Search Results (196)

Externally excited synchronous machines (EESMs) are a rare-earth-free solution for traction applications. However, variable field excitation and magnetic coupling increase control complexity. Efficient validation of the resulting control functionalities can be carried out using hardware-in-the-loop (HIL) testing, which requires high-fidelity real-time simulation models. This paper presents a semi-analytical, discrete-time EESM model tailored for HIL applications. Nonlinear magnetic saturation and magnetic coupling are captured using an inverted flux–current characteristic combined with a rotating coordinate transformation, which improves resource utilization. Spatial harmonics are included through a Fourier decomposition of the angle-dependent inverse characteristics. Additionally, different loss mechanisms are considered to accurately represent the physical behavior of the machine. The model is parameterized using finite element analysis (FEA) results from a 100$\mathrm{k}$$\mathrm{W}$ salient-pole EESM. It is implemented on a field-programmable gate array to achieve real-time capability at a simulation frequency of $2.5$$\mathrm{M}$$\mathrm{Hz}$. Validation results for the typical operating range show deviations below $0.1$% compared to detailed FEA results, demonstrating accurate real-time simulation of the electromagnetic behavior. Full article

This study introduces a modified computational scheme for handling linear and nonlinear fractal time-dependent partial differential equations. The method is constructed using three different stages that provide third-order accuracy in the fractal time variable. The stability of the approach is examined using scalar fractal models and Fourier analysis, while convergence is established for coupled convection–diffusion systems. The numerical algorithm is applied to analyze the mixed convective flow of a Carreau–Yasuda non-Newtonian fluid over stationary and oscillating plates under the influence of viscous dissipation and magnetic field effects. For spatial discretization, the incompressible continuity equation is handled by a first-order difference scheme, whereas higher-order compact schemes are implemented for the momentum, thermal, and concentration equations. The numerical findings show that increasing the Weissenberg number and magnetic field inclination reduces the velocity distribution. An accuracy assessment against existing numerical techniques demonstrates that the present method yields smaller computational errors, particularly when central difference schemes are used in space. In addition, a surrogate machine learning model is developed to predict the skin friction coefficient and local Nusselt number using Reynolds, Weissenberg, Prandtl, and Eckert numbers as input features. The predictive capability of the model is validated through Parity plots, bar charts for sensitivity analysis, scatter visualization, and Taylor Diagrams, confirming strong agreement with the numerical results. Full article

The Korteweg–de Vries (KdV) equation is a foundational model in geophysical fluid dynamics (GFD), governing the propagation of long internal and surface gravity waves in stratified and shallow ocean environments where the interplay between nonlinear steepening and frequency-dependent dispersion gives rise to solitons. Although the analytical tractability of the KdV equation through inverse scattering is well established, systematic numerical exploration of multi-soliton interactions remains valuable for benchmarking solvers, probing conservation properties under varied oceanic initial conditions, and building intuition for more complex ocean wave phenomena. This article presents sangkuriang, an open-source Python library that solves the KdV equation using Fourier pseudo-spectral spatial discretization and adaptive eighth-order Runge–Kutta time integration. The implementation leverages just-in-time (JIT) compilation to achieve research-grade computational efficiency on standard hardware, making it readily accessible for coastal and ocean engineering applications, including idealized modeling of internal solitary waves on continental shelves, rapid parameter studies for solitary wave propagation in stratified basins, and pedagogical investigations of nonlinear dispersive wave dynamics. The solver is validated through four progressively complex idealized scenarios motivated by oceanic wave dynamics: isolated soliton propagation, symmetric interactions, overtaking collisions, and three-body interactions. High-fidelity conservation of mass, momentum, and energy is demonstrated, with relative errors remaining below $\mathcal{O}\left({10}^{-4}\right)$ across all test cases. Measured soliton velocities align with theoretical predictions within 5%, confirming the capture of the amplitude-dependent dispersion characteristic of oceanic solitary waves. Complementary diagnostics, including spectral entropy and recurrence quantification analysis (RQA), verify that the numerical solutions preserve the regular phase-space structure characteristic of integrable Hamiltonian systems. These results establish sangkuriang as a robust, lightweight platform for reproducible numerical investigation of idealized nonlinear dispersive wave dynamics relevant to coastal and ocean engineering applications. Full article

