Search Results (1,303)

Thermal maturity assessment in Precambrian sedimentary successions is commonly hindered by the inapplicability of vitrinite reflectance and by the limited calibration of individual proxies. Here, we constrain the thermal maturity of a Mesoproterozoic interval from the L4F3 drill core (Alto Tapajós basin, Amazonian craton) using an integrated approach that combines the Kübler Index (KI), Raman spectroscopy of carbonaceous material measured directly on organic-walled microfossils (RSCM), and the Acritarch Alteration Index (AAI). Illite crystallinity was evaluated from the FWHM of the 10 Å (001) reflection on oriented <2 µm mounts under air-dried and ethylene glycol conditions, with peak decomposition (DecompXR) used for internal quality control. RSCM thermometry was applied to Leiosphaeridia tenuissima wall material, and AAI classes were assigned by standardized transmitted light color comparison. KI proxy values indicate conditions near the late diagenesis-to-incipient anchizone transition, with eight horizons approaching anchizone reference values. RSCM yields peak temperature estimates of 213–263 °C with an uncertainty of approximately ±30 °C, while AAI results are uniform (class 3.0), providing qualitative support consistent with advanced maturation. Together, these indicators place the studied succession broadly within late diagenesis to incipient anchizone conditions and provide constraints relevant to thermal history reconstruction and basin modeling in ancient cratonic settings. Full article

Remote sensing imagery is often degraded by cloud cover, causing severe information loss and hindering downstream Earth observation tasks. Although recent deep learning methods, including CNN- and Transformer-based models, have achieved promising progress in cloud removal, they mainly operate in the spatial domain and largely overlook the frequency-domain discrepancies introduced by clouds of different types and densities. This limitation restricts their ability to generalize across diverse cloud corruption scenarios. To address this issue, we propose a Frequency Interaction Cloud Removal Network (FI-CRNet), which introduces a novel Frequency-Aware Modulation (FAM) mechanism for high-fidelity cloud-free image reconstruction. The FAM module consists of two components. First, the Frequency Decomposition (FD) module explicitly separates input features into low-frequency cloud-affected components and high-frequency detail-rich components through spectral analysis, while aligning them with decoder features via cross-attention. Second, the Cross-Frequency Interaction (CFI) module adaptively integrates these components through a dual-gate weighting mechanism, including spatial and channel gates, to suppress cloud interference while enhancing structural and textural details. By jointly modeling frequency-domain cues and spatial features, FI-CRNet enables robust and adaptive reconstruction under diverse cloud conditions. Extensive experiments show that our method outperforms state-of-the-art techniques across diverse cloud scenarios. Full article

Power quality disturbance signals must be continuously monitored, stored, and transmitted for effective analysis, protection, and system planning in modern power systems. The large volume of data generated during power quality monitoring necessitates efficient storage techniques. The sparse representation of power quality signals can significantly reduce memory requirements while preserving important signal characteristics. Since several techniques exist for obtaining sparse representations, it is important to identify the most suitable Sparse Signal Decomposition (SSD) technique for different power quality disturbances. This paper presents a comparative study of various SSD techniques, including Orthogonal Matching Pursuit (OMP), Matching Pursuit (MP), Least Squares–Orthogonal Matching Pursuit (LS-OMP), and Thresholding and Basis Pursuit (BP), along with diverse dictionaries for the representation of power quality disturbances such as sag, swell, transients, and harmonics. Mean Square Error (MSE) and the ratio between the actual signals and reconstructed signals (A/R ratio) are used to evaluate the accuracy, while computation time is considered to compare the computational speed of different techniques. Simulation studies are carried out in MATLAB to evaluate the effectiveness of the SSD techniques. From the simulation results, it is observed that OMP and LS-OMP provide accurate representations of power quality disturbance signals. For sag, swell, and transients, the impulse dictionary performs best with OMP, offering faster computation. However, for harmonics, OMP with DCT dictionary is found to be more effective. Full article

