based watermarking techniques, affirming its applicability in scenarios demanding secure, imperceptible, and transformation-invariant image watermarking. Full article

Urban planners increasingly require high-resolution thermal comfort maps to prioritize heat adaptation, yet validating the necessary microclimate models against standard field instruments remains methodologically fraught. This study establishes an integrated measurement–modeling framework applied to a study area in Strasbourg, France. First, we evaluate the radiative physics of the LASER/F model against net radiometer measurements at a specific sub-canopy location and against incoming shortwave radiation pyranometer records across three instrumentation sites. Results demonstrate high accuracy for longwave fluxes ($R^2>0.95$) but reveal that simplified tree geometry leads to condition-dependent shortwave discrepancies. Second, we quantify the systematic divergence between Mean Radiant Temperature derived from black globe measurements and six-directional simulations across seven sites. We analyze how these inevitable discrepancies, stemming mainly from geometric mismatch, propagate into the Universal Thermal Climate Index (UTCI), resulting in (71.5–75.5%) diurnal exact categorical agreement. Finally, spatial application of the model uncovers a “masked risk”: while temporal averaging suggests that 100% of the district remains safe (mean UTCI $<{32}^{\circ }\mathrm{C}$), duration-based analysis reveals that 72.8% of surfaces actually experience critical heat stress for over a quarter of the period. To address these hidden exposure risks, we propose a “Combined Risk Score” (CRS) that integrates thermal intensity and critical exposure duration on an absolute, dataset-independent scale, with a sensitivity analysis demonstrating that spatial risk prioritization is invariant to the intensity–duration weighting choice at the operational threshold. Full article

The rotational evolution of pulsars is governed by torque mechanisms whose mathematical structure encodes fundamental symmetries of the underlying physics. We demonstrate that the standard spin-down equation $\dot{f}=-sf-r{f}^3-g{f}^5$ derives from a discrete antisymmetry requirement, namely invariance of the torque under reversal of rotation sense, which restricts the frequency dependence to odd integer powers. We show that physically motivated plasma processes systematically break this symmetry, introducing fractional frequency exponents: viscous Ekman pumping at the crust–superfluid boundary layer ($f^{3/2}$), magnetohydrodynamic turbulent dissipation via Kolmogorov and Sweet–Parker cascades ($f^{10/3}$, $f^{11/3}$), non-linear superfluid vortex dynamics ($f^{5/2}$), and saturated r-mode oscillations ($f^{7-2\beta }$). The central result is an exact analytical resolution of the long-standing Crab pulsar braking index puzzle: the observed $n=2.51\pm 0.01$, which has defied explanation for nearly four decades, emerges naturally from the superposition of magnetic dipole radiation ($\dot{f}\propto f^3$) and boundary layer Ekman pumping ($\dot{f}\propto f^{3/2}$), with analytically derived coefficients yielding a dipole-component surface field $B_p=6.2\times {10}^{12}$ G—higher than the standard $\sqrt{P\dot{P}}$ estimate of $3.8\times {10}^{12}$ G, because that formula conflates dipole and non-dipole torques, but lower than applying the Larmor formula to the full spin-down rate ($7.6\times {10}^{12}$ G), since $32.7\%$ of the total torque is non-radiative boundary-layer dissipation. We develop the Riemann–Liouville fractional calculus formalism for these equations, showing that fractional derivatives break time-translation symmetry through intrinsic memory effects, with solutions expressed in terms of Mittag-Leffler and Fox H-functions that interpolate continuously between exponential (fully symmetric) and power-law (scale-free symmetric) relaxation. Lambert–Tsallis $W_q$ functions with non-extensive parameter q encoding broken statistical symmetry enable equation-of-state-independent inference of neutron star compactness and tidal deformability. Our framework establishes a unified symmetry-based classification of pulsar spin-down mechanisms and predicts frequency-dependent braking indices evolving at rate $\mathrm{d}n/\mathrm{d}t\sim 2\times {10}^{-4}$ yr⁻¹, yielding $\mathrm{\Delta }n\approx 0.01$ over 50 years—testable with current pulsar timing programmes. The formalism provides a coherent theoretical foundation connecting plasma microphysics at the neutron star interior to macroscopic observables in electromagnetic and gravitational wave channels. Full article

