Search Results (2,903)

Functional brain networks in schizophrenia (SZ) are often characterized by covariance-based measures, yet covariance matrices live on a curved geometric structure rather than in ordinary Euclidean space, complicating noise-robust inference from scalp EEG. We develop a Riemannian Geometry-based Adaptive Nonlinear Coupling Analysis (RGA-NCA) framework that integrates the affine-invariant Riemannian metric (AIRM), tangent space mapping (TSM), and an anatomically adaptive artifact rejection (AAAR) strategy accounting for regional signal-to-noise heterogeneity. The framework is grounded in the observation that Euclidean summaries of symmetric positive definite matrices are sensitive to noise-driven volume inflation, whereas geodesic distances on the manifold emphasize shape deformation. RGA-NCA was evaluated on four benchmark dynamical systems, a supplementary multichannel EEG-like sample covariance simulation, and a public button-tone SZ/HC EEG dataset associated with the auditory feedback paradigm described by Ford et al. (81 subjects; 49 SZ, 32 healthy controls). Compared with Euclidean and linear baselines, RGA-NCA showed lower sensitivity to noise-driven distance distortion and yielded clearer group-level contrasts in the tested ROI analyses; all four pre-specified frontotemporal and parietal channel pairs remained significant after Benjamini–Hochberg FDR correction. The resulting patterns are consistent with reduced long-range connectivity together with localized hyper-synchronization-like effects in SZ. Quantitatively, the Riemannian structural sensitivity index $(\mathrm{sim}=\exp (-d^2/4))$ remained high across all tested SNR levels (−20 to +10 dB; 50 Monte Carlo trials per level; range 0.936–0.964), with only a 0.026 endpoint change between +10 and −20 dB, whereas the Euclidean metric fell from 0.922 at +10 dB to 0.000 at −20 dB. These findings support Riemannian modeling as a candidate strategy for noisy covariance-based neural data, pending validation in larger independent cohorts. Full article

This study investigates the effect of hydrogen injection pressure on combustion, energy, and emission characteristics of a spark-ignition engine under stoichiometric operating conditions. Experiments were performed on a four-cylinder Nissan HR16DE engine at 2500 rpm and 0.48 MPa brake mean effective pressure using gasoline and hydrogen-enriched blends containing 10%, 20%, and 30% hydrogen by mass. Hydrogen was injected into the intake manifold at pressures of 1.2, 1.4, 1.6, and 1.9 bar, while spark timing was adjusted to maintain peak in-cylinder pressure at 14–15 CAD after top dead center. Results showed that hydrogen mass fraction had a much stronger influence on engine performance than injection pressure. Increasing hydrogen content intensified combustion, shortened ignition delay, increased heat release rate and in-cylinder temperature, and reduced brake-specific fuel consumption by up to 36% compared with pure gasoline. Hydrogen enrichment also reduced HC and CO₂ emissions, but increased NO_x emissions. Effect of injection pressure was secondary and depended on hydrogen concentration. Under the investigated conditions, the lowest tested pressure, 1.2 bar, was generally the most favorable, especially at lower hydrogen fractions. Overall, hydrogen injection pressure acted mainly as a mixture formation control parameter, while hydrogen mass fraction remained the dominant factor determining engine behavior. Full article

This study investigates the thermo-mechanical performance of fused filament fabrication (FFF)-printed polyphthalamide reinforced with 15 wt.% short carbon fibers (PPA CF15) for engineering applications under elevated temperature and cyclic loading conditions. The material was characterized by quasi-static tensile testing, fatigue testing, dynamic mechanical analysis (DMA), scanning electron microscopy (SEM), and finite element analysis (FEA). Tensile tests performed from 20 to 180 °C revealed a strong temperature-dependent reduction in mechanical properties: the elastic modulus decreased from 2.437 to 0.401 GPa, while the ultimate tensile strength decreased from 64.537 to 9.190 MPa. In contrast, elongation at break generally increased with temperature, indicating a transition toward more ductile deformation governed by thermal softening of the polymer matrix. Fatigue tests showed reduced fatigue resistance at higher temperatures and stress levels; however, stable cyclic performance was achieved when the applied stress remained below approximately 60–70% of the ultimate tensile strength, with several specimens reaching 10⁶ cycles. DMA confirmed the viscoelastic nature of PPA CF15 and enabled the construction of frequency–temperature superposition master curves for numerical modelling. SEM observations revealed increased matrix deformation and fiber pull-out at elevated temperatures. FEA of an automotive intake manifold (IM) case study demonstrated that experimentally derived material data can be used to predict deformation, stress redistribution, and viscoelastic stabilization under combined thermal and mechanical loading. The results indicate that FFF-printed PPA CF15 is a promising lightweight composite for thermally and mechanically demanding automotive applications. Full article

