Search Results (6,607)

A classical fluid splitter produces the same patterns of energy redistribution as a Stern–Gerlach quantum device, with rotationally invariant coefficients of correlation between molecular paths. Alternative settings express a cosine squared relationship, leading to Tsirelson-type Bell violations with outcome independence. This result confirms the Correspondence Principle of quantum mechanics, where individual detection events express system-level properties according to Born’s Rule. Kochen–Specker contextuality and Bell Locality are not formally contradicted, but their interpretation is in question. Current definitions of “Local Realism” are limited to intrinsic particle properties. In contrast, quantum-like correlations require the acknowledgement of ensemble effects on dynamically inseparable entities, even when those entities are observed one at a time. 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

In airborne synthetic aperture radar (SAR) imaging, slant-range errors vary across the swath, making phase errors range-dependent. However, the conventional phase gradient autofocus (PGA) method assumes a range-invariant phase model and becomes unreliable when range-dependent phase errors are pronounced. Although range-partitioned PGA can substantially improve focusing performance, it may still introduce block-dependent azimuth shifts after compensation, causing geometric distortion in the focused image. To address this problem, this paper proposes a lightweight post-autofocus distortion-correction method for SAR images processed by range-partitioned PGA. Instead of re-estimating the full residual phase, the method operates on the block-wise phase-error estimates after global linear-phase removal, extracts the distortion-related linear trend using a sliding-window fitting strategy, converts it into azimuth-shift profiles, and performs sinc-based realignment. The proposed method is validated using both simulation and real unmanned aerial vehicle (UAV) SAR data. Experimental results demonstrate that the method effectively corrects geometric distortion while preserving the focusing gain achieved by range-partitioned PGA. In two representative real-data regions, the azimuth misalignment is reduced from 20 pixels to 3 pixels and from 34 pixels to 2 pixels, respectively. Full article

Air–ground collaborative systems leverage the complementary strengths of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) and hold significant potential for logistics in complex, unstructured environments. However, trajectory planning in infrastructure-free mountainous regions remains challenging owing to the need for continuous tight coupling, obstacle avoidance, and reliable communication-link maintenance. To address these challenges, this study proposes a cooperative trajectory planning framework that enforces strict inter-vehicle distance constraints to maintain communication connectivity. By formulating the coordination problem in terms of relative configurations between air and ground vehicles, the proposed framework exhibits translational invariance, reflecting an underlying symmetry with respect to global position shifts. This symmetry-aware formulation reduces reliance on absolute coordinates and promotes consistent cooperative behavior under environmental variability. The trajectory planning problem is mathematically formulated as a constrained multi-objective nonlinear programming (MONLP) model that balances energy consumption and trajectory smoothness. An adaptive inertia weight particle swarm optimization (AIWPSO) algorithm is developed to efficiently solve the resulting optimization problem. Simulation results demonstrate that the proposed approach generates smooth, collision-free trajectories while maintaining stable air–ground coordination, demonstrating improved feasibility and robustness over conventional planning methods in unstructured mountainous environments. Full article

Inverse scattering problems seek anomalies in a medium given data measured after the interaction with emitted waves. Due to noise, predictions about the nature of these inclusions should be complemented with uncertainty estimates. To this end, we propose a progressive framework for inverse scattering from low- to high-dimensional Bayesian formulations depending on the prior information and the problem complexity. We aim to reduce computational costs by exploiting educated prior information. When we look for a few well-separated inclusions in a known medium with information about their number, we resort to low-dimensional parameterizations in terms of a few random variables representing their shape and material constants. We test this approach detecting anomalies in tissues and deposits in stratified subsoils. In more complex situations where the anomalies may overlap, we propose high-dimensional parameterizations obtained from Karhunen–Loève (KL) or Fourier expansions of the density and velocity fields. We employ these methods to characterize oil and gas reservoirs in a salt dome configuration, where the screening effect of the dome cap prevents the obtention of adequate prior information. We characterize the posterior probability by means of affine invariant ensemble and functional ensemble MCMC samplers depending on dimensionality. This provides information on configurations with the highest a posteriori probability and the uncertainty around them, identifying factors that could reduce the uncertainty. In high-dimensional setups, techniques based on KL developments are more effective and stable. A recurring issue is the choice of the a priori covariance (which strongly affects the results) and the choice of its hyperparameters. Here, we use educated choices. Formulations that include them as additional parameters could be a next step at a higher cost. Full article