Background: This study aims to psychometrically validate the abbreviated version of the Connor–Davidson Resilience Scale (CD-RISC-10) in Peruvian nurses, evaluating its convergent validity through its association with perceived stress and empathy. Methods: A cross-sectional psychometric study was conducted in 374 Peruvian nurses to evaluate the psychometric properties of CD-RISC-10 through confirmatory factor analysis (CFA). Furthermore, concurrent validity was assessed through correlational analysis using Spearman’s rho coefficient to evaluate the relationships among resilience, perceived stress, and empathy. Results: The CFA supported the predominantly one-dimensional model showing an adequate fit when the residual covariance between Items 4 and 7 was specified after correlating the residuals of Items 4 and 7 (CFI = 0.978, TLI = 0.971, RMSEA = 0.080, and SRMR = 0.044). Ordinal Cronbach’s alpha of 0.89 and McDonald’s omega of 0.81 were obtained. Concurrent validity showed significant correlations with perceived stress (rho = −0.53, p < 0.001) and empathy (rho = 0.31, p < 0.001). Conclusions: The CD-RISC-10 has adequate psychometric properties in Peruvian nurses. Future studies are needed to evaluate its factorial invariance between clinical specialties and establish normative thresholds. Full article

Accurate calibration is essential for ensuring the performance of magnetic gradient tensor (MGT) arrays. Existing calibration methods generally rely on mechanical rotation to obtain magnetic responses under multiple orientations. However, for large-scale cubic MGT arrays, rotating the entire array using a high-precision non-magnetic turntable is often costly and impractical, while manual rotation is difficult to control and may introduce array-center offsets. To address these limitations, this paper proposes a rotation-free scalar calibration framework for cubic MGT arrays, in which a tri-axial Helmholtz coil system generates constant-magnitude magnetic fields with randomized orientations while compensating for ambient magnetic drifts. Based on the acquired data, a hierarchical calibration algorithm is developed to estimate sensor-level intrinsic errors and array-level misalignment errors. Experimental results show that the proposed method reduces the joint tensor invariant $C_T$ from $9.07\times {10}^3$ nT/m to 11.51 nT/m, corresponding to a 99.87% reduction. In addition, compared with a conventional rotation-based fast calibration method, the proposed framework further decreases the mean and RMS of the joint $C_T$ by 62.7% and 63.1%, respectively. These results demonstrate that the proposed framework improves the spatial consistency of the MGT array and provides a practical calibration solution for large-scale MGT array systems. Full article

Accurate wind speed forecasting is critical for reliable wind-power integration, yet it remains challenging due to the strongly non-stationary and inherently multi-scale nature of atmospheric processes. While deep learning models—such as LSTM, GRU, and Transformer architectures—achieve competitive short- and medium-term performance, they frequently suffer from spectral bias, hyperparameter sensitivity, and reduced generalization under heterogeneous operating regimes. To address these limitations, we propose a multi-scale spectral–recurrent framework, termed RFF-RNN, which integrates multi-band Random Fourier Feature (RFF) encodings with parameterizable recurrent backbones. A key innovation of our approach is the deliberate relaxation of strict shift-invariance constraints; by jointly optimizing spectral frequencies, phase biases, and bandwidth scales alongside the neural weights, the framework dynamically shapes a fully data-driven spectral embedding. To ensure robust adaptation, we employ a two-stage optimization strategy combining gradient-based inner-loop learning with outer-loop Bayesian hyperparameter tuning. Our extensive evaluations on a controlled synthetic benchmark and six geographically diverse real-world wind datasets (spanning the USA, China, and the Netherlands) demonstrate the superiority of the proposed framework. Statistical validation via the Friedman test confirms that RFF-enhanced models—particularly RFF-GRU and RFF-LSTM—systematically outperform standard recurrent networks and state-of-the-art Transformer architectures (Autoformer and FEDformer). The proposed approach yields significantly lower error metrics (MAE and RMSE) and higher explained variance ($R^2$), while exhibiting remarkable resilience against error accumulation at extended forecasting horizons. Full article