Emotion recognition through electroencephalogram (EEG) signals is crucial for brain–computer interfaces (BCIs), yet existing methods often struggle with heterogeneous feature fusion and capturing long-range temporal dependencies. To address these challenges, we propose MAF-TransNet, a novel unified spatiotemporal framework. Specifically, parallel Fully Connected Neural Network (FCNN) modules first non-linearly align heterogeneous differential entropy (DE) and power spectral density (PSD) features. Subsequently, an Adaptive Channel-wise Feature Encoder (ACFE) recalibrates spatial–spectral responses to highlight emotion-relevant cortical activations. Finally, a Transformer encoder dynamically models the global temporal evolution of emotional states. Evaluated on the SEED-IV and DEAP datasets, MAF-TransNet achieves superior subject-dependent (SD) accuracies of 88.80% and 96.58%, respectively, alongside robust subject-independent (SI) performance. Furthermore, Granger causality analysis reveals distinct emotion-dependent prefrontal asymmetry, while t-SNE visualizations confirm the formation of a highly discriminative, linearly separable feature manifold. Ultimately, MAF-TransNet effectively unifies local spatial–spectral extraction with global temporal modeling, providing an accurate and robust approach, while offering preliminary insights into the spatiotemporal dynamics of emotion for future affective BCI applications. Full article

Micro-expression recognition (MER) remains a formidable challenge in affective computing due to the subtle, localized, and fleeting nature of facial muscle actuations. Conventional spatial-temporal networks are easily overwhelmed by static facial topologies, leading to feature representations that are heavily biased toward identity-specific noise. To address this, we propose the Motion-Anchored Cross-Modal Fusion Network (MACFN), a novel dual-stream ViT architecture that explicitly decouples and synergizes spatial appearance and optical flow dynamics. Specifically, we introduce a motion-anchored spatial attention module, which translates latent motion features into a sparse spatial probability mask. It acts as an enhancement gate, forcing the texture stream to bypass static backgrounds and attend to genuine ME-related regions. Furthermore, we design a cross-modal bilinear fusion module to capture the second-order interactions across modalities, mapping the coupled features into a discriminative semantic manifold. Extensive experiments conducted on the CASME II, SAMM, and SMIC databases under the rigorous leave-one-subject-out composite database evaluation protocol demonstrate that MACFN is effective and achieves competitive performance compared to several recent methods. Full article

While gas-turbine combustors have received much research attention, the forced response of large atmospheric industrial flames is much less studied. To improve the understanding of thermoacoustic instabilities in industrial combustion systems, the forced response of a large natural-gas fired test furnace is computed using Scale-Adaptive Simulations (SASs) with a Flamelet Generated Manifold model. Two test burner configurations are compared. One produces a partially premixed flame (case P) and the other a non-premixed flame. Furthermore, the non-premixed configuration is simulated at both a slightly rich (case N) and a slightly lean set point (case NL). The flame is forced by perturbing the airflow using a superposition of sine waves at four discrete frequencies. That way, the gain and phase of the Flame Transfer Function (FTF) are determined in three simulations for a total of 12 discrete frequencies between 10 and 230 Hz. The results show very different behaviour of the partially premixed and non-premixed configurations. Case P is simulated to be a compact flame, with a maximum FTF gain of one around 70-80 Hz and a quasi-steady limit of 0.7. Case N and NL are characterised by slightly lifted flames acting as low-pass filters that quickly drop off towards higher frequencies. While the phase shift in case P is linearly dependent on frequency and can be related to its flame length, the non-premixed cases have a sharp initial phase shift that levels off with increasing frequency as the gain reduces to zero. Importantly, a non-zero phase shift at 0 Hz is observed for case NL. The nature of the combustion dynamics is further explored by a Proper Orthogonal Decomposition (POD) analysis. The FTFs are applied to predict the thermoacoustic stability using an Acoustic Network Model (ANM). This model is able to reproduce the stability of the cases observed in experiments. The results presented in this study provide insight on the effect of mixing and stoichiometry on the stability of large industrial furnaces. Full article