To address the drawbacks of traditional convolutional neural network-based rolling bearing fault diagnosis techniques, including poor feature extraction, low diagnostic accuracy, and poor generalization capability, a dual-channel rolling bearing fault diagnosis model based on VMD-BiGRU and GADF-ResNet-CBAM was proposed. Variational mode decomposition (VMD) was used to first break down and reconstruct the original vibration signal. The rebuilt signal was then input into a bidirectional gated recurrent unit (BiGRU) network in order to extract temporal information. Second, the Gramian angular difference field (GADF) transformed the one-dimensional vibration signal into a two-dimensional picture. This image was then fed into a residual network that was merged with the convolutional block attention module (CBAM) in order to extract spatial characteristics. After concatenating and fusing the data from the two channels, Softmax was finally employed at the output layer to classify different types of faults. The Case Western Reserve University (CWRU) bearing dataset and a self-collected independent dataset from the Xinjiang University experimental rig were utilized for validation. The model achieved diagnosis accuracies of 99.39% and 99.58%, respectively. These results demonstrate the robustness and practical applicability of the proposed method on data acquired from distinct hardware sources and experimental environments, outperforming alternative approaches. Full article

To better understand the impact that multi-scale unsteadiness has on industrial flows, we use Large Eddy Simulation (LES) data representative of a midspan compressor section operating in an idealized multi-stage environment. We collect a large number of three-dimensional flow snapshots and perform a large-scale flow decomposition using a parallel framework based on the Proper Orthogonal Decomposition (POD). Once the flow is split into orthogonal modes, we quantify kinetic energy budgets on a mode-by-mode basis. This enables us to characterize energy exchanges between these modes and analyze the flow in a multi-scale manner. As a result we are able to reconstruct an approximate energy cascade within the domain. The results provide insights into the role that various scales play in modulating the energy transfer within the flow. This work is a stepping stone towards utilizing all the information embedded in the 3D unsteady flowfield and its evolution for the purpose of informing turbulence modeling. Full article

Sweeping jet (SWJ) actuators are widely used in active flow control, but explicitly resolving actuator-scale unsteadiness in full-configuration computational fluid dynamics (CFD) remains prohibitively expensive because of the small geometric scales and high-frequency oscillations involved. Existing reduced-order boundary-condition models constructed from exit-plane data alone can reproduce the observed switching waveform, but they treat the actuator as an input–output black box and provide limited insight into the internal dynamics that generate the response. This work develops a causal state-space reduced-order modeling framework that links internal mixing-chamber dynamics to time-resolved exit-plane boundary conditions. Proper orthogonal decomposition (POD) is used to obtain a low-dimensional representation of the internal flow, and a data-driven linear evolution operator is identified in the reduced space by least-squares regression of successive snapshot pairs. A POD truncation rank of $r=60$ is selected from cumulative-energy and validation-error sensitivity analyses, capturing well above 99% of the fluctuation energy while lying within the converged performance regime. A corresponding reduced operator is identified for the exit plane, and spectral comparison reveals near-neutrally stable oscillatory modes in both regions. Using a $\pm 1\%$ relative frequency-matching tolerance, the dominant reduced-operator modes exhibit a 28.3% frequency overlap, providing operator-level evidence that exit-plane oscillations are dynamically linked to internal coherent structures. This correspondence is further supported by cross-spectral coherence analysis between representative internal and exit-plane probe signals, which shows strong coherence at dynamically relevant frequencies. A delayed causal output mapping is then formulated in which the internal reduced state drives the exit-plane response after an identified lag of 149 time steps, corresponding to $2.98\times {10}^{-3}$ s. This delay provides a physically interpretable convective transport timescale from the mixing chamber to the actuator exit. Over the validation interval, the model maintains a mean relative $L_2$ error below 0.02, with maximum normalized errors below 0.04 for most of the prediction horizon, and localized increases are confined to rapid jet-switching events. Field-level reconstructions of streamwise velocity and total pressure show that the model captures both phases of the jet-switching cycle, with errors concentrated primarily in high-gradient shear-layer regions. Compared with exit-only reduced-order models, the proposed internal-driven formulation improves amplitude and phase fidelity over extended prediction horizons. The resulting framework provides a compact, interpretable, operator-based representation of SWJ actuator dynamics suitable for use as a CFD-embeddable dynamic boundary condition. Full article