Background: Rabies is almost invariably fatal once clinical symptoms develop, yet it is preventable through canine vaccination and timely post-exposure prophylaxis (PEP). In rural Thailand, preventive behaviors likely depend on health literacy and contextual conditions that enable and reinforce protective action, but structural pathways remain unclear. Methods: We conducted a cross-sectional study among 750 adults in rabies-risk areas of Si Sa Ket Province, Thailand. A socio-ecological, One Health-informed structural equation model (SEM) examined associations among rabies-related health literacy skills (HLskill), service/system enabling conditions (ENAB), reinforcing community mechanisms (COMM), and rabies-prevention behaviors (BEHAV). Results: Model fit was acceptable (CFI = 0.948; TLI = 0.918; SRMR = 0.047; scaled RMSEA = 0.090). HLskill and COMM showed direct associations with BEHAV (β = 0.352 and 0.371, respectively), while ENAB was strongly associated with COMM (β = 0.939), indicating an indirect pathway through community reinforcement (β = 0.348; 95% CI [0.273, 0.424]). Conclusions: Rabies-prevention behaviors were associated with health literacy skills and reinforcing community mechanisms; service readiness operated primarily through community reinforcement. Rabies control should combine health literacy strengthening with community communication, coordinated dog vaccination, bite management, and timely PEP uptake. Full article

We formulate a covariant version of Maxwell-like fracton electrodynamics in six dimensions using a symmetric tensor gauge field with scalar gauge symmetry $\delta A_{\mu \nu }={\partial }_{\mu }{\partial }_{\nu }\mathsf{\Lambda }$. This provides a relativistic setting in which the characteristic fractonic restriction on mobility follows directly from gauge invariance and the allowed coupling to matter. We construct the stress–energy tensor and show that its trace has a universal dimension-dependent structure that becomes a total derivative in $d=6$. In the presence of sources, the theory enforces conservation of charge and dipole moment, capturing the immobility of isolated charges and the mobility of dipolar bound states. This structure can also be viewed as a higher-moment form of generalized global symmetry. Full article

Video-based action recognition for neural rehabilitation—spanning stroke recovery, Parkinsonian gait assessment, and cerebral palsy monitoring—faces critical challenges, including temporal ambiguity, non-causal motion correlations, and the absence of causally grounded dynamics modeling. While transformer-based architectures achieve strong performance, they often exploit spurious temporal and environmental cues, limiting reliability in safety-critical clinical settings. We propose NeuroPrisma, a neuro-prismatic video framework that integrates frequency-domain spectral decomposition with causal intervention under Structural Causal Models (SCMs) via the backdoor criterion. NeuroPrisma introduces (i) a Prismatic Spectral Attention (PSA) module, which applies discrete Fourier transforms to decompose temporal features into multi-scale frequency bands, disentangling slow postural dynamics from rapid corrective movements, and (ii) a Causal Intervention Layer (CIL), which performs do-calculus-based backdoor adjustment to remove confounding influences and produce causally invariant representations. PSA preconditions representations prior to intervention, improving confounder estimation and causal robustness. Extensive evaluation against seven state-of-the-art models (I3D, SlowFast, TimeSformer, ViViT, Video Swin Transformer, UniFormerV2, and VideoMAE) demonstrates that NeuroPrisma achieves 98.7% Top-1 accuracy on UCF101, 82.4% on HMDB51, 71.2% on Something-Something V2, and 91.5%/95.8% on NTU RGB+D (Cross-Subject/Cross-View), consistently outperforming prior methods. It further reduces the Causal Confusion Score (CCS) by 42.3%, indicating substantially lower reliance on spurious correlations, while maintaining real-time performance with 23.4 ms latency per 16-frame clip on an NVIDIA A100 GPU. All improvements are statistically significant (p < 0.001, Cohen’s d = 0.72–1.24). Evaluation was conducted exclusively on benchmark datasets (UCF101, HMDB51, Something-Something V2, and NTU RGB+D) under controlled conditions, without direct clinical validation on neurological patient cohorts. Overfitting was mitigated using three random seeds (42, 123, 456), RandAugment, Mixup (α = 0.8), weight decay (0.05), and early stopping. Cross-dataset generalization from UCF101 to HMDB51 without fine-tuning achieved 76.2% Top-1 accuracy. Future work will focus on prospective clinical validation across stroke, Parkinson’s disease, and cerebral palsy populations, including correlation with standardized clinical assessment scales such as Fugl–Meyer, UPDRS, and GMFCS. These results establish NeuroPrisma as a causally grounded and computationally efficient framework for reliable, real-time movement assessment in clinical rehabilitation systems. Full article