This study rigorously evaluates 13 Convolutional Neural Network (CNN) architectures for Thai dialect recognition. By treating Automatic Speech Recognition (ASR) as a computer vision texture classification task, we processed an extensive 840-h dataset from the Spoken Language Systems, Chulalongkorn University (SLSCU) corpus. Raw audio from four major dialects—Central, Northern (Khummuang), Northeastern (Korat), and Southern (Pat-tani)—was transformed into 2D Mel-spectrograms using the Short-Time Fourier Transform (STFT). We analyzed a diverse range of architectures, including the VGG, Inception, ResNet, DenseNet, and MobileNet families, to establish the optimal trade-off between mathematical complexity and spectral feature extraction. Our experimental results identify NASNet-Mobile as the most effective model, achieving a macro-average F1-score of 0.9425. The analysis suggests that NASNet’s search-optimized cell structure is uniquely capable of capturing the multiscale texture of phonetic formants. In contrast, we observed a catastrophic mode collapse in VGG16 (32.97% accuracy), likely due to excessive parameter bloat, while Xception and MobileNetV2 maintained robust generalization. Confusion matrix analysis reveals high acoustic distinctiveness for Southern Thai (96.7% recall), whereas Northern Thai exhibits significant spectral overlap with Central Thai. These results support the hypothesis that CNNs interpret spectrograms as textures rather than discrete objects, positioning NASNet-Mobile as a high-performance, low-latency baseline for edge-device deployment in resource-constrained environments. Full article

Sandstone uranium deposits exhibit stratabound mineralization and strong vertical heterogeneity in geological space, which complicates the identification of uranium anomaly layers and their integration into deposit-scale 3D models using borehole datasets. In this paper, we propose a UAPC Fourier layer identification (UFLI) method for uranium anomaly layer identification. The method is based on multi-log feature construction, random forest-based estimation of a depth continuous uranium anomaly probability curve (UAPC), and improved Fourier vertical variation analysis. We used 19 boreholes arranged on four exploration lines (ZKA-ZKD) of the Daying uranium deposit in the northern Ordos Basin (north central China), for the validation. The proposed UFLI method identified 51 uranium anomaly layers at a 5 m sampling interval, forming discrete vertical clusters within the drilled successions. The results indicate that anomalies are overwhelmingly concentrated in the Middle Jurassic Zhiluo Formation, particularly within the lower Zhiluo member, with an anomaly-bearing depth range of approximately 550–745 m. Comparison with known mineralization records shows that both industrial and ordinary mineralization intervals are captured within the anomaly layers. Then, based on inter-borehole continuity of anomaly layers, we reconstructed five uranium orebodies (orebodies 1–5) and describe their distribution characteristics. The proposed method provides a technical means for subsurface visualization and exploration targeting in sandstone uranium systems. Full article

Production profiling is essential for optimizing production strategies in oil and gas wells. Conventional production logging tools provide only discrete, time-limited measurements and face operational challenges in long or complex horizontal wells. Distributed fiber-optic sensing (DTS/DAS) enables continuous, full-wellbore monitoring but each sensing modality has limitations when used alone: DTS interpretation is influenced by wellbore disturbances and thermal hysteresis, while DAS acoustic energy does not always correspond to actual inflow zones. This study proposes a joint interpretation method integrating DTS-based temperature inversion with DAS frequency-band energy and apparent velocity analysis. DTS data are processed using a coupled wellbore–formation heat-transfer model to obtain segmental flow rates, while DAS data are analyzed using short-time Fourier transform, cross-correlation, and Hough transform to extract positive and negative apparent velocities indicating fluid migration directions. Field results show that high-production intervals at 4126–4486 m correlate with positive apparent velocities, whereas medium-/low-production and shut-in stages exhibit persistent negative velocities linked to backflow and reinjection. The combined interpretation effectively distinguishes reservoir inflow from wellbore flow by jointly constraining thermal response and flow direction, thereby reducing uncertainties associated with single-method analysis. Full article