With the rapid development of technologies for scramjet and combined-cycle engines, the efficient mixing of supersonic airflow and fuel within the combustor has become a key factor limiting engine performance improvement. Existing research has predominantly focused on Mach number and compressibility effects, while systematic analysis of the influence of Reynolds number remains scarce. In this study, the large eddy simulation (LES) method is employed to investigate the effects of inflow Reynolds number on the flow structures, growth characteristics, and modal evolution of a supersonic mixing layer. By adjusting the inlet pressure, three different Reynolds number conditions are established, and the evolution of vortex structures, development of mixing layer thickness, turbulence statistics, and dynamic mode decomposition (DMD) characteristics are analyzed. The results indicate that under high Reynolds numbers, the transition in the mixing layer occurs earlier, vortex breakdown intensifies, three-dimensional features become more pronounced, and the mixing layer centerline shifts significantly toward the low-speed side. The energy spectrum in the self-similar region exhibits approximate isotropy, and the inertial subrange expands with increasing Reynolds number. Moreover, DMD analysis reveals that flow field reconstruction at high Reynolds numbers requires higher-order modes, reflecting richer dynamic scales. Our study elucidates the influence of Reynolds number on the multi-scale evolution mechanisms of supersonic mixing layers, providing a theoretical basis for the prediction and control of mixing processes in high-speed propulsion systems. Full article

Microwave imaging is a promising non-ionizing technique for bedside follow-up of intracranial hemorrhage, but dynamic monitoring remains challenging under limited multistatic sampling because weak inter-frame changes can be obscured by measurement variability, model mismatch, and the high cost of frame-by-frame nonlinear inversion. To address this problem, this paper proposes a state-referenced truncated singular-value decomposition (SR-TSVD) framework for dynamic microwave monitoring of hemorrhagic evolution. The method maintains an internal gate state and reconstructs only the state-referenced increment at each monitoring instant. A row-whitened TSVD inversion is introduced to reduce channel dominance effects and improve robustness to route-dependent imbalance, while a residual-driven gate-refresh mechanism updates the internal state only when the current linearization background becomes insufficiently accurate. The proposed method was validated through two-dimensional numerical experiments and hardware phantom measurements. The numerical study examined different lesion evolution scenarios and analyzed the effects of antenna count, frequency diversity, and measurement noise. The hardware study showed that the method preserves the main dynamic evolution in a real measurement system and remains more stable than baseline linear methods under sparse array conditions. These results indicate that SR-TSVD provides an effective and computationally practical framework for repeated bedside microwave monitoring of intracranial hemorrhage. Full article

Continuous in situ marine holographic observation generates data volumes that challenge onboard storage and transmission. Parallel phase-shifting digital holography (PPSDH) is especially sensitive to compression because phase retrieval depends on consistent four-channel demodulation. We present a training-free spatiotemporal compression framework for sparse-particle marine PPSDH sequences based on background–residual decomposition and a shared four-channel processing path. The background is coded once per temporal window by a discrete wavelet transform (DWT) followed by principal component analysis (PCA), and the dynamic residual is decorrelated by temporal principal component analysis before quantization and entropy coding. The framework is evaluated on three primary 64-frame marine PPSDH sequences using a common reconstruction-and-evaluation pipeline with wrapped-phase root-mean-square error (PhaseRMSE) as the primary metric and amplitude peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as secondary references; expanded supplementary checks are also reported for nine additional selected 64-frame groups spanning sparse to transitional occupancy. On the primary sequence and within the high-fidelity achieved-rate overlap with the JPEG Pleno anchor codec INTERFERE, the proposed framework reduces PhaseRMSE by about 3.3-fold to 3.4-fold while increasing amplitude PSNR by about 11 dB and preserving amplitude SSIM above 0.99997. Lower-bitrate sweeps further quantify the rate–fidelity trade-off rather than claiming universal low-rate superiority. These results support BG–Res spatiotemporal coding as a practical phase-fidelity-oriented option for the tested sparse-to-transitional marine PPSDH conditions; extension to dense scenes, broader marine conditions, and downstream biological tasks requires separate validation. Full article