A difficult NP-hard optimization problem, designing cryptographically robust substitution-boxes (S-boxes) necessitates a careful balancing act between several conflicting properties, such as differential uniformity and nonlinearity. Genetic Algorithms (GAs) have been widely used for this task; however, their performance is often limited by premature convergence and insufficient diversity during crossover operations. This primarily occurs because genetic algorithms commence with limited a priori knowledge. This sort of “blindness” and failure to utilize local knowledge results in diminished performance. In GA, the crossover operations facilitate the dissemination of robust candidates within the population. Conventionally, GA implements crossover for each pair of parents for diversity and a robust solution. However, this is not invariably the situation. To enhance children’s candidacy, parental diversity is quite crucial. This paper proposes a similarity-aware crossover strategy, integrated with a Siamese learning framework, to guide the genetic algorithm for improved S-box optimization with better diversity and faster convergence by utilizing parental local information. The proposed model is similarity-aware to guarantee that the GA improves parental diversity. When the parents exhibit excessive similarity, a “regressive” crossover is opted, which ensures the propagation of a parental couple with sufficient diversity to produce superior offspring. The proposed similarity-aware GA model is applied and evaluated to generate cryptographically robust and optimized S-boxes. To verify the robustness in terms of diversity, the model has been tested using three different loss functions: contrastive loss, KL divergence loss, and the suggested method of combining both loss functions to form a hybrid loss function. The effectiveness of the proposed approach is demonstrated through the generation of high-quality S-boxes with strong cryptographic properties. Full article

As AI-powered learning tools become more common in educational settings, understanding their acceptance mechanisms is increasingly important. This study examines how the design attributes of AI museum guides—anthropomorphism, interactivity, and personalization—are associated with the acceptance intention and perceived learning outcomes among Chinese high school students with prior museum experience. Using structural equation modeling with 324 participants, we test whether these features relate to acceptance through two affective pathways: perceived warmth and anxiety reduction. The results reveal distinct patterns: anthropomorphism shows an indirect-only association with anxiety reduction through perceived warmth; interactivity is associated with anxiety reduction through responsive feedback; and personalization serves dual functions, enhancing both pathways. Anxiety reduction shows strong positive associations with both acceptance intention and perceived learning outcomes. The multi-group analysis shows that most pathways function equivalently across genders, with one exception where anxiety reduction more strongly predicts learning outcomes for females than males. These findings reveal distinct psychological functions within the Chinese educational context: anthropomorphism influences anxiety reduction exclusively through perceived warmth, while personalization and interactivity provide both affective and cognitive support. The implications for AI museum guide design in similar contexts are discussed. The generalizability to other cultural contexts and populations, such as Western students or adult learners, requires further investigation. 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

Remote photoplethysmography (rPPG) estimates heart rate by analyzing subtle blood-flow-induced color variations from camera videos; however, its performance is highly sensitive to illumination changes caused by variations in light intensity, position, and environmental conditions. To address this limitation, this study proposes a lightweight, training-free brightness normalization method that suppresses illumination-induced luminance fluctuations while preserving physiologically relevant color variations associated with blood perfusion. The proposed approach separates luminance and chrominance components from the frame-mean RGB vector and applies normalization only to the brightness component, thereby maintaining the intrinsic color direction essential for rPPG signal extraction and stabilizing temporal brightness without distorting chrominance relationships. Experimental evaluations show that channel-wise mean values vary only within $\pm 6$–$12\%$ with negligible changes in standard deviation, while dynamic range and temporal stability are significantly improved. Furthermore, when combined with an SNR-based signal selection strategy, the proposed method reduces the mean absolute error (MAE) of the CHROM algorithm on the DLCN dataset from approximately 18–19 BPM to 4.87 BPM under complex illumination scenarios, with consistent improvements also observed on the MR-NIRP dataset. These results suggest that the proposed preprocessing method helps preserve blood-flow-induced temporal color variations and improves the robustness of rPPG measurement under diverse illumination conditions. 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