Vision Transformers (ViTs) have achieved strong performance in computer vision but suffer from limited inductive bias, high data requirements, and reduced sensitivity to high-frequency visual details. To address these limitations, Frequency-Domain ViTs (FD-ViTs) incorporate spectral representations—such as Fourier, wavelet, and discrete cosine transforms—into the Transformer pipeline to improve feature expressiveness and robustness. This survey provides a systematic review of FD-ViT architectures and introduces a unified taxonomy based on spectral transformation type, integration level, and computational characteristics. We summarize empirical findings across image classification, image restoration, and domain-specific applications, including medical imaging and remote sensing, highlighting consistent performance patterns and task-dependent trade-offs. Our analysis shows that frequency-domain integration yields modest, context-dependent gains in large-scale classification, while offering more consistent advantages in frequency-sensitive tasks such as image restoration and noise-robust visual analysis. We further discuss key open challenges, including spectral aliasing, phase information loss, evaluation inconsistency, and deployment efficiency, and outline emerging directions toward dynamic spectral operators, multimodal integration, and hardware-aware designs. To the best of our knowledge, this work constitutes the first systematic survey that consolidates the growing body of research on FD-ViT, providing a structured conceptual and methodological reference for future studies on spectral representations in Transformer-based visual learning. Full article

This study presents a promising strategy for the fabrication of a novel chitosan-based nanogel-in-oil system by integrating the development of a water-in-oil (W/O) microemulsion containing chitosan as a template, followed by crosslinking with genipin, a natural crosslinking agent, via emulsion crosslinking. To develop the W/O microemulsion template, nanometer-sized internal aqueous droplets were successfully formed in cottonseed oil, a vegetable oil, using a blend of nonionic surfactants, polysorbate 80 and sorbitan monooleate. A pseudoternary phase diagram was constructed to investigate the phase behavior of systems composed of chitosan solution, mixed surfactant, and cottonseed oil. Compositions falling within the monophasic region were selected for further formulation optimization. The microemulsions were characterized for droplet size, size distribution, electrical conductivity, and viscosity. The optimal microemulsion exhibited W/O characteristics with the lowest viscosity. Dynamic light scattering (DLS) analysis confirmed the presence of uniformly distributed nanometer-sized droplets, as evidenced by a Z-average diameter of 92.9 ± 2.3 nm and a PDI of 0.100 ± 0.072. The microemulsion system demonstrated physical stability, as confirmed by centrifugal testing. Crosslinking of chitosan with genipin was monitored by fluorescence intensity measurements of the crosslinking products. Fourier transform infrared spectroscopy further confirmed the formation of genipin-crosslinked chitosan structure. DLS and transmission electron microscopy revealed that the nanogels possessed nanoscale dimensions and discrete spherical morphologies. Overall, this approach demonstrates a viable route for producing a nanogel-in-oil system by combining microemulsion templating with emulsion crosslinking. Full article

Background/Objectives: Early detection and intervention are critical for improving outcomes in head and neck cancer. Although endoscopy is commonly used for screening, it requires specialist expertise and may cause patient discomfort. Therefore, there is a need for a simpler and less invasive screening method. This study aimed to evaluate the clinical feasibility of Fourier-transform infrared (FTIR) spectroscopy-based exhaled breath analysis as a non-invasive screening tool for head and neck cancer. Methods: This single-center study was conducted at the Department of Otolaryngology–Head and Neck Surgery, Showa Medical University. Outpatients with head and neck cancer (n = 10) and healthy controls (n = 14) were enrolled. Exhaled breath samples and ambient air surrounding the patient and lesion were analyzed using FTIR spectroscopy. Infrared absorption spectra were obtained, divided into 7667 discrete wavenumber points across the measured range, and compared between the patient and control groups. Results: FTIR spectroscopy revealed significant differences between patients and controls, with 2691 wavenumber points showing statistically significant differences (p < 0.05). Among these, the wavenumber at 3917.3 cm⁻¹ showed a particularly strong difference (p = 0.00015). Receiver operating characteristic analysis demonstrated good discriminative performance, with an area under the curve of 0.929. The maximum Youden index was 0.829, with an optimal threshold of 0.234. Conclusions: FTIR-based exhaled breath analysis is a non-invasive and feasible approach for screening head and neck cancer. These findings suggest that this technique has potential clinical applicability as a screening tool and may also be extendable to the detection of other diseases. Full article