To tackle issues such as high data volatility, temporal dependencies, complex feature extraction, and low parameter tuning efficiency in wind power forecasting, this paper proposes a dual-optimization model for short-term wind power forecasting based on RIME-VMD and MSSA-CNN-BiLSTM. First, the Rime Optimization Algorithm (RIME) is employed to adaptively refine the key parameters of Variational Mode Decomposition (VMD), decomposing wind power into intrinsic modal functions (IMFs) of different frequencies to reduce signal complexity. Second, by integrating the local feature extraction capabilities of Convolutional Neural Network (CNN) with the bidirectional temporal dependency capture capabilities of Bidirectional Long Short-Term Memory Network (BiLSTM), a hybrid deep learning architecture is constructed. Additionally, the Multi-strategy Sparrow Search Algorithm (MSSA) is introduced to perform global hyperparameter optimization, thereby addressing the shortcomings of manual parameter tuning. The final power forecast is obtained through the prediction of each IMF component and the reconstruction of the results. Experiments demonstrate that the presented prediction model attains a root mean square error (RMSE) of 0.0333, a mean absolute error (MAE) of 0.0265, and a coefficient of determination (R²) of 0.9901. Seasonal validation shows that the model’s R² exceeds 0.983 in all four seasons—spring, summer, autumn, and winter—demonstrating good generalization capability. Relative to the BiLSTM model, its RMSE and MAE are reduced by 50.52% and 46.57%, respectively, while R² increases by 3.36%, effectively addressing the issue of insufficient accuracy in short-term wind power forecasting. Full article

The carbon allowance price series exhibits nonlinearity, non-stationarity, and high noise due to multiple factors. Accurate forecasting is crucial to the stability of the carbon market and to resource allocation. This paper proposes a forecasting framework using multi-scale decomposition and a TCN–LSTM hybrid model. First, the original carbon allowance price series is decomposed using CEEMDAN optimized by PSO. Then, VMD performs secondary decomposition of complex components based on sample entropy. Next, transfer entropy identifies causal relationships between each component and the original series, enabling reconstruction based on causality. Finally, a TCN–LSTM model uses reconstructed sequences to forecast carbon prices. The method achieves high-precision short-term forecasts using only the carbon allowance price series, avoiding reliance on external variables. Empirical results on the Hubei carbon market show an optimal lag of 3, with R² = 0.8873, outperforming the single LSTM and TCN models and achieving a lower RMSE. The forecast using January–March 2026 data shows stable carbon prices with slight fluctuations. This study provides a reliable method for data-constrained short-term carbon price forecasting, supporting decision-making and policy assessment. Full article

This work presents a compact arithmetic formulation for inverting row-major linear indices into three-dimensional coordinates. The formulation defines a bijective and reversible mapping based solely on integer division and modulo operations and avoids iteration and control-flow constructs. A traversal-based reconstruction strategy and the arithmetic formulation are evaluated on Graphics Processing Unit (GPU) hardware across multiple volumetric configurations. The experimental results show that arithmetic index decomposition yields uniform execution behavior, low run-to-run timing variability, and constant per-thread execution cost under massively parallel execution. The observed differences follow from GPU architectural characteristics, particularly sensitivity to control-flow divergence. The formulation provides a portable reference model for multidimensional index inversion suitable for parallel kernels and hardware-oriented implementations. Full article