Predictive modeling of thermal aging in solid propellants is challenging because HTPB-based propellants are highly filled particle-reinforced polymer systems with sparse experimental data, nonlinear parameter coupling, and partially unclear aging mechanisms. This study proposes a Physics-Informed Manifold Neural Operator (PIMANO) framework for multi-parameter prediction of polymer aging in HTPB solid propellants. Accelerated thermal aging, stress relaxation, and swelling experiments were used to obtain aging temperature, aging time, crosslinking density, and viscoelastic Prony-series parameters. A continuous aging-state field was first reconstructed over the temperature–time domain by radial basis function interpolation. Crosslinking density was then introduced as a physically interpretable bridge-state variable linking aging conditions with viscoelastic responses. Among three candidate kinetic models, the modified Arrhenius–Avrami model gave the best fitting performance for crosslinking-density evolution, with R² = 0.988 and MRE = 0.0199. By combining local multi-scale neighborhood features, manifold latent representations, and DeepONet-based operator learning, PIMANO established a unified mapping from aging conditions to multi-parameter viscoelastic responses while incorporating bridge-state consistency, parameter non-negativity, and evolution-direction constraints. Under the RBF-augmented validation setting, PIMANO-ae achieved RMSE = 0.7847, MAE = 0.3366, R² = 0.9995, and MRE = 0.0027. Compared with the traditional model, RMSE, MAE, and MRE were reduced by 94.93%, 96.47%, and 96.85%, respectively. Temperature leave-one-out validation further yielded average R² values of 0.9469–0.9647 and MRE values of 4.98–6.21% at unseen aging temperatures. These results demonstrate that PIMANO provides an accurate, stable, and physically interpretable framework for multi-parameter aging prediction and life-assessment modeling of polymer-based energetic materials. Full article

Hyperspectral imaging has emerged as a powerful tool for food quality assessment, yet most existing methods rely on supervised classification and require prior knowledge of adulterant categories. This study applies a non-targeted screening approach based on a deep spectral autoencoder to detect adulterants in Fritillaria. While autoencoder-based anomaly detection has been established in other hyperspectral domains, its application to congeneric species discrimination and exogenous adulterant screening in Fritillaria has not been systematically explored. A deep spectral autoencoder was constructed and trained exclusively on pure samples to learn the intrinsic spectral distribution of authentic materials. During inference, reconstruction error was used as an anomaly score, and samples deviating from the learned spectral manifold were identified as suspicious. Spectral data augmentation and band trimming were applied to enhance model robustness, while the anomaly threshold was determined solely from the distribution of pure samples. The proposed method achieved strong discrimination performance, with an area under the receiver operating characteristic curve (AUC) of 0.9903 and high detection rates across multiple adulterant types. Typical exogenous adulterants such as starch and talc powder were completely detected, while congeneric species also showed high detection sensitivity despite their spectral similarity to authentic samples. Latent space visualization and residual spectral analysis further revealed clear separation patterns and interpretable spectral deviations. These results demonstrate the proof-of-concept viability of the proposed non-targeted framework for open-set screening of adulteration risks. However, the authentic samples used for training originated from a single source, and only a limited set of anomaly types was tested. Therefore, the current model should be regarded as an early proof-of-concept only, not as a ready-to-deploy screening tool. Further validation with diverse authentic samples and a wider range of adulterants under realistic variability is necessary before the method can be considered a practical strategy for quality control. Full article