In this study, semi-interpenetrating polymer network (semi-IPN) hydrogels based on methacrylated dextran and native inulin were designed as biodegradable carriers for the colon-specific delivery of uracil as a model antitumor compound. The hydrogels were synthesized via free-radical polymerization, using diethylene glycol diacrylate (DEGDA) as a crosslinking agent at varying concentrations (5, 7.5, and 10 wt%), and their structural, thermal, and biological properties were systematically evaluated. Fourier transform infrared spectroscopy (FTIR) confirmed successful crosslinking and physical incorporation of uracil through hydrogen bonding. Concurrently, differential scanning calorimetry (DSC) revealed an increase in glass transition temperature (T_g) with increasing crosslinking density (149, 153, and 156 °C, respectively). Swelling studies demonstrated relaxation-controlled, first-order swelling kinetics under physiological conditions (pH 7.4, 37 °C) and high gel fraction values (84.75, 91.34, and 94.90%, respectively), indicating stable network formation. SEM analysis revealed that the hydrogel morphology strongly depended on crosslinking density and drug incorporation, with increasing crosslinker content leading to a more compact and wrinkled structure. Uracil loading further modified the microstructure, promoting the formation of discrete crystalline domains within the semi-IPN hydrogels, indicative of physical drug entrapment. All formulations exhibited high encapsulation efficiencies (>86%), which increased with increasing crosslinker content, consistent with the observed gel fraction values. Simulated in vitro gastrointestinal digestion showed negligible drug release under gastric conditions and controlled release in the intestinal phase, primarily governed by crosslinking density. Antimicrobial assessment against Escherichia coli and Staphylococcus epidermidis, used as an initial or indirect indicator of cytotoxic potential, revealed no inhibitory activity, suggesting low biological reactivity at the screening level. Overall, the results indicate that DEGDA-crosslinked dextran/inulin semi-interpenetrating (semi-IPN) hydrogels represent promising carriers for colon-targeted antitumor drug delivery. Full article

The expansion of renewables in modern power systems and the coordinated development of upstream and downstream industrial chains are promoting a shift on the utility side from traditional settlement by energy toward operation driven by data and models. Industrial electricity consumption data exhibit pronounced multi-scale temporal structures and sectoral heterogeneity, which makes unified long-term load and generation forecasting while maintaining accuracy, interpretability, and scalability a challenge. From a modern system identification perspective, this paper proposes a System Identification in Adaptive Bayesian Framework (SIABF) for medium- and long-term industrial load forecasting based on daily freeze electricity time series. By combining daily aggregation of high-frequency data, frequency domain analysis, sparse identification, and long-term extrapolation, we first construct daily freeze series from 15 min measurements, and then we apply discrete Fourier transforms and a spectral complexity index to extract dominant periodic components and build an interpretable sinusoidal basis library. A sparse regression formulation with ${\mathit{\ell }}_1$ regularization is employed to select a compact set of key basis functions, yielding concise representations of sector and enterprise load profiles and naturally supporting multivariate and joint multi-sector modeling. Building on this structure, we implement a state-space-implicit physics-informed Bayesian forecasting model and evaluate it on real data from three representative sectors, namely, steel, photovoltaics, and chemical, using one year of 15 min measurements. Under a one-month-ahead evaluation using one year of 15 min measurements, the proposed framework attains a Mean Absolute Percentage Error (MAPE) of 4.5% for a representative PV-related customer case and achieves low single-digit MAPE for high-inertia sectors, often outperforming classical statistical models, sparse learning baselines, and deep learning architectures. These results should be interpreted as indicative given the limited time span and sample size, and broader multi-year, population-level validation is warranted. Full article

This study proposes a novel framework for quantifying curved grain boundaries that overcomes key limitations of existing methods. Unlike Fourier-based approaches that require labor-intensive sequential analysis of individual boundaries and selectively represent only high-amplitude regions, or spline-based methods that demand complex parameter selection for interpolation points, the proposed framework integrates curvature variance filtering with U-chord curvature calculation to enable automated, comprehensive, and noise-resistant characterization of grain boundary morphology. The curvature variance filtering adaptively determines smoothing parameters based on local curve properties, while the U-chord curvature method ensures rotational invariance and robustness against digitization errors. Four heat treatment processes were applied to GH4169 alloy, producing distinct grain boundary morphologies with mean curvature (MC) values ranging from 0.0625 to 0.1252. Controlled cooling alone (Process A) yielded predominantly straight boundaries (91.06% straight, 0.12% serrated), while re-dissolution treatment (Process D) produced the highest serration degree (58.81% straight, 3.53% serrated). The quantitative analysis reveals that dispersed δ-phase precipitation creates discrete pinning points, forming serrated boundaries with sharp curvature peaks, whereas dense, parallel δ-phase arrays at specific angles produce coordinated wavy undulations. This framework provides a reliable quantitative tool for optimizing heat treatment protocols to achieve target grain boundary configurations in nickel-based superalloys. Full article