Precise object detection is critical for preventing damage to vehicle attachments during automatic car washing. However, the existing methods often suffer from low accuracy and false detections due to the diverse shapes and visual ambiguity of these attachments. To address these challenges, we propose a novel framework integrating a YOLOv11-based detector with a graph neural network. Specifically, we introduce a spatial graph module to refine object localization by capturing invariant spatial constraints within the car wash environment. Furthermore, we incorporate a class graph module to model inter-class semantic correlations, thereby improving the classification of visually ambiguous objects such as emblems. Experimental results on a real-world dataset demonstrate that our method achieves an mAP50 of 97.9%, outperforming state-of-the-art models including D-FINE 96.5% and RT-DETR 96.1%. These findings confirm the robustness of our approach under varying viewpoints and background conditions, offering a significant improvement in the safety and reliability of automatic car wash systems. Full article

In this paper, the uniform approximation of continuous functions on $[0,1]$ by rational functions with prescribed poles and bounded multiplicities is studied. A classical theorem of Fichera characterizes density in $C\left(\right[0,1\left]\right)$ through the divergence of a conformally invariant series involving the pole distribution. A modern reformulation of this result is developed and it is given an operator-theoretic interpretation in which the approximation property is equivalent to cyclicity and to the absence of nontrivial invariant subspaces in an associated Hardy-space model. In this framework, the classical Blaschke condition emerges as the fundamental obstruction to density, linking rational approximation to the structure of model spaces and non-selfadjoint operator algebras. The density criterion is interpreted in terms of symmetry: divergence corresponds to a balanced distribution of poles compatible with the conformal geometry of the slit domain, while convergence induces symmetry breaking and the emergence of invariant structures. Numerical models illustrate the sharpness of the criterion and provide a concrete manifestation of the Blaschke obstruction and cyclicity mechanism. This new approach places Fichera’s theorem within a broader operator-theoretic and spectral framework, connecting classical approximation theory with Hardy spaces, invariant subspace theory, and modern rational approximation methods. 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

In this paper, we present a mathematical analysis of within-host CHIKV dynamics by developing and studying a novel delay differential equation model that incorporates persistent infected monocytes, discrete time delays, and an antibody-mediated humoral immune response. The model includes five compartments: susceptible monocytes, persistent infected monocytes, actively infected monocytes, CHIKV pathogens, and neutralizing antibodies. To reflect key biological latencies, we introduce four distinct discrete delays accounting for the periods between viral entry and the emergence of infected cell populations, intracellular virion production, and antibody activation. We analyze the model, establishing the positivity, boundedness, and invariance of solutions, and derive the basic reproduction number ${\mathcal{R}}_0$ via the next-generation matrix method. Using Lyapunov functions and LaSalle’s Invariance Principle, we prove a threshold dynamic: the infection-free equilibrium is globally asymptotically stable (GAS) when ${\mathcal{R}}_0\le 1$, while a unique endemic equilibrium is GAS when ${\mathcal{R}}_0>1$. Numerical simulations validate the analytical results and illustrate threshold behavior. A detailed local sensitivity analysis of ${\mathcal{R}}_0$ identifies the most influential parameters, offering theoretical insights into potential intervention strategies. We further investigate the effects of antiviral therapy as a theoretical intervention, deriving a treatment-dependent reproduction number and the critical drug efficacy required for eradication, and explore how the intracellular production delay can itself serve as a critical threshold for infection clearance. The study provides a rigorous theoretical framework that highlights the roles of latency, immune response, and biological delays in CHIKV pathogenesis and offers qualitative insights that may inform future experimental and treatment design studies. Full article