We introduce the notion of an almost conformal vector field, which generalizes conformal vector fields and recently introduced m-modified conformal vector fields on a Riemannian manifold. The definition of an almost conformal vector field $\zeta$ on an n-dimensional Riemannian manifold $\left(N,g\right)$ requires two smooth functions $\sigma$ and f called the potential and copotential and a skew symmetric tensor $\phi$ called the associated tensor of $\zeta$. Many examples of almost conformal vector fields which are not conformal vector fields are provided. We find conditions using $\sigma$, f and $\phi$ under which an almost conformal vector field $\zeta$ on an n-dimensional compact Riemannian manifold $\left(N,g\right)$ is either conformal or a Killing vector field. We also find conditions under which a compact Riemannian manifold $\left(N,g\right)$ admitting an almost conformal vector field is isometric to the sphere $S^n\left(c\right)$. Finally, we find conditions under which an almost conformal vector field $\zeta$ on a noncompact Riemannian manifold $\left(N,g\right)$ is a Killing vector field. Full article

This paper proposes a robust barrier-function-based fuzzy adaptive super-twisting integral terminal sliding-mode control (BF-FAST-ITSMC) for robotic manipulators subject to external disturbances. Initially, an integral terminal sliding-mode manifold is designed to ensure finite-time error convergence and eliminate steady-state offsets. To reduce model dependence, the unknown nonlinear function is approximated and compensated using a fuzzy approximator. By combining the super-twisting algorithm (STA) and the barrier-function-based adaptive gains, the designed BF-FAST-ITSMC can suppress actuator chattering effectively, which allows control gains to increase automatically as the error approaches the prescribed boundary. This mechanism ensures that tracking errors are strictly confined within a predefined bound. Comparative simulations on an inverted pendulum and robotic manipulators with one to three degrees of freedom demonstrate that the proposed method provides superior tracking precision, smooth control torque, and enhanced robustness compared to conventional and fuzzy ITSMC schemes. Full article

An emotional neural network-based global predefined-time sliding mode control (ENN-GPTSMC) method is proposed for an uncertain hybrid mechanism. To estimate and compensate for the lumped uncertainty including discontinuous friction, an emotional neural network is developed. Simultaneously, a predefined-time terminal sliding mode control (PTTSMC) uses the estimation value. The adjustable predefined-time performance parameters are then incorporated into the PTTSMC law to extend its attractiveness for the system states to the global domain, thereby solving the limitation of the existing PTTSMC that can only locally achieve the predefined-time convergence of the system states during the reaching phase. The fast convergence of system states is subsequently achieved by embedding an integer-power linear term and its derivative into the sliding manifold and PTTSMC law, respectively. Based on these, an ENN-GPTSMC algorithm is designed. Furthermore, the saturation function of a dynamic boundary layer with an adjustable thickness is designed to avoid the singularity of ENN-GPTSMC, thereby achieving no-singularity fast global predefined-time convergence of the system. Theoretical analysis shows the Lyapunov stability of the system. Finally, simulation and prototype experiments are used to verify the effectiveness of the proposed method. Full article

Bipolar plates are critical components of proton exchange membrane fuel cells (PEMFCs), strongly influencing thermal management, mechanical stability, and overall system efficiency. In this study, an integrated framework combining multiphysics simulation, artificial intelligence (AI), and data augmentation techniques was developed for PEMFC bipolar plate optimization. A coupled thermal–structural finite element model was established in COMSOL Multiphysics to evaluate temperature distribution, thermal stress, and structural deformation under varying operating conditions. A total of 80 parametric design cases were generated by varying six key parameters: hole radius, plate thickness, heating power, manifold pressure, plate number, and heat transfer coefficient. The dataset was expanded using SMOTE, GAN, and LLM-based augmentation techniques and used to train ANN, LR, RF, XGBoost, and SVR models. Model performance was evaluated using 5-fold cross-validation with MAE, RMSE, and LogCosh metrics. The results showed that ensemble tree-based methods, particularly RF and XGBoost, achieved the highest prediction accuracy and computational efficiency. XGBoost produced the best temperature prediction performance for the SMOTE-based dataset (RMSE = 3.668), while RF achieved the lowest stress prediction error (RMSE = 0.0490). GAN-augmented datasets provided stable and reliable predictions, whereas LLM-generated datasets resulted in higher prediction errors and lower physical consistency. Feature importance analysis revealed that plate thickness dominates displacement prediction (≈0.72 importance), manifold pressure governs stress behavior (≈0.999), and heating power is the primary factor affecting temperature prediction. The proposed AI-assisted surrogate modeling framework enables rapid and accurate thermo-mechanical prediction while significantly reducing computational cost compared to conventional multiphysics simulations. The findings demonstrate that integrating physics-based simulations with data-driven approaches provides an efficient strategy for the optimization of next-generation PEM fuel cell bipolar plates. Full article