The accurate reconstruction and real-time prediction of transient three-dimensional flow fields in hydraulic turbines are critical for ensuring operational stability under renewable energy-driven variable-load conditions, yet conventional computational fluid dynamics (CFD) approaches remain too computationally expensive for digital twin applications. This paper proposes a hybrid framework that integrates Proper Orthogonal Decomposition (POD) with Long Short-Term Memory (LSTM) networks to reconstruct and predict the unsteady flow field within the draft tube of a Francis turbine using only four sparse wall-mounted pressure sensors. The methodology begins with high-fidelity Large Eddy Simulation (LES) to establish a comprehensive flow field database under Part Load (PL), Best Efficiency Point (BEP), and High Load (HL) conditions. POD is subsequently applied to extract dominant coherent structures and their temporal coefficients, achieving a low-dimensional representation of the high-dimensional flow field. A comparative analysis between standard POD and weighted POD reveals that under the PL condition characterized by a strong double-helical vortex rope, the weighting effect is significant—standard POD captures 90% of the total energy with the first 2 modes, while weighted POD requires up to 8 modes to reach the same threshold. Under the BEP and HL conditions, the energy distributions of the two methods are nearly identical, yet weighted POD still yields cleaner spatial modes with sharper vortex boundaries and fewer spurious wall-region vortices. An LSTM network is then trained to establish a mapping between time-series signals from the four sensors and the POD temporal coefficients. The results demonstrate that LSTM prediction performance is governed by the spatial correlation between each mode and the sensor locations rather than by temporal regularity. Modes that project strongly onto the sensor locations—PL Modes 1–2 (R² = 0.85 and 0.513), BEP Mode 1 (R² = 0.96), and HL Mode 1 (R² = 0.92)—are reliably predictable, while PL Mode 3 and HL Mode 2, despite their regular temporal oscillations, yield strongly negative R² values (−3.366 and −186.6) because their spatial structures are concentrated away from the wall. With a condition-adaptive strategy predicting only sensor-correlated, energetic modes, the reconstructed pressure fields achieve mean L2 relative errors of 17.01% (PL), 7.17% (BEP), and 12.91% (HL). Because the mean flow dominates total pressure energy (86.66–98.07%), the effective absolute error is substantially lower. The proposed POD-LSTM framework successfully bridges the gap between high-fidelity CFD and real-time monitoring, enabling full-field flow state estimation from sparse sensor measurements without the computational expense of online simulations. This capability is particularly valuable for digital twin applications in hydraulic turbines operating under rapidly varying renewable energy conditions. Full article

Health monitoring of marine turbochargers under real-ship operation is complicated by operating-condition variability, recurrent online cleaning, and limited fault labels. This study presents a maintenance-aware temporal contrastive autoencoder (TCCL-AE) for health index (HI) learning from multivariate real-ship monitoring data. The framework aims to learn an HI that tracks degradation while reducing sensitivity to short-term operating-condition fluctuations by incorporating maintenance information into latent-state evolution and introducing temporal contrastive learning. The model includes a temporal encoder for window-level feature extraction, a latent decomposition module for separating degradation-related and condition-related information, and a Health Coupling Module for representing maintenance-induced recovery. The training objective combines temporal contrastive learning, observation reconstruction, and maintenance consistency. Experiments on multi-voyage real-ship data indicate that the learned HI reflects long-term degradation evolution and maintenance-related recovery, while remaining comparatively smooth under variable operating conditions. The resulting HI provides a continuous representation for condition tracking and maintenance-related interpretation during long-horizon monitoring. Full article

Single-image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs and remains fundamentally ill-posed due to the inherent ambiguity of missing high-frequency details. While diffusion-based SR models achieve superior perceptual quality through iterative denoising, their multi-step sampling process results in substantial computational cost and latency. In contrast, transformer-based SR models offer efficient single-forward inference but are typically optimized for distortion-oriented objectives, limiting perceptual realism. In this paper, we propose DTKD, a diffusion-to-transformer heterogeneous knowledge distillation framework that transfers the perceptual prior of a diffusion teacher into an efficient transformer student. To effectively bridge the representational gap between generative diffusion outputs and deterministic transformer reconstructions, we introduce a frequency-group-aware distillation loss based on two-level discrete wavelet transform (DWT). The loss decomposes images into structured frequency sub-bands and assigns non-uniform weights to emphasize discrepancy-sensitive mid-frequency components. Furthermore, we adopt a progressive scheduling strategy that gradually increases the distillation weight during training to stabilize optimization and balance structural fidelity with perceptual enhancement. Extensive experiments on real-world SR benchmarks demonstrate that the proposed framework consistently improves perceptual quality over a standalone transformer student while maintaining transformer-level inference efficiency. Ablation studies further validate the importance of moderate frequency decomposition, discrepancy-aware weighting, and progressive distillation scheduling. These results suggest that heterogeneous distillation provides an effective and practical approach for transferring diffusion-based generative priors into efficient super-resolution models. Full article