Neural language models have achieved remarkable progress in semantic representation learning. However, cross-domain representation learning still suffers from prominent semantic noise propagation issues. Existing methods still face challenges in cross-domain semantic modeling, including limited robustness across different semantic granularities, difficulty in separating transferable semantics from task-irrelevant semantic interference, and insufficient adaptability to specialized scenarios. These issues may reduce feature discriminability in fine-grained semantic tasks and complex application settings. To address these problems, we propose the Diffusion-Coded Attention Network (DCANet), a novel cross-domain representation learning architecture with three synergistic core modules: a multi-granular parallel diffusion masking mechanism for cross-scale context fusion via stochastic path activation, an implicit semantic encoder that distills domain-invariant patterns into adaptive bias codes via shared latent manifolds, and a self-correcting attention topology realizing dynamic semantic purification via closed-loop interactions between local features and global bias states. Extensive evaluations are conducted on nine well-recognized benchmark datasets to verify DCANet’s effectiveness and reliability. Experimental results show that DCANet attains state-of-the-art results on the majority of the benchmark datasets, with significant accuracy improvements on text classification and sentiment analysis tasks. Full article

Electroencephalography (EEG) provides a non-invasive and high-temporal-resolution modality for decoding cognitive states, but high-density recordings remain challenging for Transformer-based models because self-attention scales quadratically with the number of channels. In addition, conventional Euclidean representations do not fully capture the intrinsic geometry of EEG covariance features, which may limit robustness in cross-subject settings. To address these issues, we propose EEG-RCformer, a Riemannian geometry-informed channel clustering Transformer for EEG decoding. The model first computes per-channel symmetric positive definite (SPD) covariance matrices from windowed EEG features and uses the affine-invariant Riemannian metric (AIRM) to identify trial-specific functional hubs. These hubs are then integrated with capacity-constrained spatial clustering to generate anatomically plausible and computationally efficient channel groups, which are encoded as tokens for a Transformer classifier. We evaluated EEG-RCformer on the MODMA and SEED datasets under both subject-dependent and -independent paradigms, achieving area under the curve (AUC) values of 0.9802 and 0.7154 on MODMA and 0.8541 and 0.8011 on SEED, respectively. Paired statistical tests further showed significant gains for MODMA in both the subject-dependent and -independent settings and for SEED in the subject-dependent setting, while SEED still showed a positive but non-significant mean improvement in the subject-independent setting. Full article

Fermentation is a promising sustainable and ecofriendly alternative for producing high-added-value chemicals such as 2,3-butanediol (2,3-BDO). The emergence of process analytical technology (PAT) tools, combined with advances in chemometrics, enables real-time process monitoring of product attributes, thereby ensuring quality. The aim of this study is to transfer near-infrared (NIR) partial least squares (PLS) models under two scenarios for the monitoring of 2,3-BDO production. PLS regression models initially developed under specific conditions were transferred across domains using dynamic orthogonal projection (DOP) and domain invariant (di)-PLS standard-free calibration transfer (CT) methods. For the 1st scenario involving model transfer from “mock samples” to “flask atline,” di-PLS was able to enhance NIR PLS model performance with improvements in RMSEC and RMSEP of 18 and 25% (2 g/L absolute error), respectively. In the 2nd scenario, however, DOP successfully transferred the model from the “flask atline” domain to the “500 mL bioreactor online” domain, achieving RMSEC and RMSEP values of 12 and 14 g/L, respectively. The feasibility of multivariate model transfer for PAT applications in complex fermentation systems from atline to online configurations using standard-free CT methods is demonstrated. This enhances model adaptability under varying conditions, fostering process scale-up and real-time monitoring. Full article