Trustworthy deployment of artificial intelligence in safety-critical systems requires accurate diagnosis of anticipated scenarios and reliable rejection of out-of-distribution (OOD) inputs that fall outside the modeled operational scope. Existing data-driven diagnostic models typically assume that test inputs are drawn from the training distribution or rely on heuristically tuned thresholds that lack enforceable safety guarantees. This article presents SCOPE (Safety-Calibrated Out-of-distribution Prediction via Contrastive Embeddings), a framework integrating supervised contrastive learning with split-conformal prediction to provide statistically grounded OOD rejection with finite-sample false-alarm control. SCOPE employs a causal residual convolutional encoder to map multivariate sensor streams into a hyperspherical embedding space with a compact, class-specific structure. A k-nearest-neighbor density nonconformity score, computed in the encoder embedding space, flags transients that occupy low-density regions relative to known accident manifolds; an ablation shows that this density score outperforms prototype distance, entropy, and conservative maximum fusion as well as a panel of standard OOD baselines (MSP, ODIN, energy, Mahalanobis, OpenMax, MC-dropout, and a reconstruction autoencoder). To support temporally evolving trajectories, SCOPE aggregates window-level scores under a monotone decision policy and performs trajectory-level conformal calibration, yielding distribution-free guarantees that bound the probability of falsely rejecting a known accident run. SCOPE is evaluated on the Nuclear Power Plant Accident Data (NPPAD) benchmark using high-openness splits that withhold entire accident families as unknowns, and all metrics are reported as mean ± standard deviation across multiple random seeds. Results demonstrate strong diagnostic accuracy on accepted trajectories, conservative false-alarm rates satisfying user-specified safety constraints across multiple operating points, and timely rejection of unseen accident mechanisms, making SCOPE suitable for deployment in safety-critical monitoring applications. Full article

The manual annotation of images remains a major challenge in building robust classifiers, especially in the Big Data era where labeled data are limited. Graph-based semi-supervised learning (SSL) provides an effective solution by leveraging both labeled and unlabeled data. In this paper, we propose a novel graph-based SSL framework that integrates labeled samples with abundant unlabeled data through an adaptive graph structure. The proposed method jointly learns three components: an auto-weighted low-rank graph, soft labels for unlabeled data, and a discriminative latent subspace. By incorporating soft labels into the subspace learning process, the model enforces consistency between the graph structure and the data manifold. This leads to improved discriminative representation. Unlike traditional approaches that treat these components separately, our method optimizes them jointly within a unified framework. Experimental results on multiple benchmark datasets demonstrate the effectiveness of the proposed approach, achieving superior performance under different labeling conditions. Full article

Z- and U-type parallel pipe network configurations are widely used in engineering applications such as solar collectors, fuel cells, microchannels, spargers, and irrigation systems. Although the Z configuration is more commonly employed, the U configuration may provide advantages under specific operating conditions. This study presents a comparative analysis of the two configurations in terms of flowdistribution uniformity and pressure drop. A three-dimensional computational fluid dynamics (CFD) model was developed to simulate realistic solar collector conditions, including both fluid and solid domains together with detailed inlet and outlet junctions. The system consists of manifolds and headers with a diameter of 20 mm and a length of 1150 mm, connected to ten parallel tubes of 7 mm diameter and 1780 mm length. The analysis was conducted over a wide range of inlet Reynolds numbers (Re_D = 100–5000) to represent diverse practical operating conditions. The CFD model was validated against experimental data from the literature and showed good agreement. Flowdistribution uniformity was evaluated using two quantitative indicators. The results show that flow maldistribution increases with Reynolds number in both configurations; however, the U configuration exhibits significantly improved flow uniformity at higher Reynolds numbers. In addition, both configurations exhibited comparable pressure drop characteristics over the investigated operating range. The findings suggest that the U configuration is better suited to high-flow-rate applications that require improved hydraulic and thermal uniformity, while the Z configuration remains effective at lower Reynolds numbers. Full article