Surface crack detection and temporal evolution analysis are fundamental tasks in remote sensing and photogrammetry, providing critical information for slope stability assessment, infrastructure safety inspection, and long-term geohazard monitoring. However, current unmanned aerial vehicle (UAV)-based crack detection pipelines typically treat spatial detection and temporal change analysis as separate processes, leading to weak geometric consistency across time and limiting the interpretability of crack evolution patterns. To overcome these limitations, we propose the Longitudinal Crack Fitting Network (LCFNet), a unified and physically interpretable framework that achieves, for the first time, integrated time-series crack detection and evolution analysis from UAV remote sensing imagery. At its core, the Longitudinal Crack Fitting Convolution (LCFConv) integrates Fourier-series decomposition with affine Lie group convolution, enabling anisotropic feature representation that preserves equivariance to translation, rotation, and scale. This design effectively captures the elongated and oscillatory morphology of surface cracks while suppressing background interference under complex aerial viewpoints. Beyond detection, a Lie-group-based Temporal Crack Change Detection (LTCCD) module is introduced to perform geometrically consistent matching between bi-temporal UAV images, guided by a partial differential equation (PDE) formulation that models the continuous propagation of surface fractures, providing a bridge between discrete perception and physical dynamics. Extensive experiments on the constructed UAV-Filiform Crack Dataset (10,588 remote sensing images) demonstrate that LCFNet surpasses advanced detection frameworks such as You only look once v12 (YOLOv12), RT-DETR, and RS-Mamba, achieving superior performance (mAP_50:95 = 75.3%, F1 = 85.5%, and CDR = 85.6%) while maintaining real-time inference speed (88.9 FPS). Field deployment on a UAV–IoT monitoring platform further confirms the robustness of LCFNet in multi-temporal remote sensing applications, accurately identifying newly formed and extended cracks under varying illumination and terrain conditions. This work establishes the first end-to-end paradigm that unifies spatial crack detection and temporal evolution modeling in UAV remote sensing, bridging discrete deep learning inference with continuous physical dynamics. The proposed LCFNet provides both algorithmic robustness and physical interpretability, offering a new foundation for intelligent remote sensing-based structural health assessment and high-precision photogrammetric monitoring. Full article

This study investigates the thermal transport behavior of a time-dependent viscoelastic nanofluid moving over a widening cylindrical surface. A steady magnetic influence is introduced along the transverse direction due to photonic heating, thermal sources, or absorbers, and modified Fourier conduction. A mixture of $CoF{e}_2{O}_4$ and $F{e}_3{O}_4$ nanoparticles are uniformly distributed in ethylene glycol to form a hybrid nanofluid. Using a suitable similarity transformation, the governing equations were reformulated into a set of nonlinear ordinary differential equations. The collocation method (CM) is employed as a discretization approach, combined with feedforward neural networks (FNNs) to enhance computational accuracy. Unsteady patterns in both fluid motion and heat distribution were identified, with the localized Nusselt coefficient influenced by relevant scaling parameters. Results are illustrated through plots and structured data formats for various nanoparticle geometries, including spherical, brick, and platelet forms. The analysis revealed that spherical nanoparticles enhance heat transfer by up to 18–22% compared with brick and platelet forms under strong unsteadiness and relaxation effects. As temporal fluctuation indicators intensify, the thermal distribution increases; however, increasing the relaxation coefficient in the heat response leads to diminished energy levels. Full article

Urban land subsidence poses a significant threat in rapidly urbanizing regions, threatening infrastructure integrity and sustainable development. This study focuses on Mandalay, Myanmar, and presents a novel clustering framework—Dynamic Time Warping and Trend-based Longest Common Subsequence with Agglomerative Hierarchical Clustering (DTLCS-AHC)—to classify spatiotemporal deformation patterns from Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) time series derived from Sentinel-1A imagery covering January 2022 to March 2025. The method identifies four characteristic deformation regimes: stable uplift, stable subsidence, primary subsidence, and secondary subsidence. Time–frequency analysis employing Empirical Mode Decomposition (EMD) and Discrete Fourier Transform (DFT) reveals seasonal oscillations in stable areas. Notably, a transition from subsidence to uplift was detected in specific areas approximately seven months prior to the Mw 7.7 earthquake, but causal relationships require further validation. This study further establishes correlations between subsidence and both urban expansion and rainfall patterns. A physically informed conceptual model is developed through multi-source data integration, and cross-city validation in Yangon confirms the robustness and generalizability of the approach. This research provides a scalable technical framework for deformation monitoring and risk assessment in tropical, data-scarce urban environments. Full article