Background: Social anxiety in adolescence is a prevalent mental health concern characterized by intense fear of negative evaluation in social situations. The Social Anxiety Questionnaire for Adolescents (CASO-N24) is a Spanish-language instrument requiring validation in Peruvian populations. Objective: This study aimed to validate the CASO-N24 in Peruvian adolescents aged 12–17 years, extending its application beyond the original 9–15-year range, and examine its psychometric properties including factorial structure, measurement invariance, nomological validity, and internal consistency. Methods: A stratified probability sample of 710 adolescents (352 males, 358 females; M = 14.82 years, SD = 1.45) from four northern Peruvian educational centers completed the CASO-N24 and ASQ-14. Exploratory and confirmatory factor analyses, multigroup invariance testing by age and gender, nomological validity assessment, and reliability estimation (Cronbach’s α and McDonald’s ω) were conducted using polychoric correlations and robust estimation methods. Results: The six-factor structure was replicated, explaining 47.13% of variance with factor loadings ranging 0.48–0.78. Model fit indices were excellent (GFI = 0.981, AGFI = 0.976, NFI = 0.971, SRMR = 0.046). Complete measurement invariance was achieved across age groups (12–15 vs. 16–17 years). Partial invariance by gender was observed, with differential item functioning identified in item 17. Nomological validity was confirmed through moderate-to-high correlations with ASQ-14 (males: r = 0.622; females: r = 0.604). Internal consistency was adequate (total scale ω = 0.95; subscales ω = 0.69–0.82). Conclusions: The CASO-N24 demonstrated robust psychometric properties for assessing social anxiety in Peruvian adolescents aged 12–17 years, supporting its multidimensional structure and utility for early detection in school settings while highlighting gender-specific response patterns warranting clinical consideration. Full article

Chirality is a pervasive and functionally critical feature of biological macromolecules, yet its distributed and emergent forms remain poorly quantified in complex systems such as membrane proteins. We present Chirobiophore, a novel paradigm for capturing biochirality across scales—from atomic geometries to global structural asymmetries. Unlike traditional stereochemical metrics, Chirobiophore employs a multidimensional model-independent vector comprising Local Tetrahedral Asymmetry (LTA), Helical Path Curvature (HPC), Asymmetric Environment Score (AES), Directional Density Profile (DDP), Leaflet Asymmetry Index (LAI), and Orientation Twist Score (OTS). This framework enables coordinate-invariant comparisons of structurally diverse proteins in a continuous chirality space. We demonstrate its application to canonical, GPCR, and topologically complex membrane proteins, revealing distinct chirality signatures and functional clustering. Furthermore, we map Chirobiophore descriptors to tissue-level asymmetry indices, providing a bridge between molecular structure and morphogenetic patterning. Chirobiophore offers a unified, extensible platform for structural biology, synthetic design, and developmental modeling of chirality. Full article

In this paper, the circular Sitnikov three-body problem is studied under the combined influence of radiation pressure and albedo. The model consists of two equal-mass primaries moving in circular orbits about their center of mass and an infinitesimal body constrained to oscillate along the perpendicular axis. The radiative emission from one primary and the reflected radiation from the other are incorporated into the effective potential through radiation and reflectivity parameters. Using the Jacobi integral, we determine the energetically admissible region for vertical motion and examine how radiative effects modify the accessible phase space. The study shows that the system admits a single vertical equilibrium point at the origin, which remains linearly stable within the physically admissible parameter range. Radiation and albedo reduce the effective restoring force and increase the oscillation period, producing a measurable rescaling of the physical time without altering the geometrical structure of the phase trajectories. The phase-space dynamics are further explored by means of Poincare (first-return) maps obtained from numerical integration of the nonlinear equation of motion. The resulting invariant curves confirm that the motion remains regular and bounded, while their progressive contraction reflects the reduction in the oscillation amplitude with increasing radiative effects. Overall, the results show that albedo acts as a quantitative modifier of the vertical Sitnikov dynamics by changing the effective potential, the admissible energy domain, and the observable time scale, without generating new qualitative phase-space structures. Full article